in the problem solving process goals are identified in which of the following phases

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DMAIC Model | The 5 Phase DMAIC Process to Problem-Solving

• 5 mins to read
• July 1, 2020
• By Reagan Pannell

Summary: An Introduction to DMAIC

Dmaic – the dmaic model.

The 6 Sigma DMAIC model remains the core roadmap for almost all Lean Six Sigma problem-solving approaches that drive quality improvement projects. It is used to ensure a robust problem-solving process is followed to give the best chance of the best solution being found.

A note about the structure and the approach used in this article.

Our approach to DMAIC follows Quentin Brook’s book “Lean Six Sigma & Minitab” which for anyone wishing to study Lean Six Sigma is a must for the  Green Belt Course  and the  Black Belt Course .

What is the DMAIC Model?

DMAIC is short for: Define, Measure, Analyse, Improve and Control. These are the key phases that each project must go through to find the right solution. This flow is the concept behind DMAIC Analysis of an issue and its the DMAIC cycle all projects must go through.

As you can quickly see from the 5 DMAIC phases they follow a logical sequence as we will go through in more detail below. But they also make sure you do not try to jump to implementing a solution before you have properly, defined and measured what you are going to be an improvement.

We all love to jump to solutions, but the DMAIC problem-solving structure helps us have a more rigorous approach so that we do not short cut the process and perhaps miss the best solution or perhaps implement the wrong solution as well. It can help companies better structure their problem-solving approaches and be more robust in their approach. 

DMAIC – The 5 DMAIC Process Phases

The phases throughout the DMAIC model have and can be broken down in many different ways. One of the best approaches we have found is from Opex Resources which shows how to examine the existing processes, and with a project team, and the sigma improvement process, we can solve complex issues.

DMAIC Define Phase

The purpose of the Define phase is ultimately to describe the problems that need to be solved and for the key business decision-makers to be aligned on the goal of the project. Its about creating and agreeing the project charter .

All too often, teams have identified solutions without actually defining what it is they will actually be trying to do or perhaps not do. This can lead to internal confusion and often solutions which completely miss the business requirements and needs.

• Define the Business Case
• Understand the Consumer
• Define The Process
• Manage the Project
• Gain Project Approval

We can’t solve problems by using the same kind of thinking we used when we created them” Albert Einstein Tweet

DMAIC Analyse Phase

The goal of the DMAIC Analyse phase with the lean six sigma improvement process is to identify which process inputs or parameters have the most critical effect on the outputs. In other words, we want to identify the root cause(s) so that we know what critical elements we need to fix.

During this phase, the teams need to explore all potential root causes using both analytical approaches, statistical approaches or even graphical tools such as VSM’s and Process maps to uncover the most important elements which need to be changed/fixed.

The Analyse phase can be broken down into:

• Analyse the Process
• Develop Theories and Ideas
• Analyse the Data
• and finally, Verify Root Causes 

DMAIC Improve Phase

The goal of the improvement phase is to identify a wide range of potential solutions before identifying the critical solutions which will give us the maximum return for our investment and directly fix the root cause we identified.

During this phase, the team brainstorm, pilot, test and validate potential improvement ideas before finally implementing the right solutions. With each pilot, the team can validate how well it improves the key measures they identified back in Define and Measure. When the team finally roll out the solution, the results should be seen if the right solution has been found and implemented correctly.

The Improve phase can be broken down into:

• Generate Potential Solutions
• Select the Best Solution
• Assess the Risks
• Pilot and Implement

DMAIC Control Phase

The final part of the DMAIC Model is the Control phase where we need to ensure that the new changes become business as normal and we do not revert to the same way of working as before.

During this phase, we want to ensure that we close the project off by validating the project savings and ensuring the new process is correctly documented. We also need to make sure that new measures and process KPI’s are in place and, finally that we get the business champion to sign off on both the project and the savings. We may need to redesign the workplace following the 5S principles .

The Control phase can be broken down into:

• Implement Ongoing Measurements
• Standardise Solutions
• Quantify the Improvement
• Close The Project

The key closing documents of the Control Phase is a Control Plan that documents all the changes and process steps with key risks, standard work instructions and the Project Close-Out document signed by the business owners to accept the change and the validated benefits.

Our approach to DMAIC follows Quentin Brook’s book “Lean Six Sigma & Minitab” which for anyone wishing to study Lean Six Sigma is a must for the  Green Belt Course  and the  Black Belt Course .

The dmaic model vs. a3 management vs. 8d problem solving.

The DMAIC model is not the only project management roadmap. Two others which are important is the A3 format which originally comes from Toyota and is very Lean focused and the 8D which draws more of the DMAIC structure but with the 1-page idea of the A3.

Everyone has their own preference but each method is interchangeable. The DMAIC Structure lends its self naturally to a multi-slide Powerpoint presentation. Whereas the A3 is a single-page document which is perfect for internal communication and adding into War Rooms and Control Towers.

What’s important is that every problem-solving approach follows the PDCA (Plan, Do, Check and Act) Scientific Problem Solving format. The reset is just a preference or using the right tool in the right circumstances.

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Reagan Pannell

Reagan Pannell is a highly accomplished professional with 15 years of experience in building lean management programs for corporate companies. With his expertise in strategy execution, he has established himself as a trusted advisor for numerous organisations seeking to improve their operational efficiency.

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PDCA Cycle Explained: 4 Steps for Continuous Learning and Improvement

The Meaning of PDCA Cycle

PDCA Cycle (also known as PDSA Cycle or Deming Cycle), is a problem-solving method used for the continuous learning and improvement of a process or product. 

There are 4 basic steps in PDCA Cycle:

• Plan : identify a problem and possible solutions
• Do : execute the plan and test the solution(s)
• Check : evaluate the results and lessons learned
• Act : improve the plan/process for better solutions

These four steps incorporate inductive-deductive interplay and have been a simple and scientific approach for problem-solving (process-improving). It follows the curve of how we acquire knowledge through constant reflection, standardization, and modification.

The PDCA framework begins with a planning phase where a problem or a process to be improved is identified. This involves not only the goal setting and finding possible solutions, but also hypothesizing methods that can be used to reach the ultimate goal. Another thing that needs special attention is defining the success metrics. This simply means a clear evaluation matrix is ideally to be set beforehand.

Then, the solution(s) will be tested in the Do process. To detach the Do, there could be two steps: making the Do multiple To-Dos by splitting the task and defining them with a specific time, personnel, and steps, and collecting real-time data and feedback. 

Check includes analyzing the results and comparing them to the hypothesis in the Plan stage: how well the solution worked, how much the goal has been achieved, and whether the methods were proven feasible. If there are any unexpected issues, you may also need to find the causes and possible solutions. Note that there might be forth and back between Do and check.

The Act step closes the cycle, which involves adjustment on the initial goal or solutions and integration of all key learnings by the entire process, to standardize successful parts and avoid error recurrence. In a nutshell, the Act phase summarizes the current cycle and prepares for the next.

However, the PDCA cycle doesn’t stop here. Instead, it can repeat from the beginning with a modified version of the Plan. There is no end to it and sustainability should be its main pitch.

How PDCA Has Evolved

Usually used interchangeably with “PDSA Cycle”, “Deming Cycle”, “Deming wheel”, “Shewhart Cycle” etc, the PDCA model has indeed confronted some misunderstanding and confusion. It remains unexplained in most cases how PDCA became what it is today and what’s the difference between those mysterious terminologies and how they interact. According to Ronald D. Moen & Clifford L. Norman , its evolution could be summarized like the following:

Shewhart cycle (1939): Specification - Production - Inspection . 

He brought up this method from the viewpoint of Quality Control.

Deming Wheel (1950): Design the product - Make the product - Sell it - Test it .

Deming built off the Shewhart cycle and emphasized the four steps should be rotated constantly to aim for the product quality. This has gained increasing popularity when Deming participated in the Japanese Union of Scientists and Engineers (JUSE).

PDCA Cycle (the 1950s):  Plan - Do - Check - Act.

A Japanese executive reworked the Deming Wheel and translated it into the PDCA Cycle for problem-solving. PDCA emphasizes more on the establishment of standards during the process and the ongoing modification of those standards. 

Extended PDCA Cycle (1985): Plan - Do - Check - Act .

Kaoru Ishikawa refined the PDCA model to include more steps in the Plan and Do steps: Identify the goals and methods to use; involve in training and education during implementation.

PDSA Cycle (1985): Plan - Do - Study - Act .

Deming claimed that the ownership of PDCA Cycle was never recognized by anyone and the word “check” was used incorrectly because it means “to hold back”. Therefore, he replaced it with “study” to emphasize the importance of the continuous learning-improvement model.

How to Implement - PDCA Examples

Now, you’ve got a clear idea of what the PDCA Cycle is and how it changes over time. As a simple and structured method widely adopted in Quality Control and Total Quality Management, can it also be applied in wider areas, such as personal growth and business development? Yes, I’ll give you a couple of examples.

Benefits of PDCA Cycle

Among all those other methods, why does the PDCA model shade some lights in the history, especially known for the “Japanese Quality” and is still widely used today? Some key benefits of it need to be valued.

PDCA methodology emphasizes minimizing errors and maximizing outcomes. When applied to business development, e.g. a product’s iterations, it could ensure a developing path where the product is shaped better and better to the market and customers. It’s the same when it comes to personal growth. It also leaves space for constant check and reflection, which can avoid wasting time on the mistakes or making the same mistakes.

PDCA framework follows a learning curve and enhances the learning-improvement process continually. This is the key factor defining PDCA as a scientific and methodical way to gain knowledge. With knowledge building up, people’s ability goes up. 

PDCA model encourages a growth mindset. Seeing continuous improvements is a good way to enhance individuals’ self-esteem levels and bring a great sense of accomplishment. People tend to find meaning in the things they do. Imagine if one stops making progress, they would stay in the static and lose meaning in repetitive work and life. 

• PDCA Cycle is a simple and scientific way for problem-solving and process improvement.
• PDCA Cycle involves four key steps: Plan, Do, Check and Act.
• PDCA works slightly differently from Deming Cycle, Shewhart Cycle, and PDSA.
• PDCA Cycle is a never-ending process that can be used on a continual basis.
• PDCA Cycle can be used for quality control, business development, and personal growth.

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• Guide: DMAIC
• June 2, 2023
• Learn Lean Sigma

The DMAIC methodology is a popular problem-solving framework that is used to drive process improvements and achieve measurable results. Businesses can improve efficiency, quality, and customer satisfaction by using a structured and data-driven approach to identify, analyze, and address issues.

Table of Contents

The dmaic steps, step 1: define.

In step one of the DMAIC process, you are focused on defining, which involves defining all of the elements of the improvement process and is one of the most important stages as it lays the foundation for stages that follow and ensures the project goes on the right track.

By the end of the define stage, the project should have the following defined:

• Define the problem
• Defined goal
• Defined the process
• Identify the customers and their needs ( VOC )
• Identify Stakeholders
• Define the project timeline

DMAIC Project Report Template- Page 1

Step 2: Measure

After successfully defining the problem you are looking to resolve or the process you want to improve, it is time to work on the measurement phase, which involves collecting data on the problem to verify what the problem is.

Within this you will work with a cross-functional team to initially create a data collection plan which will plan out what data is needed to verify the problem and also help with the analysis stage that comes next.

The data collection plan plans out what data will be collected, where it is collected from when it will be collected, the frequency, who will record it and the method. To get an in-depth understanding, take a look at the Data Collection Guide.

Once you have this data collected, you should be able to get a baseline measurement understanding of the process, which would be referred to as the current state. 

Another way the measure phase can be conducted is by creating a process map to define the process “as-is,” or the current state of how the process is being completed.

Detailed sub process map

By mapping out the process, you can analyse the current state and later create the future state process map that will look to improve the process and remove waste from the process in relation the 8 Process Wastes

Value add analysis example

Step 3: Analyze

Following the collection of the data needed to understand the problem, the next step is to conduct analysis, which in Lean Six Sigma is a huge topic as there are many ways analysis can be done depending on the problem and the type of data that you have.

In the analysis phase, some of the methods that can be used include Fishbone Diagram, 5 Whys and FMEA for non-numerical data. However, for numerical data, the list of tools includes the following:

• Hypothesis Testing
• Correlation Analysis
• Regression Analysis
• Pareto Chart
• Scatter Plot
• Time Series Plot

We could not cover all of these methods within this guide. However, if you are looking to use one or more of these methods, consult our guides section to learn more about them and how to conduct the analysis.

This analysis is done to verify the root causes of problems, understand what is causing them, and direct the improvement team to know what action needs to be taken to address them. 

DMAIC-Template-Example-Page-2

Step 4: Improve

After the analysis has been completed, and the root cause of the problem has been identified, the project team should conduct a brainstorming session to gather various potential solutions to the problem. 

Once the brainstorming has been done and the solutions have been identified, the team should then create a plan for implementation. This will likely be in the form of an action plan that sets out what the actions are when they need to be done, and who will do them.

The plan will likely involve pilot testing the improvements by conducting small tests or trials and analyzing the results before full implementation. It is also common practice to conduct a cost-benefit analysis to evaluate if it is feasible to take full advantage of the potential solutions.

Step 5: Control

Once the improvements are in place, they need to be controlled and sustained to ensure that they are long-term solutions and not short-term ones, following the improvement and then reverting back to pre-improvement levels. 

This is usually done using tools such as control charts that collect data at regular intervals to measure process performance, supported by updating documentation and standard operating procedures that confirm and formalize any changes to the process. It could also include updating any training to individuals involved with the process and regular audits of the process.

DMAIC-Template-Example-Page-3

DMAIC Template

If you are looking to use the DMAIC methodology you might find it useful to use the DMAIC project report template to structure and communicate your project to the business. Feel free to download it from the template section.

The DMAIC methodology is a proven, structured approach for process improvement that stands for Define, Measure, Analyze, Improve, and Control. As a backbone of Lean Six Sigma, it offers a comprehensive roadmap for problem-solving and optimizing business processes.

Starting with the ‘Define’ phase, the methodology emphasizes laying a strong foundation by setting objectives and assembling a team. The ‘Measure’ phase focuses on collecting relevant data to understand the current state. Then comes ‘Analyze,’ where the focus shifts to identifying root causes through a variety of analytical tools. ‘Improve’ involves brainstorming solutions and pilot testing, while the ‘Control’ phase ensures that improvements are sustainable over the long term. Altogether, DMAIC offers a complete, data-driven strategy for achieving measurable improvements in efficiency, quality, and customer satisfaction.

• Berardinelli, C.F., 2012. TO DMAIC or not to DMAIC? .  Quality Progress ,  45 (11), p.72.
• De Mast, J. and Lokkerbol, J., 2012. An analysis of the Six Sigma DMAIC method from the perspective of problem solving.  International Journal of Production Economics ,  139 (2), pp.604-614.

A: DMAIC stands for Define, Measure, Analyze, Improve, and Control. It is a structured problem-solving methodology used in Six Sigma to improve processes and reduce defects.

A: The purpose of DMAIC is to identify and address problems or inefficiencies in a process, leading to measurable and sustainable improvements. It provides a framework for problem-solving and continuous improvement.

A: The steps in DMAIC are as follows:

• Define: Clearly define the problem and project goals.
• Measure: Gather data and measure the current state of the process.
• Analyze: Analyze the data to identify the root causes of the problem.
• Improve: Develop and implement solutions to address the identified causes.
• Control: Establish control mechanisms to sustain the improvements made and prevent future issues.

A: No, DMAIC is a versatile problem-solving methodology that can be applied to various industries and processes, including manufacturing, service, healthcare, software development, and more. It is applicable wherever there is a need for process improvement and reducing defects.

Daniel Croft

Daniel Croft is a seasoned continuous improvement manager with a Black Belt in Lean Six Sigma. With over 10 years of real-world application experience across diverse sectors, Daniel has a passion for optimizing processes and fostering a culture of efficiency. He's not just a practitioner but also an avid learner, constantly seeking to expand his knowledge. Outside of his professional life, Daniel has a keen Investing, statistics and knowledge-sharing, which led him to create the website learnleansigma.com, a platform dedicated to Lean Six Sigma and process improvement insights.

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Overview of the Problem-Solving Mental Process

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

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You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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The Problem-Solving Process

Looking at the basic problem-solving process to help keep you on the right track.

By the Mind Tools Content Team

Problem-solving is an important part of planning and decision-making. The process has much in common with the decision-making process, and in the case of complex decisions, can form part of the process itself.

We face and solve problems every day, in a variety of guises and of differing complexity. Some, such as the resolution of a serious complaint, require a significant amount of time, thought and investigation. Others, such as a printer running out of paper, are so quickly resolved they barely register as a problem at all.

Despite the everyday occurrence of problems, many people lack confidence when it comes to solving them, and as a result may chose to stay with the status quo rather than tackle the issue. Broken down into steps, however, the problem-solving process is very simple. While there are many tools and techniques available to help us solve problems, the outline process remains the same.

The main stages of problem-solving are outlined below, though not all are required for every problem that needs to be solved.

1. Define the Problem

Clarify the problem before trying to solve it. A common mistake with problem-solving is to react to what the problem appears to be, rather than what it actually is. Write down a simple statement of the problem, and then underline the key words. Be certain there are no hidden assumptions in the key words you have underlined. One way of doing this is to use a synonym to replace the key words. For example, ‘We need to encourage higher productivity ’ might become ‘We need to promote superior output ’ which has a different meaning.

2. Analyze the Problem

Ask yourself, and others, the following questions.

• Where is the problem occurring?
• When is it occurring?
• Why is it happening?

Be careful not to jump to ‘who is causing the problem?’. When stressed and faced with a problem it is all too easy to assign blame. This, however, can cause negative feeling and does not help to solve the problem. As an example, if an employee is underperforming, the root of the problem might lie in a number of areas, such as lack of training, workplace bullying or management style. To assign immediate blame to the employee would not therefore resolve the underlying issue.

Once the answers to the where, when and why have been determined, the following questions should also be asked:

• Where can further information be found?
• Is this information correct, up-to-date and unbiased?
• What does this information mean in terms of the available options?

3. Generate Potential Solutions

When generating potential solutions it can be a good idea to have a mixture of ‘right brain’ and ‘left brain’ thinkers. In other words, some people who think laterally and some who think logically. This provides a balance in terms of generating the widest possible variety of solutions while also being realistic about what can be achieved. There are many tools and techniques which can help produce solutions, including thinking about the problem from a number of different perspectives, and brainstorming, where a team or individual write as many possibilities as they can think of to encourage lateral thinking and generate a broad range of potential solutions.

4. Select Best Solution

When selecting the best solution, consider:

• Is this a long-term solution, or a ‘quick fix’?
• Is the solution achievable in terms of available resources and time?
• Are there any risks associated with the chosen solution?
• Could the solution, in itself, lead to other problems?

This stage in particular demonstrates why problem-solving and decision-making are so closely related.

5. Take Action

In order to implement the chosen solution effectively, consider the following:

• What will the situation look like when the problem is resolved?
• What needs to be done to implement the solution? Are there systems or processes that need to be adjusted?
• What will be the success indicators?
• What are the timescales for the implementation? Does the scale of the problem/implementation require a project plan?
• Who is responsible?

Once the answers to all the above questions are written down, they can form the basis of an action plan.

6. Monitor and Review

One of the most important factors in successful problem-solving is continual observation and feedback. Use the success indicators in the action plan to monitor progress on a regular basis. Is everything as expected? Is everything on schedule? Keep an eye on priorities and timelines to prevent them from slipping.

If the indicators are not being met, or if timescales are slipping, consider what can be done. Was the plan realistic? If so, are sufficient resources being made available? Are these resources targeting the correct part of the plan? Or does the plan need to be amended? Regular review and discussion of the action plan is important so small adjustments can be made on a regular basis to help keep everything on track.

Once all the indicators have been met and the problem has been resolved, consider what steps can now be taken to prevent this type of problem recurring? It may be that the chosen solution already prevents a recurrence, however if an interim or partial solution has been chosen it is important not to lose momentum.

Problems, by their very nature, will not always fit neatly into a structured problem-solving process. This process, therefore, is designed as a framework which can be adapted to individual needs and nature.

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The Ultimate Problem-Solving Process Guide: 31 Steps and Resources

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16 Min Read

Got Challenges with Your Problem-Solving Process? Are You Frustrated?

prob·lem-solv·ing noun

-the process of finding solutions to difficult or complex issues.

It sounds so simple, doesn’t it?

But in reality problem-solving is hard. It’s almost always more complex than it seems. That’s why problem-solving can be so frustrating sometimes. You can feel like you’re spinning your wheels, arguing in circles, or just failing to find answers that actually work.

And when you’ve got a group working on a problem, it can get even muddier …differences of opinions, viewpoints colored by different backgrounds, history, life experiences, you name it. We’re all looking at life and work from different angles, and that often means disagreement. Sometimes sharp disagreement.

That human element, figuring out how to take ourselves out of the equation and make solid, fact-based decisions , is precisely why there’s been so much written on problem-solving. Which creates its own set of problems.

Whose method is best? How can you possibly sift through them all? Are we to have one person complete the entire problem-solving process by themselves or rely on a larger team to find answers to our most vexing challenges in the workplace ?

Today, we’re going to make sense of it all. We’ll take a close look at nine top problem-solving methods. Then we’ll grab the best elements of all of them to give you a process that will have your team solving problems faster, with better results , and maybe with less sharp disagreement.

Ready to dive in?

9 Profitable Problem-Solving Techniques and Methods

While there are loads of methods to choose from, we are going to focus on nine of the more common ones. You can use some of these problem-solving techniques reactively to solve a known issue or proactively to find more efficient or effective ways of performing tasks. If you want to explore other methods, check out this resource here .

A helpful bit of advice here is to reassure people that you aren’t here to identify the person that caused the problem . You’re working to surface the issue, solve it and make sure it doesn’t happen again, regardless of the person working on the process. It can’t be understated how important it is to continually reassure people of this so that you get unfiltered access to information.

Without this, people will often hide things to protect themselves . After all, nobody wants to look bad, do they?

With that said, let’s get started…

Alex Osborn coined the term “Creative Problem Solving” in the 1940s with this simple four-step process:

• Clarify : Explore the vision, gather data, and formulate questions.
• Ideate : This stage should use brainstorming to generate divergent thinking and ideas rather than the random ideas normally associated with brainstorming.
• Develop : Formulate solutions as part of an overall plan.
• Implement : Put the plan into practice and communicate it to all parties.

Source: http://www.davidcooperrider.com/ai-process/

This method seeks, first and foremost, to identify the strengths in people and organizations and play to that “positive core” rather than focus our energies on improving weaknesses . It starts with an “affirmative topic,” followed by the “positive core (strengths).” Then this method delves into the following stages:

• Discovery (fact-finding)
• Dream (visioning the future)
• Design (strategic purpose)
• Destiny (continuous improvement)

This method simply suggests that we ask “Why” at least five times during our review of the problem and in search of a fix. This helps us dig deeper to find the the true reason for the problem, or the root cause. Now, this doesn’t mean we just keeping asking the same question five times. Once we get an answer to our first “why”, we ask why to that answer until we get to five “whys”.

Using the “five whys” is part of the “Analyze” phase of Six Sigma but can be used with or without the full Six Sigma process.

Review this simple Wikipedia example of the 5 Whys in action:

The vehicle will not start. (the problem)

• Why? – The battery is dead. (First why)
• Why? – The alternator is not functioning. (Second why)
• Why? – The alternator belt has broken. (Third why)
• Why? – The alternator belt was well beyond its useful service life and not replaced. (Fourth why)
• Why? – The vehicle was not maintained according to the recommended service schedule. (Fifth why, a root cause)

While many people have at least heard of Lean or Six Sigma, do we know what it is? Like many problem-solving processes, it has five main steps to follow.

• Define : Clearly laying out the problem and soliciting feedback from those who are customers of the process is necessary to starting off on the right foot.
• Measure : Quantifying the current state of the problem is a key to measuring how well the fix performed once it was implemented.
• Analyze : Finding out the root cause of the problem (see number 5 “Root Cause Analysis” below) is one of the hardest and least explored steps of Six Sigma.
• Improve : Crafting, executing, and testing the solution for measureable improvement is key. What doesn’t get implemented and measured really won’t make a difference.
• Control : Sustaining the fix through a monitoring plan will ensure things continue to stay on track rather than being a short-lived solution.

Compared to other methods, you’ll more often find this technique in a reactive problem-solving mode, but it is helpful nonetheless. Put simply, it requires a persistent approach to finding the highest-level cause , since most reasons you’ll uncover for a problem don’t tell the whole story.

Most of the time, there are many factors that contributed to an issue. The main reason is often shrouded in either intentional or unintentional secrecy. T aking the time to drill down to the root of the issue is key to truly solving the problem.

Named for W. Edwards Deming and Walter A. Shewhart, this model follows a four-step process:

• Plan : Establish goals and objectives at the outset to gain agreement . It’s best to start on a small scale in order to test results and get a quick win.
• Do : This step is all about the implementation and execution of the solution .
• Check : Study and compare actual to expected results. Chart this data to identify trends.
• Act/Adjust : If the check phase showed different results, then adjust accordingly . If worse than expected, then try another fix. If the same or better than expected, then use that as the new baseline for future improvements.

While this is named “8D” for eight disciplines, there are actually nine , because the first is listed as step zero. Each of the disciplines represents a phase of this process. Its aim is to implement a quick fix in the short term while working on a more permanent solution with no recurring issues.

• Prepare and Plan : Collecting initial information from the team and preparing your approach to the process is a necessary first step.
• Form a Team : Select a cross-functional team of people, one leader to run meetings and the process, and one champion/sponsor who will be the final decision-maker.
• Describe the Problem : Using inductive and deductive reasoning approaches , lay out the precise issue to be corrected.
• Interim Containment Action : Determine if an interim solution needs to be implemented or if it can wait until the final fix is firmed up. If necessary, the interim action is usually removed once the permanent solution is ready for implementation.
• Root Cause Analysis and Escape Point : Finding the root of the issue and where in the process it could’ve been found but was not will help identify where and why the issue happened.
• Permanent Corrective Action : Incorporating key criteria into the solution, including requirements and wants, will help ensure buy-in from the team and your champion.
• Implement and Validate the Permanent Corrective Action : Measuring results from the fix implemented validates it or sends the team back to the drawing board to identity a more robust solution.
• Prevent Recurrence : Updating work procedure documents and regular communication about the changes are important to keep old habits in check.
• Closure and Team Celebration : Taking time to praise the team for their efforts in resolving the problem acknowledges the part each person played and offers a way to move forward.

The US Army has been solving problems for more than a couple of centuries , so why not take a look at the problem-solving process they’ve refined over many years? They recommend this five step process:

• Identify the Problem : Take time to understand the situation and define a scope and limitations before moving forward.
• Gather Information : Uncover facts, assumptions, and opinions about the problem, and challenge them to get to the truth.
• Five screening items should be questioned. Is it feasible, acceptable, distinguishable, and complete?
• Evaluation criteria should have these 5 elements: short title, definition, unit of measure, benchmark, and formula.
• Generate, Analyze, and Compare Possible Solutions : Most fixes are analyzed, but do you compare yours to one another as a final vetting method?
• Choose a Solution and Implement : Put the fix into practice and follow up to ensure it is being followed consistently and having the desired effect.

Tim Hurson introduced this model in 2007 with his book, Think Better. It consists of the following six actions.

• Ask “What is going on?” : Define the impact of the problem and the aim of its solution.
• Ask “What is success?” : Spell out the expected outcome, what should not be in fix, values to be considered, and how things will be evaluated.
• Ask “What is the question?” : Tailor questions to the problem type. Valuable resources can be wasted asking questions that aren’t truly relevant to the issue.
• Generate answers : Prioritize answers that are the most relevant to solutions, without excluding any suggestion to present to the decision-makers.
• Forge the solution : Refine the raw list of prioritized fixes, looking for ways to combine them for a more powerful solution or eliminate fixes that don’t fit the evaluation criteria.
• Align resources: Identify resources, team, and stakeholders needed to implement and maintain the solution.

Steal This Thorough 8-Step Problem-Solving Process

Now that we’ve reviewed a number of problem-solving methods, we’ve compiled the various steps into a straightforward, yet in-depth, s tep-by-step process to use the best of all methods.

Dig Deep: Identify, Define, and Clarify the Issue

“Elementary, my dear Watson,” you might say.

This is true, but we often forget the fundamentals before trying to solve a problem. So take some time to gain understanding of critical stakeholder’s viewpoints to clarify the problem and cement consensus behind what the issue really is.

Sometimes it feels like you’re on the same page, but minor misunderstandings mean you’re not really in full agreement.. It’s better to take the time to drill down on an issue before you get too far into solving a problem that may not be the exact problem . Which leads us to…

Dig Deeper: Root Cause Analysis

This part of the process involves identifying these three items :

• What happened?
• Why did it happen?
• What process do we need to employ to significantly reduce the chances of it happening again ?

You’ll usually need to sort through a series of situations to find the primary cause. So be careful not to stop at the first cause you uncover . Dig further into the situation to expose the root of the issue. We don’t want to install a solution that only fixes a surface-level issue and not the root. T here are typically three types of causes :

• Physical: Perhaps a part failed due to poor design or manufacturing.
• Human error: A person either did something wrong or didn’t do what needed to be done.
• Organizational: This one is mostly about a system, process, or policy that contributed to the error .

When searching for the root cause, it is important to ensure people that you aren’t there to assign blame to a person but rather identify the problem so a fix can prevent future issues.

Produce a Variety of Solution Options

So far, you’ve approached the problem as a data scientist, searching for clues to the real issue. Now, it’s important to keep your eyes and ears open, in case you run across a fix suggested by one of those involved in the process failure. Because they are closest to the problem, they will often have an idea of how to fix things. In other cases, they may be too close, and unable to see how the process could change.

The bottom line is to solicit solution ideas from a variety of sources , both close to and far away from the process you’re trying to improve.

You just never know where the top fix might come from!

Fully Evaluate and Select Planned Fix(es)

Evaluating solutions to a defined problem can be tricky since each one will have cost, political, or other factors associated with it. Running each fix through a filter of cost and impact is a vital step toward identifying a solid solution and hopefully settling on the one with the highest impact and low or acceptable cost.

Categorizing each solution in one of these four categories can help teams sift through them:

• High Cost/Low Impact: Implement these last, if at all, since t hey are expensive and won’t move the needle much .
• Low Cost/Low Impact: These are cheap, but you won’t get much impact.
• High Cost/High Impact: These can be used but should be second to the next category.
• Low Cost/High Impact: Getting a solid “bang for your buck” is what these fixes are all about. Start with these first .

Document the Final Solution and What Success Looks Like

Formalize a document that all interested parties (front-line staff, supervisors, leadership, etc.) agree to follow. This will go a long way towards making sure everyone fully understands what the new process looks like, as well as what success will look like .

While it might seem tedious, try to be overly descriptive in the explanation of the solution and how success will be achieved. This is usually necessary to gain full buy-in and commitment to continually following the solution. We often assume certain things that others may not know unless we are more explicit with our communications.

Successfully Sell and Execute the Fix

Arriving at this stage in the process only to forget to consistently apply the solution would be a waste of time, yet many organizations fall down in the execution phase . Part of making sure that doesn’t happen is to communicate the fix and ask for questions multiple times until all parties have a solid grasp on what is now required of them.

One often-overlooked element of this is the politics involved in gaining approval for your solution. Knowing and anticipating objections of those in senior or key leadership positions is central to gaining buy-in before fix implementation.

Rinse and Repeat: Evaluate, Monitor, and Follow Up

Next, doing check-ins with the new process will ensure that the solution is working (or identity if further reforms are necessary) . You’ll also see if the measure of predefined success has been attained (or is making progress in that regard).

Without regularly monitoring the fix, you can only gauge the success or failure of the solution by speculation and hearsay. And without hard data to review, most people will tell their own version of the story.

Collaborative Contingencies, Iteration, and Course Correction

Going into any problem-solving process, we should take note that we will not be done once the solution is implemented (or even if it seems to be working better at the moment). Any part of any process will always be subject to the need for future iterations and course corrections . To think otherwise would be either foolish or naive.

There might need to be slight, moderate, or wholesale changes to the solution previously implemented as new information is gained, new technologies are discovered, etc.

14 Fruitful Resources and Exercises for Your Problem-Solving Journey

Want to test your problem-solving skills?

Take a look at these twenty case study scenario exercises to see how well you can come up with solutions to these problems.

Still have a desire to discover more about solving problems? Check out these 14 articles and books…

The Lean Six Sigma Pocket Toolbook: A Quick Reference Guide to Nearly 100 Tools for Improving Quality and Speed

This book is like a Bible for Lean Six Sigma , all in a pocket-sized package.

The American Society for Quality has a short article on how it’s important to focus on the problem before searching for a solution. Wondering if you are solving the right problems? Check out this Harvard Business Review article. Looking for a fun and easy problem-solving book that was written by a McKinsey consultant? Take a look!

If you want a deeper dive into the seven steps of Creative Problem Solving , see this article. Appreciative Inquiry has been proven effective in organizations ranging from Roadway Express and British Airways to the United Nations and the United States Navy. Review this book to join the positive revolution. The Seattle Police Department has put together nine case studies that you can practice solving . While they are about police work, they have practical application in the sleuthing of work-related problems. Need a resource to delve further into Root Cause Analysis? Look no further than this book for answers to your most vexing questions .

This solid case study illustrates the complexities of solving problems in business. Learn all about the “8Ds” with this concise primer. Need to reduce groupthink in your organization’s problem-solving process ? Check out this article from the Harvard Business Review.

Tim Hurson details his own Productive Thinking Model at great length in this book from the author. This simple five-step process will help you break down the problem, analyze it, prioritize solutions, and sell them internally.

Critical Thinking : A Beginner’s Guide To Critical Thinking, Better Decision Making, And Problem Solving!

Looking for assistance with your problem-solving process.

There’s a lot to take in here, but following some of these methods are sure to improve your problem-solving process. However, if you really want to take problem-solving to the next level, InitiativeOne can come alongside your team to help you solve problems much faster than you ever have before.

There are several parts to this leadership transformation process provided by InitiativeOne, including a personal profile assessment, cognitive learning, group sessions with real-world challenges, personal discovery, and a toolkit to empower leaders to perform at their best.

There are really only two things stopping good teams from being great. One is how they make decisions and two is how they solve problems. Contact us today to grow your team’s leadership performance by making decisions and solving problems more swiftly than ever before!

Originally published at www.initiative-one.com

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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Psychology Spot

All About Psychology

The 5 phases of problem solving

Problem solving is a complex psychological process through which we try to find the best way to overcome an obstacle or face a challenge. Unfortunately, this process is not always linear, but can follow tortuous paths, plunging us into a situation of psychological anguish when we believe that there is no possible solution.

On the other hand, knowing the phases of problem solving will save us a lot of headaches. Providing a coherent structure to the situation that concerns us, and having a common thread that guides us along the way, will help us to put some order in the mental chaos that problems usually generate.

To Solve a Problem, Experience Does not Always Work in Our Favor

Experience can be a plus or, on the contrary, become an impediment to solving problems. Psychologists from the universities of Hong Kong and Princeton examined how we implement problem-solving strategies by asking a group of people to solve a series of problems with matches.

Participants were presented with a series of linked squares. Each square in the matrix was made up of separate pieces, and people had to remove a certain number of matches while keeping a specified number of squares intact. The interesting thing about these types of problems is that they generally have more than one solution, different strategies can be used and these must change according to the configuration of the matrix, just as it usually happens with life problems.

These researchers found that participants went through two major stages in problem solving. At first they let themselves be carried away by the perceptual characteristics of the problem and began to explore different strategies, some successful and others not.

In a second moment they used the accumulated experience to narrow their options of strategies, focusing on those that were more successful. The problem is that the more the participants trusted their strategic knowledge, the more difficulties they had in solving problems that demanded the application of novel strategies. In practice, they suffered from a kind of functional fixation.

These series of experiments show us that to solve a problem we must keep an open mind because along the way circumstances are likely to change and we need the mental flexibility necessary to change our problem-solving strategies.

The Stages of Problem Solving We Can All Apply

1. Identify the problem

It may seem like a truism, but the truth is that identifying the real problem is not as easy as it seems, especially when it comes to a situation that affects us emotionally. In fact, when the problem is too scary or we sense that we do not have the psychological tools to solve it, we usually put into practice defense mechanisms such as displacement that allow us to erase the problematic situation from our conscious mind.

Instead, being able to identify the problem is the first step in finding a solution. Many times that means stopping looking outside for the culprits and searching within, wondering why a situation is particularly bothering or hindering us.

2. Understand the problem

Many times the problem brings with it the seed of the solution. So one of the steps in solving a problem is making sure we understand it. It is not enough to identify the problem, we need to define it. For this we need to analyze it from different perspectives.

For example, if we are trying to carry out a professional project that does not finish taking off, we have to clarify the reasons. Do we need more training? Are we in an overly competitive sector? Do we have enough resources? We need to understand the source of the problem.

Organizing the information available is another crucial step in the problem-solving process. We have to ask ourselves both, what we know about the problem and everything we do not know. Ultimately, the accuracy of the solution will largely depend on the amount of information available.

3. Assume a psychological distance

Most of the major problems in life have the potential to generate an emotional tsunami. However, many times that affective involvement obfuscates and prevents us from thinking clearly. That is why on many occasions one of the most important but least known phases for solving problems consists of moving away from what concerns us. To assume a psychological distance , we can take a few days away from the problematic environment or try to stop thinking about what worries us for a while.

During that time the unconscious mind will continue to work and is likely to generate creative and perfectly valid insights that lead to the solution of the problem. That distance to allow us to overcome the functional fixations that prevent us from thinking outside the box, giving way to a mental restructuring that will allow us to see the problem from another perspective.

4. Find solutions and develop strategies

Each problem is different, so it will require a specific solution. A solution cannot always be reached by insight, so it will be necessary to think of possible alternatives to solve the problem. Synectics , for example, is a problem-solving method that uses creativity to find original solutions.

The next step is to develop a strategy, since solutions that do not materialize in concrete steps are very difficult to implement. Therefore, we must ask ourselves how we are going to implement our solution. In this phase of problem solving it is important to be honest with ourselves and “land” that strategy taking into account our resources and real availability. It is useless to develop a great strategy if we cannot apply it later.

5. Evaluation of progress

Very few problems are solved overnight. These are generally complex situations that we must patiently “unwind” over time. Therefore, another of the phases to solve a problem consists of monitoring the results that we are achieving. This way we make sure that we are on the right track and we are not wasting energy and time uselessly.

In this last stage of problem solving it is important to be able to adapt our expectations. It is difficult for a professional project to take off in the blink of an eye, so we must focus on the small steps that indicate that the solution is paying off. To do this, it is important to sit down and reflect on the impact of the solution from time to time.

We must also bear in mind that circumstances often change, so we may need to make adjustments to our initial solution. This requires great mental flexibility to change course when we realize that the strategy is not as effective as we would like.

Fedor, A. et. Al. (2015) Problem solving stages in the five square problem.  Front. Psychol ; 6: 1050.

Louis Lee, N. Y. & Johnson-Laird, P. N. (2013) Strategic changes in problem solving.  Journal of Cognitive Psychology ; 25: 165–173. 

Gillen, G. (2009) Managing Executive Function Impairments to Optimize Function.  Cognitive and Perceptual Rehabilitation ; 245-283.

Jennifer Delgado

I am a psychologist and I spent several years writing articles for scientific journals specialized in Health and Psychology. I want to help you create great experiences. Learn more about me .

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8-step problem solving process, organizational effectiveness.

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Step 1: Define the Problem

• What is the problem?
• How did you discover the problem?
• When did the problem start and how long has this problem been going on?
• Is there enough data available to contain the problem and prevent it from getting passed to the next process step? If yes, contain the problem.

Step 2: Clarify the Problem

• What data is available or needed to help clarify, or fully understand the problem?
• Is it a top priority to resolve the problem at this point in time?
• Are additional resources required to clarify the problem? If yes, elevate the problem to your leader to help locate the right resources and form a team. 
•  Consider a Lean Event (Do-it, Burst, RPI, Project).
• ∙Ensure the problem is contained and does not get passed to the next process step.

Step 3: Define the Goals

• What is your end goal or desired future state?
• What will you accomplish if you fix this problem?
• What is the desired timeline for solving this problem?

Step 4: Identify Root Cause of the Problem

• Identify possible causes of the problem.
• Prioritize possible root causes of the problem.
• What information or data is there to validate the root cause?

Step 5: Develop Action Plan

• Generate a list of actions required to address the root cause and prevent problem from getting to others.
• Assign an owner and timeline to each action.
• Status actions to ensure completion.

Step 6: Execute Action Plan

• Implement action plan to address the root cause.
• Verify actions are completed.

Step 7: Evaluate the Results

• Monitor and Collect Data.
• Did you meet your goals defined in step 3? If not, repeat the 8-Step Process. 
• Were there any unforeseen consequences?
• If problem is resolved, remove activities that were added previously to contain the problem.

Step 8: Continuously Improve

• Look for additional opportunities to implement solution.
• Ensure problem will not come back and communicate lessons learned.
• If needed, repeat the 8-Step Problem Solving Process to drive further improvements.

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Five Phases of Lean Six Sigma: A Complete Guide

• Written by Contributing Writer
• Updated on March 21, 2023

Customer expectations and behavior are constantly changing. Competition in nearly every industry seems to be intensifying. Profitability and growth now depend in large part on the efficiency of products and services and their speed to market.

Six Sigma is a process that organizations employ to optimize business processes for the most efficient and cost-effective approach while producing continuous product improvements.

In this article, we’ll explain the Six Sigma methodology, explore its benefits and challenges, and detail the five phases of Lean Six Sigma with examples.

What is the Six Sigma Methodology?

The Six Sigma methodology identifies defects and roadblocks that hinder performance, enabling companies to use strategies to streamline processes, decrease production variation, and improve the quality of products and services.

Six Sigma is a data-driven approach to establishing a culture committed to continuous process improvement. When implemented effectively and consistently, Six Sigma provides a framework for repeatable and systematic problem-solving.

Also Read: Six Sigma Methodologies for Process Improvement

What Is the Difference Between Six Sigma and Lean Six Sigma?

Six Sigma focuses on creating rigid consistency to reduce process variation and enhance process control. Lean Six Sigma eliminates processes that do not add value to promote more streamlined workflows.

In short, Six Sigma emphasizes reduction in variation using processes such as:

• Statistical data analysis
• Design of experiments
• Hypothesis testing

Lean Six Sigma focuses on reductions in waste using processes such as:

• Workplace organization
• Visual controls

In most cases, organizations today incorporate both methodologies into their Six Sigma quality management practices.

What Are the Benefits of Six Sigma?

The benefits of Six Sigma and the DMAIC process include:

Improvements in Quality

By focusing on identifying and eliminating defects and wasted steps in processes, organizations can improve the quality of operations and products or services.

Increases in Efficiency

Six Sigma identifies roadblocks and inefficiencies in systems. Efficiency and productivity gains provide significant results in many cases. In manufacturing, for example, these efficiencies can improve quality, turnaround times, and cycle times for equipment.

More Efficient Decision Making

By putting quantifiable data at the heart of decisions, organizations can reduce making decisions based on intuition or assumptions. Instead, decisions are made based on facts and evidence and are measured against baselines for continuous improvement.

Improved Customer or End-User Satisfaction

When organizations can streamline processes to produce faster or higher-quality results, it improves customer satisfaction. This, in turn, can generate more loyal customers and higher customer retention levels.

Cost Savings

There can be significant financial benefits from implementing a Six Sigma methodology. Increased efficiency reduces waste and defects, creates efficiencies and streamlines processes, and produces better customer satisfaction.

Competitive Advantage

Organizations that utilize the five phases of Lean Six Sigma methodology can gain a competitive advantage in several ways, including delivering higher-quality products or services more efficiently and cost-effectively than their competitors.

Employee Engagement

Not to be overlooked in the five phases of Six Sigma are the benefits to employee engagement. Project teams typically include employees from various disciplines. Working together to understand problems and develop solutions encourages teamwork and is fundamental to creating a culture of continuous improvement.

Involving team members in the process also creates buy-in since team members have a stake in the success of the Six Sigma process.

Also Read: Six Sigma Principles: A Comprehensive Guide to Implementing and Optimizing Your Processes

What is DMAIC?

DMAIC is an acronym for the five phases of Six Sigma.

The DMAIC phases are an iterative process used to seek quality improvement by focusing on the process to create more efficient and permanent solutions. DMAIC provides the structure to the process, enabling project teams to use specific tools and deliverables that lead to process improvements. While most teams work through DMAIC in a linear fashion, it isn’t mandatory to do it this way. The process itself encourages team members to backtrack to previous steps, especially when additional information or insight is needed.

The Five Phases of Lean Six Sigma

Each of the five phases of Six Sigma is data-driven and focuses on standardizing an organization’s approach to problem-solving. So, let’s dig deeper into the DMAIC phases.

Phase 1: Define

In the define stage, team members work together to identify the information they’ll need to break down components of a problem or process and create smaller actionable terms. Rather than focusing on abstract goals, it seeks quantifiable and qualifiable data to clearly identify the objective of the project.

Teams will identify the Critical to Quality (CTQ) attributes as determined by the end-user or customer — called the voice of the customer (VOC) — and create a process map, including process inputs and process outputs.

For example, a manufacturing company has identified a problem somewhere in their production process that is leading to product defects outside of acceptable ranges but doesn’t know where or why these defects occur. In the define phase, they would design the project scope and establish objectives, such as reducing the number of acceptable defects in the production process to a specific number.

Phase 2: Measure

The measure step of the DMAIC phases of the Six Sigma process assesses current processes and capabilities. While the goal is to make process improvements, teams need a clear understanding of the current conditions to judge the effectiveness of any future changes. Without a baseline, it’s hard to measure if you’ve made any progress.

In this phase, team members will measure the current process and create a baseline for future comparison.

For example, this phase would identify the frequency of the defects and information about potential causes, such as production line speed or equipment breakdowns, deviations in material or suppliers, or other mitigating factors.

Phase 3: Analyze

The analysis phase of the Six Sigma methodology takes a deep dive into the data that’s been gathered to isolate root causes of inefficiencies and identify defects. Teams in this phase often create detailed subprocess maps for every step with the goal of eliminating and streamlining steps to improve performance and quality.

Teams might deploy several analysis tools, such as Pareto charts or fishbone diagrams, to analyze the root causes of the high defect rate.

Teams also analyze the performance and financial benefits of solving a problem or improving a process.

Phase 4: Improve

After identifying the root cause of any issues, this phase focuses on finding the solution or improvement. Common methods include deploying a design of experiment model to isolate variables and test hypotheses until obstacles are uncovered.

Once process improvements are identified, teams create a proposed solution and then implement pilot programs to test, ensuring solutions meet project objectives and are financially viable.

As an outcome of analyzing the data, teams might redesign the production process, recommend upgrading equipment or maintenance procedures, or provide additional training to operators.

Phase 5: Control

The final step of the five phases of Lean Six Sigma involves documentation of the solutions created for process owners. This includes actionable steps, timelines, and milestones for implementation. In this phase, the control plan details the daily workflow.

The project team then monitors the project for a prescribed period to make sure the process meets performance expectations in real-world environments before turning it over to process owners.

In the control phase, organizations might implement control charts to tightly monitor production output and defect rates, the impact of operator training, and any other solutions deployed.

Each of the five phases of Six Sigma works together to create a repeatable template for improving business processes. When fully integrated into an organization’s culture, it enables teams to innovate new solutions, measure effectiveness and efficiency, and create quantifiable process improvements.

Also Read: Six Sigma vs. Lean Six Sigma: Which Methodology Is Right for Your Business?

Challenges with Implementing Six Sigma

Even though Six Sigma can help your team become more efficient and cost-effective, there are implementation challenges when it comes to DMAIC phases.

The five phases of Lean Six Sigma examine increasingly minute details and micro-steps in every aspect of task completion. This can be challenging for team members to accomplish without experienced project managers that are trained in Six Sigma methodologies. Organizations need team members that understand statistical training and quantifying data points effectively in order to successfully implement this methodology — it’s not something you can pick up on the fly.

Implementing Six Sigma Tools

There’s also a learning curve for the diverse set of tools required to uncover root causes and validate potential solutions. Throughout the DMAIC process, teams may need to be proficient in using tools such as:

• Pareto charts
• Gage R&R
• Process capability upper and lower bounds
• Attribute agreement analysis
• ANOVA statistical modeling
• Regression equations
• Control charts
• Statistical process control (SPC)
• Value stream mapping

Other tools may also be needed depending on industry and functionality. As you can see, some of these tools are incredibly specific and scientific, so it can be tricky to implement if someone on your team isn’t familiar enough with them while going through the five phases of Lean Six Sigma

Organizational Commitment

Effective Six Sigma implementation required buy-in across the wider organization, starting at the very top. Without proper funding, resources, and continued support, the process can be ineffective. In some cases, this leads to incomplete solutions, lack of follow-through, or failure to embrace workplace changes.

To be successful, Six Sigma needs to be embraced and become part of the company culture.

Also Read: Value Stream Mapping in Six Sigma

Fast Track Your Career in Quality Management

Efficiently deploying Six Sigma methodologies and DMAIC phases requires training and expertise. Professionally-trained and certified Six Sigma professionals can make substantive contributions to organizational improvement and earn a lucrative career as a Six Sigma expert.

If you are looking to fast-track your career in Quality Management, an accredited Six Sigma course can help. You can get hands-on experience and mentoring as you solve real-world business processes and learn about:

• Agile management
• Lean management
• Six Sigma Green Belt
• Lean Six Sigma Black Belt
• Quality management
• Digital transformation

Become a Lean Six Sigma Expert with the post graduate program delivered by Simplilearn in collaboration with the University of Massachusetts. Download the program brochure to learn more about the Lean Six Sigma course. This program is accredited by the International Association for Six Sigma Certification (IASSC) and has professors who can share real-world experiences to help bring these complicated concepts to life.

You might also like to read:

The Top 24 Lean Six Sigma Interview Questions for 2023

Six Sigma Methodologies for Process Improvement

Ultimate Guide to Six Sigma Control Charts

Process Mapping in Six Sigma: Here’s All You Need to Know

What Are the Elements of a Six Sigma Project Charter?

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By following this process, you can avoid jumping to conclusions, overlooking important details, or making hasty decisions. Instead, you can approach problems with a clear and structured mindset, which can help you make better decisions and achieve better outcomes.

In this article, we'll explore each step of the 7-Step Problem-Solving Process in detail so you can start mastering this valuable skill. You can download the process's free PowerPoint and PDF templates at the end of the blog post .

Step 1: Define the Problem

The first step in the problem-solving process is to define the problem. This step is crucial because finding a solution is only accessible if the problem is clearly defined. The problem must be specific, measurable, and achievable.

One way to define the problem is to ask the right questions. Questions like "What is the problem?" and "What are the causes of the problem?" can help. Gathering data and information about the issue to assist in the definition process is also essential.

Another critical aspect of defining the problem is identifying the stakeholders. Who is affected by it? Who has a stake in finding a solution? Identifying the stakeholders can help ensure that the problem is defined in a way that considers the needs and concerns of all those affected by it.

Once the problem is defined, it is essential to communicate the definition to all stakeholders. This helps to ensure that everyone is on the same page and that there is a shared understanding of the problem.

Step 2: Disaggregate

After defining the problem, the next step in the 7-step problem-solving process is to disaggregate the problem into smaller, more manageable parts. Disaggregation helps break down the problem into smaller pieces that can be analyzed individually. This step is crucial in understanding the root cause of the problem and identifying the most effective solutions.

Disaggregation can be achieved by breaking down the problem into sub-problems, identifying the contributing factors, and analyzing the relationships between these factors. This step helps identify the most critical factors that must be addressed to solve the problem.

A tree or fishbone diagram is one effective way to disaggregate a problem. These diagrams help identify the different factors contributing to the problem and how they are related. Another way is to use a table to list the other factors contributing to the situation and their corresponding impact on the issue.

Disaggregation helps in breaking down complex problems into smaller, more manageable parts. It helps understand the relationships between different factors contributing to the problem and identify the most critical factors that must be addressed. By disaggregating the problem, decision-makers can focus on the most vital areas, leading to more effective solutions.

Step 3: Prioritize

After defining the problem and disaggregating it into smaller parts, the next step in the 7-step problem-solving process is prioritizing the issues that need addressing. Prioritizing helps to focus on the most pressing issues and allocate resources more effectively.

There are several ways to prioritize issues, including:

• Urgency: Prioritize issues based on their urgency. Problems that require immediate attention should be addressed first.
• Impact: Prioritize issues based on their impact on the organization or stakeholders. Problems with a high impact should be given priority.
• Resources: Prioritize issues based on the resources required to address them. Problems that require fewer resources should be dealt with first.

Considering their concerns and needs, it is important to involve stakeholders in the prioritization process. This can be done through surveys, focus groups, or other forms of engagement.

Once the issues have been prioritized, developing a plan of action to address them is essential. This involves identifying the resources required, setting timelines, and assigning responsibilities.

Prioritizing issues is a critical step in problem-solving. By focusing on the most pressing problems, organizations can allocate resources more effectively and make better decisions.

Step 4: Workplan

After defining the problem, disaggregating, and prioritizing the issues, the next step in the 7-step problem-solving process is to develop a work plan. This step involves creating a roadmap that outlines the steps needed to solve the problem.

The work plan should include a list of tasks, deadlines, and responsibilities for each team member involved in the problem-solving process. Assigning tasks based on each team member's strengths and expertise ensures the work is completed efficiently and effectively.

Creating a work plan can help keep the team on track and ensure everyone is working towards the same goal. It can also help to identify potential roadblocks or challenges that may arise during the problem-solving process and develop contingency plans to address them.

Several tools and techniques can be used to develop a work plan, including Gantt charts, flowcharts, and mind maps. These tools can help to visualize the steps needed to solve the problem and identify dependencies between tasks.

Developing a work plan is a critical step in the problem-solving process. It provides a clear roadmap for solving the problem and ensures everyone involved is aligned and working towards the same goal.

Step 5: Analysis

Once the problem has been defined and disaggregated, the next step is to analyze the information gathered. This step involves examining the data, identifying patterns, and determining the root cause of the problem.

Several methods can be used during the analysis phase, including:

• Root cause analysis
• Pareto analysis
• SWOT analysis

Root cause analysis is a popular method used to identify the underlying cause of a problem. This method involves asking a series of "why" questions to get to the root cause of the issue.

Pareto analysis is another method that can be used during the analysis phase. This method involves identifying the 20% of causes responsible for 80% of the problems. By focusing on these critical causes, organizations can make significant improvements.

Finally, SWOT analysis is a valuable tool for analyzing the internal and external factors that may impact the problem. This method involves identifying the strengths, weaknesses, opportunities, and threats related to the issue.

Overall, the analysis phase is critical for identifying the root cause of the problem and developing practical solutions. Organizations can gain a deeper understanding of the issue and make informed decisions by using a combination of methods.

Step 6: Synthesize

Once the analysis phase is complete, it is time to synthesize the information gathered to arrive at a solution. During this step, the focus is on identifying the most viable solution that addresses the problem. This involves examining and combining the analysis results for a clear and concise conclusion.

One way to synthesize the information is to use a decision matrix. This involves creating a table that lists the potential solutions and the essential criteria for making a decision. Each answer is then rated against each standard, and the scores are tallied to arrive at a final decision.

Another approach to synthesizing the information is to use a mind map. This involves creating a visual representation of the problem and the potential solutions. The mind map can identify the relationships between the different pieces of information and help prioritize the solutions.

During the synthesis phase, remaining open-minded and considering all potential solutions is vital. To ensure everyone's perspectives are considered, it is also essential to involve all stakeholders in the decision-making process.

Step 7: Communicate

After synthesizing the information, the next step is communicating the findings to the relevant stakeholders. This is a crucial step because it helps to ensure that everyone is on the same page and that the decision-making process is transparent.

One effective way to communicate the findings is through a well-organized report. The report should include the problem statement, the analysis, the synthesis, and the recommended solution. It should be clear, concise, and easy to understand.

In addition to the report, a presentation explaining the findings is essential. The presentation should be tailored to the audience and highlight the report's key points. Visual aids such as tables, graphs, and charts can make the presentation more engaging.

During the presentation, it is essential to be open to feedback and questions from the audience. This helps ensure everyone agrees with the recommended solution and addresses concerns or objections.

Effective communication is vital to ensuring the decision-making process is successful. Stakeholders can make informed decisions and work towards a common goal by communicating the findings clearly and concisely.

The 7-step problem-solving process is a powerful tool for helping individuals and organizations make better decisions. By following these steps, individuals can identify the root cause of a problem, prioritize potential solutions, and develop a clear plan of action. This process can be applied to various scenarios, from personal challenges to complex business problems.

Through disaggregation, individuals can break down complex problems into smaller, more manageable parts. By prioritizing potential solutions, individuals can focus their efforts on the most impactful actions. The work step allows individuals to develop a clear action plan, while the analysis step provides a framework for evaluating possible solutions.

The synthesis step combines all the information gathered to develop a comprehensive solution. Finally, the communication step allows individuals to share their answers with others and gather feedback.

By mastering the 7-step problem-solving process, individuals can become more effective decision-makers and problem-solvers. This process can help individuals and organizations save time and resources while improving outcomes. With practice, individuals can develop the skills to apply this process to a wide range of scenarios and make better decisions in all areas of life.

7-Step Problem-Solving Process PPT Template

Free powerpoint and pdf template, executive summary: the 7-step problem-solving process.

The 7-Step Problem-Solving Process is a robust and systematic method to help individuals and organizations make better decisions by tackling complex issues and finding practical solutions. This process comprises defining the problem, disaggregating it into smaller parts, prioritizing the issues, creating a work plan, analyzing the data, synthesizing the information, and communicating the findings.

By following these steps, individuals can identify the root cause of a problem, break it down into manageable components, and prioritize the most impactful actions. The work plan, analysis, and synthesis steps provide a framework for developing comprehensive solutions, while the communication step ensures transparency and stakeholder engagement.

Mastering this process can improve decision-making and problem-solving capabilities, save time and resources, and improve outcomes in personal and professional contexts.

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Social Work Theory and Ethics pp 1–19 Cite as

Problem-Solving Theory: The Task-Centred Model

• Blanca M. Ramos 5 &
• Randall L. Stetson 6  
• Living reference work entry
• First Online: 12 April 2022

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Part of the book series: Social Work ((SOWO))

This chapter examines the task-centred model to illustrate the application of problem-solving theory for social work intervention. First, it provides a brief description of the problem-solving model. Its historical development and key principles and concepts are presented. Next, the chapter offers a general overview of the crisis intervention model. The task-centred model and crisis intervention share principles and methods drawn from problem-solving theory. The remainder of the chapter focuses on the task-centred model. It reviews its historical background, viability as a framework for social work generalist practice, as well as its applicability with diverse client populations and across cultural settings. The structured steps that guide task-centred implementation throughout the helping process are described. A brief critical review of the model’s strengths and limitations is provided. The chapter concludes with a brief summary and some closing thoughts.

• Problem-solving theory
• Task-centered model
• Task-centered practice
• Generalist social work practice
• Crisis intervention
• Multiculturalism

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Ramos, B.M., Stetson, R.L. (2022). Problem-Solving Theory: The Task-Centred Model. In: Hölscher, D., Hugman, R., McAuliffe, D. (eds) Social Work Theory and Ethics. Social Work. Springer, Singapore. https://doi.org/10.1007/978-981-16-3059-0_9-1

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7 important steps in the decision making process

The decision making process is a method of gathering information, assessing alternatives, and making a final choice with the goal of making the best decision possible. In this article, we detail the step-by-step process on how to make a good decision and explain different decision making methodologies.

We make decisions every day. Take the bus to work or call a car? Chocolate or vanilla ice cream? Whole milk or two percent?

There's an entire process that goes into making those tiny decisions, and while these are simple, easy choices, how do we end up making more challenging decisions? 

At work, decisions aren't as simple as choosing what kind of milk you want in your latte in the morning. That’s why understanding the decision making process is so important. 

What is the decision making process?

The decision making process is the method of gathering information, assessing alternatives, and, ultimately, making a final choice. 

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The 7 steps of the decision making process

Step 1: identify the decision that needs to be made.

When you're identifying the decision, ask yourself a few questions: 

What is the problem that needs to be solved?

What is the goal you plan to achieve by implementing this decision?

How will you measure success?

These questions are all common goal setting techniques that will ultimately help you come up with possible solutions. When the problem is clearly defined, you then have more information to come up with the best decision to solve the problem.

Step 2: Gather relevant information

​Gathering information related to the decision being made is an important step to making an informed decision. Does your team have any historical data as it relates to this issue? Has anybody attempted to solve this problem before?

It's also important to look for information outside of your team or company. Effective decision making requires information from many different sources. Find external resources, whether it’s doing market research, working with a consultant, or talking with colleagues at a different company who have relevant experience. Gathering information helps your team identify different solutions to your problem.

Step 3: Identify alternative solutions

This step requires you to look for many different solutions for the problem at hand. Finding more than one possible alternative is important when it comes to business decision-making, because different stakeholders may have different needs depending on their role. For example, if a company is looking for a work management tool, the design team may have different needs than a development team. Choosing only one solution right off the bat might not be the right course of action. 

Step 4: Weigh the evidence

This is when you take all of the different solutions you’ve come up with and analyze how they would address your initial problem. Your team begins identifying the pros and cons of each option, and eliminating alternatives from those choices.

There are a few common ways your team can analyze and weigh the evidence of options:

Pros and cons list

SWOT analysis

Decision matrix

Step 5: Choose among the alternatives

The next step is to make your final decision. Consider all of the information you've collected and how this decision may affect each stakeholder. 

Sometimes the right decision is not one of the alternatives, but a blend of a few different alternatives. Effective decision-making involves creative problem solving and thinking out of the box, so don't limit you or your teams to clear-cut options.

One of the key values at Asana is to reject false tradeoffs. Choosing just one decision can mean losing benefits in others. If you can, try and find options that go beyond just the alternatives presented.

Step 6: Take action

Once the final decision maker gives the green light, it's time to put the solution into action. Take the time to create an implementation plan so that your team is on the same page for next steps. Then it’s time to put your plan into action and monitor progress to determine whether or not this decision was a good one. 

Step 7: Review your decision and its impact (both good and bad)

Once you’ve made a decision, you can monitor the success metrics you outlined in step 1. This is how you determine whether or not this solution meets your team's criteria of success.

Here are a few questions to consider when reviewing your decision:

Did it solve the problem your team identified in step 1? 

Did this decision impact your team in a positive or negative way?

Which stakeholders benefited from this decision? Which stakeholders were impacted negatively?

If this solution was not the best alternative, your team might benefit from using an iterative form of project management. This enables your team to quickly adapt to changes, and make the best decisions with the resources they have. 

Types of decision making models

While most decision making models revolve around the same seven steps, here are a few different methodologies to help you make a good decision.

​Rational decision making models

This type of decision making model is the most common type that you'll see. It's logical and sequential. The seven steps listed above are an example of the rational decision making model. 

When your decision has a big impact on your team and you need to maximize outcomes, this is the type of decision making process you should use. It requires you to consider a wide range of viewpoints with little bias so you can make the best decision possible. 

Intuitive decision making models

This type of decision making model is dictated not by information or data, but by gut instincts. This form of decision making requires previous experience and pattern recognition to form strong instincts.

This type of decision making is often made by decision makers who have a lot of experience with similar kinds of problems. They have already had proven success with the solution they're looking to implement. 

Creative decision making model

The creative decision making model involves collecting information and insights about a problem and coming up with potential ideas for a solution, similar to the rational decision making model. 

The difference here is that instead of identifying the pros and cons of each alternative, the decision maker enters a period in which they try not to actively think about the solution at all. The goal is to have their subconscious take over and lead them to the right decision, similar to the intuitive decision making model. 

This situation is best used in an iterative process so that teams can test their solutions and adapt as things change.

Track key decisions with a work management tool

Tracking key decisions can be challenging when not documented correctly. Learn more about how a work management tool like Asana can help your team track key decisions, collaborate with teammates, and stay on top of progress all in one place.

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Complex Problem Solving in Teams: The Impact of Collective Orientation on Team Process Demands

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Complex problem solving is challenging and a high-level cognitive process for individuals. When analyzing complex problem solving in teams, an additional, new dimension has to be considered, as teamwork processes increase the requirements already put on individual team members. After introducing an idealized teamwork process model, that complex problem solving teams pass through, and integrating the relevant teamwork skills for interdependently working teams into the model and combining it with the four kinds of team processes (transition, action, interpersonal, and learning processes), the paper demonstrates the importance of fulfilling team process demands for successful complex problem solving within teams. Therefore, results from a controlled team study within complex situations are presented. The study focused on factors that influence action processes, like coordination, such as emergent states like collective orientation, cohesion, and trust and that dynamically enable effective teamwork in complex situations. Before conducting the experiments, participants were divided by median split into two-person teams with either high ( n = 58) or low ( n = 58) collective orientation values. The study was conducted with the microworld C3Fire, simulating dynamic decision making, and acting in complex situations within a teamwork context. The microworld includes interdependent tasks such as extinguishing forest fires or protecting houses. Two firefighting scenarios had been developed, which takes a maximum of 15 min each. All teams worked on these two scenarios. Coordination within the team and the resulting team performance were calculated based on a log-file analysis. The results show that no relationships between trust and action processes and team performance exist. Likewise, no relationships were found for cohesion. Only collective orientation of team members positively influences team performance in complex environments mediated by action processes such as coordination within the team. The results are discussed in relation to previous empirical findings and to learning processes within the team with a focus on feedback strategies.

Introduction

Complex problems in organizational contexts are seldom solved by individuals. Generally, interdependently working teams of experts deal with complex problems (Fiore et al., 2010 ), which are characterized by element interactivity/ interconnectedness, dynamic developments, non-transparency and multiple, and/or conflicting goals (Dörner et al., 1983 ; Brehmer, 1992 ; Funke, 1995 ). Complex problem solving “takes place for reducing the barrier between a given start state and an intended goal state with the help of cognitive activities and behavior. Start state, intended goal state, and barriers prove complexity, change dynamically over time, and can be partially intransparent” (Funke, 2012 , p. 682). Teams dealing with complex problems in interdependent work contexts, for example in disaster, crisis or accident management, are called High Responsibility Teams. They are named High Responsibility Teams (HRTs; Hagemann, 2011 ; Hagemann et al., 2011 ) due to their dynamic and often unpredictable working conditions and demanding work contexts, in which technical faults and slips have severe consequences for human beings and the environment if they are not identified and resolved within the team immediately (Kluge et al., 2009 ). HRTs bear responsibility regarding lives of third parties and their own lives based on their actions and consequences.

The context of interdependently working HRTs, dealing with complex problems, is described as follows (Zsambok, 1997 ): Members of interdependently working teams have to reach ill-defined or competing goals in common in poor structured, non-transparent and dynamically changing situations under the consideration of rules of engagement and based on several cycles of joint action. Some or all goals are critical in terms of time and the consequences of actions result in decision-based outcomes with high importance for the culture (e.g., human life). In HRT contexts, added to the features of the complexity of the problem, is the complexity of relationships, which is called social complexity (Dörner, 1989/2003 ) or crew coordination complexity (Kluge, 2014 ), which results from the interconnectedness between multiple agents through coordination requirements. The dynamic control aspect of the continuous process is coupled with the need to coordinate multiple highly interactive processes imposing high coordination demands (Roth and Woods, 1988 ; Waller et al., 2004 ; Hagemann et al., 2012 ).

Within this article, it is important to us to describe the theoretical background of complex problem solving in teams in depth and to combine different but compatible theoretical approaches, in order to demonstrate their theoretical and practical use in the context of the analysis of complex problem solving in teams. In Industrial and Organizational Psychology, a detailed description of tasks and work contexts that are in the focus of the analysis is essential. The individual or team task is the point of intersection between organization and individual as a “psychologically most relevant part” of the working conditions (Ulich, 1995 ). Thus, the tasks and the teamwork context of teams that deal with complex problems is of high relevance in the present paper. We will comprehensively describe the context of complex problem solving in teams by introducing a model of an idealized teamwork process that complex problem solving teams pass through and extensively integrate the relevant teamwork skills for these interdependently working teams into the idealized teamwork process model.

Furthermore, we will highlight the episodic aspect concerning complex problem solving in teams and combine the agreed on transition, action, interpersonal and learning processes of teamwork with the idealized teamwork process model. Because we are interested in investigating teamwork competencies and action processes of complex problem solving teams, we will analyze the indirect effect of collective orientation on team performance through the teams' coordination behavior. The focusing of the study will be owed to its validity. Even though that we know that more aspects of the theoretical framework might be of interest and could be analyzed, we will focus on a detail within the laboratory experiment for getting reliable and valid results.

Goal, task, and outcome interdependence in teamwork

Concerning interdependence, teamwork research focuses on three designated features, which are in accordance with general process models of human action (Hertel et al., 2004 ). One type is goal interdependence, which refers to the degree to which teams have distinct goals as well as a linkage between individual members and team goals (Campion et al., 1993 ; Wageman, 1995 ). A second type is task interdependence, which refers to the interaction between team members. The team members depend on each other for work accomplishment, and the actions of one member have strong implications for the work process of all members (Shea and Guzzo, 1987 ; Campion et al., 1993 ; Hertel et al., 2004 ). The third type is outcome interdependence, which is defined as the extent to which one team member's outcomes depend on the performance of other members (Wageman, 1995 ). Accordingly, the rewards for each member are based on the total team performance (Hertel et al., 2004 ). This can occur, for instance, if a team receives a reward based on specific performance criteria. Although interdependence is often the reason why teams are formed in the first place, and it is stated as a defining attribute of teams (Salas et al., 2008 ), different levels of task interdependence exist (Van de Ven et al., 1976 ; Arthur et al., 2005 ).

The workflow pattern of teams can be

• Independent or pooled (activities are performed separately),
• Sequential (activities flow from one member to another in a unidirectional manner),
• Reciprocal (activities flow between team members in a back and forth manner) or
• Intensive (team members must simultaneously diagnose, problem-solve, and coordinate as a team to accomplish a task).

Teams that deal with complex problems work within intensive interdependence, which requires greater coordination patterns compared to lower levels of interdependence (Van de Ven et al., 1976 ; Wageman, 1995 ) and necessitates mutual adjustments as well as frequent interaction and information integration within the team (Gibson, 1999 ; Stajkovic et al., 2009 ).

Thus, in addition to the cognitive requirements related to information processing (e.g., encoding, storage and retrieval processes (Hinsz et al., 1997 ), simultaneously representing and anticipating the dynamic elements and predicting future states of the problem, balancing contradictory objectives and decide on the right timing for actions to execute) of individual team members, the interconnectedness between the experts in the team imposes high team process demands on the team members. These team process demands follow from the required interdependent actions of all team members for effectively using all resources, such as equipment, money, time, and expertise, to reach high team performance (Marks et al., 2001 ). Examples for team process demands are the communication for building a shared situation awareness, negotiating conflicting perspectives on how to proceed or coordinating and orchestrating actions of all team members.

A comprehensive model of the idealized teamwork process

The cognitive requirements, that complex problem solving teams face, and the team process demands are consolidated within our model of an idealized teamwork process in Figure ​ Figure1 1 (Hagemann, 2011 ; Kluge et al., 2014 ). Individual and team processes converge sequential and in parallel and influencing factors as well as process demands concerning complex problem solving in teams can be extracted. The core elements of the model are situation awareness, information transfer, individual and shared mental models, coordination and leadership, and decision making.

Relevant teamwork skills (orange color) for interdependently working teams (see Wilson et al., 2010 ) integrated into the model of an idealized teamwork process.

Complex problem solving teams are responsible for finding solutions and reaching specified goals. Based on the overall goals various sub goals will be identified at the beginning of the teamwork process in the course of mission analysis, strategy formulation and planning, all aspects of the transition phase (Marks et al., 2001 ). The transition phase processes occur during periods of time when teams focus predominantly on evaluation and/or planning activities. The identified and communicated goals within the team represent relevant input variables for each team member in order to build up a Situation Awareness (SA). SA contains three steps and is the foundation for an ideal and goal directed collaboration within a team (Endsley, 1999 ; Flin et al., 2008 ). The individual SA is the start and end within the idealized teamwork process model. SA means the assessment of a situation which is important for complex problem solving teams, as they work based on the division of labor as well as interdependently and each team member needs to achieve a correct SA and to share it within the team. Each single team member needs to utilize all technical and interpersonal resources in order to collect and interpret up-to-date goal directed information and to share this information with other team members via “closed-loop communication.”

This information transfer focuses on sending and receiving single SA between team members in order to build up a Shared Situation Awareness (SSA). Overlapping cuts of individual SA are synchronized within the team and a bigger picture of the situation is developed. Creating a SSA means sharing a common perspective of the members concerning current events within their environment, their meaning and their future development. This shared perspective enables problem-solving teams to attain high performance standards through corresponding and goal directed actions (Cannon-Bowers et al., 1993 ).

Expectations of each team member based on briefings, individual mental models and interpositional knowledge influence the SA, the information transfer and the consolidation process. Mental models are internal and cognitive representations of relations and processes (e.g., execution of tactics) between various aspects or elements of a situation. They help team members to describe, explain and predict circumstances (Mathieu et al., 2000 ). Mental models possess knowledge elements required by team members in order to assess a current situation in terms of SA. Interpositional knowledge refers to an individual understanding concerning the tasks and duties of all team members, in order to develop an understanding about the impact of own actions on the actions of other team members and vice versa. It supports the team in identifying the information needs and the amount of required help of other members and in avoiding team conflicts (Smith-Jentsch et al., 2001 ). This knowledge is the foundation for anticipating the team members' needs for information and it is important for matching information within the team.

Based on the information matching process within the team, a common understanding of the problem, the goals and the current situation is developed in terms of a Shared Mental Model (SMM), which is important for the subsequent decisions. SMM are commonly shared mental models within a team and refer to the organized knowledge structures of all team members, that are shared with each other and which enable the team to interact goal-oriented (Mathieu et al., 2000 ). SMM help complex problem solving teams during high workload to adapt fast and efficiently to changing situations (Waller et al., 2004 ). They also enhance the teams' performance and communication processes (Cannon-Bowers et al., 1993 ; Mathieu et al., 2000 ). Especially under time pressure and in crucial situations when overt verbal communication and explicit coordination is not applicable, SMM are fundamental in order to coordinate implicitly. This information matching process fosters the building of a shared understanding of the current situation and the required actions. In order to do so teamwork skills (see Wilson et al., 2010 ) such as communication, coordination , and cooperation within the team are vitally important. Figure ​ Figure1 1 incorporates the teamwork skills into the model of an idealized teamwork process.

Depending on the shared knowledge and SA within the team, the coordination can be based either on well-known procedures or shared expectations within the team or on explicit communication based on task specific phraseology or closed-loop communication. Cooperation needs mutual performance monitoring within the team, for example, in order to apply task strategies to accurately monitor teammate performance and prevent errors (Salas et al., 2005 ). Cooperation also needs backup behavior of each team member, for example, and continuous actions in reference to the collective events. The anticipation of other team members' needs under high workload maintains the teams' performance and the well-being of each team member (Badke-Schaub, 2008 ). A successful pass through the teamwork process model also depends e.g., on the trust and the cohesion within the team and the collective orientation of each team member.

Collective orientation (CO) is defined “as the propensity to work in a collective manner in team settings” (Driskell et al., 2010 , p. 317). Highly collectively oriented people work with others on a task-activity and team-activity track (Morgan et al., 1993 ) in a goal-oriented manner, seek others' input, contribute to team outcomes, enjoy team membership, and value cooperativeness more than power (Driskell et al., 2010 ). Thus, teams with collectively oriented members perform better than teams with non-collectively oriented members (Driskell and Salas, 1992 ). CO, trust and cohesion as well as other coordination and cooperation skills are so called emergent sates that represent cognitive, affective, and motivational states, and not traits, of teams and team members, and which are influenced, for example, by team experience, so that emergent states can be considered as team inputs but also as team outcomes (Marks et al., 2001 ).

Based on the information matching process the complex problem solving team or the team leader needs to make decisions in order to execute actions. The task prioritization and distribution is an integrated part of this step (Waller et al., 2004 ). Depending on the progress of the dynamic, non-transparent and heavily foreseeable situation tasks have to be re-prioritized during episodes of teamwork. Episodes are “temporal cycles of goal-directed activity” in which teams perform (Marks et al., 2001 , p. 359). Thus, the team acts adaptive and is able to react flexible to situation changes. The team coordinates implicitly when each team member knows what he/she has to do in his/her job, what the others expect from him/her and how he/she interacts with the others. In contrast, when abnormal events occur and they are recognized during SA processes, the team starts coordinating explicitly via communication, for example. Via closed-loop communication and based on interpositional knowledge new strategies are communicated within the team and tasks are re-prioritized.

The result of the decision making and action taking flows back into the individual SA and the as-is state will be compared with the original goals. This model of an idealized teamwork process (Figure ​ (Figure1) 1 ) is a regulator circuit with feedback loops, which enables a team to adapt flexible to changing environments and goals. The foundation of this model is the classic Input-Process-Outcome (IPO) framework (Hackman, 1987 ) with a strong focus on the process part. IPO models view processes as mechanisms linking variables such as member, team, or organizational features with outcomes such as performance quality and quantity or members' reactions. This mediating mechanism, the team process , can be defined as “members' interdependent acts that convert inputs to outcomes through cognitive, verbal, and behavioral activities directed toward organizing taskwork to achieve collective goals” (Marks et al., 2001 , p. 357). That means team members interact interdependently with other members as well as with their environment. These cognitive, verbal, and behavioral activities directed toward taskwork and goal attainment are represented as gathering situation awareness, communication, coordination, cooperation, the consolidation of information, and task prioritization within our model of an idealized teamwork process. Within the context of complex problem solving, teams have to face team process demands in addition to cognitive challenges related to individual information processing. That means teamwork processes and taskwork to solve complex problems co-occur, the processes guide the execution of taskwork.

The dynamic nature of teamwork and temporal influences on complex problem solving teams are considered within adapted versions (Marks et al., 2001 ; Ilgen et al., 2005 ) of the original IPO framework. These adaptations propose that teams experience cycles of joint action, so called episodes, in which teams perform and also receive feedback for further actions. The IPO cycles occur sequentially and simultaneously and are nested in transition and action phases within episodes in which outcomes from initial episodes serve as inputs for the next cycle (see Figure ​ Figure2). 2 ). These repetitive IPO cycles are a vital element of our idealized teamwork process model, as it incorporates feedback loops in such a way, that the outcomes, e.g., changes within the as-is state, are continuously compared with the original goals. Detected discrepancies within the step of updating SA motivate the team members to consider further actions for goal accomplishment.

Teamwork episodes with repetitive IPO cycles (Marks et al., 2001 ).

When applying this episodic framework to complex problem solving teams it becomes obvious that teams handle different types of taskwork at different phases of task accomplishment (Marks et al., 2001 ). That means episodes consist of two phases, so-called action and transition phases , in which teams are engaged in activities related to goal attainment and in other time in reflecting on past performance and planning for further common actions. The addition of the social complexity to the complexity of the problem within collaborative complex problem solving comes to the fore here. During transition phases teams evaluate their performance, compare the as-is state against goals, reflect on their strategies and plan future activities to guide their goal accomplishment. For example, team members discuss alternative courses of action, if their activities for simulated firefighting, such as splitting team members in order to cover more space of the map, are not successful. During action phases, teams focus directly on the taskwork and are engaged in activities such as exchanging information about the development of the dynamic situation or supporting each other. For example, a team member recognizes high workload of another team member and supports him/her in collecting information or in taking over the required communication with other involved parties.

Transition and action phases

The idealized teamwork process model covers these transition and action phases as well as the processes occurring during these two phases of team functioning, which can be clustered into transition, action, and interpersonal processes. That means during complex problem solving the relevant or activated teamwork processes in the transition and action phases change as teams move back and forth between these phases. As this taxonomy of team processes from Marks et al. ( 2001 ) states that a team process is multidimensional and teams use different processes simultaneously, some processes can occur either during transition periods or during action periods or during both periods. Transition processes especially occur during transition phases and enable the team to understand their tasks, guide their attention, specify goals and develop courses of action for task accomplishment. Thus, transition processes include (see Marks et al., 2001 ) mission analysis, formulation and planning (Prince and Salas, 1993 ), e.g., fighting a forest fire, goal specification (Prussia and Kinicki, 1996 ), e.g., saving as much houses and vegetation as possible, and strategy formulation (Prince and Salas, 1993 ; Cannon-Bowers et al., 1995 ), e.g., spreading team members into different geographic directions. Action processes predominantly occur during action phases and support the team in conducting activities directly related to goal accomplishment. Thus, action processes are monitoring progress toward goals (Cannon-Bowers et al., 1995 ), e.g., collecting information how many cells in a firefighting simulation are still burning, systems monitoring (Fleishman and Zaccaro, 1992 ), e.g., tracking team resources such as water for firefighting, team monitoring and backup behavior (Stevens and Campion, 1994 ; Salas et al., 2005 ), e.g., helping a team member and completing a task for him/her, and coordination (Fleishman and Zaccaro, 1992 ; Serfaty et al., 1998 ), e.g., orchestrating the interdependent actions of the team members such as exchanging information during firefighting about positions of team members for meeting at the right time at the right place in order to refill the firefighters water tanks. Especially the coordination process is influenced by the amount of task interdependence as coordination becomes more and more important for effective team functioning when interdependence increases (Marks et al., 2001 ). Interpersonal processes occur during transition and action phases equally and lay the foundation for the effectiveness of other processes and govern interpersonal activities (Marks et al., 2001 ). Thus, interpersonal processes include conflict management (Cannon-Bowers et al., 1995 ), like the development of team rules, motivation and confidence building (Fleishman and Zaccaro, 1992 ), like encourage team members to perform better, and affect management (Cannon-Bowers et al., 1995 ), e.g., regulating member emotions during complex problem solving.

Summing up, process demands such as transition processes that complex problem solving teams pass through, are mission analysis, planning, briefing and goal specification, visualized on the left side of the idealized teamwork process model (see Figure ​ Figure3). 3 ). The results of these IPO cycles lay the foundation for gathering a good SA and initiating activities directed toward taskwork and goal accomplishment and therefore initiating action processes. The effective execution of action processes depends on the communication, coordination, cooperation, matching of information, and task prioritization as well as emergent team cognition variables (SSA and SMM) within the team. The results, like decisions, of these IPO cycles flow back into the next episode and may initiate further transition processes. In addition, interpersonal processes play a crucial role for complex problem solving teams. That means, conflict management, motivating and confidence building, and affect management are permanently important, no matter whether a team runs through transition or action phases and these interpersonal processes frame the whole idealized teamwork process model. Therefore, interpersonal processes are also able to impede successful teamwork at any point as breakdowns in conflict or affect management can lead to coordination breakdowns (Wilson et al., 2010 ) or problems with monitoring or backing up teammates (Marks et al., 2001 ). Thus, complex problem solving teams have to face these multidimensional team process demands in addition to cognitive challenges, e.g., information storage or retrieval (Hinsz et al., 1997 ), related to individual information processing.

The integration of transition, action, interpersonal, and learning processes into the model of an idealized teamwork process.

Team learning opportunities for handling complex problems

In order to support teams in handling complex situations or problems, learning opportunities seem to be very important for successful task accomplishment and for reducing possible negative effects of team process demands. Learning means any kind of relative outlasted changes in potential of human behavior that cannot be traced back to age-related changes (Bower and Hilgard, 1981 ; Bredenkamp, 1998 ). Therefore, Schmutz et al. ( 2016 ) amended the taxonomy of team processes developed by Marks et al. ( 2001 ) and added learning processes as a fourth category of processes, which occur during transition and action phases and contribute to overall team effectiveness. Learning processes (see also Edmondson, 1999 ) include observation, e.g., observing own and other team members' actions such as the teammate's positioning of firewalls in order to protect houses in case of firefighting, feedback, like giving a teammate information about the wind direction for effective positioning of firewalls, and reflection, e.g., talking about procedures for firefighting or refilling water tanks, for example, within the team. Learning from success and failure and identifying future problems is crucial for the effectiveness of complex problem solving teams and therefore possibilities for learning based on repetitive cycles of joint action or episodes and reflection of team members' activities during action and transition phases should be used effectively (Edmondson, 1999 ; Marks et al., 2001 ). The processes of the idealized teamwork model are embedded into these learning processes (see Figure ​ Figure3 3 ).

The fulfillment of transition, action, interpersonal and learning processes contribute significantly to successful team performance in complex problem solving. For clustering these processes, transition and action processes could be seen as operational processes and interpersonal and learning process as support processes. When dealing with complex and dynamic situations teams have to face these team process demands more strongly than in non-complex situations. For example, goal specification and prioritization or strategy formulation, both aspects of transition processes, are strongly influenced by multiple goals, interconnectedness or dynamically and constantly changing conditions. The same is true for action processes, such as monitoring progress toward goals, team monitoring and backup behavior or coordination of interdependent actions. Interpersonal processes, such as conflict and affect management or confidence building enhance the demands put on team members compared to individuals working on complex problems. Interpersonal processes are essential for effective teamwork and need to be cultivated during episodes of team working, because breakdowns in confidence building or affect management can lead to coordination breakdowns or problems with monitoring or backing up teammates (Marks et al., 2001 ). Especially within complex situations aspects such as interdependence, delayed feedback, multiple goals and dynamic changes put high demands on interpersonal processes within teams. Learning processes, supporting interpersonal processes and the result of effective teamwork are e.g., observation of others' as well as own actions and receiving feedback by others or the system and are strongly influenced by situational characteristics such as non-transparency or delayed feedback concerning actions. It is assumed that amongst others team learning happens through repetitive cycles of joint action within the action phases and reflection of team members within the transition phases (Edmondson, 1999 ; Gabelica et al., 2014 ; Schmutz et al., 2016 ). The repetitive cycles help to generate SMM (Cannon-Bowers et al., 1993 ; Mathieu et al., 2000 ), SSA (Endsley and Robertson, 2000 ) or transactive memory systems (Hollingshead et al., 2012 ) within the team.

Emergent states in complex team work and the role of collective orientation

IPO models propose that input variables and emergent states are able to influence team processes and therefore outcomes such as team performance positively. Emergent states represent team members' attitudes or motivations and are “properties of the team that are typically dynamic in nature and vary as a function of team context, inputs, processes, and outcomes” (Marks et al., 2001 , p. 357). Both emergent states and interaction processes are relevant for team effectiveness (Kozlowski and Ilgen, 2006 ).

Emergent states refer to conditions that underlie and dynamically enable effective teamwork (DeChurch and Mesmer-Magnus, 2010 ) and can be differentiated from team process, which refers to interdependent actions of team members that transform inputs into outcomes based on activities directed toward task accomplishment (Marks et al., 2001 ). Emergent states mainly support the execution of behavioral processes (e.g., planning, coordination, backup behavior) during the action phase, meaning during episodes when members are engaged in acts that focus on task work and goal accomplishment. Emergent states like trust, cohesion and CO are “products of team experiences (including team processes) and become new inputs to subsequent processes and outcomes” (Marks et al., 2001 , p. 358). Trust between team members and cohesion within the team are emergent states that develop over time and only while experiencing teamwork in a specific team. CO is an emergent state that a team member brings along with him/her into the teamwork, is assumed to be more persistent than trust and cohesion, and can, but does not have to, be positively and negatively influenced by experiencing teamwork in a specific team for a while or by means of training (Eby and Dobbins, 1997 ; Driskell et al., 2010 ). Thus, viewing emergent states on a continuum, trust and cohesion are assumed more fluctuating than CO, but CO is much more sensitive to change and direct experience than a stable trait such as a personality trait.

CO of team members is one of the teamwork-relevant competencies that facilitates team processes, such as collecting and sharing information between team members, and positively affects the success of teams, as people who are high in CO work with others in a goal-oriented manner, seek others' input and contribute to team outcomes (Driskell et al., 2010 ). CO is an emergent state, as it can be an input variable as well as a teamwork outcome. CO is context-dependent, becomes visible in reactions to situations and people, and can be influenced by experience (e.g., individual learning experiences with various types of teamwork) or knowledge or training (Eby and Dobbins, 1997 ; Bell, 2007 ). CO enhances team performance through activating transition and action processes such as coordination, evaluation and consideration of task inputs from other team members while performing a team task (Driskell and Salas, 1992 ; Salas et al., 2005 ). Collectively oriented people effectively use available resources in due consideration of the team's goals, participate actively and adapt teamwork processes adequately to the situation.

Driskell et al. ( 2010 ) and Hagemann ( 2017 ) provide a sound overview of the evidence of discriminant and convergent validity of CO compared to other teamwork-relevant constructs, such as cohesion, also an emergent state, or cooperative interdependence or preference for solitude. Studies analyzing collectively and non-collectively oriented persons' decision-making in an interdependent task demonstrated that teams with non-collectively oriented members performed poorly in problem solving and that members with CO judged inputs from teammates as more valuable and considered these inputs more frequently (Driskell and Salas, 1992 ). Eby and Dobbins ( 1997 ) also showed that CO results in increased coordination among team members, which may enhance team performance through information sharing, goal setting and strategizing (Salas et al., 2005 ). Driskell et al. ( 2010 ) and Hagemann ( 2017 ) analyzed CO in relation to team performance and showed that the effect of CO on team performance depends on the task type (see McGrath, 1984 ). Significant positive relationships between team members' CO and performance were found in relation to the task types choosing/decision making and negotiating (Driskell et al., 2010 ) respectively choosing/decision making (Hagemann, 2017 ). These kinds of tasks are characterized by much more interdependence than task types such as executing or generating tasks. As research shows that the positive influence of CO on team performance unfolds especially in interdependent teamwork contexts (Driskell et al., 2010 ), which require more team processes such as coordination patterns (Van de Ven et al., 1976 ; Wageman, 1995 ) and necessitate mutual adjustments as well as frequent information integration within the team (Gibson, 1999 ; Stajkovic et al., 2009 ), CO might be vitally important for complex problem solving teams. Thus, CO as an emergent state of single team members might be a valuable resource for enhancing the team's performance when exposed to solving complex problems. Therefore, it will be of interest to analyze the influence of CO on team process demands such as coordination processes and performance within complex problem solving teams. We predict that the positive effect of CO on team performance is an indirect effect through coordination processes within the team, which are vitally important for teams working in intensive interdependent work contexts.

• Hypothesis 1: CO leads to a better coordination behavior, which in turn leads to a higher team performance.

As has been shown in team research that emergent states like trust and cohesion (see also Figure ​ Figure1) 1 ) affect team performance, these two constructs are analyzed in conjunction with CO concerning action processes, such as coordination behavior and team performance. Trust between team members supports information sharing and the willingness to accept feedback, and therefore positively influences teamwork processes (McAllister, 1995 ; Salas et al., 2005 ). Cohesion within a team facilitates motivational factors and group processes like coordination and enhances team performance (Beal et al., 2003 ; Kozlowski and Ilgen, 2006 ).

• Hypothesis 2: Trust shows a positive relationship with (a) action processes (team coordination) and with (b) team performance.
• Hypothesis 3: Cohesion shows a positive relationship with (a) action processes (team coordination) and with (b) team performance.

Materials and methods

In order to demonstrate the importance of team process demands for complex problem solving in teams, we used a computer-based microworld in a laboratory study. We analyzed the effectiveness of complex problem solving teams while considering the influence of input variables, like collective orientation of team members and trust and cohesion within the team, on action processes within teams, like coordination.

The microworld for investigating teams process demands

We used the simulation-based team task C 3 Fire (Granlund et al., 2001 ; Granlund and Johansson, 2004 ), which is described as an intensive interdependence team task for complex problem solving (Arthur et al., 2005 ). C 3 Fire is a command, control and communications simulation environment that allows teams' coordination and communication in complex and dynamic environments to be analyzed. C 3 Fire is a microworld, as important characteristics of the real world are transferred to a small and well-controlled simulation system. The task environment in C 3 Fire is complex, dynamic and opaque (see Table ​ Table1) 1 ) and therefore similar to the cognitive tasks people usually encounter in real-life settings, in and outside their work place (Brehmer and Dörner, 1993 ; Funke, 2001 ). Figure ​ Figure4 4 demonstrates how the complexity characteristics mentioned in Table ​ Table1 1 are realized in C 3 Fire. The screenshot represents the simulation manager's point of view, who is able to observe all units and actions and the scenario development. For more information about the units and scenarios, please (see the text below and the Supplementary Material). Complexity requires people to consider a number of facts. Because executed actions in C 3 Fire influence the ongoing process, the sequencing of actions is free and not stringent, such as a fixed (if X then Y) or parallel (if X then Y and Z) sequence (Ormerod et al., 1998 ). This can lead to stressful situations. Taking these characteristics of microworlds into consideration, team processes during complex problem solving can be analyzed within laboratories under controlled conditions. Simulated microworlds such as C 3 Fire allow the gap to be bridged between laboratory studies, which might show deficiencies regarding ecological validity, and field studies, which have been criticized due to their small amount of control (see Brehmer and Dörner, 1993 ).

Overview of complexity characteristics of microworlds in general and in C 3 Fire (cf. Funke, 2001 ).

Examples for the complexity characteristics in Table ​ Table1 1 represented within a simulation scenario in C 3 Fire.

In C 3 Fire, the teams' task is to coordinate their actions to extinguish a forest fire whilst protecting houses and saving lives. The team members' actions are interdependent. The simulation includes, e.g., forest fires, houses, tents, gas tanks, different kinds of vegetation and computer-simulated agents such as firefighting units (Granlund, 2003 ). It is possible, for example, that the direction of wind will change during firefighting and the time until different kinds of vegetation are burned down varies between those. In the present study, two simulation scenarios were developed for two-person teams and consisted of two firefighting units, one mobile water tank unit (responsible for re-filling the firefighting units' water tanks that contain a predefined amount of water) and one fire-break unit (a field defended with a fire-break cannot be ignited; the fire spreads around its ends). The two developed scenarios lasted for 15 min maximum. Each team member was responsible for two units in each scenario; person one for firefighting and water tank unit and person two for firefighting and fire-break unit. The user interface was a map system (40 × 40 square grid) with all relevant geographic information and positions of all symbols representing houses, water tank units and so on. All parts of the map with houses and vegetation were visible for the subjects, but not the fire itself or the other units; instead, the subjects were close to them with their own units (restricted visibility field; 3 × 3 square grid). The simulation was run on computers networked in a client-server configuration. The subjects used a chat system for communication that was logged. For each scenario, C 3 Fire creates a detailed log file containing all events that occurred over the course of the simulation. Examples of the C 3 Fire scenarios are provided in the Figures S1 – 3 and a short introduction into the microworld is given in the video. Detailed information regarding the scenario characteristics are given in Table S1 . From scenario one to two, the complexity and interdependence increased.

Participants

The study was conducted from Mai 2014 until March 2015. Undergraduate and graduate students ( N = 116) studying applied cognitive sciences participated in the study (68.1% female). Their mean age was 21.17 years ( SD = 3.11). Participants were assigned to 58 two-person teams, with team assignments being based on the pre-measured CO values (see procedure). They received 2 hourly credits as a trial subject and giveaways such as pencils and non-alcoholic canned drinks. The study was approved by the university's ethics committee in February 2014.

The study was conducted within a laboratory setting at a university department for business psychology. Prior to the experiment, the participants filled in the CO instrument online and gave written informed consent (see Figure ​ Figure5). 5 ). The median was calculated subsequently ( Md = 3.12; range: 1.69–4.06; scale range: 1–5) relating to the variable CO and two individuals with either high ( n = 58) or low ( n = 58) CO values were randomly matched as teammates. The matching process was random in part, as those two subjects were matched to form a team, whose preferred indicated time for participation in a specific week during data collection were identical. The participants were invited to the experimental study by e-mail 1–2 weeks after filling in the CO instrument. The study began with an introduction to the experimental procedure and the teams' task. The individuals received time to familiarize themselves with the simulation, received 20 min of training and completed two practice trials. After the training, participants answered a questionnaire collecting demographic data. Following this, a simulation scenario started and the participants had a maximum of 15 min to coordinate their actions to extinguish a forest fire whilst protecting houses and saving lives. After that, at measuring time T1, participants answered questionnaires assessing trust and cohesion within the team. Again, the teams worked on the following scenario 2 followed by a last round of questionnaires assessing trust and cohesion at T2.

Overview about the procedure and measures.

Demographic data such as age, sex, and study course were assessed after the training at the beginning of the experiment.

Collective Orientation was measured at an individual level with 16 items rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree ) developed by the authors (Hagemann, 2017 ) based on the work of Driskell et al. ( 2010 ). The factorial structure concerning the German-language CO scale was proven prior to this study (χ 2 = 162.25, df = 92, p = 0.000, χ 2 /df = 1.76, CFI = 0.97, TLI = 0.96, RMSEA = 0.040, CI = 0.030-0.051, SRMR = 0.043) and correlations for testing convergent and discriminant evidence of validity were satisfying. For example, CO correlated r = 0.09 ( p > 0.10) with cohesion, r = 0.34 ( p < 0.01) with cooperative interdependence and r = −0.28 ( p < 0.01) with preference for solitude (Hagemann, 2017 ). An example item is “ I find working on team projects to be very satisfying ”. Coefficient alpha for this scale was 0.81.

Trust in team members' integrity, trust in members' task abilities and trust in members' work-related attitudes (Geister et al., 2006 ) was measured with seven items rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree ). An example item is “ I can trust that I will have no additional demands due to lack of motivation of my team member .” Coefficient alpha for this scale was 0.83 (T1) and 0.87 (T2).

Cohesion was measured with a six-item scale from Riordan and Weatherly ( 1999 ) rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree ). An example item is “ In this team, there is a lot of team spirit among the members .” Coefficient alpha for this scale was 0.87 (T1) and 0.87 (T2).

Action process: coordination

Successful coordination requires mechanisms that serve to manage dependencies between the teams' activities and their resources. Coordination effectiveness was assessed based on the time the firefighting units spent without water in the field in relation to the total scenario time. This measure is an indicator of the effectiveness of resource-oriented coordination, as it reflects an efficient performance regarding the water refill process in C 3 Fire, which requires coordinated actions between the two firefighting units and one water tank unit (Lafond et al., 2011 ). The underlying assumption is that a more successful coordination process leads to fewer delays in conducting the refill process. Coordination was calculated by a formula and values ranged between 0 and 1, with lower values indicating better coordination in the team (see Jobidon et al., 2012 ).

Team performance

This measure related to the teams' goals (limiting the number of burned out cells and saving as many houses/buildings as possible) and was quantified as the number of protected houses and the number of protected fields and bushes/trees in relation to the number of houses, fields, and bushes/trees, respectively, which would burn in a worst case scenario. This formula takes into account that teams needing more time for firefighting also have more burning cells and show a less successful performance than teams that are quick in firefighting. To determine the worst case scenario, both 15-min scenarios were run with no firefighting action taken. Thus, the particularities (e.g., how many houses would burn down if no action was taken) of each scenario were considered. Furthermore, the houses, bushes/trees and fields were weighted according to their differing importance, mirroring the teams' goals. Houses should be protected and were most important. Bushes/trees (middle importance) burn faster than fields (lowest importance) and foster the expansion of the fire. Values regarding team performance ranged between 0 and 7.99, with higher values indicating a better overall performance. Team performance was calculated as follows (see Table ​ Table2 2 ):

Explanation of formula for calculating team performance in both scenarios.

Means, standard deviations, internal consistencies, and correlations for all study variables are provided in Table ​ Table3 3 .

Means, standard deviations, internal consistencies, and correlations for all study variables.

Performance range from 0 to 7.99; Time without Water range from 0 to 1 (lower values indicate a more effective handling of water); CO range from 1 to 5 .

Team complex problem solving in scenario 1 correlated significantly negative with time without water in scenario 1, indicating that a high team performance is attended by the coordination behavior (as a team process). The same was true for scenario 2. In addition, time without water as an indicator for team coordination correlated significantly negative with the team members' CO, indicating that team members with high CO values experience less time without water in the microworld than teams with members with low CO values.

In order to analyze the influence of CO on team process demands such as coordination processes and thereby performance within complex problem solving teams we tested whether CO would show an indirect effect on team performance through the teams' coordination processes. To analyze this assumption, indirect effects in simple mediation models were estimated for both scenarios (see Preacher and Hayes, 2004 ). The mean for CO was 3.44 ( SD = 0.32) for teams with high CO values and it was 2.79 ( SD = 0.35) for teams with low CO values. The mean concerning team performance in scenario 1 for teams with high CO values was 6.30 ( SD = 1.64) and with low CO values 5.35 ( SD = 2.30). The mean concerning time without water (coordination behavior) for teams with high CO values was 0.16 ( SD = 0.08) and with low CO values 0.20 ( SD = 0.09). In scenario 2 the mean for team performance was 6.26 ( SD = 2.51) for teams with high CO values and it was 4.36 ( SD = 2.24) for teams with low CO values. The mean concerning time without water for teams with high CO values was 0.18 ( SD = 0.08) and with low CO values 0.25 ( SD = 0.11).

For analyzing indirect effects, CO was the independent variable, time without water the mediator and team performance the dependent variable. The findings indicated that CO has an indirect effect on team performance mediated by time without water for scenario 1 (Table ​ (Table4) 4 ) and scenario 2 (Table ​ (Table5). 5 ). In scenario 1, CO had no direct effect on team performance ( b(YX) ), but CO significantly predicted time without water ( b(MX) ). A significant total effect ( b(YX) ) is not an assumption in the assessment of indirect effects, and therefore the non-significance of this relationship does not violate the analysis (see Preacher and Hayes, 2004 , p. 719). Furthermore, time without water significantly predicted team performance when controlling for CO ( b(YM.X) ), whereas the effect of CO on team performance was not significant when controlling for time without water ( b(YX.M) ). The indirect effect was 0.40 and significant when using normal distribution and estimated with the Sobel test ( z = 1.97, p < 0.05). The bootstrap procedure was applied to estimate the effect size not based on the assumption of normal distribution. As displayed in Table ​ Table4, 4 , the bootstrapped estimate of the indirect effect was 0.41 and the true indirect effect was estimated to lie between 0.0084 and 0.9215 with a 95% confidence interval. As zero is not in the 95% confidence interval, it can be concluded that the indirect effect is indeed significantly different from zero at p < 0.05 (two-tailed).

Indirect Effect for Coordination and Team Performance in Scenario 1.

Y = Team Performance Scenario 1; X = Collective Orientation T0; M = Coordination (time without water in scenario 1); Number of Bootstrap Resamples 5000 .

Indirect Effect for Coordination and Team Performance in Scenario 2.

Y = Team Performance Scenario 2; X = Collective Orientation T0; M = Coordination (time without water in scenario 2); Number of Bootstrap Resamples 5000 .

Regarding scenario 2, CO had a direct effect on team performance ( b(YX) ) and on time without water ( b(MX) ). Again, time without water significantly predicted team performance when controlling for CO ( b(YM.X) ), whereas the effect of CO on team performance was not significant when controlling for time without water ( b(YX.M) ). This time, the indirect effect was 0.60 (Sobel test, z = 2.31, p < 0.05). As displayed in Table ​ Table5, 5 , the bootstrapped estimate of the indirect effect was 0.61 and the true indirect effect was estimated to lie between 0.1876 and 1.1014 with a 95% confidence interval and between 0.0340 and 1.2578 with a 99% confidence interval. Because zero is not in the 99% confidence interval, it can be concluded that the indirect effect is indeed significantly different from zero at p < 0.01 (two-tailed).

The indirect effects for both scenarios are visualized in Figure ​ Figure6. 6 . Summing up, the results support hypothesis 1 and indicate that CO has an indirect effect on team performance mediated by the teams' coordination behavior, an action process. That means, fulfilling team process demands affect the dynamic decision making quality of teams acting in complex situations and input variables such as CO influence the action processes within teams positively.

Indirect effect of collective orientation on team performance via coordination within the teams for scenario 1 and 2, * p < 0.05, ** p < 0.01, *** p < 0.001, numbers in italic represent results from scenario 2, non-italic numbers are from scenario 1.

Trust between team members assessed after scenario 1 (T1) and after scenario 2 (T2) did not show any significant correlation with the coordination behavior or with team complex problem solving in scenarios 1 and 2 (Table ​ (Table3). 3 ). Thus, hypotheses 2a and 2b are not supported. Cohesion at T1 showed no significant relationship with team performance in both scenarios, one significant negative correlation ( r = −0.22, p < 0.05) with the coordination behavior in scenario 1 and no correlation with the coordination behavior in scenario 2. Cohesion at T2 did not show any significant correlation with the coordination behavior or with team performance in both scenarios. Thus, hypotheses 3a and 3b could also not be supported. Furthermore, the results showed no significant relations between CO and trust and cohesion. The correlations between trust and cohesion ranged between r = 0.39 and r = 0.51 ( p < 0.01).

The purpose of our paper was first to give a sound theoretical overview and to combine theoretical approaches about team competencies and team process demands in collaborative complex problem solving and second to demonstrate the importance of selected team competencies and processes on team performance in complex problem solving by means of results from a laboratory study. We introduced the model of an idealized teamwork process that complex problem solving team pass through and integrated the relevant teamwork skills for interdependently working teams into it. Moreover, we highlighted the episodic aspect concerning complex problem solving in teams and combined the well-known transition, action, interpersonal and learning processes of teamwork with the idealized teamwork process model. Finally, we investigated the influence of trust, cohesion, and CO on action processes, such as coordination behavior of complex problem solving teams and on team performance.

Regarding hypothesis 1, studies have indicated that teams whose members have high CO values are more successful in their coordination processes and task accomplishment (Eby and Dobbins, 1997 ; Driskell et al., 2010 ; Hagemann, 2017 ), which may enhance team performance through considering task inputs from other team members, information sharing and strategizing (Salas et al., 2005 ). Thus, we had a close look on CO as an emergent state in the present study, because emergent states support the execution of behavioral processes. In order to analyze this indirect effect of CO on team performance via coordination processes, we used the time, which firefighters spent without water in a scenario, as an indicator for high-quality coordination within the team. A small amount of time without water represents sharing information and resources between team members in a reciprocal manner, which are essential qualities of effective coordination (Ellington and Dierdorff, 2014 ). One of the two team members was in charge of the mobile water tank unit and therefore responsible for filling up the water tanks of his/her own firefighting unit and that of the other team member on time. In order to avoid running out of water for firefighting, the team members had to exchange information about, for example, their firefighting units' current and future positions in the field, their water levels, their strategies for extinguishing one or two fires, and the water tank unit's current and future position in the field. The simple mediation models showed that CO has an indirect effect on team performance mediated by time without water, supporting hypothesis 1. Thus, CO facilitates high-quality coordination within complex problem solving teams and this in turn influences decision-making and team performance positively (cf. Figure ​ Figure1). 1 ). These results support previous findings concerning the relationships between emergent states, such as CO, and the team process, such as action processes like coordination (Cannon-Bowers et al., 1995 ; Driskell et al., 2010 ) and between the team process and the team performance (Stevens and Campion, 1994 ; Dierdorff et al., 2011 ).

Hypotheses 2 and 3 analyzed the relationships between trust and cohesion and coordination and team performance. Because no correlations between trust and cohesion and the coordination behavior and team complex problem solving existed, further analyses, like mediation analyses, were unnecessary. In contrast to other studies (McAllister, 1995 ; Beal et al., 2003 ; Salas et al., 2005 ; Kozlowski and Ilgen, 2006 ), the present study was not able to detect effects of trust and cohesion on team processes, like action processes, or on team performance. This can be attributed to the restricted sample composition or the rather small sample size. Nevertheless, effect sizes were small to medium, so that they would have become significant with an increased sample sizes. The prerequisite, mentioned by the authors, that interdependence of the teamwork is important for identifying those effects, was given in the present study. Therefore, this aspect could not have been the reason for finding no effects concerning trust and cohesion. Trust and cohesion within the teams developed during working on the simulation scenarios while fighting fires, showed significant correlations with each other, and were unrelated to CO, which showed an effect on the coordination behavior and the team performance indeed. The results seem to implicate, that the influence of CO on action processes and team performance might be much more stronger than those of trust and cohesion. If these results can be replicated should be analyzed in future studies.

As the interdependent complex problem-solving task was a computer-based simulation, the results might have been affected by the participants' attitudes to using a computer. For example, computer affinity seems to be able to minimize potential fear of working with a simulation environment and might therefore, be able to contribute to successful performance in a computer-based team task. Although computers and other electronic devices are pervasive in present-day life, computer aversion has to be considered in future studies within complex problem-solving research when applying computer-based simulation team tasks. As all of the participants were studying applied cognitive science, which is a mix of psychology and computer science, this problem might not have been influenced the present results. However, the specific composition of the sample reduces the external validity of the study and the generalizability of the results. A further limitation is the small sample size, so that moderate to small effects are difficult to detect.

Furthermore, laboratory research of teamwork might have certain limitations. Teamwork as demonstrated in this study fails to account for the fact that teams are not simple, static and isolated entities (McGrath et al., 2000 ). The validity of the results could be reduced insofar as the complex relationships in teams were not represented, the teamwork context was not considered, not all teammates and teams were comparable, and the characteristic as a dynamic system with a team history and future was not given in the present study. This could be a possible explanation why no effects of trust and cohesion were found in the present study. Maybe, the teams need more time working together on the simulation scenarios in order to show that trust and cohesion influence the coordination with the team and the team performance. Furthermore, Bell ( 2007 ) demonstrated in her meta-analysis that the relationship between team members' attitudes and the team's performance was proven more strongly in the field compared to the laboratory. In consideration of this fact, the findings of the present study concerning CO are remarkable and the simulation based microworld C3Fire (Granlund et al., 2001 ; Granlund, 2003 ) seems to be appropriate for analyzing complex problem solving in interdependently working teams.

An asset of the present study is, that the teams' action processes, the coordination performance, was assessed objectively based on logged data and was not a subjective measure, as is often the case in group and team research studies (cf. Van de Ven et al., 1976 ; Antoni and Hertel, 2009 ; Dierdorff et al., 2011 ; Ellington and Dierdorff, 2014 ). As coordination was the mediator in the analysis, this objective measurement supports the validity of the results.

As no transition processes such as mission analysis, formulation, and planning (Prince and Salas, 1993 ), goal specification (Prussia and Kinicki, 1996 ), and strategy formulation (Prince and Salas, 1993 ; Cannon-Bowers et al., 1995 ) as well as action processes such as monitoring progress toward goals (Cannon-Bowers et al., 1995 ) and systems monitoring (Fleishman and Zaccaro, 1992 ) were analyzed within the present study, future studies should collect data concerning these processes in order to show their importance on performance within complex problem solving teams. Because these processes are difficult to observe, subjective measurements are needed, for example asking the participants after each scenario how they have prioritized various tasks, if and when they have changed their strategy concerning protecting houses or fighting fires, and on which data within the scenarios they focused for collecting information for goal and systems monitoring. Another possibility could be using eye-tracking methods in order to collect data about collecting information for monitoring progress toward goals, e.g., collecting information how many cells are still burning, and systems monitoring, e.g., tracking team resources like water for firefighting.

CO is an emergent state and emergent states can be influenced by experience or learning, for example (Kozlowski and Ilgen, 2006 ). Learning processes (Edmondson, 1999 ), that Schmutz et al. ( 2016 ) added to the taxonomy of team processes developed by Marks et al. ( 2001 ) and which occur during transition and action phases and contribute to team effectiveness include e.g., feedback . Feedback can be useful for team learning when team learning is seen as a form of information processing (Hinsz et al., 1997 ). Because CO supports action processes, such as coordination and it can be influenced by learning, learning opportunities, such as feedback, seem to be important for successful task accomplishment and for supporting teams in handling complex situations or problems. If the team is temporarily and interpersonally unstable, as it is the case for most of the disaster or crisis management teams dealing with complex problems, there might be less opportunities for generating shared mental models by experiencing repetitive cycles of joint action (cf. Figure ​ Figure2) 2 ) and strategies such as cross training (Salas et al., 2007 ) or feedback might become more and more important for successful complex problem solving in teams. Thus, for future research it would be of interest to analyze what kind of feedback is able to influence CO positively and therefore is able to enhance coordination and performance within complex problem-solving teams.

Depending on the type of feedback, different main points will be focused during the feedback (see Gabelica et al., 2012 ). Feedback can be differentiated into performance and process feedback. Process feedback can be further divided into task-related and interpersonal feedback. Besides these aspects, feedback can be given on a team-level or an individual-level. Combinations of the various kinds of feedback are possible and are analyzed in research concerning their influence on e.g., self- and team-regulatory processes and team performance (Prussia and Kinicki, 1996 ; Hinsz et al., 1997 ; Jung and Sosik, 2003 ; Gabelica et al., 2012 ). For future studies it would be relevant to analyze, whether it is possible to positively influence the CO of team members and therefore action processes such as coordination and team performance or not. A focus could be on the learning processes, especially on feedback, and its influence on CO in complex problem solving teams. So far, no studies exist that analyzed the relationship between feedback and a change in CO, even though researchers already discuss the possibility that team-level process feedback shifts attention processes on team actions and team learning (McLeod et al., 1992 ; Hinsz et al., 1997 ). These results would be very helpful for training programs for fire service or police or medical teams working in complex environments and solving problems collaboratively, in order to support their team working and their performance.

In summary, the idealized teamwork process model is in combination with the transition, action, interpersonal and learning processes a good framework for analyzing the impact of teamwork competencies and teamwork processes in detail on team performance in complex environments. Overall, the framework offers further possibilities for investigating the influence of teamwork competencies on diverse processes and teamwork outcomes in complex problem solving teams than demonstrated here. The results of our study provide evidence of how CO influences complex problem solving teams and their performance. Accordingly, future researchers and practitioners would be well advised to find interventions how to influence CO and support interdependently working teams.

Ethics statement

This study was carried out in accordance with the recommendations of Ethical guidelines of the German Association of Psychology, Ethics committee of the University of Duisburg-Essen, Department of Computer Science and Applied Cognitive Science with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Ethics committee of the University of Duisburg-Essen, Department of Computer Science and Applied Cognitive Science.

Author contributions

VH and AK were responsible for the conception of the work and the study design. VH analyzed and interpreted the collected data. VH and AK drafted the manuscript. They approved it for publication and act as guarantors for the overall content.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fpsyg.2017.01730/full#supplementary-material

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