Identify Goal
Define Problem
Define Problem
Gather Data
Define Causes
Identify Options
Clarify Problem
Generate Ideas
Evaluate Options
Generate Ideas
Choose the Best Solution
Implement Solution
Select Solution
Take Action
MacLeod offers her own problem solving procedure, which echoes the above steps:
“1. Recognize the Problem: State what you see. Sometimes the problem is covert. 2. Identify: Get the facts — What exactly happened? What is the issue? 3. and 4. Explore and Connect: Dig deeper and encourage group members to relate their similar experiences. Now you're getting more into the feelings and background [of the situation], not just the facts. 5. Possible Solutions: Consider and brainstorm ideas for resolution. 6. Implement: Choose a solution and try it out — this could be role play and/or a discussion of how the solution would be put in place. 7. Evaluate: Revisit to see if the solution was successful or not.”
Many of these problem solving techniques can be used in concert with one another, or multiple can be appropriate for any given problem. It’s less about facilitating a perfect CPS session, and more about encouraging team members to continually think outside the box and push beyond personal boundaries that inhibit their innovative thinking. So, try out several methods, find those that resonate best with your team, and continue adopting new techniques and adapting your processes along the way.
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June 14, 2022 - 10 min read
Solving complex problems may be difficult but it doesn't have to be excruciating. You just need the right frame of mind and a process for untangling the problem at hand.
Luckily for you, there are plenty of techniques available to solve whatever problems come at you in the workplace.
When faced with a doozy of a problem, where do you start? And what problem-solving techniques can you use right now that can help you make good decisions?
Today's post will give you tips and techniques for solving complex problems so you can untangle any complication like an expert.
At its core, problem-solving is a methodical four-step process. You may even recall these steps from when you were first introduced to the Scientific Method.
When applying problem-solving techniques, you will be using a variation of these steps as your foundation.
Takeaway: Before you can solve a problem, seek to understand it fully.
Time to get creative! You might think this will just be a list of out-of-the-box ways to brainstorm ideas. Not exactly.
Creative problem solving (CPS) is actually a formal process formulated by Sidney Parnes and Alex Faickney Osborn , who is thought of as the father of traditional brainstorming (and the "O" in famous advertising agency BBDO).
Their creative problem solving process emphasizes several things, namely:
Takeaway: When brainstorming solutions, generate ideas first by using questions and building off of existing ideas. Do all evaluating and judging later.
If you take a look at the history of problem-solving techniques in psychology, you'll come across a wide spectrum of interesting ideas that could be helpful.
In 1911, the American psychologist Edward Thorndike observed cats figuring out how to escape from the cage he placed them in. From this, Thorndike developed his law of effect , which states: If you succeed via trial-and-error, you're more likely to use those same actions and ideas that led to your previous success when you face the problem again.
Takeaway: Your past experience can inform and shed light on the problem you face now. Recall. Explore.
The Gestalt psychologists built on Thorndike's ideas when they proposed that problem-solving can happen via reproductive thinking — which is not about sex, but rather solving a problem by using past experience and reproducing that experience to solve the current problem.
What's interesting about Gestalt psychology is how they view barriers to problem-solving. Here are two such barriers:
Takeaway: Think outside of the box! And by box, we mean outside of the past experience you're holding on to, or outside any preconceived ideas on how a tool is conventionally used.
Hurson's productive thinking model.
In his book "Think Better," author and creativity guru Tim Hurson proposed a six-step model for solving problems creatively. The steps in his Productive Thinking Model are:
The most important part of defining the problem is looking at the possible root cause. You'll need to ask yourself questions like: Where and when is it happening? How is it occurring? With whom is it happening? Why is it happening?
You can get to the root cause with a fishbone diagram (also known as an Ishikawa diagram or a cause and effect diagram).
Basically, you put the effect on the right side as the problem statement. Then you list all possible causes on the left, grouped into larger cause categories. The resulting shape resembles a fish skeleton. Which is a perfect way to say, "This problem smells fishy."
Analogical thinking uses information from one area to help with a problem in a different area. In short, solving a different problem can lead you to find a solution to the actual problem. Watch out though! Analogies are difficult for beginners and take some getting used to.
An example: In the "radiation problem," a doctor has a patient with a tumor that cannot be operated on. The doctor can use rays to destroy the tumor but it also destroys healthy tissue.
Two researchers, Gick and Holyoak , noted that people solved the radiation problem much more easily after being asked to read a story about an invading general who must capture the fortress of a king but be careful to avoid landmines that will detonate if large forces traverse the streets. The general then sends small forces of men down different streets so the army can converge at the fortress at the same time and can capture it at full force.
In her book " The Architecture of All Abundance ," author Lenedra J. Carroll (aka the mother of pop star Jewel) talks about a question-and-answer technique for getting out of a problem.
When faced with a problem, ask yourself a question about it and brainstorm 12 answers ("12 what elses") to that problem. Then you can go further by taking one answer, turning it into a question and generating 12 more "what elses." Repeat until the solution is golden brown, fully baked, and ready to take out of the oven.
Hopefully you find these different techniques useful and they get your imagination rolling with ideas on how to solve different problems.
And if that's the case, then you have four different takeaways to use the next time a problem gets you tangled up:
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Want to streamline your processes and ease future problem-solving? Get started with a free two-week trial of Wrike today.
Do you have a problem-solving technique that has worked wonders for your organization? Hit the comments below and share your wisdom!
Lionel is a former Content Marketing Manager of Wrike. He is also a blogger since 1997, a productivity enthusiast, a project management newbie, a musician and producer of electronic downtempo music, a father of three, and a husband of one.
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Author: Daniel Croft
Daniel Croft is an experienced continuous improvement manager with a Lean Six Sigma Black Belt and a Bachelor's degree in Business Management. With more than ten years of experience applying his skills across various industries, Daniel specializes in optimizing processes and improving efficiency. His approach combines practical experience with a deep understanding of business fundamentals to drive meaningful change.
Problem-solving is an important component of any business or organization. It entails identifying, analyzing, and resolving problems in order to improve processes, drive results, and foster a culture of continuous improvement. A3 Problem solving is one of the most effective problem-solving methodologies.
A3 Problem solving is a structured and systematic approach to problem-solving that originated with the lean manufacturing methodology. It visualizes the problem-solving process using a one-page document known as an A3 report. The A3 report provides an overview of the problem, data analysis, root causes, solutions, and results in a clear and concise manner.
A3 Problem Solving has numerous advantages, including improved communication, better decision-making, increased efficiency, and reduced waste. It is a powerful tool for businesses of all sizes and industries, and it is especially useful for solving complex and multi-faceted problems.
In this blog post, we will walk you through the A3 Problem Solving methodology step by step. Whether you are new to A3 Problem Solving or simply want to improve your skills, this guide will help you understand and apply the process in your workplace.
A3 Problem Solving is a structured and systematic approach to problem-solving that makes use of a one-page document called an A3 report to visually represent the process. The A3 report provides an overview of the problem, data analysis, root causes, solutions, and results in a clear and concise manner. The method was created within the framework of the Lean manufacturing methodology and is based on the principles of continuous improvement and visual management.
Looking for a A3 Problem solving template? Click here
A3 Problem Solving was developed by Toyota Motor Corporation and was first used in the manufacture of automobiles. The term “A3” refers to the size of the paper used to create the report, which is an ISO standard known as “A3”. The goal of the A3 report is to provide a visual representation of the problem-solving process that all members of the organisation can easily understand and share. A3 Problem Solving has been adopted by organisations in a variety of industries over the years, and it has become a widely used and recognised method for problem-solving.
The following are the key principles of A3 Problem Solving:
These principles serve as the foundation of the A3 Problem Solving methodology and are intended to assist organisations in continuously improving and achieving their objectives. Organizations can effectively solve problems, identify areas for improvement, and drive results by adhering to these principles.
Importance of clearly defining the problem.
The first step in the A3 Problem Solving process is critical because it lays the groundwork for the remaining steps. To define the problem clearly and accurately, you must first understand the problem and identify the underlying root cause. This step is critical because if the problem is not correctly defined, the rest of the process will be based on incorrect information, and the solution developed may not address the issue effectively.
The significance of defining the problem clearly cannot be overstated. It aids in the collection and analysis of relevant data, which is critical for developing effective solutions. When the problem is clearly defined, the data gathered is more relevant and targeted, resulting in a more comprehensive understanding of the issue. This will enable the development of solutions that are more likely to be effective because they are founded on a thorough and accurate understanding of the problem.
However, if the problem is not clearly defined, the data gathered may be irrelevant or incorrect, resulting in incorrect conclusions and ineffective solutions. Furthermore, the process of collecting and analysing data can become time-consuming and inefficient, resulting in resource waste. Furthermore, if the problem is not accurately defined, the solutions developed may fail to address the root cause of the problem, resulting in ongoing issues and a lack of improvement.
The first step in the A3 Problem Solving process is to clearly and accurately define the problem. This is an important step because a clearly defined problem will help to ensure that the appropriate data is collected and solutions are developed. If the problem is not clearly defined, incorrect data may be collected, solutions that do not address the root cause of the problem, and time and resources may be wasted.
A problem can be defined using a variety of techniques, including brainstorming , root cause analysis , process mapping , and Ishikawa diagrams . Each of these techniques has its own advantages and disadvantages and can be used in a variety of situations depending on the nature of the problem.
In addition to brainstorming, root cause analysis, process mapping, and Ishikawa diagram s, best practices should be followed when defining a problem in A3 Problem Solving. Among these best practices are:
Organizations can ensure that their problem is defined in a way that allows for effective data collection, analysis, and solution development by following these best practices. This will aid in the development of appropriate solutions and the effective resolution of the problem, resulting in improvements in the organization’s processes and outcomes.
Gathering data in a3 problem solving.
Data collection is an important step in the A3 Problem Solving process because it allows organisations to gain a thorough understanding of the problem they are attempting to solve. This step entails gathering pertinent information about the problem, such as data on its origin, impact, and any related factors. This information is then used to help identify root causes and develop effective solutions.
One of the most important advantages of data collection in A3 Problem Solving is that it allows organisations to identify patterns and trends in data, which can be useful in determining the root cause of the problem. This information can then be used to create effective solutions that address the problem’s root cause rather than just its symptoms.
In A3 Problem Solving, data collection is a collaborative effort involving all stakeholders, including those directly impacted by the problem and those with relevant expertise or experience. Stakeholders can ensure that all relevant information is collected and that the data is accurate and complete by working together.
Overall, data collection is an important step in the A3 Problem Solving process because it serves as the foundation for effective problem-solving. Organizations can gain a deep understanding of the problem they are attempting to solve and develop effective solutions that address its root cause by collecting and analysing relevant data.
In A3 Problem Solving, several data collection methods are available, including:
The best data collection method will be determined by the problem being solved and the type of data required. To gain a complete understanding of the problem, it is critical to use multiple data collection methods.
Once the data has been collected, it must be analysed and visualised in order to gain insights into the problem. This process can be aided by the following tools:
These tools can assist in organising data and making it easier to understand. They can also be used to generate visual representations of data, such as graphs and charts, to communicate the findings to others.
Finally, the data collection and analysis step is an important part of the A3 Problem Solving process. Organizations can gain a better understanding of the problem and develop effective solutions by collecting and analysing relevant data.
Identifying the root causes of the problem is the third step in the A3 Problem Solving process. This step is critical because it assists organisations in understanding the root causes of a problem rather than just its symptoms. Once the underlying cause of the problem is identified, it can be addressed more effectively, leading to more long-term solutions.
The process of determining the underlying causes of a problem is known as root cause analysis. This process can assist organisations in determining why a problem is occurring and what can be done to prevent it from recurring in the future. The goal of root cause analysis is to identify the underlying cause of a problem rather than just its symptoms, allowing it to be addressed more effectively.
To understand Root cause analysis in more detail check out RCA in our Lean Six Sigma Yellow Belt Course Root Cause Analysis section
There are several techniques for determining the root causes of a problem, including:
These methods can be used to investigate the issue in-depth and identify potential root causes. Organizations can gain a deeper understanding of the problem and identify the underlying causes that must be addressed by using these techniques.
It is critical to follow these best practices when conducting root cause analysis in A3 Problem Solving:
Organizations can ensure that root cause analysis is carried out effectively and that the root cause of the problem is identified by adhering to these best practises. This will aid in the development of appropriate solutions and the effective resolution of the problem.
Developing solutions is the fourth step in the A3 Problem Solving process. This entails generating ideas and options for dealing with the problem, followed by selecting the best solution. The goal is to develop a solution that addresses the root cause of the problem and prevents it from recurring.
A3 solution development Problem solving is an iterative process in which options are generated and evaluated. The data gathered in the previous steps, as well as the insights and understanding gained from the root cause analysis, guide this process. The solution should be based on a thorough understanding of the problem and address the underlying cause.
There are several techniques that can be used to develop solutions in A3 Problem Solving, including:
These techniques can help to generate a range of options and to select the best solution.
It is critical to follow the following best practices when developing solutions in A3 Problem Solving:
Organizations can ensure that the solutions they develop are effective and sustainable by adhering to these best practises. This will help to ensure that the problem is addressed effectively and that it does not reoccur.
The final and most important step in the A3 Problem Solving methodology is solution implementation. This is the stage at which the identified and developed solutions are put into action to address the problem. This step’s goal is to ensure that the solutions are effective, efficient, and long-lasting.
The implementation process entails putting the solutions developed in the previous step into action. This could include changes to processes, procedures, and systems, as well as employee training and education. To ensure that the solutions are effective, the implementation process should be well-planned and meticulously executed.
A3 Problem Solving solutions can be implemented using a variety of techniques, including:
It is critical to follow these best practices when implementing solutions in A3 Problem Solving:
Make certain that all relevant stakeholders are involved and supportive of the solution. Have a clear implementation plan that outlines the steps, timeline, and resources required. Continuously monitor and evaluate the solution to determine its efficacy and make any necessary changes. Encourage all stakeholders to communicate and collaborate openly. Organizations can ensure that solutions are effectively implemented and problems are effectively addressed by adhering to these best practices. The ultimate goal is to find a long-term solution to the problem and improve the organization’s overall performance.
In conclusion, A3 Problem Solving is a comprehensive and structured methodology for problem-solving that can be applied in various industries and organisations. The A3 Problem Solving process’s five steps – Define the Problem, Gather Data, Identify Root Causes, Develop Solutions, and Implement Solutions – provide a road map for effectively addressing problems and making long-term improvements.
Organizations can improve their problem-solving skills and achieve better results by following the key principles, techniques, and best practices outlined in this guide. As a result, both the organisation and its stakeholders will benefit from increased efficiency, effectiveness, and satisfaction. So, whether you’re an experienced problem solver or just getting started, consider incorporating the A3 Problem Solving methodology into your work and start reaping the benefits right away.
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 www.learnleansigma.com, a platform dedicated to Lean Six Sigma and process improvement insights.
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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.
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.
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.
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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Last Updated on February 29, 2024 by Ossian Muscad
Every business, regardless of its industry, encounters the inevitable challenge of problem-solving. This critical aspect of business operations is often multifaceted, whether it’s in the process of anticipating potential obstacles, addressing immediate issues at hand, or refining systems for improved efficiency. The intricate and often time-intensive nature of solving these problems demands not only a systematic approach but also the right set of tools to navigate the complexities effectively.
Problem-solving tools are, therefore, not just useful but essential for any business professional. This guide delves into the intricacies of these tools, equipping you with the necessary understanding and skills to employ them within your organizational context. From the nuances of selecting the appropriate tool for a specific challenge to the strategic implementation for maximum impact, we offer a comprehensive exploration into the world of problem-solving tools designed to streamline and enhance your decision-making processes.
Problem-solving tools are structured methodologies or frameworks designed to aid individuals and organizations in navigating the complexities of various challenges. They offer a systematic way to deconstruct and comprehend issues, thereby empowering you to tackle each component with clarity and strategic insight. These tools serve as a kind of compass in the intricate maze of decision-making, providing a step-by-step guide to dissect problems and identify their underlying causes.
By enabling focused brainstorming, these methodologies help in the generation and assessment of potential resolutions, ensuring that the execution of solutions is not only strategic but also monitored for effectiveness. Diverse in their application, problem-solving tools adapt to a range of scenarios, each with its distinct intricacies and nuances. They encourage a proactive approach to conflict resolution, fostering a mindset geared towards continual improvement and preemptive action.
Utilizing visual aids like charts or diagrams, these tools can chart the most efficient path from a problem-ridden present to a solution-oriented future, mapping out the journey from dysfunction to functional success in a clear and accessible manner.
A visual problem-solving process will help reinforce your understanding of the issue and is an excellent way for you and your team to convert abstract ideas into an actual, reconstructive plan. With that said, here are six examples of common problem-mapping diagrams that you can try:
The Fishbone Diagram , also known as the Cause-and-effect Diagram or the Ishikawa diagram , is a powerful problem-solving tool that visually maps out the potential causes of an issue. Its distinctive shape resembles the skeleton of a fish, where the “head” represents the problem and the “bones” branching out from the “spine” symbolize different categories of root causes.
Each of these categories, such as Methods, Equipment, People, and Materials, is probed to identify possible contributing factors to the problem at hand. The simplicity and effectiveness of the Ishikawa diagram lie in its ability to facilitate comprehensive brainstorming sessions based on the principle of cause and effect.
With its widespread popularity, teams across industries value this type of diagram for its ease of use and its competence in stimulating the identification and analysis of various potential causes for a problem. Fishbone diagrams are versatile tools that can be applied in numerous situations. Here’s a comprehensive list detailing common uses:
Flowcharts are among the most prevalent and straightforward problem-solving tools utilized across various industries due to their visual representation of processes or systems. They consist of shapes and arrows that designate sequential steps, illustrating the flow from one stage to another. This visual mapping allows individuals and teams to follow a problem from initiation to resolution, providing a bird’s-eye view of the entire process.
The primary advantage of employing a flowchart lies in its capacity to lay out each step in a predictable manner, thereby enabling the observer to identify bottlenecks, redundancies, or inefficiencies within any step. By using flowcharts, you can dissect complex processes into manageable parts, ensuring a more precise understanding of how each component interacts with others.
This comprehensive perspective not only aids in pinpointing where the issues arise but also enhances the potential for optimizing different segments of the workflow to achieve a more efficient, streamlined process. Flowcharts are a universal tool used to break down complex processes and illustrate step-by-step sequences. Common uses for flowcharts that span across various disciplines include, but are not limited to:
Strategy Maps are a frequently used tool for strategic planning within companies, but they also hold significant value in problem-solving tasks. The essence of a strategy map is to illustrate the connections among various aspects of the organization, such as objectives, measures, initiatives, and activities. These diagrams provide a visual representation of how each component supports the overarching goals and serve as a guide to understanding which areas require attention in order to solve a problem. There are three main types of strategy maps:
While each type of map has its specific applications and nuances, the underlying premise remains constant: to provide a comprehensive view of the strategic interdependencies within an organization. In doing so, strategy maps facilitate the identification of areas where resources can be optimized or where shifts in strategy may resolve existing problems.
Strategy Maps enable leaders and teams to align their efforts toward a common goal and to comprehend how changes in one segment of the business can ripple through and impact the organization as a whole. Strategy Maps are incredibly versatile and can be applied to a number of uses within an organization to enhance strategic understanding and alignment. Below is a detailed list of the common uses of Strategy Maps:
The 5 Whys is a problem-solving method developed by Sakichi Toyoda and later used within Toyota Motor Corporation during its manufacturing process improvements. It is a simple yet effective technique used to uncover the root cause of a problem by asking “Why?” a minimum of five times or until the underlying issue is identified.
The process involves a team collaboratively pinpointing an issue and then questioning the cause of that issue iteratively. This exercise forces deeper inquiry beyond surface-level symptoms, leading to the discovery of the fundamental problem at hand. By addressing the root cause, organizations can implement substantive, long-term solutions rather than temporary fixes.
The 5 Whys technique is versatile and can be applied in various industries and scenarios, making it a staple in the toolbox of lean manufacturing, quality management, and process improvement. The 5 Whys technique has a multitude of applications across different fields and industries due to its simplicity and effectiveness in root cause analysis. Here is a comprehensive list detailing seven common uses of the 5 Whys:
Pareto charts are statistical tools named after the Italian economist Vilfredo Pareto, who is best known for the Pareto Principle. This principle, also known as the 80/20 rule, posits that for many events, roughly 80% of the effects come from 20% of the causes. A Pareto Chart conveys this concept through a visual graph that combines both bar and line graphs.
The bars represent individual values in descending order from left to right and are typically related to frequency or cost. Next to these, a line graph indicates the cumulative total, enabling users to easily identify which factors contribute the most to the problem they are examining. Pareto Charts are especially useful in quality control and process improvement because they help stakeholders prioritize issues or defects in a process.
By concentrating efforts on the most significant problems, organizations can significantly improve their overall performance, often with considerably less effort than by treating all problems equally. Pareto Charts are widely recognized for their ability to visually represent and prioritize problems to focus improvement efforts. Here are seven common uses of Pareto Charts across various disciplines:
A decision tree is a flowchart-like structure that is used as a decision support tool, representing a series of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It’s a visual representation that maps out multiple decision paths and evaluates them based on various scenarios, which can be immensely beneficial in making informed choices in complex situations.
Decision trees feature branches that depict decision nodes and leaf nodes representing outcomes, allowing individuals or organizations to review all possible solutions and identify the best course of action before committing. This analytical tool is widely used across business, engineering, law, and medical fields due to its straightforward graphical approach, which promotes clarity in the decision-making process.
Decision Trees are versatile tools, and their application can be found in diverse areas. By exploring different scenarios and their potential outcomes, you can uncover hidden insights and make more informed decisions. Here are some common uses of Decision Trees:
An objective map is a strategic tool utilized by companies to visualize their objectives and the connections between them. This graphical illustration aids in clarifying how various goals relate to one another and often reveals how addressing one objective can influence the achievement of others.
By mapping out objectives in a clear and structured way, an objective map enables team members and stakeholders to gain insight into the hierarchy and interdependencies of their collective goals. It’s particularly valuable when trying to solve complex problems within an organization, as it helps to pinpoint which objectives need to be prioritized and tackled in order to address and resolve specific challenges effectively.
This visual mapping can become a critical step in strategic planning, ensuring that efforts are directed toward the most impactful areas. Objective Maps are crucial in a multitude of organizational processes. Here are seven common uses:
A Balanced Scorecard Map is a strategic planning and management tool that provides a visual representation of an organization’s performance measures and objectives across different perspectives. It brings together financial, customer, internal, and growth-related goals in a coherent manner, articulating how the organization creates value.
By identifying key performance indicators (KPIs) and targets within these areas, the map serves as a guide to translate a company’s vision into actionable goals. It allows managers and teams to understand at a glance how various objectives tie into overall corporate strategy and how they contribute to resolving organizational issues.
With its clear, graphical design, a Balanced Scorecard Map can highlight specific areas that require attention to drive improvement and achieve strategic balance. Balanced Scorecard Maps are used widely across various industries to enhance strategic alignment and improve organizational performance. Here is a comprehensive list detailing their common uses:
No matter what type of problem you’re facing, there’s a diagram that can help you solve it. Therefore, by understanding the different types of diagrams and how to use them, you’ll need to prepare for any issue that comes your way.
Choosing the ideal problem-solving tool largely depends on the nature of the problem and its complexity. Here are some steps you can follow to select the most suitable tool:
The goal is not just to solve the problem but to learn from the process. This learning can be applied to future problem-solving efforts, continuously improving your approach.
Q1: what differentiates a simple problem from a complex one in problem-solving.
Simple problems usually have a clear and predictable solution, whereas complex problems have many interconnected components that change dynamically and may require holistic and flexible approaches to solve. By understanding the nature of the problem, you can choose a suitable tool for effective problem-solving.
Review and update your problem-solving tools as needed when there is a significant change in the organization’s objectives or market conditions or when feedback or results indicate an improvement is warranted. Make sure your tools are aligned with your current strategy and goals to ensure their effectiveness.
Not typically. Problem-solving tools are varied and designed to address specific types of issues. It’s crucial to match the right tool to the problem to ensure an effective solution. At the same time, some tools may be adaptable and useful for multiple types of problems.
If the tool isn’t working, it’s essential to re-evaluate the situation, possibly redefining the problem or choosing a different tool that may be better suited to the circumstances. That way, you can find a solution that is more likely to yield the expected results. If necessary, seek guidance or assistance from experts in the field to help identify a suitable tool and approach.
Collaborative problem-solving can foster diverse ideas and lead to innovative solutions. However, the situation and type of problem should guide the necessity and extent of collaboration. By understanding these factors, you can choose the most effective problem-solving approach and tool for your organization’s needs.
Proficiency in various tools allows for flexibility and adaptability in tackling different problems. Continuous learning and training in multiple methodologies can significantly improve problem-solving effectiveness. Making sure your team is well-versed in various tools can also increase their confidence and competence in addressing complex issues. So, it’s beneficial to invest in training and development programs that expose individuals to a variety of problem-solving approaches and tools.
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The art of problem-solving necessitates a careful evaluation of the problem, consideration of the tools and resources available, and a sound understanding of your organization’s capabilities. Whether the problem at hand is simple, complicated, or complex, the key lies in matching the right problem-solving tool to the task.
Regular review and adaptation of tools, coupled with a readiness to embrace training and collaboration, bolsters the problem-solving process. Through an iterative and informed approach to problem-solving, it is possible to enhance decision-making, drive continuous improvement, and achieve strategic organizational goals.
Picture this, you're handling your daily tasks at work and your boss calls you in and says, "We have a problem."
Unfortunately, we don't live in a world in which problems are instantly resolved with the snap of our fingers. Knowing how to effectively solve problems is an important professional skill to hone. If you have a problem that needs to be solved, what is the right process to use to ensure you get the most effective solution?
In this article we'll break down the problem-solving process and how you can find the most effective solutions for complex problems.
Problem solving is the process of finding a resolution for a specific issue or conflict. There are many possible solutions for solving a problem, which is why it's important to go through a problem-solving process to find the best solution. You could use a flathead screwdriver to unscrew a Phillips head screw, but there is a better tool for the situation. Utilizing common problem-solving techniques helps you find the best solution to fit the needs of the specific situation, much like using the right tools.
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While it might be tempting to dive into a problem head first, take the time to move step by step. Here’s how you can effectively break down the problem-solving process with your team:
One of the easiest ways to identify a problem is to ask questions. A good place to start is to ask journalistic questions, like:
Who : Who is involved with this problem? Who caused the problem? Who is most affected by this issue?
What: What is happening? What is the extent of the issue? What does this problem prevent from moving forward?
Where: Where did this problem take place? Does this problem affect anything else in the immediate area?
When: When did this problem happen? When does this problem take effect? Is this an urgent issue that needs to be solved within a certain timeframe?
Why: Why is it happening? Why does it impact workflows?
How: How did this problem occur? How is it affecting workflows and team members from being productive?
Asking journalistic questions can help you define a strong problem statement so you can highlight the current situation objectively, and create a plan around that situation.
Here’s an example of how a design team uses journalistic questions to identify their problem:
Overarching problem: Design requests are being missed
Who: Design team, digital marketing team, web development team
What: Design requests are forgotten, lost, or being created ad hoc.
Where: Email requests, design request spreadsheet
When: Missed requests on January 20th, January 31st, February 4th, February 6th
How : Email request was lost in inbox and the intake spreadsheet was not updated correctly. The digital marketing team had to delay launching ads for a few days while design requests were bottlenecked. Designers had to work extra hours to ensure all requests were completed.
In this example, there are many different aspects of this problem that can be solved. Using journalistic questions can help you identify different issues and who you should involve in the process.
If at all possible, bring in a facilitator who doesn't have a major stake in the solution. Bringing an individual who has little-to-no stake in the matter can help keep your team on track and encourage good problem-solving skills.
Here are a few brainstorming techniques to encourage creative thinking:
Brainstorm alone before hand: Before you come together as a group, provide some context to your team on what exactly the issue is that you're brainstorming. This will give time for you and your teammates to have some ideas ready by the time you meet.
Say yes to everything (at first): When you first start brainstorming, don't say no to any ideas just yet—try to get as many ideas down as possible. Having as many ideas as possible ensures that you’ll get a variety of solutions. Save the trimming for the next step of the strategy.
Talk to team members one-on-one: Some people may be less comfortable sharing their ideas in a group setting. Discuss the issue with team members individually and encourage them to share their opinions without restrictions—you might find some more detailed insights than originally anticipated.
Break out of your routine: If you're used to brainstorming in a conference room or over Zoom calls, do something a little different! Take your brainstorming meeting to a coffee shop or have your Zoom call while you're taking a walk. Getting out of your routine can force your brain out of its usual rut and increase critical thinking.
After you brainstorm with team members to get their unique perspectives on a scenario, it's time to look at the different strategies and decide which option is the best solution for the problem at hand. When defining the solution, consider these main two questions: What is the desired outcome of this solution and who stands to benefit from this solution?
Set a deadline for when this decision needs to be made and update stakeholders accordingly. Sometimes there's too many people who need to make a decision. Use your best judgement based on the limitations provided to do great things fast.
To implement your solution, start by working with the individuals who are as closest to the problem. This can help those most affected by the problem get unblocked. Then move farther out to those who are less affected, and so on and so forth. Some solutions are simple enough that you don’t need to work through multiple teams.
After you prioritize implementation with the right teams, assign out the ongoing work that needs to be completed by the rest of the team. This can prevent people from becoming overburdened during the implementation plan . Once your solution is in place, schedule check-ins to see how the solution is working and course-correct if necessary.
There are a few ways to go about identifying problems (and solutions). Here are some strategies you can try, as well as common ways to apply them:
Trial and error problem solving doesn't usually require a whole team of people to solve. To use trial and error problem solving, identify the cause of the problem, and then rapidly test possible solutions to see if anything changes.
This problem-solving method is often used in tech support teams through troubleshooting.
The 5 whys problem-solving method helps get to the root cause of an issue. You start by asking once, “Why did this issue happen?” After answering the first why, ask again, “Why did that happen?” You'll do this five times until you can attribute the problem to a root cause.
This technique can help you dig in and find the human error that caused something to go wrong. More importantly, it also helps you and your team develop an actionable plan so that you can prevent the issue from happening again.
Here’s an example:
Problem: The email marketing campaign was accidentally sent to the wrong audience.
“Why did this happen?” Because the audience name was not updated in our email platform.
“Why were the audience names not changed?” Because the audience segment was not renamed after editing.
“Why was the audience segment not renamed?” Because everybody has an individual way of creating an audience segment.
“Why does everybody have an individual way of creating an audience segment?” Because there is no standardized process for creating audience segments.
“Why is there no standardized process for creating audience segments?” Because the team hasn't decided on a way to standardize the process as the team introduced new members.
In this example, we can see a few areas that could be optimized to prevent this mistake from happening again. When working through these questions, make sure that everyone who was involved in the situation is present so that you can co-create next steps to avoid the same problem.
A SWOT analysis can help you highlight the strengths and weaknesses of a specific solution. SWOT stands for:
Strength: Why is this specific solution a good fit for this problem?
Weaknesses: What are the weak points of this solution? Is there anything that you can do to strengthen those weaknesses?
Opportunities: What other benefits could arise from implementing this solution?
Threats: Is there anything about this decision that can detrimentally impact your team?
As you identify specific solutions, you can highlight the different strengths, weaknesses, opportunities, and threats of each solution.
This particular problem-solving strategy is good to use when you're narrowing down the answers and need to compare and contrast the differences between different solutions.
After you’ve worked through a tough problem, don't forget to celebrate how far you've come. Not only is this important for your team of problem solvers to see their work in action, but this can also help you become a more efficient, effective , and flexible team. The more problems you tackle together, the more you’ll achieve.
Looking for a tool to help solve problems on your team? Track project implementation with a work management tool like Asana .
Experiential Education
When solving problems, dig at the roots instead of just hacking at the leaves. Anthony J. D'Angelo
What will you learn?
Effective problem solving often requires you to collect, process, analyse and evaluate information to find a solution/s. Along with other transferable skills like communication, planning and organisation, teamwork, critical thinking, digital literacy, and active inquiry (for example), it is essential in work and life more generally.
There are a few different stages in the problem solving process, which makes it a little more complicated than it probably sounds. For example, you first have to define the problem, then collect more information about it and do some research and investigations into causes. After that, you need to process the material to understand it, and then analyse and evaluate it (whilst generating possible solutions along the way). The final solution/s and action plan then come from there.
There are various stages to problem solving. We have described those that we see as key in the process, starting with recognising the problem to begin with and defining and labelling it; through to solving it, evaluating it and planning for contingencies. In connection with this, we have introduced certain problem solving tools and techniques that can be used at each stage. This is because some methods work better at different stages than others. The methods are presented in more detail in the section following this one, titled ‘Tools of the Trade’.
So, what are the key stages in the problem solving process?
This first stage is where you identify the symptoms. You look for facts and analyse data, as well as explore history and human factors such as attitudes, values and reactions (soft data). Symptom identification can be achieved through collecting all kinds of data, including through interviews and focus groups, brainstorming and mind mapping. More on these methods can be found later in this module.
Define and label the Problem (and determine causes)It is important to have a clear definition of the problem otherwise the solutions generated will not work. This is because the solutions you come up with will not necessarily address the actual problem (i.e. ‘the real problem’ or ‘key stone’). For example, if you define the problem as being poor performance by employees, when in fact it is a lack of training or high expectations, the solutions generated to address the problem are unlikely to be effective.
In this stage, it is good to write down some problem statements. Begin by noting the problem and why it is important to solve. Consider the benefits to solving the problem (e.g. costs, consumer satisfaction, employee wellbeing, time, product quality).
Work out stakeholders (both internal and external) and who all the members of the team are. In regards to the problem, think through - who, what, where, when, how and why.
Find the root cause of the problem (do not be caught on symptoms otherwise you won’t find the real issue).
The tools we recommend to help you define the problem and determine the cause (including the root cause) are:
Mind mapping, brainstorming.
To explore some of these further, see below under ‘Tools of the Trade’.
After defining the problem and finding the root cause, you need to generate ideas and solutions. This usually happens through brainstorming and mind mapping. These are good techniques for generating ideas quickly (and just getting it all down).
These methods help to generate thought not only around problem statements, definitions or causes (in stage one) but about potential solutions, impacts, and much more. These techniques are more concerned with ‘quantity’ in terms of getting as many ideas as possible, rather than ‘quality’. You can also do a force-field analysis in this stage or positive and negative analysis.
Students interested in watch in a lecture on critical thinking and problem solving in the context of agriculture can watch the following video:
Critical thinking and Problem solving | 19:08 mins
To explore brainstorming and mind mapping, please see below under ‘Tools of the Trade’.
Once you have generated optimal solutions, you then need to make some decisions about what to choose. There are many decision-making tools that can be used. Some of these include:
Once you arrived at your solution to your problem, an action plan should be developed. Elements to include in your action plan could be a series of questions. There are no hard and fast rules on what to include in your action plan. However, it must be highly effective in solving the problem or problems. It must be well thought through, achievable and highly relevant to your business.
Some elements to include in your plan may be:
This section of the module provides some detail on problem solving tools – and when to use them. Not all possible tools are described but we have tried to include those that are often used in business/enterprise.
The 5W’s approach is most useful when defining the problem and then later on when identifying if more data or analysis is required.
In most cases, you can define a problem by noting:
What are the attributes of the problem – what is the issue? Then move on to ask questions such as, ‘What do I need to know? What would be ideal? What will we use to measure if we solve the problem?’ You can ask the ‘what question’ repeatedly to drill down as well. For example, if a what question points to skill shortages as a problem, you would then ask, ‘what skills?’ if the answer is ‘communication skills’, you would ask ‘what communication skills’? | |
Where is the structural or physical location of the problem? So, where does the problem typically generate from? Or, where might we expect or foresee issues? | |
When does the problem occur in regards to time? When does the problem need to be fixed? When will we implement a solution? | |
Who are the people affected by the problem? Who is the source of the problem? Who are the key stakeholders involved in the problem (including customers, partners/external parties, employees)? Who will decide if we have solved the problem? | |
Why do we want to/need to solve the problem? Why is this happening to begin with? Why are people reacting in certain ways? The why question gets to the underlying causes of the problem and contributing factors. Quite often the why question is asked 5 times. This is to ensure drilling down as much as possible to find the root causes. This part is called the 5 WHY’s, which is considered an approach to root cause analysis. The point being to find causes to the problem not symptoms. |
SWOT analysis is a strategic planning tool that is used to generate information on which rigorous, logical, defendable decision can be based. As the diagram highlights, SWOT stands for Strengths, Weaknesses, Opportunities and Threats.
To complete a SWOT Analysis for your business/company/enterprise or workplace, start with asking the following questions:
Weaknesses:
Opportunities:
To explore SWOT Analysis further (and to find blank templates to use) please follow this link to a module on SWOT Analysis
Mind mapping is an effective means to brainstorm and collect your thoughts. A mind map is a diagram that visually organises information and involves writing down a central theme/concept and thinking of new and related ideas which radiate out from the centre.
By focussing on key ideas written down in your own words and looking for connections between them, you can map knowledge in a way that will help you better understand and retain information.
To see an example from the contemporary issues and sustainability unit, look below. You will see a mind map by Dr Nissen of economic, environmental and social issues that enterprises may face (and various strategies and plans that can be used to sustainably manage these issues). The map shows you a way of viewing how to undertake the process. It is best to produce a mind map that is suited to your own agricultural enterprise.
Find more on Mind Mapping here including examples.
Brainstorming is a popular group problem-solving technique.
A desktop search of definitions of brainstorming revealed that it is typically referred to as a technique for:
If you are interested in exploring the method of brainstorming beyond this brief description, please follow this link to a short module on Brainstorming .
A fishbone analysis is often useful for trying to organise information generated from a root cause analysis, brainstorming or mind mapping exercise. You input this information into a fishbone diagram i.e. a picture of a fish where you write the main problem in the head, then record causes in the spines. The best way to do this is to write the problem as a statement or question and then add to the backbones of the fish with potential causes. The picture below provides an example of what a fishbone diagram looks like.
For other examples and access to fishbone templates, please click here .
GoLeanSixSigma also have a great Excel tool for fishbone diagrams:
After you have undertaken the initial research on a topic, affinity mapping is a useful strategy to visualise and categorise this information. It is an effective and dynamic way of recording the qualitative and quantitative data you have collected such as on-site observations and interviews. The first step to affinity mapping involves documenting your findings on separate sticky notes, followed by grouping them according to topics (e.g. trends, challenges, themes) and then categorising them with headings. Be prepared to move your sticky notes around; affinity mapping is a dynamic process.
Pokars, S. 1989. Systematic Problem Solving and Decision Making. The Viability Group, Inc. USA.
Porter, M. E. 1979 How competitive forces shape strategy, Harvard Business Review, vol. 59, no. 2, pp. 137-145.
Porter, M. E. 2008. The five forces that shape strategy. Harvard Business Review, vol. 88, pp. 78-93
< https://globaldigitalcitizen.org/critical-thinking-skills-cheatsheet-infographic >.
The first step to solving a problem is to define the problem precisely. It is the heart of problem-solving.
Root cause analysis is the second important element of problem-solving in quality management. The reason is if you don't know what the problem is, you can never solve the exact problem that is hurting the quality.
Manufacturers have a variety of problem-solving tools at hand. However, they need to know when to use which tool in a manner that is appropriate for the situation. In this article, we discuss 7 tools including:
The model introduced by Ishikawa (also known as the fishbone diagram) is considered one of the most robust methods for conducting root cause analysis. This model uses the assessment of the 6Ms as a methodology for identifying the true or most probable root cause to determine corrective and preventive actions. The 6Ms include:
Related Training: Fishbone Diagramming
The Pareto Chart is a series of bars whose heights reflect the frequency or impact of problems. On the Chart, bars are arranged in descending order of height from left to right, which means the categories represented by the tall bars on the left are relatively more frequent than those on the right.
Related Training: EFFECTIVE INVESTIGATIONS AND CORRECTIVE ACTIONS (CAPA) Establishing and resolving the root causes of deviations, problems and failures
This model uses the 5 Why by asking why 5 times to find the root cause of the problem. It generally takes five iterations of the questioning process to arrive at the root cause of the problem and that's why this model got its name as 5 Whys. But it is perfectly fine for a facilitator to ask less or more questions depending on the needs.
Related training: Accident/Incident Investigation and Root Cause Analysis
Process | Failure | Effect | S | Causes | O | D | RPN |
---|---|---|---|---|---|---|---|
FMEA is a technique used to identify process and product problems before they occur. It focuses on how and when a system will fail, not if it will fail. In this model, each failure mode is assessed for:
A combination of the three scores produces a risk priority number (RPN). The RPN is then provided a ranking system to prioritize which problem must gain more attention first.
Related Training: Failure Mode Effects Analysis
A scatter diagram also known as a scatter plot is a graph in which the values of two variables are plotted along two axes, the pattern of the resulting points revealing any correlation present.
To use scatter plots in root cause analysis, an independent variable or suspected cause is plotted on the x-axis and the dependent variable (the effect) is plotted on the y-axis. If the pattern reflects a clear curve or line, it means they are correlated. If required, more sophisticated correlation analyses can be continued.
Related Training: Excel Charting Basics - Produce Professional-Looking Excel Charts
Also known as KJ Diagram, this model is used to represent the structure of big and complex factors that impact a problem or a situation. It divides these factors into small classifications according to their similarity to assist in identifying the major causes of the problem.
The Fault Tree Analysis uses Boolean logic to arrive at the cause of a problem. It begins with a defined problem and works backward to identify what factors contributed to the problem using a graphical representation called the Fault Tree. It takes a top-down approach starting with the problem and evaluating the factors that caused the problem.
Finding the root cause isn't an easy because there is not always one root cause. You may have to repeat your experiment several times to arrive at it to eliminate the encountered problem. Using a scientific approach to solving problem works. So, its important to learn the several problem-solving tools and techniques at your fingertips so you can use the ones appropriate for different situations.
P&PC, SPC/6Sigma, Failure Investigation, Root Cause Analysis, PDCA, DMAIC, A3 This webinar will define what are the US FDA's expectation for Production and Process Control / Product Realization, the use of statistical tehniques, 6 sigma, SPC, for establishing, controlling , and verifying the acceptability of process capability and product characteristics, product acceptance or validation and other studies. Non-conformance, OOS, deviations Failure Investigations, and Root Cause Analysis, PDCA, DMAIC, and similar project drivers to improvement, A# and similar dash boards.
Accident/Incident Investigation and Root Cause Analysis If a major workplace injury or illness occurred, what would you do? How would you properly investigate it? What could be done to prevent it from happening again? A properly executed accident/incident investigation drives to the root causes of the workplace accident to prevent a repeat occurrence. A good accident/incident investigation process includes identifying the investigation team, establishing/reviewing written procedures, identifying root causes and tracking of all safety hazards found to completion.
Root Cause Analysis - The Heart of Corrective Action This presentation will explain the importance of root cause analysis and how it fits into an effective corrective and preventive action system. It will cover where else in your quality management system root cause analysis can be used and will give examples of some of the techniques for doing an effective root cause analysis. Attendees will learn how root cause analysis can be used in process control.
Addressing Non-Conformances using Root Cause Analysis (RCA) RCA assumes that systems and events are interrelated. An action in one area triggers an action in another, and another, and so on. By tracing back these actions, you can discover where the issue started and how it grew into the problem you're now facing.
Risk Management Under ISO 14971 ISO 14971:2019 is the definitive standard for risk management for medical devices and IVDs. The standard lays out a comprehensive approach to managing risks in the life sciences. The course will discuss practical approaches to complying with the standard.
Introduction to Root Cause Investigation for CAPA If you have reoccurring problems showing up in your quality systems, your CAPA system is not effective and you have not performed an in-depth root cause analysis to be able to detect through proper problem solving tools and quality data sources, the true root cause of your problem. Unless you can get to the true root cause of a failure, nonconformity, defect or other undesirable situation, your CAPA system will not be successful.
Root Cause Analysis and CAPA Controls for a Compliant Quality System In this CAPA webinar, learn various regulations governing Corrective and Preventive Actions (CAPA) and how organization should collect information, analyze information, identify, investigate product and quality problems, and take appropriate and effective corrective and/or preventive action to prevent their recurrence.
How to Design and Implement a Dynamic Control Plan This webinar training will discuss how to design a dynamic control plan that combines FMEA and the control plan by extending the FMEA to encompass the elements of the control plan and create a living document that helps to drive continual improvement.
An Easy to Implement Integrated Risk Management Approach Compliant with ISO 14971 This integrated risk management training for medical devices will discuss how to incorporate risk management as per ISO 14971 guidelines in all phases of medical device development. It will highlight the documentation needed to support the decisions made as part of the risk management process.
The Use and Mis-use of FMEA in Medical Device Risk Management The presentation will discuss the proper use of FMEA in risk management and how to recognize and avoid the traps associated with this tool in order to have a more efficient risk management process. Most medical device manufacturers use FMEA as a part of their risk management system. Most medical device manufacturers use FMEA as a part of their risk management system.
Root Cause Analysis for CAPA Management (Shutting Down the Alligator Farm) Emphasis will be placed on realizing system interactions and cultural environment that often lies at the root of the problem and prevents true root cause analysis. This webinar will benefit any organization that wants to improve the effectiveness of their CAPA and failure investigation processes.
Root Cause Analysis for Corrective and Preventive Action (CAPA) The Quality Systems Regulation (21 CFR 820) and the Quality Management Standard for Medical Devices (ISO 13485:2003), require medical device companies to establish and maintain procedures for implementing corrective and preventive action (CAPA) as an integral part of the quality system.
Strategies for an Effective Root Cause Analysis and CAPA Program This webinar will provide valuable assistance to all regulated companies, a CAPA program is a requirement across the Medical Device, Diagnostic, Pharmaceutical, and Biologics fields. This session will discuss the importance, requirements, and elements of a root cause-based CAPA program, as well as detailing the most effective ways to determine root cause and describing the uses of CAPA data.
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By Sebastian Traeger
Updated: April 21, 2024
Reading Time: 5 minutes
2. pareto chart, 4. failure mode and effects analysis (fmea), 5. proact® rca method, 6. affinity diagram, 7. fault tree analysis (fta).
With over two decades in business – spanning strategy consulting, tech startups and executive leadership – I am committed to helping your organization thrive. At Reliability, we’re on a mission to help enhance strategic decision-making and operational excellence through the power of Root Cause Analysis, and I hope this article will be helpful! Our goal is to help you better understand these root cause analysis techniques by offering insights and practical tips based on years of experience. Whether you’re new to doing RCAs or a seasoned pro, we trust this will be useful in your journey towards working hard and working smart.
Root Cause Analysis (RCA) shines as a pivotal process that helps organizations identify the underlying reasons for problems, failures, and inefficiencies. The goal is simple: find the cause, fix it, and prevent it from happening again. But the process can be complex, and that’s where various RCA techniques come into play.
Let’s dive into seven widely utilized RCA techniques and explore how they can empower your team’s problem-solving efforts.
Named after Japanese quality control statistician Kaoru Ishikawa, the Fishbone Diagram is a visual tool designed for group discussions. It helps teams track back to the potential root causes of a problem by sorting and relating them in a structured way. The diagram resembles a fishbone, with the problem at the head and the causes branching off the spine like bones. This visualization aids in categorizing potential causes and studying their complex interrelationships.
The Pareto Chart, rooted in the Pareto Principle, is a visual tool that helps teams identify the most significant factors in a set of data. In most situations, 80% of problems can be traced back to about 20% of causes. By arranging bar heights from tallest to shortest, teams can prioritize the most significant factors and focus their improvement efforts where they can have the most impact.
The 5 Whys method is the epitome of simplicity in getting to the bottom of a problem. By repeatedly asking ‘why’ (typically five times), you can delve beneath the surface-level symptoms of a problem to unearth the root cause. This iterative interrogation is most effective when answers are grounded in factual evidence.
When prevention is better than cure, Failure Mode and Effects Analysis (FMEA) steps in. This systematic, proactive method helps teams identify where and how a process might fail. By predicting and examining potential process breakdowns and their impacts, teams can rectify issues before they turn into failures. FMEA is a three-step process that involves identifying potential failures, analyzing their effects, and prioritizing them based on severity, occurrence, and detection ratings.
The PROACT ® RCA technique is a robust process designed to drive significant business results. Notably used to identify and analyze ‘chronic failures,’ which can otherwise be overlooked, this method is defined by its name:
PReserving Evidence and Acquiring Data: Initial evidence collection step based on the 5-P’s – Parts, Position, People, Paper, and Paradigms.
Order Your Analysis Team and Assign Resources: Assembling an unbiased team to analyze a specific failure.
Analyze the Event: Reconstructing the event using a logic tree to identify Physical, Human, and Latent Root Causes.
Communicate Findings and Recommendations: Developing and implementing solutions to prevent root cause recurrence.
Track and Measure Impact for Bottom Line Results: Tracking the success of implemented recommendations and correlating the RCA’s effectiveness with ROI.
PROACT® RCA excels in mitigating risk, optimizing cost, and boosting performance, making it a valuable addition to any RCA toolkit.
The Affinity Diagram is a powerful tool for dealing with large amounts of data. It organizes a broad range of information into groups based on their natural relationships, creating a clear, visual representation of complex situations. It’s particularly beneficial for condensing feedback from brainstorming sessions into manageable categories, fostering a better understanding of the broader picture.
Fault Tree Analysis (FTA) is a top-down, deductive failure analysis that explores the causes of faults or problems. It involves graphically mapping multiple causal chains to track back to possible root causes, using a tree-like diagram. FTA is particularly useful in high-risk industries, such as aerospace and nuclear power, where preventing failure is crucial.
Each RCA technique provides a unique approach for viewing and understanding problems, helping you pinpoint the root cause more effectively. The key is to understand when and how to use each tool, which can significantly enhance your team’s problem-solving capabilities.
Power up your RCA analysis with our EasyRCA and revolutionize your problem-solving process. Start Your Free Trial.
Ishikawa Fishbone Diagram | Visual representation of complex relationships | When there are many possible causes to a problem |
Pareto Chart | Prioritizes problem areas based on impact | When trying to identify the most significant causes |
5 Whys | Simple, iterative problem-solving technique | When the problem is straightforward and the solution is not immediately apparent |
FMEA | Proactive, preventative approach | When addressing complex processes that could lead to serious consequences if failed |
PROACT® RCA Method | Comprehensive, result-driven approach | When dealing with chronic, recurrent failures |
Affinity Diagram | Groups large data into manageable categories | When trying to find patterns and connections in large amounts of data |
Fault Tree Analysis (FTA) | Visual mapping of causal chains | When working in high-risk industries where prevention is crucial |
In conclusion, the techniques presented offer a diverse set of tools to help organizations address problems and inefficiencies effectively. From visual representations like the Ishikawa Fishbone Diagram and Pareto Chart to more proactive approaches such as the 5 Whys and Failure Mode and Effects Analysis (FMEA), each technique provides a unique perspective on identifying and mitigating root causes.
The PROACT® RCA Method stands out for its comprehensive process, particularly suited for chronic failures. Additionally, the Affinity Diagram and Fault Tree Analysis (FTA) contribute valuable insights by organizing data and exploring causal chains, respectively. Leveraging these techniques strategically enhances a team’s problem-solving capabilities, enabling them to make informed decisions and drive continuous improvement.
I hope you found these 7 techniques insightful and actionable! Stay tuned for more thought-provoking articles as we continue to share our knowledge. Success is rooted in a thorough understanding and consistent application, and we hope this article was a step in unlocking the full potential of Root Cause Analysis for your organization. Reliability runs initiatives such as an online learning center focused on the proprietary PROACT® RCA methodology and EasyRCA.com software. For additional resources, visit our Reliability Resources .
Ultimate Guide to Swiss Cheese Model and Its Applications
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Root Cause Analysis with 5 Whys Technique (With Examples)
What Is Fault Tree Analysis (FTA)? Definition & Examples
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How do you make seven even? Use these tools to solve the big math problems in life.
The test questions, wolframalpha, microsoft mathsolver.
While OpenAI's ChatGPT is one of the most widely known AI tools, there are numerous other platforms that students can use to improve their math skills.
I tested seven AI tools on two common math problems so you know what to expect from each platform and how to use each of them.
I used two math problems to test each tool and standardize the inputs.
These two problems give each AI tool a chance to show reasoning, problem-solving, accuracy, and how it can guide a learner through the process.
Thetawise provides more than simple answers; you can also opt to have the AI model tutor you by sharing a detailed step-by-step breakdown of the solution. Using the platform is fairly straightforward, given that all you need to do is navigate to the platform and key in the math problem at hand. Alternatively, you can even upload a photo of the math problem onto the platform, and the AI will analyze the image and provide you with an answer.
The AI platform gave us a step-by-step breakdown of the problem:
It resulted in the answer:
While the answer is correct, the tool also provides further options for students to generate a more detailed breakdown of the steps or ask more specific questions.
WolframAlpha is an AI tool capable of solving advanced arithmetic, calculus, and algebra equations. While WolframAlpha's free version provides you with a direct answer, the paid version of the tool generates step-by-step solutions. If you want to make the best use of WolframAlpha's capabilities, you can sign up for the Pro version, which costs $5 per month for the annual plan if you're a student.
As expected, Wolfram Alpha solved both problems, showcasing its ability to handle different problems and provide precise answers quickly.
Julius works pretty similarly to the other AI tools on this list. That said, the highlight of this platform is that it has a built-in community forum, which users can use to discuss their prompts, results, or even issues they might be facing with the platform. Its active user base helps you quickly exchange ideas and receive feedback or advice. The platform's default version uses a combination of GPT-4 and Calude-3, based on whichever model best suits the prompt you input.
We tested the platform's accuracy by submitting the same problems that we did with the other AI tools. When submitting your prompt, you have the option of typing your question or uploading an image or a Google Sheet.
Julius provided correct solutions and offered options to help users verify the solution.
One of the oldest AI platforms, Microsoft's MathSolver is a great option if you want a tool capable of providing free step-by-step solutions to calculus, algebra, and other math problems. Here's how it fared when we submitted our math problems.
Microsoft's MathSolver provided the correct answers, and you can view the steps to the solution, take a quiz, solve similar problems, and more. This can be a great way to practice and perfect your understanding of different concepts.
Symbolab allows you to practice your math skills via quizzes, track your progress, and provide solutions to mathematical problems of different types, including calculus, fractions, trigonometry, and more. You can also use the Digital Notebook feature to keep track of any math problems you solve and share them with your friends. Another highlight of this platform is that educators can use the tool to create a virtual classroom, generate assessments, and share feedback, among other things.
The platform not only displays the answer but also lets you view a breakdown of the steps involved in solving the problem. You can also share the answers and steps via email or social media or print them for reference.
Anthropic launched its Claude 3 AI models in March 2024. Anthropic stated that Claude Opus, the most advanced Claude 3 model, outperforms comparable AI tools on most benchmarks for AI systems, including basic mathematics, undergraduate-level expert knowledge, and graduate-level expert reasoning. To test the platform's accuracy and ease of use, we submitted our two math problems. Here's how the platform performed:
While Claude initially got the answer wrong, probing it and requesting further clarification led to a correct solution.
Remember that we used the free version of Claude to solve this problem; subscribing to Opus (its more advanced model) is recommended if you want to take advantage of Claude's more advanced problem-solving capabilities.
Given that Claude got the previous problem wrong, our second, more basic fraction-based problem will indicate if the AI's performance was an anomaly or part of a consistent pattern.
As you can see, Claude correctly solved this problem and provided a detailed step-by-step breakdown of how it arrived at the answer.
GPT-4 can solve problems with far greater accuracy than its predecessor, GPT-3.5. If you're using the free version of ChatGPT, you'll likely only have access to GPT 3.5 and GPT-4o . However, for $20 per month, you can subscribe to the Plus model, which gives you access to GPT-4 and allows you to input five times the number of messages per day compared to the free version. That said, let's check how it performs with math problems.
In both cases, GPT-4o provided the correct answer with a detailed breakdown of the steps. While the platform is free, unlike other models, it does not have a quiz feature or a community forum.
These AI tools offer unique features and capabilities that make them a good option for math problems. Ultimately, the best way to pick a tool is by testing different models to determine which platform best fits your preferences and learning needs.
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Title: tool-planner: dynamic solution tree planning for large language model with tool clustering.
Abstract: Large language models (LLMs) have demonstrated exceptional reasoning capabilities, enabling them to solve various complex problems. Recently, this ability has been applied to the paradigm of tool learning. Tool learning involves providing examples of tool usage and their corresponding functions, allowing LLMs to formulate plans and demonstrate the process of invoking and executing each tool. LLMs can address tasks that they cannot complete independently, thereby enhancing their potential across different tasks. However, this approach faces two key challenges. First, redundant error correction leads to unstable planning and long execution time. Additionally, designing a correct plan among multiple tools is also a challenge in tool learning. To address these issues, we propose Tool-Planner, a task-processing framework based on toolkits. Tool-Planner groups tools based on the API functions with the same function into a toolkit and allows LLMs to implement planning across the various toolkits. When a tool error occurs, the language model can reselect and adjust tools based on the toolkit. Experiments show that our approach demonstrates a high pass and win rate across different datasets and optimizes the planning scheme for tool learning in models such as GPT-4 and Claude 3, showcasing the potential of our method.
Comments: | 46pages first version |
Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO) |
Cite as: | [cs.AI] |
(or [cs.AI] for this version) | |
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Problem solving software is the best way to take advantage of multiple problem solving tools in one platform. While some software programs are geared toward specific industries or processes - like manufacturing or customer relationship management, for example - others, like MindManager , are purpose-built to work across multiple trades ...
Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward. Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.
9 Problem-solving tools for gathering and selecting ideas. Problem-solving tools support your meeting with easy-to-use graphs, visualisations and techniques. By implementing a problem-solving tool, you break the cycle of mundane verbal discussion, enabling you to maintain engagement throughout the session. 28. Fishbone Diagram
The first step in solving a problem is understanding what that problem actually is. You need to be sure that you're dealing with the real problem - not its symptoms. For example, if performance in your department is substandard, you might think that the problem lies with the individuals submitting work. However, if you look a bit deeper, the ...
2. Omnex Systems. via Omnex. Omnex's problem-solving software has many helpful features to track, manage, and solve problems quickly. It's a one-stop shop for dealing with internal and external issues. The platform is also customer-centric, which responds to customers in their preferred formats.
The McKinsey guide to problem solving. Become a better problem solver with insights and advice from leaders around the world on topics including developing a problem-solving mindset, solving problems in uncertain times, problem solving with AI, and much more.
Problem Solving. 56 Resources. Problems can occur at any time, and solutions often need to be found quickly. Delve into this wide variety of tools that will help you to identify the source of a problem, brainstorm solutions and select the best option.
The 5 Whys strategy is a simple, effective tool for uncovering the root of a problem. You can use it in troubleshooting, problem-solving, and quality-improvement initiatives. Start with a problem and ask why it is occurring. Make sure that your answer is grounded in fact, and then ask the question again.
Balance divergent and convergent thinking. Ask problems as questions. Defer or suspend judgement. Focus on "Yes, and…" rather than "No, but…". According to Carella, "Creative problem solving is the mental process used for generating innovative and imaginative ideas as a solution to a problem or a challenge.
Solving complex problems may be difficult but it doesn't have to be excruciating. You just need the right frame of mind and a process for untangling the problem at hand. ... More problem-solving tools Hurson's Productive Thinking Model. In his book "Think Better," author and creativity guru Tim Hurson proposed a six-step model for solving ...
Brainstorming and team problem-solving techniques are both useful tools in this stage of problem solving. Many alternative solutions to the problem should be generated before final evaluation. A common mistake in problem solving is that alternatives are evaluated as they are proposed, so the first acceptable solution is chosen, even if it's ...
It is a powerful tool for businesses of all sizes and industries, and it is especially useful for solving complex and multi-faceted problems. In this blog post, we will walk you through the A3 Problem Solving methodology step by step. Whether you are new to A3 Problem Solving or simply want to improve your skills, this guide will help you ...
Problem-solving tools refer to strategies that can help determine the cause of a problem and identify the best solutions available. The first step in addressing an issue at work is to outline your objectives. Once you establish the cause, you can isolate variables that can help contribute to a potential solution.
NGT is used to generate and evaluate or rank a series of actions or solutions. It is often used after a brainstorming session to organize and evaluate the products of the brainstorming session. The problem or decision is stated by the facilitator. Ideas are generated or imported from a previous brainstorming session.
Structured problem solving strategies can be used to address almost any complex challenge in business or public policy. ... 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 ...
Fishbone Diagram. The Fishbone Diagram, also known as the Cause-and-effect Diagram or the Ishikawa diagram, is a powerful problem-solving tool that visually maps out the potential causes of an issue. Its distinctive shape resembles the skeleton of a fish, where the "head" represents the problem and the "bones" branching out from the ...
4 steps to better problem solving. While it might be tempting to dive into a problem head first, take the time to move step by step. Here's how you can effectively break down the problem-solving process with your team: 1. Identify the problem that needs to be solved. One of the easiest ways to identify a problem is to ask questions.
Key Stages. Problem Recognition - determining what the problem is. Labelling the problem. Conducting a problem-cause analysis. Optional solutions. Making a decision based on the best options you have generated. Developing an action plan to solve the problem. Evaluating and monitoring your solution to the problem.
Here are some commonly used tools and methodologies: 1. THE 5 WHYS. This is a simple but effective technique that involves asking "why" repeatedly (usually five times) to drill down to the ...
It is the heart of problem-solving. Root cause analysis is the second important element of problem-solving in quality management. The reason is if you don't know what the problem is, you can never solve the exact problem that is hurting the quality. Manufacturers have a variety of problem-solving tools at hand.
Let's dive into seven widely utilized RCA techniques and explore how they can empower your team's problem-solving efforts. 1. The Ishikawa Fishbone Diagram (IFD) Named after Japanese quality control statistician Kaoru Ishikawa, the Fishbone Diagram is a visual tool designed for group discussions.
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.
Although problem-solving is a skill in its own right, a subset of seven skills can help make the process of problem-solving easier. These include analysis, communication, emotional intelligence, resilience, creativity, adaptability, and teamwork. 1. Analysis. As a manager, you'll solve each problem by assessing the situation first.
Get math help in your language. Works in Spanish, Hindi, German, and more. Online math solver with free step by step solutions to algebra, calculus, and other math problems. Get help on the web or with our math app.
Thetawise. WolframAlpha. Julius. Microsoft MathSolver. Symbolab. Claude. ChatGPT-4o. While OpenAI's ChatGPT is one of the most widely known AI tools, there are numerous other platforms that students can use to improve their math skills. I tested seven AI tools on two common math problems so you know what to expect from each platform and how to ...
Find troubleshooting steps for problems such as your Creative Cloud desktop app not showing available Photoshop updates, you cannot activate Photoshop or it is appearing in trial mode, Photoshop is not recognizing your camera's raw files, you are experiencing image rendering issues or slow performance, Photoshop is crashing, or some tool, font, or plug-in is not working properly.
Problem solving is an exceptionally important workplace skill. Being a competent and confident problem solver will create many opportunities for you. By using a well-developed model like Simplexity Thinking for solving problems, you can approach the process systematically, and be comfortable that the decisions you make are solid.
This Pillsbury IP Partner Developed an AI Tool to Solve the Problem. Josh Tucker, a Pillsbury Winthrop Shaw Pittman IP partner in Austin, had one goal: "I want to be able to yell at my computer ...
Large language models (LLMs) have demonstrated exceptional reasoning capabilities, enabling them to solve various complex problems. Recently, this ability has been applied to the paradigm of tool learning. Tool learning involves providing examples of tool usage and their corresponding functions, allowing LLMs to formulate plans and demonstrate the process of invoking and executing each tool ...