The reason that conducting a root cause analysis is so important is that it is the only way you can identify exactly what is the root cause of the problem. This will result in the best possible solution that will be both effective and efficient.

The Best RCA Tools to Play the Blame Game

1. The 5 Whys: This Lean Six Sigma tool is so easy that kids already play it. Just keep asking why until a meaningful conclusion has been established. Many times it could take as long as it has taken to answer one of your kid’s most challenging questions.
2. Flowcharts: Everyone can visually see what is being impacted with flowcharts; these make things clear to kids as well. Make sure you keep the flowcharts simple.
3. Fishbone Diagram: This is also known as the cause and effect diagram. Used in conjunction with the 5 Whys when the 5 Whys are too general. The Fishbone diagram will put causes into specific categories, indicating how that cause impacts the outcome.
4. Brainstorming: This will bring the entire family together so everyone can have their say or input. Everyone’s input counts and is added, so this is a great Lean Six Sigma tool for family participation. The result in the brainstorming session should identify the root cause of the problem (why the garage always gets messy) and try to come up with possible solutions.
5. Affinity Diagram: This can be used with the information gathered from the brainstorming session, by organizing and possibly consolidating that information to further relate to the issue at hand.

This is yet another creative way to use the magic of Six Sigma and Lean Six Sigma tools in our daily lives to deal with common, everyday organization issues.

Interested in learning more about root cause analysis? Learn about our RCA training!

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If you’ve been in business for a while, you’ve probably already had to work with affinity diagrams more than once. It’s a very useful tool and despite being half a century old, it’s aged quite well and still has a prominent place in many companies’ workflows. A lot of work has been simplified and streamlined with the help of the affinity diagram, but how exactly did it come to be, and who’s responsible for it?

Affinity diagrams are known under a different name in some circles the KJ method referring to the original inventor of the tool, Jiro Kawakita. They were originally designed as a tool to help groups of people reach a consensus on a difficult topic, and even though the diagram is a very basic tool without any fancy bells and whistles, it still gets the job done perfectly in this regard.

Working with Subjective Data

The important point is that affinity diagrams have to be based on some workable data, subjective facts and not the opinions of people. There is a commonly used anecdote used to explain how the KJ method has evolved, describing a military exercise in the US Army some time ago. In it, commanders were tasked with predicting enemy movements. It turned out that alone, their analyses were very far from the actual mark, but when each of them was given the opportunity to review what others had come up with, the situation suddenly became much better, despite the fact that no real new information was introduced.

That’s the goal of affinity diagrams too to consolidate multiple different points of view in a way that makes sense across the board. It has gone through some iterations over time and the method has been simplified somewhat since its original inception, but in the end, its main point remains the same. It allows you to combine information from multiple sources sometimes conflicting ones and get a better overview of the current situation by taking all those factors into consideration with appropriate weights.

Does it Really Work?

An obvious question that can arise from all of this would be about the efficiency of the method, and its actual reliability. Can we really trust the affinity diagram to consolidate multiple different points of view so effectively? There have been multiple experiments over the years, as many people have tried to verify the legitimacy of the claims of those who support the use of affinity diagrams. And so far, we keep seeing the same story over and over again the method simply works.

There are a few caveats, however. Most importantly, there are going to be some significant differences in the outcome depending on how the method is carried out exactly. Following a standardized multi-step strategy is recommended, and even though affinity diagrams leave a lot of freedom for experimentation, it’s good to know that you’re following some rigid system that can produce expected results.

Consistency is key in implementing the KJ method effectively, and if you end up modifying your approach, you should do your best to document all changes and come up with a modified plan of action. Otherwise, even if you do come across a good improvement that works better in your specific case, it might end up lost the next time you apply the method.

Conclusion

Affinity diagrams have been around for quite a while, but few people realize where they come from and what kinds of developments they’ve seen over time. They’re a very important tool when working with larger teams of people, or even smaller ones where there is still a lot of misunderstanding between the different members. It’s easily one of the best ways to come up with a solution that works well enough for everyone, even when there are some significant disagreements between the different points of view.

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How Do We Use Machine Data Capture?

The types of machine data your computer systems collect will vary. It can range from the volume of satisfactory products to sell vs. rejected sub-standard ones to machine capacity effectiveness and utilization. Additionally, your systems should also monitor factors like machine cycles, production time, availability and reliability. Machine status is an equally critical component of MDC and denotes a whole spectrum of important production factors. These factors include primary and secondary time, breakdowns and maintenance, as well as service requirements.

But what happens to all this data? How do businesses use it? It’s simple. All your captured data travels through interfaces where your manufacturing execution system (MES) collects and collates it. Think of your MDC as the gold miner and the MES as the sieve through which they filter all the information they collect. The combined MDC data and MES findings allow you to use data effectively. Furthermore, your goal is to use your data to plan and push production in the direction you want it to go. That is, toward achieving optimum conditions. This allows for increased productivity and better results.

How Can Six Sigma Improve Data Collection?

You’re probably wondering, how does Six Sigma fit into the equation? Six Sigma improvement projects are an extremely reliable method for process improvement. It allows you to make the changes necessary for a strong data collection plan. Moreover, Six Sigma project leaders, usually Black Belts, employ DMAIC to define, measure, analyze, improve, and control data collection processes. There are several prerequisites you must meet for an effective data collection plan.

First, we have the pre-data collection steps. Ensure your project team defines your data collection goals. What data do you need? Equally, for what purpose will you use it? What insight will it offer and what do you wish to achieve through collecting it? Using tools like brainstorming, affinity diagrams, and root cause analysis can help generate answers to these questions.

The project team should then be able to agree on your plan’s methodology and operational definitions. Teamwork is always most effective here, and we recommend examining previous data to compare to the current. It’s essential that you determine whether past, present, and future data will factor into the data collection plan, plus what methodologies you are likely to use. Skipping this step will certainly deliver insufficient, if not deceptive, results. Finally, it is essential you ensure the repeatability, accuracy, stability, and reproducibility of your data collection and measurement. Once you have defined and planned your data collection process using the above data, you can then move forward. Black Belts can oversee implementation and to nudge it in the right direction, should you run into any obstacles.

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Ever noticed why there are whiteboards or strawboards at work places? Over the past decade, there has been a rising trend in the demand of whiteboards and strawboards by companies across the world. You find them everywhere irrespective of the type of job a person is doing or the role he is playing in his company. These boards help people to write down ideas and work on them as and when it is convenient for them. Often innovating or problem solving are not easy tasks. Such tasks require a holistic approach and multi-dimensional analysis which involves playing with a lot of ideas and broad themes.

Affinity Diagrams:

Affinity Diagrams are pictorial business tools that help a person to identify ideas (for innovation) or issues (for problem solving) and organize them. There is no harm in having a long list of ideas (for innovating) or a list of causes to a problem. Once the list is complete, the issue of organizing the ideas into similar categories is dealt with. Finally, the person tries to understand if there is any ‘affinity’ or relationship between all or some of the causes and looks for new ideas and solutions. This process is cyclic and not linear. It may not be possible for a person to come up with a solution or an idea for days. It may not be possible to do this alone and sometimes, it is good to work with teams and have brainstorming sessions as well.

Case Study:

Let us apply using Affinity Diagrams to understand why people are happy with the way Amazon (the online e-commerce portal) operates and identify areas of improvement.

People may be happy with Amazon due to the following reasons:
1. Convenience of shopping online
2. A vast product portfolio offering competitive prices
3. Easy billing and payment methods
4. Reliable delivery
5. Reliable vendors
6. Prompt order status via email alerts
7. Uninterrupted web browsing experience with non-intrusive advertisements
8. Customization of e-marketplace, creation of wish lists, etc
9. Reliable problem-solving and a great customer support system
10. In some states, saving taxes may be a priority
11. Ability to write reviews, rate products and have a community in the e-marketplace

It is to be remembered that to deal with competition, Amazon has to deal with the same set of issues as stated above to understand the following issues:
1. Why a majority of people is shopping elsewhere online?
2. How is the web traffic being driven to Amazon?
3. What are the causes for poor satisfaction of the consumers?
4. What can be done to overcome these problems?

This stage involves clustering of various issues under sub-heads.

Let us take a look at two situations:

If there is a delivery problem, identify what is delaying order processing, what are the errors in order processing, are the people processing the orders well-trained, are the people processing the orders happy with their jobs, well paid and productive, are the items stocked properly, is there a scenario where the items are not in stock and the website shows ‘in stock’, is it possible that the carrier is no longer reliable (USPS, FedEx, etc).

If there is a web browsing problem, identify how a consumer browses on the website, what tools he uses to optimize his results, is he distracted by advertisements and eventually buying elsewhere, is he able to find what he is looking for, are the prices of the products properly displayed, is he able to proceed to checkout easily, is he able to follow payment procedures properly, does he make wish lists, add items to his watch list etc.

Conclusion: These are only a few examples, but the idea is clear. Once ideas or issues have been identified , the ‘affinity’ between various causes  and various effects and ‘cause and effects’ has to mapped so that problem solving can be easier and new ideas can be proposed. Affinity Diagrams may be drawn by using white board pens drawing rectangular boxes and writing text in them or with note slips which can stick on the board and can be used to place anywhere. Affinity Diagrams are simple to make, easy to understand and a good tool to process large amounts of information and ideas which would otherwise be just floating in one’s mind!

Learn more information about 6Sigma.com’s Lean Six Sigma training coursework, available as classroom, onsite, or online options.

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