The Taguchi methods can be seen in many industries nowadays, and they’ve become an integral part of the optimization strategies of many companies. And yet, despite the fact that they tend to be so popular in a diverse range of fields including even areas like biotech it’s important to recognize the actual origins of the methods, namely the manufacturing sector. There is a lot to gain for a company that works in this field if it applies the Taguchi methods to its work properly, and mastering them is an important step in the development of any responsible leader.

The Loss Function

The use of a loss function is a somewhat controversial topic in statistical analysis, and depending on who you ask, you’re going to get different responses on its usefulness. It’s true that the Taguchi methods tend to deviate from the original norms in the field to some extent, but in the end that’s actually what makes them so powerful and widely accepted. The fact that they provide an alternative outlook on the way data is processed and analyzed is seen as a positive factor by many.

Taguchi himself paid a lot of attention to the development of a proper methodology for the use of loss functions, and he studied them in detail. His findings are still being repeated to this day, and it’s important to understand where the Taguchi methods come from in the first place, in order to develop an adequate ability for implementing them into the company’s daily work.

The most important point here is the realization that in most production environments, companies typically try to reach a specific target in their output, and a lot of the optimization can revolve around bringing the results closer to that specific target.

Off-Line Quality Control

Another important point to drive home about the Taguchi methods is the importance of controlling quality off the production line itself. According to Taguchi’s original findings, the best way to ensure that the product is produced with a strong approach to quality control is to ensure that it follows sound design principles and that the manufacturing process is developed in a way that eliminates common errors in an automated manner.

This might sound straightforward, and it’s actually a common element to how most manufacturing organizations work today, but the roots of this philosophy can be traced back to Taguchi to a good extent. Of course, some types of errors that occur off the production line can be impossible to detect in advance and work around, which makes it important to have adequate statistical analysis methods that can point out any irregularities early on which, in turn, brings us back to the point from above.

Everything involved in the Taguchi methods is tightly interconnected, and understanding these relationships is crucial for applying the methods correctly in an organization’s work.

Design of Experiments

Last but not least, Taguchi paid a lot of attention to experiments and their design. The main idea was that the methods should provide a deep understanding of the way variation works and how it affects the operation of any given organization, and he was dissatisfied with the methods that were traditionally used at the time, such as regular sampling techniques.

One of the interesting ideas proposed by Taguchi in the development of his methodologies is to use an external simulated environment which is supposed to mimic the environment the product will be required to perform in later on. That way, random variance can be introduced in a controlled manner, allowing observers to identify exactly how it impacts the work of an organization and to figure out what measures if any should be taken to fix issues with variance.

Conclusion

The Taguchi methods are important to understand for anyone in a leadership position, regardless of the nature of the company’s work. As long as there is room for optimization in the standard processes carried out on a regular basis, it doesn’t matter if the company does manufacturing or anything else the Taguchi methods will likely prove to be a very viable addition to your arsenal of tools for statistical analysis. Of course, studying alternative methodologies is just as important in the long run as well, and you should not limit yourself to one specific area.

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Using the design of experiments method (DOE) is a great way to determine what factors are in control of the final output of your processes. It’s a good methodology for establishing all relevant relationships in your operation, and it can show you what variables you can change in order to push the final result in the right direction. Therefore, mastering its use as early as possible is strongly encouraged if you want to perfect the design of your product, provide a better service, etc. Let’s have a look at some examples of how DOE is used in practice, and what kinds of problems it can be used to address.

The Classic Weight Problem Example

A commonly given example that’s often used to explain how DOE works involves weighing objects in a pan, and attempting to identify the correct weight of each one. Different approaches to weighing are evaluated, with the result of each one recorded step by step for easy comparison. From there, it can be easily inferred which of the two approaches has a better potential for identifying the correct results more quickly, and you can additionally test how they change when you modify the size of the input. This is more of a thought experiment, but it has plenty of real-life implications and it’s commonly given as an example in case studies.

As you’ll see below, the way this experiment is carried out has some real-life applications and it can be used to study how certain other processes are carried out. If you’re looking for a good introduction to how DOE works and what kinds of effects it can have on your operations, you should start with this one and give it a good study.

Perfection in Silicon Wafer Design

The computer industry is a common adopter of DOE, and the methodology has seen plenty of uses in that area. Silicon wafers are very sensitive to tiny changes in many of the parameters they operate under, making it important to study their design very carefully, and ensure that there are no unexpected deviations. Unfortunately, this can sometimes be a huge challenge with the number of parameters typically involved in the standard wafer production facility, making DOE a good candidate for these types of studies.

Indeed, a correct application of DOE can help a facility quickly identify the exact variables that may cause the final product to fall out of line with regards to the intended safe parameter constraints, and it can help the facility operators to improve their overall work by focusing on those specific factors.

Optimizing the Yield of Chemical Processes

You’ll probably see DOE used frequently in the chemical industry, and for a good reason. It’s not rare to make attempts to optimize the outcome of a certain process when working with complex chemical reactions and multi-stage processes, and the sheer number of variables that can be tweaked can sometimes make it impossible to get the right balance by hand.

This is where design of experiments comes in, as it can help you identify the exact appropriate balance levels between the different variables involved in the process, and fine-tune them to perfection until you’re able to maximize the yield of each run. It’s worth noting that this requires you to have a very good underlying system for data collection, as otherwise you can’t expect to be able to adequately compare the results of different runs. On the other hand, once you’ve got that system in place, it’s trivial to keep it running and ensure that the outcome of each iteration is recorded and stored properly.

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Drive Energy Efficiency with Six Sigma Projects

When it comes to running a business, electricity is an essential resource. It powers the lights, computers, machinery, and allows production lines to keep running. Some companies produce energy, but all of them use it. As such, it’s important for us to conserve electricity wherever we can. Through error or inefficiency, we often end up wasting vast amounts of power performing everyday actions. Furthermore, big businesses and small have a responsibility to monitor and control their energy usage. Six Sigma projects are a valuable tool if you wish to increase your energy efficiency.

Streamlining Your Processes to Reduce Energy Usage

Without a streamlined organizational system, you are likely to waste energy where it need not be wasted. Poor energy efficiency tends to take on a snowball effect. Once it starts rolling, it will increase with time. Without safeguards in place to monitor your systems and cash flow, you will suffer significant losses through insufficient maintenance. Six Sigma projects can target problems and eliminate them. Using Six Sigma, you can replace inefficiencies with more practical solutions or excise the issue altogether. None-value-adding processes contribute to over-processing waste, which drains power and bloats your energy usage. It also saps your funds, minimizing cash flow.

Creating a Six Sigma Project to Target Energy Efficiency

When creating a Six Sigma project, ensure you have multiple Yellow, Green, and Black Belts on your team. Additional team members such as White or Orange Belts will complement the project, but this powerful trio provides all you need to make effective process changes. When drafting your project charter, ensure your team is fully aware of all the implications. Use techniques like DMAIC to define, measure, analyze, improve and control your energy efficiency problems. It’s always helpful to identify the origins of certain issues. Root Cause Analysis is a critical tool in Six Sigma work as it allows you to locate the underlying source of your process issues.

Where Does Poor Energy Efficiency Come From?

Poor energy efficiency may be the result of many problems, such as faulty machinery, over-processing, or human error. Applying Six Sigma tools to this problem allows you access to a different perspective, which makes correcting your inefficiencies much simpler. Similarly, affinity diagrams will help you identify connections between issues, allowing you to trace one problem back to another. Moreover, Design of Experiments enables you to create effective testing parameters so you can design more energy efficient operations. Energy efficiency is critical to modern business operations. Don’t hold back progress, and don’t hinder your own. Use Six Sigma to make lasting and needed changes for your business and the planet.

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White Belt

The White Belt is the beginning of your Six Sigma journey. The shallow end of the pool. You study a basic overview of Six Sigma knowledge and teamworking skills here. White Belts will track Six Sigma’s evolution from its earliest beginnings to present day. Key Six Sigma skills like DMAIC and value-stream-mapping provide the foundation for further belt progression.

Yellow Belt

Once you have earned your White Belt, you can take the step up to Yellow. This signifies the inciting moment on your journey. From here, it’s either give up or full steam ahead. While White Belts are foundation level, Yellow Belts are the first rung on the ladder. Equally, you will learn how to utilize basic measuring and analytical tools so you can understand and leverage data. Key problem-solving and process improvement skills allow you to perform novice Six Sigma project work while preparing you for further study.

Orange Belt

Often forgotten about, the Orange Belt is as important as Yellow or Green. Yellow Belts must earn their Orange before they can move up to Green. Highly effective team members, it is here you learn how to hone your leadership and team-building abilities. Orange Belts, like Green, are also potential management material. Furthermore, management strategies like PDCA and the A-3 report, alongside practical project work will allow you to handle a team effectively.

Green Belt

Highly flexible, Green Belts can take on the management responsibilities of a Black Belt, while performing advanced data analysis. Combining all heretofore acquired knowledge from White, Yellow and Orange Belt education, you will put complex Six Sigma concepts into practice. Green Belt study is both comprehensive and intensive. Here, you will take concepts like Lean and Kaizen and incorporate them into your process improvement work. Moreover, Green Belts work alongside Black Belts to cultivate a culture of continuous improvement, while aiding your team in identifying and eliminating problems as they occur. You will also implement preventative measures to ensure these same problems do not recur.

Black Belt / Master Black Belt

If you’ve made it this far, you’re a born leader. Black Belts are primarily responsible for coaching and directing Green, Orange, Yellow and White Belts. You will also liaise with Master Black Belts to encourage and support improvement goals at all levels of your Six Sigma journey. Black Belt training is the penultimate stop on your journey. Taking all your prior education, from DMAIC to complex data analysis, you will combine your expertise with advanced team dynamics management. Moreover, you will acquire an extensive range of process improvement techniques like

Black Belt training is the penultimate stop on your journey. Taking all your prior education, from DMAIC to complex data analysis, you will combine your expertise with advanced team dynamics management. Moreover, you will acquire an extensive range of process improvement techniques like design of experiments, process mapping, and root cause analysis. Similarly, with Green Belts to support you, Black Belts learn how to implement Lean and Kaizen to enhance and bolster your Six Sigma work. Furthermore, from here you can progress to Master Black Belt level, in which you further enrich your leadership skills. You will also learn how to design your own Six Sigma curriculums, and implement both classical and advanced Six Sigma metrics.

Furthermore, from here you can progress to Master Black Belt level, in which you further enrich your leadership skills. You will also learn how to design your own Six Sigma curriculums, and implement both classical and advanced Six Sigma metrics.

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What Does Lean Focus On?

Lean focuses on reducing the eight types of waste (Muda).  Defects, overproduction, waiting, non-utilized talent, waste from transportation, inventory waste, waste from motion, unnecessary processing. Additionally, Lean principles aim to reduce waste by identifying and eliminating it. Lean also improves production by maximizing flow and identifying non-value-adding steps you should remove. Anything that does not add value for the customer is a potential threat to production. As such, Lean uses a holistic approach that aims to build a culture of continuous improvement and in-depth analysis.

Lean Principles, Tools, and Techniques

• PDCA. Standing for Plan, Do, Check, Act, PDCA is a rapid cycle-based strategy used to drive process improvement.

• 5S is a 5-step method for creating and maintaining an intuitive and efficient workplace. The 5 Ss stand for Sort, Set In Order, Shine, Standardize, Sustain.

• 8 Types of Waste. Lean aims to eliminate the eight waste types: defects, overproduction, waiting, non-utilized talent, waste from transportation, inventory waste, waste from motion, and unnecessary

• Value Stream Maps. VSMs are a visual method for displaying the key process steps in production.

• Flow is the unhindered movement of a process.

• Pull describes how customer demand is used to dictate process flow, i.e. what the customer wants, or might want, determines what a company produces.

What does Six Sigma Focus On?

Six Sigma and Lean share many similarities. As such, they complement each other very well. However, Six Sigma focuses primarily on reducing variation, just one of the seven types of waste Lean tackles. Six Sigma is used to complete improvement projects, aimed at solving process issues. It is also highly data-oriented, involving validation of hypotheses using statistics. Six Sigma knowledge is classified using a belt-based hierarchy styled on martial arts (Yellow, Green, Black, and Master Black Belt). The higher the belt, the more adept you are at using Six Sigma. Furthermore, one of Six Sigma’s primary tools is a 5-step method with which to complete improvement projects.

Six Sigma Ideas, Tools, and Techniques

• DMAIC. This 5-step method uses the following steps, Define, Measure, Analyze, Improve, and Control to improve production processes. Furthermore, DMAIC also allows you to identify the problem and develop creative solutions through deep analysis.

• Project Charter. Six Sigma uses a single-page document to outline the process issue, project goal, scope, and a timeline. Moreover, the charter forms an essential framework for the trajectory of an improvement project.

• Pareto Chart. Pareto Charts display information about potential causes of process issues in a cascading bar chart format. Additionally, you should also organize problems from largest to smallest.

• Hypothesis Testing. Hypothesis Testing is a way of providing statistical precision to root causes of process problems, so you can make the best decisions.

• Design of Experiments. Methods of controlled testing, with which to assess how efficient processes are. DoE also allows you to select the best conditions, materials, and methods for each.

• Statistical Process Control. SPC enables you to monitor your processes, ensuring they consistently satisfy customer demand.

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Six Sigma Black Belt Curriculum

Our Six Sigma Black Belt course is a direct continuation of our Green Belt course. On top of the Green Belt’s requisite two weeks, an additional 3^rd and 4^th weeks focus on Black Belt training. Furthermore, our Black Belt curriculum is based on the DMAIC structure, focussing on Analyze, Improve, and Control:

Design

The first stage of DMAIC involves setting project goals and customer deliverables. Additionally, project planning and deliverables are other essential components of Six Sigma, for which your MBB course leader will provide review and support.

1. Project Management. Black Belts are leaders and should know how to deploy changes smoothly. Moreover, all our course leaders are certified Master Black Belts with a wealth of real-world project experience to impart. Our MBBs also provide support and consultation each week.
2. Charter a Project Team. When chartering a project team, Black Belts need to ensure all team members have value to add. If not, that would go against Six Sigma! Similarly, when drawing up your project charter, we can provide useful all-purpose models, alongside with basic project examples.

Measure

1. The second stage of DMAIC requires you to measure your processes to quantify the problem and determine performance.
2.  Process Mapping. Black Belts should be able to draw a clear picture of how business processes function and are interconnected. SixSigmaUS will also provide you with useful templates and data files to aid your project work.
3. Calculate business sigma level. Learning to define and measure opportunities, defects, and to calculate yield, in training will help Black Belts in practice.

Analyze

The third stage of DMAIC involves analysis of the root causes of variation, as well as how to prevent the same changes from returning.

1. Define performance goals. Establishing performance goals will allow Black Belts to pitch the necessity of a Six Sigma project to company executives. Furthermore, it will enable you to manage deployment effectively.
2. Identify root causes of problems. Black Belts should be trained in Root Cause Analysis to identify production issues. RCA will also allow you to develop innovative solutions to specific issues.

Improve

The fourth stage of DMAIC is where process improvement eliminates defect.

1. Design of Experiments. With a working knowledge of Design of Experiments, Black Belts can then find cause-and-effect relationships between process factors and outputs. We will also provide you with a reference manual for DoE, and a trial version of the Design Experiment program. Furthermore, additional instruction in Six Sigma software like Minitab software will be provided, with MINITAB macros to help simplify your work and improve graphics quality.
2. Developing and Implementing solutions. It’s one thing to theorize a creative solution, but another to implement it successfully. Moreover, if Black Belts don’t understand the mechanics of change deployment, then your project will not progress.

Control

The fifth and final stage of DMAIC seeks to regulate performance and drive future successes. At the end of the course, once your project undergoes certification review, you will receive your certificate.

1. Developing process standards and procedures. Processes should also be standardized to ensure variation does not occur.
2. Developing a transfer plan. Black Belts handle Six Sigma implementation while also working with their clients. But it’s up to Black Belts to hand over responsibility when the time comes.

Additional Black Belt Curriculum Contents: Week 3 

Additional Black Belt Curriculum Contents: Week 4

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Key Principles of Design of Experiments and How to Use Them

Design of Experiments (DOE) is a sophisticated device with which to perfect your production processes. With DOE, you can optimize your processes for the better. Likewise, this is done by ensuring they run faster, smoother, and are more cost-effective in the future. One of many benefits is the data that comes with excellent experiment design. Good data will allow you to make the most informed decisions, which in turn will provide measures of predictability for future results and consequences. Having this level of awareness will only benefit you in future experiments. Likewise, it will help you to fine-tune your production processes according to your needs.

The Key Principles

• View DOE as a factor-based concept. By varying your factors together, through the use of a factorial grid, you can get a better idea of how each factor influences the other, and how they interact in production.Additionally,  varying one single factor at a time will only aggravate time constraints and will fail to give you as clear a picture of the inter-factor connectivity when viewed in suspension.

• Randomizing experiment order. Randomizing the order in which you run your experiments in the factorial grid can help prevent partiality from creeping in. It can also help recognize any unidentified extraneous variables that are holding your production back. Variables like these can cause processes to stagnate, which is why quick excision can make a world of difference.

• Blocking noise. Nuisance and/or extraneous variables that have no stake in production other than that they exist to cause problems should be blocked as soon as they are identified. These bothersome variables have no data-value and are the cause of much difficulty for those working (or trying to work) with DOE. Removing these issues may seem tricky, but it’s as easy as removing a particular category from your dataset, e.g. name or gender.

• Replicate your experiments. Persistence and repeat experiments almost always lead to the best (or at least most revealing) results, as some experiments may not always yield the same outcomes with a second run. Replication can help reduce any potential noise and/or variation masking preventing you from recognizing factors pertinent to production. This enables you to change your processes in such a way that will benefit the entire company. By making small adjustments here and there, you can transform the entirety of your production.

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