Author: Tim Akerman
Why does variation and the type of variation matter?
Everything varies. We know it happens, and if you can’t see it, the variation may not be that significant to your process. However, it may be that your measurement systems are incapable of detecting significant variation that is important to your process, more about that in another post. Variation leads to production problems, waste and ultimately quality and delivery problems. Control the variation, you control the waste and costs. If waste and costs are a problem in your business, you may be interested in reading on.
There are two types of variation, common cause and special cause. Common cause variation is natural, characteristic of the process and most importantly, predictable. Special cause variation is caused by external factors acting on the process and is not predictable. This is an important distinction because the methodologies for investigating special and common cause variation are different, and if you investigate the wrong sort of variation it can waste a huge amount of time and cause frustration.
Take the process shown above. Just creating a graph of the data isn’t really useful, since it is unclear what should be investigated, or how to proceed. Typically a manager will look at a trend line to see if the process data is trending up or down. If the process is in control and (often) a manager observes an undesirable deviation from target, it is common to ask for that to be investigated. If the investigation focuses on special cause variation which is likely, since the investigator is likely to assume something is “wrong” therefore there must be a root cause. In businesses that do not use process control charts, there is no objective assessment of process performance before launching into seeking the root cause. The problem this creates is that there may not be a root cause. If common cause variation is at work, it is a fruitless exercise.
Where a root cause analysis finds nothing, managers can assume that the investigation is flawed and demand more work to identify the root cause. At this point willing workers are perplexed, nothing they look at can explain what they have seen. Eventually, the pressure leads to the willing worker picking the most likely “cause” and ascribing the failure to this cause. Success! The manager is happy and “corrective action” is taken. The problem is that system tampering will increase the variability in the system, making failures more likely.
The danger is then clear, if we investigate common cause variation using special cause techniques, we can increase variation through system tampering.
What then of the reverse, chasing common cause corrections for special cause variation. The basic performance of the process is unlikely to change, and every time there is a perceived “breakthrough” in performance, as soon as the special cause happens again the process exhibits more variation. The process does not see an increase in variation however, neither is there any improvement in the variation.
The only way to determine if the process is in control, or if a significant process change has occurred is to look at the data in a control chart. Using a control chart we can see which variation should be investigated as a special cause, and where we should seek variation reduction. In this example, the only result that should be investigated is result 8. This is a special cause and will have a specific reason. Eliminate the root cause of that and the process is in normal control. Everything else appears to be in control. Analysing the process data in this way leads to a focused investigation. If after removal of the special cause the process limits are inconsistent with the customer specification, variation reduction efforts should focus on common cause variation.
Why does the type of variation matter?
Everything varies. We know it happens, and if you can’t see it, the variation may not be that significant to your process. However, it may be that your measurement systems are incapable of detecting significant variation that is important to your process, more
There are two types of variation, common cause and special cause. Common cause variation is natural, characteristic of the process and most importantly, predictable. Special cause variation is caused by external factors acting on the process and is not predicable. This is an important distinction, because the methodologies for investigating special and common cause variation are different, and if you investigate the wrong sort of variation it can waste a huge amount of time and cause frustration.
Take the process shown above. Just creating a graph of the data isn’t really useful, since it is unclear what should be investigated, or how to proceed. Typically a manager will look at a trend line to see if the process data is
Where a root cause analysis finds nothing, managers can assume that the investigation is flawed and demand more work to identify the root cause. At this point willing workers are perplexed, nothing they look at can explain what they have seen. Eventually, the pressure leads to the willing worker picking the most likely “cause” and ascribing the failure to this cause. Success! The manager is happy and “corrective action” is taken. The problem is that system tampering will increase the variability in the system, making failures more likely.
The danger is then clear, if we investigate common cause variation using special cause techniques, we can increase variation through system tampering.
What then of the reverse, chasing common cause corrections for special cause variation. The basic performance of the process is unlikely to change, and every time there is a perceived “breakthrough” in performance, as soon as the special cause happens again the process exhibits more variation. The process does not see an increase in variation, however neither is there any improvement in the variation.
The only way to determine if the process is in control, or if a significant process change has occurred is to look at the data in a control chart. Using a control chart we can see which variation should be investigated as a special cause, and where we should seek variation reduction. In this example, the only result that should be investigated is result 8. This is a special cause and will have a specific reason. Eliminate the root cause of that and the process is in normal control. Everything else appears to be in control. Analysing the process data in this way leads to a focused investigation. If after removal of the special cause the process limits are inconsistent with the customer specification, variation reduction efforts should focus on common cause variation.
If you are interested in understanding more about variation and how it affects your process, please get in touch or visit me on stand C23 at the E3 Business Expo on 3rd April. Details can be found at https://www.1eventsmedia.co.uk/e3businessexpo/blog/2019/01/13/visitor-registrations-now-open-for-e3-business-expo-2019/
Where is the evidence for sigma shift?
This is a longer post than normal, since the topic is one that is debated and discussed wherever six sigma interacts with Lean and industrial engineering.
In lean six sigma and six sigma methodology there is a controversial little mechanism called sigma shift. Ask anyone who has been trained and they will tell you that all sigma ratings are given as short-term sigma ratings and that if you are using long term data you must add 1.5 to the sigma rating to get a true reflection of the process effectiveness. Ask where this 1.5 sigma shift comes from and you will be told with varying degrees of certainty that is has been evidenced by Motorola and industry in general. So should we just accept this?
The argument is presented as a shift in the mean by up to 1.5 sigma as shown below.
Isn’t it strange in a discipline that is so exacting for evidence in so many aspects, this idea that the process sigma value must increase by 1.5 if you are using long term data is accepted without empirical evidence? The argument that Motorola or some other corporation has observed it, so it must be true sounds a lot like ‘We’ve always done it that way’. Suddenly this assertion doesn’t feel so comfortable does it? I set out to track down the source of this 1.5 sigma shift and find the source of the data, some study with actual data to prove the theory.
As soon as one starts to ask for data and search for studies, it becomes apparent that the data is not readily available to support this statement. Every paper referring to the 1.5 sigma shift seems to refer to it as ‘previous work’. Several studies came up consistently during my search.
- An article on tolerancing from 1962 by A. Bender (Bender, 1962)
- An article on statistical tolerancing by David Evans (Evans, 1975)
- An article on Six Sigma in Quality Progress from 1993 (McFadden, 1993)
- A treatise on the source of 1.5 sigma shift by Davis R. Bothe (Bothe, 2002)
So why am I focusing on these 4 citations? I perceive a migration across these papers from a simplified method of calculating cumulative tolerances to a theoretical explanation of where the 1.5 sigma shift comes from.
The first article in this series was written in 1962. At this time all calculations were done by hand, complex calculations with the aid of a slide rule. Mistakes were easy to make, and the process was time consuming. This was before electronic calculators, and before computers. Bender was seeking a shortcut to reduce the time taken to calculate tolerance stacks, whilst retaining some scientific basis for their calculation. The proposed solution was to use a fudge factor to arrive at a perceived practical tolerance limit. The fudge was to multiply the variance by 1.5, a figure based on “probability, approximation, and experience”. There is nothing wrong with this approach, however it cannot be called a data driven basis. It should also be understood that the purpose of the 1.5 sigma shift in this case was to provide a window for tolerancing that would give an acceptable engineering tolerance for manufactured parts.
The paper by Evans then provides a critical review of the methods available and uses the Bender example as a low technology method for setting tolerances that appears to work in that situation. One interesting comment in Evans paper is in his closing remarks
“Basic to tolerancing, as we have looked at it here as a science, is the need to have a well-defined, sufficiently accurate relationship between the values of the components and the response of the mechanism.”
Is there evidence that the relationship between the values of the components and the response of the mechanism is sufficiently well defined to use it as a basis for generalisation of tolerancing? I would argue that in most processes this is not the case. Commercial and manufacturing functions are eager to get an acceptable product to market, which is in most cases the correct response to market need. What most businesses fail to do thereafter is invest time, money and effort into understanding these causal relationships, until there is a problem. Once there is a problem, there is an expectation of instant understanding. In his concluding remarks Evans also notes that
“As for the other area discussed, the shifting and drifting of component distributions, there does not exist a good enough theory to provide practical answers in a sufficiently general manner.”
It seems then, that as of 1975 there was inadequate evidence to support the notion of 1.5 sigma shift.
The next paper identified, is an article by McFadden published in Quality Progress in 1993. In this article, MCfadden makes a strong mathematical case that when tolerancing, aiming for a Cp of 2 and Cpk of 1.5 yields a robust process. This is based upon a predicted shift in the process mean of 1.5σ. Under these circumstances, a defect rate of 3.4 defects per million opportunities would be achieved. Again, a sound mathematical analysis of a theoretical change, however there remains no evidence that it is real. Reference is made here to the paper by Bender.
The paper by Bothe, is very similar to the one by McFadden. Both papers express a view that there is evidence for this process shift somewhere, usually with Motorola sources quoted. The articles by Evans, McFadden, and Bothe are all referring to the case where the process mean shifts by up to 1.5 σ with no change in the standard deviation itself. Evans notes that there is no evidence this is a true case.
If you keep searching eventually you find an explanation of the source of 1.5 sigma shift from the author of Six Sigma itself, Mikel J. Harry. Harry addressed the issue of 1.5σ shift in his book Resolving the Mysteries of Six Sigma (Harry, 2003). On page 28 there is the most compelling evidence I have found for the origin of the 1.5σ shift. Harry states in his footnote
“Many practitioners that are fairly new to six sigma work are often erroneously informed that the proverbial “1.5σ shift factor” is a comprehensive empirical correction that should somehow be overlaid on active processes for purposes of “real time” capability reporting. In other words, some unjustifiably believe that the measurement of long-term performance is fully unwarranted (as it could be algebraically established). Although the “typical” shift factor will frequently tend toward 1.5σ (over the many heterogeneous CTQ’s within a relatively complex product or service), each CTQ will retain its ow unique magnitude of dynamic variance expansion (expressed in the form of an equivalent mean offset.”
This statement confirms that there is no comprehensive empirical evidence for the 1.5σ shift. Furthermore, Harry clearly states that the long-term behaviour of a process can only be established through long term study of that process. A perfectly reasonable assertion. There is another change here, in that Harry explains the 1.5 σ shift in terms of an increase in the standard deviation due to long term sampling variations, not as is often postulated in other texts, movements in the sample mean. Harry’s explanation is consistent with one of the central precepts of six sigma, namely that the sampling regime is representative. If the regime is representative, it is clear that the sample mean can vary only within the confidence interval associated with the sample. Any deviation beyond this would constitute a special cause since the process mean will have shifted, yielding a different process. The impact of different samples will be to yield an inflated standard deviation, not a shift of mean. This means that the 1.5sigma shift should be represented as below, not as a shift of the mean
In his book Harry expands on the six sigma methodology as a mechanism for setting tolerances and examining the capability of a process to meet those tolerances with a high degree of reproducibility in the long term. Much of the discussion in this section relates to setting of tolerances using a safety margin M=0.50 for setting of design tolerances.
It seems the 1.5σ shift is a best guess estimation of the long-term tolerances required to ensure compliance with specification. It is not, and never has been a profound evidence-based relationship between long term and short-term data sets. The source of this statement is none other than Mikel J. Harry, stated in his book and reproduced above. Harry has stated that
“…those of us at Motorola involved in the initial formulation of six sigma (1984 – 1985) decided to adopt and support the idea of a ‘1.5σ equivalent mean shift’ as a simplistic (but effective) way to account for the underlying influence of long-term, random sampling error.”
For me it is a significant coincidence that Bender proposed an estimating formula for tolerancing of processes based on 1.5 * √variance of x. Variance is a statistical term. It is defined as follows
The square root of variance is the standard deviation. Or put another way, we can estimate the likely behaviour over time of a process parameter using 1.5 sigma as the basis of variation to allow for shifts and drifts in the sampling of the process.
Given the dynamic nature of processes and process set-up, the methodology employed in process setting can greatly influence the observed result. For example if the process set up instruction requires the process to be inside specification before committing the run, then there may be genuine differences in the process mean. This will be far less likely if the process setup instruction requires the process to be on target with minimum variance.
It seems to me that the 1.5 sigma shift is a ‘Benderized tolerance’ based on ‘probability, approximation, and experience’. If tolerances are set on this basis, it is vital that the practitioner has knowledge and experience appropriate to justify and validate their assertion.
Harry refers to Bender’s research, citing this paper as a scientific basis for non-random shifts and drifts. The basis of Bender’s adjustment must be remembered – ‘probability, approximation and experience’. Two of these can be quantified and measured, what is unclear is how much of the adjustment is based on the nebulous parameter of experience.
In conclusion, it is clear that the 1.5 sigma shift quoted in almost every six sigma and lean six sigma course as a reliable estimate of long term shift and drift of a process is at best a reasonable guess based on a process safety margin of 0.50. Harry has stated in footnote 1 of his book
“While serving at Motorola, this author was kindly asked by Mr Robert ‘Bob’ Galvin not to publish the underlying theoretical constructs associated with the shift factor, as such ‘mystery’ helped to keep the idea of six sigma alive. He explained that such a mystery would ‘keep people talking about six-sigma in the many hallways of our company’.”
Given this information, I will continue to recommend that if a process improvement practitioner wishes to make design tolerance predictions then a 1.5 sigma shift is as good an estimate as any and at least has some basis in the process. However, if you want to know what the long-term process capability will be and how it compares to the short-term process capability, continue to collect data and analyse when you have both long and short term data. Otherwise, focus on process control, investigating and eliminating sources of special cause variation.
None of us can change where or how we are trained, nor can we be blamed for reasonably believing that which is presented as fact. The deliberate withholding of critical information to create mystery and debate demonstrates a key difference in the roots of six sigma compared to lean. Such disinformation does not respect the individual and promotes a clear delineation between the statisticians and scientists trained to understand the statistical basis of the data, and those chosen to implement the methodology. This deliberate act of those with knowledge withholding information, has created a fundamental misunderstanding of the methodology. Is it then any wonder that those who have worked diligently to learn, having been misinformed by the originators of the technique now propagate and defend this misinformation?
What does this mean for the much vaunted 3.4 DPMO for six sigma processes?
The argument for this level of defects is mathematically correct, however the validity of the value is brought into question when the objective evidence supporting the calculation is based in supposition not process data. I think it is an interesting mathematical calculation, but if you want to know how well your process meets the specification limits, the process capability indices Cp and Cpk are more useful. After all, we can make up any set of numbers and claim compliance if we are not concerned with data, facts and evidence.
This seems to be a triumph of management style over sense and reason, creating a waste of time and effort through debating something that has simply been taught incorrectly, initially through a conscious decision to withhold essential information, later through a failure to insist on data, evidence and proof.
However, if we continue to accept doctrine without evidence can we really regard ourselves and data driven scientists? Isn’t that the remit of blind faith? It is up to six sigma teachers and practitioners to now ensure this misinformation is corrected with all future teachings and to ensure that the 1.5 sigma shift is given its proper place as an approximation to ensure robust tolerances, not a proven process independent variation supported by robust process data.
Bibliography
Bender, A. (1962). Benderizing Tolerances – A Simple Practical Probability Method of Handling Tolerances for Limit-Stack-Ups. Graphic Science, 17.
Bothe, D. R. (2002). Statistical Reason for the 1.5σ Shift. Quality Engineering, 14(3), 479-487. Retrieved 2 22, 2018, from http://tandfonline.com/doi/full/10.1081/qen-120001884
Evans, D. H. (1975). Statistical Tolerancing: The State of the Art, Part III. Shifts and Drifts. Journal of Quality Technology, 72-76.
Harry, M. J. (2003). inflating the error. In M. J. Harry, Resolving the Mysteries of Six Sigma: Statistical Constructs and Engineering Rationale (p. 28).
McFadden, F. (1993). Six Sigma Quality Programs. Quality Progress, 26(6).
I’m in the news!
Check out this article in Lancashire Business News.
https://businesslancashire.co.uk/2017/11/02/boost-helps-cheesecake-firm-get-just-desserts/
It’s great to have a satisfied customer, even better when they tell other companies how happy they are
Boost & Co Partner
I am delighted to have been accepted as a Boost & Co partner.
I set out 18 months ago to help businesses learn and apply process improvement techniques to enable profitability and growth. Like everyone else I have bills to pay, but I wanted a way to help small business owners learn about continuous improvement. The Boost growth mentoring programme enabled me to do just that.
It has been a pleasure and a privilege to help small business owners understand the most effective ways for them to grow their businesses, whilst managing the amount of time they must devote to working in the business. Along the way I have helped business owners to do the following;
- Reduce their working hours whilst increasing capacity through a 30% increase in productivity
- Identify the vital few actions required for their business to grow and thrive
- Implement world class manufacturing techniques normally found in large multinationals into a small engineering company
- Identify the right investment questions to ask when looking at growing through acquisition
In each case the business owner really knew the answers, what they needed from me was structure and a ‘sounding board’ to enable them to sense check their plans.
I believe the key to successful mentoring lies within each mentor. If you are focused on how to directly gain from the mentoring programme and transition the mentee to a paying customer you will not deliver their needs. The key is to focus on delivering value to that mentee as if they were a paying customer. Be generous, give freely of your skills and knowledge with the aim of truly helping. The return may not happen for many years, but I believe that every mentee I help in the right way is another link in forging a reputation for capability and reliability. With each session you get to know each other better and develop a relationship that leads to trust.
When you have established trust, you set the foundation for future recommendations. It is those recommendations that will deliver the return on investment. Mentoring is about ensuring that manufacturing businesses thrive so that there are consulting opportunities in the future. Delivering today’s mentoring at high quality with added value builds tomorrows reputation and income streams.
So with that in mind, I am ‘paying it forward’ delivering high added value mentoring that has a positive impact on quality, cost, and lead time in my customers businesses.
If that sounds like something you need, get in touch, let’s have a conversation.
What do I gain from mentoring?
I was recently asked, as a mentor what do I get from mentoring. The answers are quite basic and rooted in the reason I set up my business.
I set up my own business because I repeatedly saw small and medium businesses struggling with quality issues, high stock levels and long lead times caused by ineffective processes. I wanted to help businesses that are struggling to improve and grow, so I became a consultant specifically to help and support these businesses.
What I gain from mentoring small businesses is
· The satisfaction of helping businesses learn to use world class techniques.
· The opportunity to make a difference for small business owners
· Networking with small businesses demonstrating the value I can add
· Opportunities to actively practice coaching and mentoring skills
· Exposure for my business and my brand
The most important reward by far is the difference I have been allowed to make in peoples lives through removing obstacles to growth, both now and in the future.
What I have learned since starting my own business?
I started my business, Tamarind Tree Consulting in June 2017.The last 16 months have been the most rewarding, and uncertain adventure of my life. Starting a new business as a sole trader is a huge change, particularly when you have worked for someone else for thirty years.
What have I learned in this time?
The first thing I have learned is that I can do far more than I would have believed. I have designed a website, written a business plan, worked on marketing plans, developed a social media presence, become familiar with finance requirements, the list goes on and on. I had believed that I was incapable of many of these things.
A trusted advisor is vital, in my case it quickly became clear this role would be filled by my accountant. Getting the right accountant will result in clear guidance, good advice and a solid grasp on the numbers. It is vital that you don’t confuse turnover with profit, and have a clear picture of what return you need to make it worthwhile to be your own boss.
You will be tempted to use spreadsheets for your accounts – I was! What I learned quickly is that a professional accounts package, such as Xero is a massive timesaver – other packages are available! It is critical to select the most suitable package for your business. It appears to be a cost you can do without, however it is worth the expense.
A clear brand image is really important. Understand your “why”and hone a pitch that can be delivered in under 30 seconds. It is easy to fill time, it is very hard to shorten your pitch. Clear branding with a clear value adding proposition will help you win business.
The most surprising thing I have learned is how easy it is to set up a limited company. I had a perception that it was a significant legal undertaking. In truth, it was almost trivial.
The most important thing I have learned is to enjoy what you have. When you are busy, enjoy your good fortune, and deliver quality products and services. When you are quiet, enjoy the free time and don’t feel guilty. Neither extreme is likely to be prolonged, so enjoy each time for its own merits.
If you are thinking of starting a business, I think the best advice comes in the form of a quote from Henry Ford;
“If you think you can do a thing or think you can’t do a thing, you’re right.”
What does quality mean and why should everyone be involved?
Everyone knows what quality is don’t they?
Or do we? I suspect there are a few words missing from that statement.
Everyone knows what quality means to them.
So why do we run into quality issues in business? In my experience it is usually because the person making promises (specifications) doesn’t have to deliver against them (product or service provision). The meaning of quality is variable, depending on the purpose of the product or service, and the customers need. For example, if I want a quality car, my criteria are very different if I am a lottery winner, or earning minimum wage.
It goes hand in hand with the idea of “fit for purpose”. Often when judging if something is fit for purpose we are making a commercial judgement of risk to our business of receiving a complaint, when a product is outside of the agreed specification. If his commercial question becomes a technical question the standards drift. By the time our customer complains we are a long way from what was agreed and often don’t understand how we got here, and more importantly how to fix it. I believe the question we should really be asking is this
“If I was the customer receiving this product or service, with the customers standards, would I accept it?” . If the answer is yes, check you are right. Respect your customer enough to ask them.
We must always remember that decisions about fitness for purpose and quality will always belong to the customer.
It is vital to ensure everyone is involved. If our employees understand why the specification is important to our customer, and the impact of being out of specification, they are more likely to ensure the specifications are met.
Why?
Simple. Tell me I forget, show me I remember, involve me I understand. It is only through open communication and transparent understanding that we can engage all of employees in delivering excellence for the customer.
Let’s engage all of our employees in delivering the customer’s vision of excellence within the cost constraints agreed.
Why Employee Engagement is important
I see many articles about engagement linked to skills shortages recently. There is an abundance of advice of how to attract and retain suitably skilled staff. This got me thinking what is wrong with this picture?
The problem that I see is one of development. If every business wants to avoid spending money on training, development, apprenticeships, where exactly will those trained individuals come from? Someone, somewhere must create the pool of trained labour. There is talk of apprenticeships, but companies want apprentices trained more quickly, with less depth, then complain that their in house trained employees lack skills. The government receive much criticism for not funding training and apprenticeships, but is that really a government responsibility? I would argue that it is not. If you look at the latest version of ISO9001, there is a specific clause about ‘knowledge’. The standard talks about the responsibilities of senior management to ensure that the knowledge and skills required now, and for the future are identified and planned for.
I would ask when did educating employees and providing proper training for them become a burden? Is it not in the best interests of an organisation to ensure that every employee has the relevant skills for their role fully developed, to the highest standard possible? If employees are properly trained, they add value by ensuring their process is effective and efficient, eliminating non-value added steps. There is an odd by-product of investing in your employees and ensuring they have the best available skills. When you invest in them through training, they are more engaged with the business and believe in what they do. They also start to identify with the aims and objectives of the business.
If you want more engaged staff, who will make your business more effective, take the time to invest in their skills and develop their capabilities. You won’t be disappointed!
New Ltd company in Lancashire
I am delighted to announce that Tamarind Tree Consulting has become a Ltd company. My aim remains to help and support profitable and sustainable growth in the north through process excellence. Tamarind Tree Consulting Ltd will continue to provide the same high standards of quality, process improvement, and management mentoring consultancy that my customers have enjoyed from Tamarind Tree Consulting.
These are exciting times for Tamarind Tree Consulting Ltd, if you want someone to support and guide you to improved business performance through outstanding customer service, please get in touch for a coffee and a chat, with no obligation.