Firstly, this article is my own view and not representative of SigmaPro as a whole. Secondly it is not intended in any way to trivialise the fact that so far, over 66,000 people have lost their lives as a result of Coronavirus across the World. To lose a loved one under any circumstances is a terrible thing, especially when one feels that it could have been prevented, and our thoughts and prayers go out to anyone that has been affected directly or indirectly by coronavirus.

According to the World Health Organisation (WHO) coronavirus disease (Covid-19) is an infectious disease caused by a newly discovered coronavirus. The Covid-19 virus spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, and the droplets land on another person close by and may in turn infect them.

The first recognised death from Covid-19 was in China was on 10^th January 2020, one month later the number dying per day being above 100. So far, over 1.2 million people around the World have been infected, and while more than 250,000 have recovered there remain over 900,000 active cases. But how long will this go on for and how many people will die in the UK? Professor Neil Ferguson, of Imperial College London, warned that up to 400,000 people could possibly die of Covid-19 in the UK. This is based on 60% of the population being affected, and a roughly 1% death rate.

(68,000,000 x 0.60 x 0.01 = 408,000)

But is that likely? Professor Ferguson is clear that he is not forecasting these numbers, but it is a possible worst case scenario. So, if not 400,000 what’s the most likely outcome?

This is a question I have been asked quite a few times, and I’m sure many others have also been asked the same question. So, how does a qualified engineer, not an epidemiologist, but a certified Master Black Belt with some basic understanding of statistics approach this kind of question, and what’s the answer?

Well the answer is 16,600, and the deaths from coronavirus in UK could continue to seriously affect us for another 40-45 days until the 18^th May 2020.

But, how do we get to 16,600 and what are the assumptions involved, assumptions being a key part of the answer, because we don’t know everything we need to calculate the answer accurately, and of course the statistics involved are a simplification of what’s actually happening. But, for those involved in running improvement projects, think of this as the Define phase, we often use data which we are not sure of, and calculate estimates using it. In the Measure phase we move on to making sure we get the data we need and that it is accurate data.

The first answer I gave some weeks ago when first asked the question after there had been only a handful of deaths in Europe, was to look at the situation in China, which was then being brought under control, use their death and infection rate proportions and apply them to the UK population.

• China: 1.5 Bn people, 3,300 deaths = 2 deaths/million of population
• UK: 68 M people x 2 = 136 likely deaths

The problem with this approach is that it assumes the same infection and death rate and also assumes that the statistics quoted from China apply to the whole country, which is not actually correct. In fact the outbreak was mostly restricted to Hubei province, which has a population of 57 million, a similar size to Italy or indeed the UK. If we do the same calculation again but using 57 M, then we get:

Hubei: 57 M people, 3,300 deaths = 58 deaths/million of population

UK: 68M people x 58 = 3,944 likely deaths

However, the lockdown restrictions in China (Hubei) were stricter than those we have seen in Europe, in Wuhan for example all public transport, including buses, railways, flights, and ferry services were suspended. The Wuhan Airport, railway station, and the Metro were all closed. The residents of Wuhan were also not allowed to leave the city without permission from the authorities.

These restrictions, introduced on the 23^rd January 2020, did seem to have an effect, but the deaths continued to rise for some weeks afterwards, with the daily rate starting to reduce in mid February. It took until mid March for the numbers dying each day to be consistently in single digit numbers.

Figure 1 - source www.worldometers.info

In Europe, Italy was the first country to have an outbreak of Coronavirus, with the first recognised death being recorded on February 21^st. The spread of the virus in Italy was far more rapid than had been seen in Hubei, by 10^th March the number of people dying per day was into the 100’s and after 30 days had reached almost 800 deaths per day.

Figure 2 - 30 days deaths in Italy

The lockdown restrictions in Italy were not as stringent as in Hubei, and were introduced more slowly, starting with restrictions on the 24th February in certain areas with lockdown across the whole country on the 9^th March. However, even then public transport was allowed to continue.

So, we can assume the spread of the virus is different in Europe (Italy in particular) to that seen in Hubei. Using data from the first 30 days, we are able to fit an equation that describes the pattern of deaths in Italy.

Regression Equation

Italy       =            67.4 - 21.54 Day + 1.356 Day*Day

We can use this equation to predict the number of deaths going forward in Italy beyond 30 days and compare them with actuals. This allows us to see whether the lockdown measures in Italy had any effect.

It would certainly appear that the predicted deaths do not match what actually happened in Italy, supporting the fact that the measures did have, and are having, an effect. The actual curve no longer looks like an exponential increase. Note that it appears to be actually reducing from a peak of around 900 deaths per day. If we use a Poisson regression analysis to find this new equation, the best fit now looks like this:

Regression Equation

Again, we can use this to estimate the number of deaths in the coming days, and if we add these predictions to the chart we get the picture below:

The graph shows a typical scenario. Worst case (95% confidence) shows that by day 75, deaths should be in single figures in Italy. Day 75 is the 5^th May. The total number of people dying from Covid-19 in Italy during the period is the area under the curve, which is around 20,000 deaths. 95% confidence upper limit shows around 21,500 deaths.

But what about the UK? Although the number of deaths per day in the early period in the UK was similar to China and not Italy, the implementation of the lockdown measures in Italy, rather than China, seem more similar to the way measures have been implemented in the UK in both timing and detail, and in the last 2 weeks the number dying has risen more steeply than seen in China. Therefore, one could argue that Italy makes a better model to use if we want to estimate how the lockdown might affect number of deaths in the UK.

Assuming that the populations are the same, we could use the first 30 days of deaths in the UK, and then the prediction equation data for Italy for the days from 31 to 75 with an adjustment for the slightly lower values generally seen in each day in the UK (on average 86 less), then find the equation that best describes this data. This equation is:

Regression Equation

If we use this equation to draw the prediction graph for the UK it looks like this:

If we add the area under the curve going forward from today to the existing deaths that gives a total number of deaths of 16,617, around 17,500 if we take a 95% confidence worst case scenario. It is likely that the deaths will continue to affect us for another 40-45 days, going through to the 18^th May.

As stated at the beginning of this article, there are a number of assumptions involved, because we don’t know everything we need to calculate the answer accurately and the statistics involved are a simplification of what’s actually happening. It also assumes the underlying data is accurate, which of course is not guaranteed. It also, of course, assumes that the lockdowns continue and people stick to the restrictions introduced to safeguard lives and protect the health services.

There also many other questions that are not covered in this article, not least the differences in regions, the concept of “herd immunity”, the role of testing and PPE, the economic impact, and what happens when restrictions are lifted.

And finally, just to emphasise that our thoughts are with anyone who has been or will be affected by Covid-19, their sadness and loss cannot be measured in statistical terms.

Chris D. Rees

Director of Operations, SigmaPro Limited

06^th April 2020

References

https://www.worldometers.info/

https://www.who.int/health-topics/coronavirus

https://www.standard.co.uk/news/health/coronavirus-britain-uk-deaths-a4362866.html

In a previous blog, I have covered process stability, which is the first part of assessing the current (or baseline) performance of the process, typically involving control charts. The biggest mistake people make when examining process stability using control charts is to confuse control limits and specification limits. Specification limits have nothing to do with statistical control, and control limits have nothing to do with specification limits!

The measure that relates process performance to specification is process capability.

What is Process Capability?

Process Capability is an assessment of how the process is performing with respect to a desired outcome. It could be described as allowed variation divided by actual variation. It could also be expressed as Voice of the Customer divided by Voice of the Process. In concept terms, it is the specification divided by the range of the process, where range is expressed as maximum value minus minimum value.

As we know, process range is not the best way to describe variation, but it is a starting point if you are trying to explain it to someone! So, let’s start by saying process capability will be good if range is less than the specification, and poor if range is greater than the specification.

When we calculate capability we actually use standard deviation (σ or s depending on whether the data is from a sample or population) and not range. We know that for a normal distribution 99.7% of all items produced will fall within ±3σ, so, if instead of using the process range we use ±3σ we will get a better statistical estimate of our process capability.

The basic formula for process capability is therefore:

Capability=spec / 6σ

Where spec = USL-LSL (upper spec limit – lower spec limit)

We can see from this that a capability value of 1 means that 99.7% of our process output will be within specification, and 0.3% outside specification. If capability is less than one then more than 0.3% will be outside specification

The Process Capability Index C_p

If we consider a car trying to park in a city, then we can think about the parking bay width being equivalent to the spec. Let us assume that the city council will fine anyone that parks over the lines in the bays! If the car park bay is 3 metres wide and our car is 2 metres wide, then we will need to park within ½ meter of the centre line each time we park. This can be considered to be the specification. To reduce the risk of a fine to a minimum we need to park our car consistently within ½ meter of the centreline.

Assume we are great at parking and that we can park within ± ½ meter of the centreline more than 99.9% of the time! Doing this will produce a good process capability value of greater than one and means we are unlikely to get fined!

However, let’s now assume that we are not that great at parking. To be more precise we are consistent, but when we park we are on average ¼ meter to the left of where we want to be, this means that we will sometimes overlap the line on the left hand side. We will get fined! But, our capability value will not have changed.

This capability value is called C_p , and it does not consider how close we are to the specification limits, only our variation in relation to the limits. The formula for C_p is:

C_{p =} USL - LSL / 6s

Where:

• USL is upper spec limit
• LSL is lower spec limit
• s is standard deviation (s is used as an estimate of population σ)

The Process Capability Ratio C_pK

The process capability index C_pK however does consider where our process is performing in relation to our specification. The concept of C_pK is that we split our distribution into two halves at the mean value, and consider each part separately, comparing each to the width of the specification in that part. We then take the lowest of the two to be our C_pK value.

C_pK = min(C_pK_U, C_pK_L)

For our parking example, if we consider our parking is ¼ meter to the left then we have a spec width of 0.25 metres on the left side, and 0.75 on the right. Now, our actual parking variation is still the same at 1 metre, and the spec overall has not changed, but we now have two numbers to calculate: CpK _U and CpK_L. Note that 3s is used instead of 6s.

CpK_{L =} x̄ - LSL / 3s       CpK_U = USL - x̄ / 3s

If we take each side,

CpK_U =.75/.5

CpK_L =.25/.5

We take the lowest of these as our CpK value. CpK_L is the lower at 0.5 so our CpK value is 0.5.

So now we have two indices, Cp and CpK.

Dynamic Mean Behaviour

Over an extended period we expect to see more variation than just considering short term data. If we track someone’s parking and work out the mean and standard deviation position for a week we will see a certain amount of variation. If we repeat the exercise a week later then we may find that the standard deviation is about the same, but the mean has moved slightly. If we repeat again in another week, we will find again that standard deviation is about the same, but the mean has moved again.

This is called dynamic mean behaviour and the result is that over time we find there is more variation than we are expecting if we just consider short term variation. Motorola found that this variation equated to about 1.5 times the standard deviation.

There are therefore two other indices, P_p and P_pK. These two indices also compare process variation with process specification, but using long term data rather than short term samples. In simple terms, P_p and P_pK are long term estimates of capability. They are calculated in the same was as C_p and C_pK as shown below:

P_p = USL – LSL / 6σ

To be more precise however, the difference between C_p and P_p is in the way that the standard deviation is calculated, for C_p, standard deviation is calculated using an estimate based on short term sub groups, and for P_p the data is assumed to be representative of the whole population and so the standard deviation used is taken from continuous data collected over a period of time.

The way the indices are calculated is in other respects the same.

Summary of Capability Metrics

The table below shows a summary of C_p, C_pK, P_p and P_pK and which each one relates to.

C_{p =} USL - LSL / 6s

C_PK = min(C_PK_U, C_PK_L)    CpK_{L =} x̄ - LSL / 3s     CpK_U = USL - x̄ / 3s

For C_P Values, s is calculated from sub group data using the formula: s = R /d2. Where R is the average of the sub group ranges, and d2 is a constant dependant on sub group size.

P_p = USL – LSL / 6σ

P_pK = min(P_PK_U, P_PK_L)     P_PK_L = x̄ - LSL / 3σ   P_PK_U = USL - x̄ / 3σ

For P_P Values, σ is calculated from continuous data using the formula:

In this article we will look at a technique called FMEA (Failure Modes and Effects Analysis, and how it can be used to identify and reduce risk. Although an FMEA is typically used in the Control phase, it can also be used in Improve, or indeed in any phase where a structured approach to risk identification is required.

Why FMEA?

FMEA is a tool to help identify and manage risk. The thought process behind FMEA is implicit in any process or product design effort: teams and individuals continuously ask “what could go wrong?” and “how could problems be prevented?” FMEA is a structured way of ensuring this thought process happens which in turn helps identify and manage risk.

If FMEA is carried out correctly, it will reduce risk and in turn this will lead to:

· Improved Customer Satisfaction – through improving reliability and safety in general.

· Improved Thoroughness - increase the likelihood that all potential failures and effects will be considered.

· Reduced Development Effort - reduced product development time and cost.

What is FMEA?

FMEA is a product development or process analysis tool used to anticipate modes of failure and mitigate potential risk. FMEA is an approach used to eliminate, control, and reduce risk. The method is used to focus limited resources on critical design trade-offs and decisions leading to improved reliability, quality, and safety. FMEA will help to identify modes of failure, assess how likely they are to occur and their severity and determine the likelihood of detection. It will then calculate a quantitative score to be placed on the risk, and help to prioritise areas requiring action. Once the actions have been taken it will help to assess the action suitability and keep track of implementation progress.

Who, When and Where Used

Failure Modes & Effects Analysis (FMEA) is a tool which can be used to assess & address risks within the context of:

· Project/Program: Identifies what can go wrong with a major project

· Design: Identifies what can go wrong with the design of a product or service. Consideration for System, Subsystem, Component.

· Process: Identifies what can go wrong with a process

· Service: Identifies what can go wrong with a service function

· Application: Identifies what can go wrong with customers using your product or service

It can be used by design teams, process development or improvement teams, or in fact any team looking at something new where there is risk.

The Basic Idea

The basic idea with FMEA is to start with a design or material requirement, or process step, and think what could go wrong here? For example, if implementing a change to a process for assembling components, one of the components could be assembled the wrong way round.

Once the potential failure modes have been identified, the effect of the failure on the customer (which could include the next step in the process) is determined. In the example of our incorrect component, let’s assume that the effect would be a test failure.

Having established what could go wrong, and its effect, the FMEA approach then seeks to either prevent the problem from occurring by eliminating the cause of the problem (for example by putting in an error proofing device that prevents incorrect assembly) or increasing the chance of detecting the problem before it gets to the test, maybe a secondary check on the assembly process which would pick up the missing component.

The aim is to prevent the effect by preventing the cause of failure or detecting it early.

FMEA Approach

To build on what has been stated already, the approach for FMEA is:

· Function – identify the process step or design feature, and the requirements it is intended to achieve. For example consider a tyre fitted to car, the requirement is to remain inflated to give a supple ride and good road holding.

· Failure Mode – brainstorm ways in which the process step or design feature could go wrong, the ways in which it might fail to meet intended requirements. For the tyre example, the failure mode could be a burst tyre and sudden deflation.

· Severity – decide how severe the failure might be to the customer of the product or process. For the tyre, the failure mode would be potentially severe with loss of control of the vehicle that may cause a crash.

· Cause Occurrence – identify what might cause the problem to occur. For the tyre example, cause could be a defect in the sidewall.

· Detection – list the current controls on the process that would detect the problem before it became an issue for the customer. There may not be any detection methods existing for our tyre example.

The three areas of severity, likelihood of occurrence and detection are then scored marks out of 10, where 1 is the best case scenario and 10 the worst. The 3 numbers are multiplied together to give an overall number (maximum of 1,000, minimum of 1) called a risk priority number. The scoring was originally subjective, but some moves have been made over the years to make it less subjective.

The FMEA System Explained

The table below shows the scoring system used for most FMEA approaches...

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This article will consider the importance of linking improvement approach to organisation strategy and propose a four step cyclical approach to ongoing change to make it a part of sustainable improvement.

Strategic Alignment

Strategic Thinking is a combination of Customer Focus, Leadership, Business Planning, Goal Deployment and Performance Measurement. The starting point is a clearly defined strategic planning process which in turn leads to a clearly defined strategy.

Strategic planning provides a framework for managers and others in an organisation to assess strategic situations similarly, discuss the alternatives in a common language and decide on actions based on a shared set of values. Strategic planning increases the capacity of an organisation to implement the strategic plan completely in a specified timeframe. It provides a regular opportunity to adjust the business to current events and competitors, provides an opportunity for managers and executives to look “down board” and is the basis of a well-run Lean Six Sigma project identification and selection activity.

Leonard Goodstein proposes a 9 step strategic planning process, which is shown in the pictorial above. Planning to plan is the first step, followed by a scan of organisation values. Mission, Vision and Values is the framework used to define strategic direction of the organization. Business modelling is the process used to determine the major lines of business (LOBs) or strategic profile that the organisation has or will develop to fulfil its mission.

Mark Denton is a round the world yachtsmen who took a crew of twenty inexperienced sailors on a round the world yacht race. The team developed a top level vision statement which was safe, happy, fast and this statement fulfils all the requirements of an excellent vision.

The vision allowed Mark and his crew to make crucial decisions during the voyage. On one occasion they were faced with a decision whether or not to go through the eye of one of the largest storms that had been seen in the Pacific Ocean for some years. They had a choice whether to proceed through the centre of the storm knowing that this was in all probability the fastest route or to go round the edge knowing they that would be safe but that the route would be slightly longer. The crew took the decision based on their priorities of firstly safe, secondly happy and thirdly fast that they would take the safe route and go towards the edge of the storm rather than through the centre where the winds would mean that they could be in danger. This is a good illustration of how a top-level statement can be used to guide people’s actions.

The business will need to establish the critical success indicators (CSIs) that will enable the organisation to track progress in each LOB that it intends to pursue. It will then identify the strategic thrusts by which the organisation will achieve its vision of the ideal future state and the culture necessary to sustain those thrusts.

CSIs are typically a mix of hard financial figures (revenues, margins and ROI) and more soft indices of success (employee morale and customer satisfaction). Other metrics may be included such as the number of new product launches or new markets established, so long as they are clear and measurable.

Strategic Thrusts are the ways in which the organisation will achieve the strategy, and may include lean six sigma projects. It is also necessary to consider the culture required to support the strategic thrusts required.

It is important to understand that this process is about defining a direction, and not an end point. The strategic thrusts move the organisation in the direction selected.

Change as a Cycle

Elisabeth Kubler Ross, and John Kotter’s models are presented as linear models for managing change, although Kotter makes it clear that after consolidating improvements the goal should be to produce more change. The Deming PDCA model however is clearly a cycle, and is just as much about change as the other two models discussed.

I would propose using the PDCA model as the basis for managing change as a cyclical rather than a linear process, and will look below at how the Kubler-Ross and Kotter model principles could apply within a cyclical model.

Plan

The first stage of the PDCA cycle is Plan. In other words establish the objective and the processes necessary to deliver that objective. Deming proposed that whenever possible start on a small scale or pilot to test possible effects. SigmaPro’s first step in any change programme would always be review, or observe the current situation, before planning any future change, some people propose a variation of PDCA that includes an O, with the O representing the Observational phase which is entirely consistent with this approach, but others believe that proper observation should automatically be included in the plan phase.

Kotters first 3 steps, create a sense of urgency, form a change coalition, and create a vision for change would seem to be consistent with activities in the Plan phase of PDCA. Steps 4 & 5, communicate the vision, and empower others to act on the vision are less clear and would in my opinion be better positioned as part of the Do phase. Step 6, Plan for and create short term wins seems to be mixed, with the planning aspect part of Plan, but the creating the short term wins in the Do phase. If step 6 is split into planning and creating short term wins then it could be positioned as half in Plan and half in Do.

Kubler Ross’s first stage is “Status Quo”, and concerns people’s emotional reaction to the announced change; this would be part of the reaction to the communication of the change, and as such in Do. However, planning for this reaction could be included as part of the Plan phase of PDCA.

Do

The second stage of PDCA is to implement the plan, execute the process, make the product and so on. Data needs to be collected data for analysis in the Check step.

Kotter’s step 6 has been discussed in the above section, and the creation of short term wins can be considered as part of the Do phase, with steps 4 & 5 also being included.

Kubler-Ross stage 2 of the change curve relates to peoples’ emotional response to the change and active resistance so fits well into the Do phase.

Check

Deming’s Check phase is to study the data collected during the Do phase and compare against the expected results to ascertain any differences.

Kotter’s step 7 is to consolidate improvements and produce more change, but is about analysing what went right and what went wrong, and making sure that successful changes made are sustained. Analysing what went right and what wrong is very much what Deming would propose in the Check phase. Consolidation and sustainability is more closely aligned with the Act phase.

Kubler-Ross stage 3 of the change curve involves testing and exploring what the changes mean and how people must adapt. This could be considered as part of either the Do or Check phases. Exploring what the changes mean seems to fit better in the Check phase.

Act

The Act phase of PDCA is to adopt the new approach going forward if it is an improvement, make any adjustments needed, seek learning from the experience, and identify potential future improvements which can be fed into the next PDCA cycle.

As stated above, consolidation and sustainability in Kotter’s step 7 is closely aligned with the Act phase., and anchoring the changes in corporate culture (step 8) also seems to align well with the Act phase. Setting goals to continue building on what’s been achieved can also be seen to align with the Act phase.

Kubler Ross identifies the fourth stage as rebuilding, where people rebuild their ways of working. Only when people get to this stage can the organization really start to reap the benefits of change. This rebuilding aligns well with the Act phase.

A 4 phase cyclical change model

It can be seen from the above analysis that many of the components of the change models proposed by Kotter and Kubler-Ross fit well when considered as components of a four phase change model based on the PDCA cycle made famous by Deming. However, there is some need of realignment to fit a cyclical rather than linear process.

The model below shows a potential four step cyclical change model based on the PDCA which could be used as the basis for managing improvement in organisations, splitting components into two types, “Technical” based on PDCA and “Change” drawn from Kubler-Ross and Kotter.

The model is proposed as a cyclical rather than a linear process.

It is hoped that by consolidating these linear models of change into the PDCA cycle a more comprehensive and holistic approach to cyclical change can be developed.

Both Lean and Six Sigma originated as strategic enablers. Lean originated with Taiichi Ohno in Toyota, Toyota’s need was to be able to compete with the American automobile industry, which was dominating globally in the 1950’s using volume production. The Japanese strategy was to compete by offering different models with different options, but this required a change in manufacturing capability, to be able to produce faster, with lower stock and higher variety.

Motorola sold a TV business to Matsushita because they could not get product quality consistent. The company was losing market share in many key segments, including televisions, car radios and semiconductors. Motorola's President and CEO Bob Galvin decided to launch a strategic improvement initiative, focusing on improving consistency and customer satisfaction to ensure that Motorola reclaimed their position as a global leader in electronic products.

Implementing strategy means changing the organisation. So any structured improvement approach that works involves changing the organisation. Most change theory looks at change as a linear process, with a start point and an end point, but if we recognise that improvement requires change, and we want to achieve sustainable, ongoing improvement, then we cannot purely consider change as a process with a start and end point. This article will look in detail at some of the theory behind managing change in organisations, a second one will consider the importance of linking improvement approach to organisation strategy and propose a four step cyclical approach to ongoing change to make it a part of sustainable improvement.

Organisation Competences

People talk about “deploying” lean six sigma, it can also be described as introducing lean six sigma into the organisation, but the word deployment does give an indication of what may lie ahead, as deployment can be defined as “getting troops ready for battle”! We could talk about deployment in a non-military sense however as a systematic method of introducing a sustainable improvement approach within an organisation.

In an earlier article we discussed the importance of five organisation competences for creating a sustainable approach to organisation improvement:

• Strategic Thinking
• Operational Excellence
• Data Driven Decision Making
• Continual & Breakthrough Improvement
• Culture, values and leadership

Champions need to consider the development of these five competences as part of their deployment plans. Champions also need to remember that organisations build maturity over time,

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Now consider the graph (A Pareto chart) below, and once again ask how easy is it to see which customer gives the most revenue.

The vast majority of people will answer that it is much easier to see from the graph which customer gives most revenue!

The power of graphical relationships to convey meaningful information quickly should not be underestimated, particularly where busy stakeholders are concerned! Graphical Analysis is often the first attempt at understanding a problem using data. These techniques will help reveal relationships, trends and patterns within the data, and are quite often at the heart of communication strategies with Customers, Champions, PO’s and other Stakeholders.

Graphical techniques are some of the simplest but most powerful tools available. They will also be useful to identify theories, or hypotheses, that can be tested subsequently with statistical methods.

The table below shows typical graphs and their applicability. The techniques range from a simple time series plot which shows trends of data (X or Y) over time to a more complex multi-vary chart which allows complex relationships between a number of X’s and one Y, for example how does sales vary by customer, product type and sales person.

Understanding what these charts are and how to use them is a basic need for any self-respecting improvement project leader!

Hypothesis testing gives answers to practical questions such as:

· Is the mean height of undergraduate women equal to 66 inches?

· Is the mean height of undergraduate men greater than undergraduate women?

· Is performance on shift A different from shift B?

· Does my new process perform better than my old process?

Hypothesis testing provides objective, data driven answers to questions which are traditionally answered subjectively. For example, we make a change to a process and look at the data before and after. It appears that the mean performance has improved so we conclude that our change has been successful, but how confident are we that this is really the case? What is the risk if we conclude that there has been a change when in fact actually there has not been a change? Assume that we only have a few days of data and we will either invest or not invest a large sum of money based on the answer!

Setting up and testing hypotheses is an essential part of statistical inference and it quantifies the risk associated with the decision making process.

Hypothesis Testing has four stages:

1. Practical Question - State the question you want answered in practical terms.

2. Statistical Question – Turn this into a statistical question, by formulating two hypotheses, the null (Ho) and the alternative (Ha). The null hypothesis assumes that there is no change, ...

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For example, we want to find the average height of men aged 40 in the United Kingdom, so we take a sample of 50 people at random and work out the mean and standard deviation. How likely is it that our calculated parameters are the same as the whole population? If they are not then how close are they likely to be?

The answers come from confidence intervals (CI’s). Confidence is the basis for Risk Based Decision Making.

Consider the example of a Belt asked by his Operation Director to improve a process. The Belt makes a change and the Director then asks for some data to confirm that the process performance has improved as he wants to role the same idea out across 300 other similar processes. The Belt takes 20 samples and works out a mean performance and standard deviation and it appears that there has been a slight improvement in performance. What would be the recommendation? What are the risks involved in making a decision at this point?

Understanding the level of risk and uncertainty involved when making conclusions & recommendations is a fundamental part of CI’s. CI’s are useful whenever you want to understand the range of values within which a population parameter is expected to fall, together with a level of risk of getting it wrong. Take the process improvement example above, if the Belt could calculate the likely range of values within which the true mean would fall, and state how confident he was in that result, then the decision making process is a lot more clear.

A definition of a CI is a range within which the true population parameter is expected to fall with repeated sampling (Mean, Median, Sigma, Proportion etc.). We quote a range (minimum and maximum) and the level of confidence we have in that range actually containing the true parameter value.

The range of the CI depends on:

· Variability observed in the sample – the more variable the less confidence we have so the greater the range quoted

· The sample size – the more samples we take, the more confidence we have, so the smaller the range quoted

· The level of confidence required, the more certainty we need the greater the range quoted.

A confidence interval is the range of values in which we have confidence that it contains the true population parameter. So, a 95% CI suggests that approximately 95 out of 100 confidence intervals will contain the true population parameter. Bear in mind that we rarely know the true population parameter, which is why confidence is expressed in this way.

Most of the time, we calculate 95% confidence intervals, but any value of CI can be quoted. 99% or 90% are also quite common. The higher the confidence we want that our values quoted include the true parameter, the larger the range of values we need to quote.

CALCULATING CONFIDENCE INTERVALS

It is relatively easy to calculate CI’s...

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In spending much of my time working with organisations on the development of high performance teams and their leaders, I have often worked alongside a number of quality management professionals and in particular, those who are involved in Six Sigma initiatives. Six Sigma is another case in point of course where there is considerable debate about just how you would define it. Some put the emphasis on things like Cost of Quality, others on unbeatable measures, many emphasise the value of arriving at a common measure throughout the business e.g. DPMO (Defects Per Million Opportunities) with almost as many different emphases as people that you talk to. What I have found though is that all of the serious Six Sigma exponents, those who have invested heavily in the training of Black Belts and who are taking the initiative right the way through their companies, is an understanding from the start that the people issues are critical. There is also a growing awareness that having tackled the measurement and training issues that to achieve the next breakthrough probably means that the culture has to receive even more attention.

What then is culture? More importantly, from the point of view of continuous improvement initiatives, can we agree a definition that allows us to measure the culture of the company and to arrive at a common language for culture across departments and divisions? If we can, then we may be in with a chance of measuring cultural change and also be able to measure our progress towards achieving alignment between our strategy and our culture.

Whether we align our strategy with our existing culture or seek to change our culture to fit our agreed strategic plans will depend on what view we take of culture.

One view might be described as the roots/external view. It says that culture comes from a variety of roots and external factors and is largely “unchangeable”. It is seen to be influenced by factors such as beliefs brought into the workplace by those from outside e.g. religious beliefs, family beliefs, combined with company lore passed down through the years and embedded in the values, behaviours and rituals that we meet as soon as we join and continue to learn throughout our time in the company. This view would say that culture can be changed only over long periods of time and in the short term we would do better to seek to modify people’s behaviour to fit the culture.

A second view, the internal/behavioural view, is that the culture is the behaviour of the company’s people. This view would say that the culture can be led and changed; indeed that it is often highly desirable to do so, even in the short term, when faced with the need for major change.

In reality there is probably some truth in both positions and indeed in the myriad of more detailed views which exist as sub-sets of these. Whichever view we take we need to understand behavioural change. For example, Schein (1985) defined culture as:

“ a pattern of basic assumptions – invented, discovered or developed by a given group as it learns to cope with its problems of external adaptation and internal integration – that has worked well enough to be considered valid and, therefore, to be taught to new members as the correct way to perceive, think and feel in relation to these problems”

Although this fits more with the external/root view, he would not subscribe to the view that this meant culture is unchangeable; the way we perceive think and feel will have a marked effect on our behaviour. Perhaps a simpler working definition would be:

“ That set of attitudes, values and beliefs that you see being enacted on a day to day basis in the organisation”

or, more simply still,

“ The way things are done around here”

Understanding people’s behaviour is a necessary pre-requisite therefore whichever view we take of culture. The emphasis needs to be on action - what we do, our behaviour, that will deliver the results. For example, when we use the simple 3 Ps version of the EFQM Excellence model of People - Process - Performance, it is People acting on and in Processes, i.e. their behaviour, that will deliver the Performance. Some of the advantages of adopting this behavioural approach to the cultural issues of continuous improvement are:

· There are some well proven (reliable and valid) behavioural instruments around and we don’t need to re-invent the wheel

· Some excellent work has already been done in translating these across to the area of measuring culture

· The spin off benefits for individuals in the organisation are high (e.g. facilitates the individual change process, aids stress management, adds considerable value to the appraisal process, more satisfying working life)

· Common non-threatening language for comparing cultural issues across departments and divisions

· Strong links between people’s motivation, behaviour and consequent organisational performance

How do we use a behavioural approach? Watch this space - it will be the subject of another blog post shortly!

As a reminder, Value Stream Mapping is a lean approach to improving the Value Stream, and as such has 4 main stages:

1. Select the product family to work on
2. Map the current state
3. Establish the future state
4. Action plan the implementation of the future state

This article will cover how the current state map can be evaluated, or interrogated, to eliminate waste and create a future state map which is more efficient and effective.

There are 10 key questions to ask when interrogating the Value Stream!

1. Is the operation designed around the value stream – traditionally operations have been designed around functional layouts, for example machining areas, assembly areas. This approach does not facilitate flow and pull.
2. Can we produce to TAKT time – it is important that each part of the operation can produce at a rate that meets customer demand.
3. Is there opportunity to improve quality- quality problems prevent flow and increase cost, they should be eliminated or reduced
4. Does value flow – are the operations designed so that product can flow one piece at a time or are products produced and moved in batches
5. How can we minimise inventory – inventory not only costs money it also prevents flow and increases throughput time.
6. Do we need to reduce set up times – changeovers may be preventing flow and increasing batch sizes, they need to be reduced or eliminated wherever possible.
7. Where do we need to introduce pull – product should flow through the system at the rate of customer demand, when flow is not possible, pull systems should be introduced.
8. Can we reduce manpower – reducing waste and improving efficiency often means that operational staff numbers can be reduced.
9. How will we schedule the operation – historically organisations have used ERP systems to schedule the operation in detail. With flow and pull this is no longer required and it is necessary to rethink how to schedule.
10. Can we improve information flow – following on from reducing the need for complex ERP, there is a need to think how information will flow through the operation.

When creating a future state it is better to consider all 10 questions first before finalising the future state or trying to action plan as subsequent questions may change things.

1. Operations Design around Value Stream

The first question to consider is “Is the operation designed around the value stream”. Traditionally operations have been designed around functional layouts, for example machining areas, assembly areas and this approach does not ...

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f we know nothing about statistics and variation, then we may try to
adjust the machine or do something about call answering times each time
we see a value either above or below the average. But what about if the
diameter is too low and we adjust it upward but the next value would
have been higher anyway? Adjusting the process in this way just makes
performance worse, it is technically known as over adjustment.

If we plot the values on a chart, with the average value at the centre,
what are the chances of an individual value being either above or below
the line for the average? They are 50:50. What about two points in a row
being one side of the line (1 in 4), or three points (1 in 8). As the
number of points increases it becomes less and less likely that the
points will all be on the same side of the line.

We can work out that 8 points in a row on one side of the average has
only a 4 in 1000 chance of happening if the process is operating
normally, 9 pints indicate less than 2 in 1,000. So if this does happen,
chances are something is going on that we ought to investigate!

Statistical control is using probability theory to only adjust the
process when it is unlikely that the variation is caused by normal
process performance. A process with only variation caused by normal
process performance is said to be in control, or stable.

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Wikipedia defines states: “Business process mapping refers to activities involved in defining what a business entity does, who is responsible, to what standard the process should be completed, and how the success of the process can be determined. The main purpose behind business process mapping is to assist organizations in becoming more efficient”. We would add to that “or more effective”.

A clear and detailed business process map allows people to look the process and see whether or not improvements can be made to the current process. Process mapping helps people ‘see’ the process, breaking it down into its smaller elements and showing the flow. It also makes issues within the process visible.

Without the map there can be no common understanding of what the process is, so documenting the current state starts the journey of improving the process. Process mapping is also a valuable training & communication tool, which can be used not only at the initial phases of a project but also in the Improve and Control phases as well.

BASIC MAPPING CONCEPTS 

Although there are many different approaches to mapping processes, they all share one basic concept. That is a process is an activity that turns inputs into outputs to achieve a specific goal.

All processes consume resources including people’s time, equipment and utilities such as power and most have some kind of controls that determine how the process flows from start to finish. This building block is the basis for most process mapping techniques.

Most organisations will have some kind of documentation that describes the way in which at least some of their processes are carried out. However, if you engage operators of the process in discussion about how the process operates, they will very often describe how the actual process operation varies from what the documentation describes, and also how they think the process should operate.

An example would be an order intake process where the customer information is required to operate the process. The documentation would describe how the order is processed when all the customer information arrives, the actual process may need an extra step after initial entry to go back to the customer to obtain the missing data. The operator would also describe how it would be nice to have all the customer data emailed over in a set format for easy entry: that would be the ideal process!

Anyone that has tried to map a process will also have come across the dilemma of how much detail to consider. For example if you are mapping the order intake process described above, then do you consider how the email is opened, which bit of data is typed onto the system first, or even which keys to press first and when to press enter.

The reason this problem arises that any activity at any level can be described as a process, from the overall organisation to the sales order intake, to the activity of pressing the keys on the computer keyboard. They are all processes in their own right. The best advice is to keep it as high level as possible for as long as possible. Process mapping is best carried out as a team effort. It may be led by an individual but should not be carried out in isolation from others.

In future articles we will look at some process mapping techniques.

Consider the diagram below. If you measure a component and find the result is just inside the tolerance limit, do you accept or reject the component? This could be a product with a tolerance, or a service feature. Most people would accept it.

But what about if you found out that there was a measurement system error that meant you were unsure of the actual result? The true result could be inside or outside the limit. Would you now accept or reject the component or service?

If you accept it there is a risk that it is actually outside spec and will not meet customer requirements, either as a product or service. Doing this passes the risk on to the customer. If you reject it there is a risk that it is acceptable, so the risk is with the organisation, you are wasting a perfectly good component!

Measurement systems can take many forms, they are not restricted to physical measurement systems such as micrometres and gauges, but also include surveys, exams, reports. All these things potentially have inaccuracies.

A measurement system can be considered in the same way as any other process, and we can use the fishbone diagram to consider what could cause measurement system error. Causes could include manpower (people using a gauge in different ways for example, or calculating on time delivery in different ways), methods (carrying out the measurement in different sequence), machine (using different equipment to carry out the same measurement), material (cleanliness of parts affecting the results), mother nature (temperature affecting the result).

If we consider our measurement systems as processes then we can start to see the potential for error is significant.

MEASUREMENT SYSTEM ERROR

If we have a measurement system result, we are invariably looking at the combination of the part to part variation and the measurement system variation.

Consider the diagram below. If we observe an amount of variation on the right hand side, which process is best, A or B? The answer is we cannot tell, they appear both the same.

Now however suppose we understand that the measurement system variation for process A is much less than for process B, which process is best? The answer is process B, because we understand that what we are observing is mostly due to measurement system variation and not process variation.

We can see from this example that an understanding of how much our measurement systems varies compared to our process is essential to make proper decisions.

There are six components of measurement system error:

• Resolution/Discrimination
• Accuracy (bias)
• Linearity
• Stability (consistency)
• Repeatability (Precision)
• Reproducibility (Precision)

All six of them need to be in order for the measurement system to be adequate!

COMPONENTS OF MEASUREMENT SYSTEM ERROR IN DETAIL

Resolution

Resolution is the capability to detect the smallest acceptable change. An example of poor resolution would be trying to measure a component to the nearest fraction of a millimetre with a ruler that has a scale that only goes down to millimetres.

General guidance is that the measuring method should be able to “resolve” to at least 10 times more than the tolerance, so for example if the tolerance for measuring the length of a shaft is ±0.5 mm than the equipment should be capable of resolving to at least 0.1 mm. A ruler of course will not be suitable for this.

Resolution is not just an issue in the factory, but also in the office. Consider for example the form shown here designed to collect data on time to fulfil a customer request. The first form does not collect time in hours, only in days, so it does not have enough resolution to answer the question how many hours does it take.

Adding a row to include time of raising the order and shipping the order improves the resolution.

To deal with resolution issues there are several choices. These include:

• Use a device that can measure to a greater resolution
• Move to a sample and record an average
• Live with it, but understand the repercussions – which may be:
• Cannot tell one component from another
• Cannot tell where component lies within upper and lower specification limits
• Cannot accurately Centre Process
• Cannot Improve the Process

Accuracy/Bias

Accuracy or bias is the difference between the observed average value of measurements and the “true” or master value. The master value should be traceable to some reference standard. In calibration this traceability is a key aspect of the process, and must be traceable back to a National standard reference.

Consider the example of throwing darts at a bull’s eye. If the throwing is accurate then on average the darts will hit the bull’s eye, even if none of them actually do! The pattern observed will be darts equally distributed around the bull’s eye.

A less accurate thrower will not have darts around the bull’s eye, but will have darts clustered either to the left or right. An example of an inaccurate gauge could be a tape measure with a worn end, so it will always read lower than the true reading.

Accuracy or bias can also be an issue with attribute data for example consider the employee satisfaction form shown, is there any potential for bias in the results? What could be done to reduce bias?

Actions to reduce the likelihood of bias include:

• Calibrate regularly
• Use operations instructions
• Validate Data Systems input accuracy
• Create Operational Definitions
• Data Cross Checks

Linearity

Linearity is an extension of accuracy, and means that over some of the rage the measuring system is accurate but over other ranges it is not. An example of this could be a tape measure that stretches. The tape will be close at lower readings, but as the distance increases the tape will stretch more and the inaccuracy will get worse.

Linearity is also relevant in attribute systems, consider the example shown here of a survey to capture customer feedback. The scale is biased towards good feedback and is not linear. Survey respondents are likely to find difficulty differentiating between the good points on the scale and the survey is likely to give results biased towards good feedback.

Linearity actions could include:

• Rebuild/Replace Gauge
• Use only over a restricted range
• Use with a correction factor/table/curve

Stability

Stability means the ability of the measurement system to give consistent and predictable results over time, in other words accuracy remains constant. Unstable equipment will vary over time.

For a stable instrument there should be no drifts, sudden shifts or cycles. Wear in the instrument could cause instability. An example of the instability would be a measuring device that requires a part to be replaced every six months

Stability actions can include:

• Ensure equipment is properly cleaned and maintained
• Use control charts
• Use/update current SOP
• Ensure adequate training
• Regular audit

Precision

Precision is the ability of the measurement system to consistently give the same result. Precision is different to accuracy which is about hitting a target value on average.

There are two components of precisions: repeatability and reproducibility. Repeatability is the ability of the equipment to give the same result when repeated measurements are taken under the same conditions; reproducibility is the ability of the equipment to give the same result under different conditions (for example different operator).

A study to look at the effects of repeatability and reproducibility is called an R&R study.

Actions to reduce repeatability and reproducibility include:

• Repair, replace, adjust equipment
• SOP
• Training

When we run an improvement project, we will be collecting data. Take as an example a project to reduce scrap on an assembly line, or a project to improve throughput time on processing an insurance claim. In both cases we will have questions such as which are the biggest problems, what is the current performance of the process, how variable is it? We must collect and use data to answer these questions. Once collected, turning data into meaningful information is essential to our project.

Once we have collected data and analysed it, and hopefully made some improvements, we will then have further questions such as has my process performance improved, have we made a difference, or is in fact the process performance worse?

Lean Six Sigma is a data driven decision making approach. An understanding of data, and how to turn data into meaningful information, is a key skill for any lean Six Sigma practitioner. Statistics are used in all phases of a Lean Six Sigma project, but are particularly important in the Measure, Analyse and Improve phase.

Data Types

There are two basic types of data, continuous and categorical.

• Continuous data can take upon infinite number of real values. Examples of continuous variables are weight, age, distance, or time. For example, a weight of a person can be anywhere on a continuum from 140 to 230 pounds.
• Categorical data is where data falls into categories, such as colours (green, red, blue) or days of the week. With categorical data we must put the item in question into a “bucket”. We can’t be half in Monday and half in Tuesday, it’s either Monday or Tuesday!        

The type of data is important because the calculations involved finding parameters for each type are different.

Continuous Data

There are some basic principles which apply to any continuous variable.

1. Variation always exists. If we have a process producing a shaft, a surgeon performing an operation, a clerk processing an invoice or indeed any other process there will be variation. The shafts produced will have different diameters, the surgeon’s times will be different, and the clerks processing times will vary. Variation will always exist. Some people will argue that their processes do not vary, but in fact all processes vary, some more than others. If the variation is small it may be difficult to detect but there will be variation.
2. Assuming we have an adequate measurement system, then the variation can be measured and the results plotted. If we count the number of shafts produced within certain size groupings then we can plot the number in each group on a chart.
3. The chart we produce will at first look like random groupings, with one or two shafts showing in each group. The graphs is called a histogram, and as we measure more and more shafts we will start to see a pattern emerge, the shape of the histogram will start to look like a bell, it is called a distribution.

Descriptive Statistics

Distributions can be described by certain characteristics or parameters, the parameters describe the central location, spread (dispersion) and symmetry of the distribution.

There are three parameters to describe the central location of a distribution; these are mean, median and mode.

• Mean is the average of a set of values. Found by adding up all the numbers in the data and dividing by the number of values you have. It is the measure of location for normal data but can be influenced by outliers or skewed data.
• Median is the midpoint in a string of sorted data, where 50% of the values are below it and 50% are above. It is the best measure of location for skewed data.
• Mode is the most frequently occurring value. It is particularly useful for attribute data.

Spread can be measured by range, which is the difference between the largest and the smallest observations, its purpose is to measure the dispersion between the highest and lowest values of a data set. The advantage of range is that it is easy to calculate and easy to understand. What are some of the disadvantages?

Range has limited value as a parameter to describe spread and shape of a distribution because it does not consider the shape of the distribution. Another parameter which can be used to measure spread is called variance.

Variance measures how far a set of numbers is spread out. A variance of zero indicates that all the values are identical. Variance is always non-negative: a small variance indicates that the data points tend to be very close to the mean and hence to each other, while a high variance indicates that the data points are very spread out around the mean and from each other. The way to calculate variance is to take each data point and subtract the mean value from it. Square the result obtained so that the data does not end up being zero if the mean is zero. Add up all the calculated values and divide by the number of data points.

An example would be of we have 10 people in a room and we record their heights in metres with the following results:

1.67, 1.68, 1.55, 1.45, 1.45, 1.67, 1.26, 1.42, 1.61, 1.53

To calculate the variance, first find the average by adding up all the numbers and dividing by 10. (The answer is 1.53). Then take each height reading, subtract the mean and square the result. For example:

(1.67-1.53) * (1.67-1.53) = 0.14 * 0.14 = 0.0199

Do this for all values and add them all up, this gives 0.166

Divide by the number of values (10) gives 0.0166, this is the variance!

One problem with the variance is that it is not in the same units as the rest of the data (it is squared). To overcome this, we can take the square root, which gives us a parameter in the same units. This parameter is called standard deviation.

The standard deviation for the data above is 0.1289

Inferential Statistics

Descriptive statistics is solely concerned with properties of the observed data, and does not assume that the data came from a larger population or otherwise. Inferential statistical analysis infers properties about a population from a sample. The whole population is assumed to be larger than the observed sample. Data is obtained using samples because we seldom know the entire population, or it is impractical to obtain this data.

For example, assume that there in fact 200 people in our height example previously, but we have only measured 10. If we are trying to understand the heights of people in the room using the sample data, we are inferring population parameters by using sample data.

Although the statistical parameters are called the same thing, the symbols and in some cases the formula is different for a population.

Population is the totality of the observations with which we are concerned; sample is a subset of observations selected from the population

The Normal Distribution

If we plot 600 observations of aging in accounts receivables we will get a distribution that approaches a curve, but it will still be a little “lumpy” and not a precise curve. If however we plot 6,000 observations we will get a much smoother curve, which appears to be evenly distributed around the mean value.

The resulting curve is called a normal, or Gaussian, distribution. The normal distribution occurs frequently in nature and in organisation processes. A normal distribution will have data points equally distributed around the mean and have a smooth bell shaped curve. The normal distribution is a theoretical concept that is the basis for statistical techniques/tests. A normal distribution is completely described by its mean and standard deviation, the tails of  the distribution extend to ± infinity, the area under the curve represents 100% of all possible observations and the curve is symmetrical with 50% of data points each side of the mean.

Normality is an assumption behind many statistical tests. Based on our understanding of the normal distribution we can make predictions about the probability of events (such as the probability of a process producing a defect). For any normal distribution, we can calculate the proportion of data points that will fall within a certain range by using the mean and standard deviation.

• 68% of the data points will fall between ± 1 standard deviation
• 95% will fall between ± 2 standard deviations
• 99.7% will fall between ± 3 standard deviations

This empirical rule is extremely useful in calculating performance statistics for processes.

Now consider throwing an (unbiased!) coin with an equal chance of heads or tails. The probability of throwing a head or tail is of course 50:50. What is the chance of throwing 2 heads in a row? Extending the logic, what is the chance of a defect in a process if the previous week’s production there were equal numbers of defects and good production? What is the chance of producing a defect if there were 5 defects out of 100 produced?

If we know the specification limits, the probability of a defect being produced can be obtained from a Z table which shows the area under the standard normal curve for a particular number of standard deviations.

Categorical Data

Categorical data (or attribute data as it is often called) is where data falls into categories, such as colours (green, red, blue) or days of the week. Categorical data can be used for data such as names, categories, number of defects and so on. In fact any data where items have to be put into “buckets”.

Categorical data includes naming, grading and counting.

It is possible to further subdivide categorical data into two sub-categories:

• Defectives – a defective unit is one which does not meet the specification
• Defects - there are defects on the unit, although that in itself does not make the unit a defective unit! An example would be a car door which has been painted, up to 2 minor defects cold be allowed on the door if they were in inconspicuous areas. So although the door has defects it is not a defective door.

Data for defective units is modelled using the Binomial distribution and data for defects is modelled using Poisson.

This article will illustrate a four step process to decide what data to collect and describe some simple tools to aid that process.

Why do we need data?

In lean Six Sigma projects we are trying to do one of two things: identify and eliminate waste, or identify and reduce variation. Both need data. For waste elimination, often once we have carried out process mapping we have data available to us of the waste in the process and may not need further data (or we may have a top level view and need further data to support our investigation, for example the value stream map shows that non value added is 95% of the time in the process, and we need to find out where precisely this time is being lost).

For variability reduction, it is often not clear what data we need and we will need to think about what data is required. In essence this is the start of the investigative process of finding the root cause of our variability. The equation Y=f(x) means Y is a function of x, or if preferred outputs are a function of inputs.

The Analyse phase is all about investigating relationships and deciding which factors are most significant in influencing the output, for example which factors in a moulding process are most significant in producing scrap, which factors in the learning process are most significant in ensuring a pass in the exam! The purpose of the Measure phase is firstly to ensure we can quantify our Y, or output, baseline performance, and also to ensure that the data required for our x’s is available for analysis when we get to the Analyse phase!

The reason we do process mapping is to understand the steps that are involved in the process under investigation. Once we know the process steps we have to determine the variables within each step that may be cause of our problem. In most cases there will be many more variables than we can collect data for with the limited resources we have and so there is a need for prioritisation. Typically this has two stages, the first pass to eliminate those factors that are of no interest, and the second stage to reduce the remaining factors to a more manageable number. Having reduced the variables to a manageable number we then can plan the data collection.

The four stages we would recommend are as follows:

1. List the process variables
2. Eliminate those of no interest
3. Prioritise the remaining variables
4. Prepare the data collection plan

List the Process Variables

The first step is to brainstorm with the team those potential factors that are considered as possible factors, or causes of the effect being studied.

An example would be which factors in a semi-conductor manufacturing facility may be causing high scrap, the voltage applied, the chemical solution, the silicon purity, or other factors? Or which factors in barbequing a burger may be causing it to be burnt, the burger itself, the cook time, the cooking temperature, the chef and so on.

The cause and effect diagram is a good tool to assist the thinking process in the team, giving 6 key areas to consider:

• Manpower
• Material
• Methods
• Machinery
• Measurements
• Mother Nature (environment)

They all start with M which makes it easier to remember what they are. It is not essential to use these 6 categories but they are a widely accepted framework for identifying potential causes. An example is shown below for potential causes for High Scrap Rate

The diagram is drawn on a flip chart or white board, with a horizontal line pointing towards the effect, which is recorded on the right hand side of the diagram. The six effect lines are drawn at 45 degrees to the horizontal line and marked up with the heading above. The potential causes of the effect are then brainstormed by the team, and listed on the diagram as they are identified by the team. It is not crucial that they are listed under the correct heading, so best not to spend too much time debating which headings they go under, use the first one that comes to mind unless everyone agrees it is the wrong category.

For complex processes with a series of steps involved there may be a need to construct several cause and effect diagrams. An example might be investigating the cause of damaged units being discovered at the final stage in a machining, plating and finishing facility, the cause(s) may lie in any of the three stages, and it makes sense to construct three diagrams to cover all areas. It would be too complex to list all potential causes on a single diagram.

Eliminate Variables of No Interest

The biggest mistake students make at this stage (unless they know better!) is to then try and collect data, or even try to fix, all the areas brainstormed onto the cause and effect diagram. The crucial step that prevents this is to eliminate process variable of no interest. The tool that is best suited to this is called pen dot voting.

Pen Dot voting is a quick & efficient way of conducting a ‘first pass’ assessment to prioritise variables & eliminate variables of no interest.  It is a qualitative tool as no data is required to carry it out, just process knowledge. Each team member is given a marker pen and asked to place (say) 10 votes on the Cause & Effect diagram. No more than (say) 3 votes are allowed on any one item. Once all team members have voted, tally the votes for a quick, simple way of prioritising the variables. All variables without any votes are of no further interest.

Prioritise remaining Process Variables

If too many variables remain, then they can be further prioritised using the Pareto approach. We are looking to find the 20% of potential causes that have 80% of the votes made during pen dot voting.

Add up the total number of votes cast (probably 50 if there are 5 people voting) and then count the votes for each item, listing them in quantity order, with the highest quantity first. Draw a bar chart if necessary showing the votes for each item, and plot the cumulative percentage of total votes on the graph with the percentage scale shown on the right hand axis.

Select the 20% of the items that have 80% of the votes, or alternatively the number of items for which resource is available for collection.

Prepare the Data Collection Plan

The data collection plan should identify:

• The practical problem or question to be answered by data collection
• The metric and units of measurement
• Type of Data (Attribute or Continuous)
• How measured (Gauge, Measurement System)
• Related Data to Collect (traceability)
• Sampling Method
• Where collected
• Who is responsible
• How data will be recorded
• When/Frequency of data collection

Here is an example of a pro-forma for a data collection plan.

In summary, data is essential to any lean six Sigma project. Inexperienced practitioners often try and collect too much, or the wrong data. Following the four stages of List the process variables, eliminate those of no interest, prioritise the remaining variables and then prepare the data collection plan will help practitioners ensure they capture the right data.

So what is VSM, why the interest in it, and why the apparent problems with little work being done as a result of the activity?

We can define value as anything the customer is prepared to pay for. If there is no paying customer, then this can be redefined as anything that increases the form or function of a product or service. A Value Stream is the series of events that take a product or service from its beginning through to the customer, and the value stream map is a diagrammatic representation of that series of events showing the main physical features.

The reason for the current interest is that most organisations recognise the importance of customers, and delivering value, and recognise that they need to understand how they do this.

The reason why little is done is because practitioners don’t realise that the value stream map is primarily a method for communicating current and potentially improved process performance, and don’t understand the steps involved in creating a clear and simple representation of the value stream.

A value stream map is defined in Wikipedia as “a lean-management method for analysing the current state and designing a future state for the series of events that take a product or service from its beginning through to the customer”. This definition gives us a clue about the purpose of VSM, the definition of the future state is the crucial aspect. The Value Stream Map is a means to an end, its purpose is to help create a more efficient and effective organisation.

Stephen Covey, in The 7 habits of highly effective people, quotes habit 2 as “begin with the end in mind”. We should adopt the same approach with Value Stream Mapping, before starting, decide why we are doing it, who the map will be shown to and how to present it in a way that can be easily understood. VSM is different to process mapping, process mapping is about understanding a process at a detailed level. Value Stream Mapping is about understanding a Value Stream at a high level to uncover opportunities for improvement.

Value Stream Mapping also links material and information flow and includes critical process data to make waste visible and summarizes actual lead times and process times. It is also a great way to establish a common language amongst those with an interest in understanding the value stream. The most important reason for using it is it exposes opportunities for waste elimination and variation reduction events and projects.

Having established why we should carry out Value Stream Mapping, who it is for and its true purpose, what are the steps involved to make sure it is a useful activity and leads to improvement?

In our view there are eight key steps to build an effective Value Stream Map:

1. Select and scope the value stream
2. Identify the major process steps
3. Collect customer data
4. Collect process performance data
5. Update the VSM with the process data
6. Record stock information
7. Show information and material flow
8. Calculate lead and process times and PCE and finalise the map

In the previous article we advised improvement project leaders to have a structured approach to five key elements when managing change in projects:

• Focus on the “A” side of the Q x A = E equation
• Establish clear goals and objectives
• Provide Leadership
• Manage resistance
• Communicate, communicate, communicate

Focusing on the A side of the Q x A = E equation means improvement project leaders should spend time on ensuring cultural acceptance and not just time on technical tools and data. Master Black Belts should spend 50% of their time on the A side of the equation. Probably Black Belts should spend at least 30% of their time on A, and Green Belts 20%. This will vary depending on the challenges faced of course but it gives an idea of how significant a commitment is required.

Establishing clear goals and targets will flow from a correct project charter, with the problem statement clarifying why this project is being tackled and what pain the organisation is feeling. The project objective should be derived from to the problem statement, and reflect the improvement sought through the project. A useful framework often used to help set objectives is SMART. SMART helps to clearly define the objective by checking it is:

• Specific: to the problem/situation
• Measurable: and confirmable
• Agreed: by all parties involved
• Realistic: a stretch goal but not too far out of reach
• Time-bound: specified when it will be achieved by

This activity is always best to do in conjunction with the team, although there may be ideas of what needs to be achieved beforehand. Agreeing it with the team will encourage ownership and buy-in from them.

Providing leadership is also an important aspect of managing any improvement project. The original model of the Black Belt was someone that could develop further within the organisation, someone who would be able to take on a more senior position and provide leadership. So, demonstrating leadership qualities in the project is expected.

In their book first published in 1987, The Leadership Challenge, Jim Kouzes and Barry Posner describe a model for leadership consisting of five key elements. The Leadership Challenge uses case studies to examine "The Five Practices of Exemplary Leadership". Their first surveys for the five practices started in 1983, by asking people "What do you do as a leader when you're performing at your personal best?". Over 30 years, Kouzes and Posner did thousands of interviews and collected over 75,000 written responses. They identified five common concepts in their survey, which are: Model the Way, Inspire a Shared Vision, Challenge the Process, Enable Others to Act and Encourage the Heart.

Model the Way 
Leaders establish principles concerning the way people (constituents, peers, colleagues, and customers alike) should be treated and the way goals should be pursued. They create standards of excellence and then set an example for others to follow. Because the prospect of complex change can overwhelm people and stifle action, they set interim goals so that people can achieve small wins as they work toward larger objectives. They unravel bureaucracy when it impedes action; they put up signposts when people are unsure of where to go or how to get there; and they create opportunities for victory.

Inspire a Shared Vision 
Leaders passionately believe that they can make a difference. They envision the future, creating an ideal and unique image of what the organization can become. Through their magnetism and quiet persuasion, leaders enlist others in their dreams. They breathe life into their visions and get people to see exciting possibilities for the future.

Challenge the Process 
Leaders search for opportunities to change the status quo. They look for innovative ways to improve the organization. In doing so, they experiment and take risks. And because leaders know that risk taking involves mistakes and failures, they accept the inevitable disappointments as learning opportunities.

Enable Others to Act 
Leaders foster collaboration and build spirited teams. They actively involve others. Leaders understand that mutual respect is what sustains extraordinary efforts; they strive to create an atmosphere of trust and human dignity. They strengthen others, making each person feel capable and powerful.

Encourage the Heart 
Accomplishing extraordinary things in organizations is hard work. To keep hope and determination alive, leaders recognize contributions that individuals make. In every winning team, the members need to share in the rewards of their efforts, so leaders celebrate accomplishments. They make people feel like heroes.

Extracted from The Leadership challenge, Jim Kouzes and Barry Posner

Identifying and managing resistance is an essential task of any leader, and Lean Six Sigma promotes a structured approach to this which can be summarised in 5 steps:

1. Identify project stakeholders
2. Define current level of support & influence
3. Determine desired level of support & influence
4. Define influencing tactics
5. Create action and communications plans
   
   
   

Within the process consider the people that actually operate the process day-to-day. It is very often the case that these people will be the ones that will be involved in data collection, and implementing improvements, so their support will probably be very useful. Consider an improvement project looking at improving the way invoices are processed in an accounting function, the support of the accounts clerks will be helpful when implementing process changes.

Within the organisation consider those that are not directly involved in the process or project, but may also have a stake in the project, for example a project to improve customer service or on time delivery may not involve sales, but sales managers and directors have a very keen interest in improving these areas as it makes it easier to retain existing customers and attract new ones.

Lastly don’t forget the wider environment, customers and suppliers may also have a stake in the project outcome, for example suppliers if any changes may affect their levels of supply, customers if service will be improved, or if the interface with them could change.

2.     Define current level of support & influence

Once the stakeholders are listed, consider two aspects: their level of support for the project, and their level of influence.

Key questions here are when push comes to shove, will this person support me and what we need to do, or not? The level of support for the project may vary from enthusiastic through to uncooperative. Remember that resistance is not always shown as overt resistance, and it may be covert in nature.

The level of influence may range from critical, through to inconsequential, in other words ranging from it makes a massive difference to it makes no difference at all. A key question to ask is if I wanted to, could I push through a change to this process without the support of this person?

3.     Determine desired level of support & influence

Once stakeholders are listed and their level of support and influence has been determined, they can be mapped on a matrix as shown below.

If post-it notes or similar are used to record each stakeholder, then the post it notes can be positioned onto the grid and easily moved if necessary. In simple terms there are 4 segments: from high influence high support to low influence low support. In reality there will be a continuum and you may decide level of support is medium rather than low or high! Be careful not to overcomplicate things though.

Different approaches are taken for each of the four quadrants. Low support low influence stakeholders can be “ignored”, this means do not waste too much time on this group. They can be included in general communications but they are not the area where resource is best used. High support low influence stakeholders should be kept informed of progress to keep them supportive, but once again don’t expend too much resource on this group. The stakeholders in high support, high influence are those with whom alliances should be sought, try and keep good contact with these people, and make sure they are communicated to regularly. These people can be extremely useful when you need support during the project.

The last area, high influence, low support is the area where most time is needed. Their level of support needs to change so that they become more supportive. This is likely to require more than just standard communication, it will require thought as to how their level of support can be changed, and in the next section we will discuss some of the tactics that can be used to achieve this.

4.     Define influencing tactics

We can group influencing tactics into two broad areas; business level and individual. Business level influencing is about thinking through what the business benefits are from doing the project in question, individual influencing is about thinking through why a specific individual might want this project done.

At a business level, think through the business & customer case. Consider the project from two dimensions, internally within the business, and externally from a customer perspective. Ask and try and answer the following questions:

• Why is the project worth doing?
• Why should it be done now
• What might the consequences of not doing it be?

You should also consider how the project fits with the business goals and targets, and what other activities have higher or equal priority.

Consider the example of a project to improve service delivery at a book store. The book store is losing money because customers are moving away to other suppliers.

The project is important to the business because it will stop revenue decreasing, if is not done now then the company will continue to lose money and ultimately may be in danger of going out of business. From a customer perspective, the better service will provide a more pleasant experience and they will be happier. The sooner the project is done the faster the customers will benefit, and if it’s not done they will continue to be dissatisfied.

The book store is doing plenty of other things, introducing a new warehouse, new IT systems planned, sales office upgrade, but tell me are any of these things going to ultimately be useful if the project to improve service is not done? Surely the project to improve service is imperative, whilst the others are nice to haves once service has been enhanced and the business is no longer losing money with its customers dissatisfied?

Individual tactics involve thinking about the individual or possibly group of stakeholders and asking WIIFM, which stands for “What’s in it for me”. Most people when asked to do something will have this thought, to a greater or lesser extent. If the project team can find examples of things that answer the question “What’s in it for me” then they will have more chance of influencing the stakeholder.

Consider the example of a sales manager in the previous illustration, she wants the office upgrade because it will help the sales environment and whilst we are not arguing that this should not be done, let’s think through what might be in it for our sales manager if we were to complete our service enhancement project. No doubt once you think about it there are several potential benefits for her, better service means easier sales, easier sales means more satisfied sales staff, sales values will rise, our sales manager will have an easier time getting budget for an even better office upgrade and may even be up for promotion with all those extra sales!

NIH stands for Not Invented Here, and we all can think of people that won’t accept an idea purely because it has not come from them. The best way to deal with this is to try and get the other person to think that the idea came from them in the first place! Place articles on their desk (or get a colleague to do so) about the type of project being tackled and how it has improved things in other companies, send emails with references to websites discussing the kind of improvements required, get colleagues talking about improvement in service.

With a bit of luck our sales manager will suggest the very thing we want to do anyway, believing it is their idea!

The final influencing tactic is called Data, Demonstrate, Demand, or the 3 “D”’s.

This tool is intended to be used in sequence:

• Data – use data to show how the improvement proposed will work.
• Demonstrate – how this approach has worked elsewhere, for example for a competitor, elsewhere in the supply chain or in another segment.
• Demand – If all else fails then get someone in authority to exert some influence and persuade the stakeholder to support you!

These tools are magic, in that they won’t always ensure that you get the support you need, but using them and learning to use them well will help the maximum chance of success.

5.     Create action and communications plans

Once tactics have been established where they are required for individual stakeholders communication plans can be put together for the project as a whole. This communication plan should address the stakeholder/stakeholder group, objective of the communication, frequency, due date and any feedback measures to make sure that the communication activity is achieving the desired results.

The communication should also consider the MESSENGER, which does not have to be the project leader, it can be anyone that the stakeholder is most likely to take note of, someone they report to or respect. The MEDIUM relates to the way in which the message is delivered, for example face –to-face, telephone, phone, email and so on. MESSAGE relates to the message that will be communicated, for example progress update, issues raised, and concerns. Make sure the message is honest, simple and clear and relevant to the stakeholders.

Toll Gate Reviews

Part of the governance for Lean Six Sigma projects should be toll gate reviews. These should be carried out at the end of each phase of the project, and/or at preset review dates. Typical attendees include the champion; process owner; belt and project mentor if there is one.

The agenda should include a reminder of the project problem and goal statements, project timing plan, progress against the plan, any points requiring discussion and the next steps planned for the project.

If the review is to sign off the project for the next phase, then a checklist of items which should have been completed by that phase of the project can be used. Potential outcomes of such reviews include agreement to progress to the next phase, the need to go back and do more work before progressing or possibly even to postpone, cancel or reorganize the project.

Keys to Success

The final component of communication planning is to recognise that it is just that, planning. The real key to successful project communication is to actually carry out the communication plan! In summary, remember the following points and your chances of project success will be greatly enhanced.

• Always be a leader
• Build a guiding coalition of senior influencers
• Build consensus for project outcomes
• Keep key stakeholders informed of progress
• Utilise informal as well as formal communication routes
• It’s difficult to over communicate
• Always be proactive in managing resistance
• Remember Q x A = E!

Knowledge of technical tools will therefore not be enough to ensure a successful project; knowledge of change management tools will also be required.

Consider when you have been involved in either a successful or unsuccessful change in the past and list the features of both situations. Components often found in successful change initiatives include:

• Strong leadership - a person or perhaps group of people were providing direction and driving the change to make sure it happened. There are many examples of strong leadership, great political leaders of the past are often quoted, Margaret Thatcher, Winston Churchill, or sporting figures such as Alex Ferguson. We may or may not agree with the direction and policies they promoted but there is no denying their leadership qualities and the fact that they directed change effectively.
• Clear and motivating goals and objectives - there was a vision for the future, and this vision was something that inspired and motivated those involved. It was a vision of a better situation than the present conditions. Examples include Google’s goal to “Organize the world's information and make it universally accessible and useful”, or John F Kennedy's Moon Challenge that “This nation should commit itself to achieving the goal, before this decade is out, of landing a man on the moon and returning him safely to the earth”.
• The need for the change was communicated – leadership was constantly communicating both the need for the change, the vision for the future and the steps involved in getting there. Consider once again Winston Churchill and those famous radio broadcasts, which kept a Nation firmly aware of the situation, the need for the changes being made and the progress towards the goal.
• Resistance was managed – we already know that there will always be resistance to any change, in successful change initiatives the resistance is effectively managed, to ensure that those against the change are not allowed to prevent progress being made. Of course resistance should not be ignored; it must be listened to and managed. A political example was the miners’ strike in 1984 in the UK when there was huge resistance and extensive strike action against the closure of many coal mining pits. The strike became a symbolic struggle, as the NUM was one of the strongest unions in the country, but it ended in March 1985 following a vote to return to work. It was a defining moment in British industrial relations, and the NUM defeat significantly weakened the British trade union movement. It was seen as a major political victory for Margaret Thatcher and the Conservative Party.
• The culture was modified to encourage change – successful change initiatives involve changing the culture, or “the ways things are done around here”, to make the change itself seem easy to achieve, and to encourage further change in the future. Michael Hyatt, president of Thomas Nelson, confirms the importance of culture, and of changing culture in his efforts to improve performance of the company over short timescales.

Unsuccessful changes initiatives involve the opposite of the above points, lack of clear goals, the need for the change was never understood by those affected, there was a lack of leadership, lack of communication or those resisting the change were allowed to “win”.

Interestingly none of the above points include technical tool usage, all of them focus on people issues.

Q x A = E^#

Jack Welch, CEO of GE during the 1990’s, recognised the importance of people issues during his tenure at GE, and wanted to accelerate rate of change and improve take up of new initiatives such as six Sigma. Jack challenged a team of consultants to study best practices in change management and come back to GE with a tool kit that GE managers could easily implement.  The result was the Change Acceleration Process, commonly referred to within GE simply as “CAP.”

The team studied hundreds of projects and business initiatives.  One of their key insights was that application of good technical tools is not sufficient to guarantee success.  A high percentage of failed projects had excellent technical tools.  As an example of such a project, consider a business adopting a new Customer Relationship Management (CRM) system enterprise-wide.  Typically a great deal of effort is put into the technical tools – to deploy the hardware and software, train the employees and so on. The team found that it is lack of attention to the cultural factors that cause project failure of such systems – not the technical tools. 

The consultants created the Change Effectiveness Equation, “Q x A = E” as a simple way to describe the importance of cultural acceptance.  It means the Effectiveness (E) of any initiative is equal to the product of the Technical Quality (Q) of the approach and the Cultural Acceptance (A) of that approach.  In other words, paying attention to the people side of the equation is just as important to success as the technical side. Jack advised his Master Black Belts to spend at least 50% if their time on the “A” side of the equation, and not just focus on technical tools.

At Sigma we advise improvement project leaders to consider five key elements when managing change in projects:

• Focus on the “A” side of the Q x A = E equation
• Provide Leadership
• Establish clear goals and objectives
• Manage resistance
• Communicate, communicate, communicate

Of course, being involved in lean six Sigma means having a structured approach for the above points, and that is covered in the next article!

# Overview of GE’s Change Acceleration Process (CAP) - January 25, 2009, Bob Von Der Linn's HPT Blog

1. Review – the Organisation to determine strengths, weakness and opportunities for improvement and establish the business case
2. Align – thinking at senior level to ensure that there is support to take action
3. Launch – the initiative by tackling some problems or improvement opportunities using the lean six Sigma methods, either in a pilot area or across the organisation
4. Progress – to build on initial success and start putting in place the components required for ensuring a sustainable approach
5. Sustain – the initiative by making it part of everyday life

Phases 2-5 are described in more detail in other articles, but the first phase, that of reviewing the organisation to determine strengths, weakness and opportunities for improvement and establish the business case is covered here.

The first stage is to assess the organisation to determine current status and identify opportunities for improvement. A tool such as value stream mapping is useful to do this, and Sigma'smaturity assessment tool can also be used. Cultural mapping can also be carried out so that the culture can be compared with those of more mature organisations. As recommended in previous articles, it is best to do this by involving people rather than alone.

Suppliers, Customers and others in the organisations industry may be good sources of information to provide insights into how they improve their own performance, and their experience with using lean six Sigma methods and tools.

If the people carrying out the business review are not familiar with lean and six Sigma approaches then the organisation may wish to consider involving a LSS consultancy or attending training to find out more about the approach.

Once the organisation review has been carried out, the business case can be properly assessed by evaluating short to medium term opportunities with the costs involved in training people and running projects.

The generic case for six Sigma is well established. Research from Mikel Harry in the year 2000 found that Six Sigma projects created on average around £100,000 savings for the organisation. This was across 3,000 projects. Sigma research more recently has found similar results with an average project value in Europe of £121,000 per project.

Each Black Belt is expected to run around 4 projects per year if operating full time, and typically 1% of employees will be full time Black Belts. Therefore the total savings can be worked out as somewhere around £400,000. If belts are to operate part time then this number of projects can be reduced pro-rata. Green Belt projects should be expected to contribute far less than this, and there is less data around to quantify these, but in our experience a Green belt project delivers around £25,000, and GB’s will run on average 1 project per year as it takes longer to carry out due to part time working.

A rule of thumb is that around 1% of employees become Black Belts, and 5% become Green Belts.

It costs around £7,000 to fully train a Black Belt to a recognised level of competence, and a Black Belt salary is somewhere around £45,000 per annum. Green Belt training is around £4,000, but because Green Belts carry on in their existing roles and work part time on improvement their salaries are not included in business case calculations.

For a 100 employee organisation, there would typically be 1 Black Belt and 5 Green Belts. The salary costs and employee costs would be £72,000 for the first year. Benefits would be £262,000 in the first year (assuming that it takes six months before the first projects are completed). So there would be a clear business case at a generic level for a payback within the first year.

But of course many organisations are reluctant to accept such broad generic business case figures, on the basis that their own situation may well be different, for example more or less opportunities for improvement, different level of maturity and so on.  A further indication of the potential financial benefits can come from the level of maturity. Sigma research has confirmed that as maturity increases the Cost of Quality (COS) reduces as a percentage of Cost of Sales (COS). For low level maturity organisations (level 1) COQ is typically around 25% of COS, so for an organisation with a £10M COS COQ will be £2.5M. For a more mature organisation, COQ will reduce to around half of this or £1,25M. That is a £1.25M improvement, so the opportunity will be there to achieve financial savings

The full answer of course is to carry out a comprehensive business review on the organisation and determine the specific opportunities that exist and estimate the financial savings that can be made.

One other factor that needs to be remembered is that research has shown that there is a clear correlation between success and how well goals are deployed throughout the organisation.

Effective programmes are most often seen in organisations with effective goal deployment strategies. So, effective goal deployment needs to be a clear part of any overall programme plan.

The next decision that needs to be made is where to start in the organisation. Most organisations will find it easiest to start in manufacturing or operations, as this is where there is most financial opportunity short term, and most performance measurement. Over time the improvement approach can progress through administrative areas and ultimately into all areas. Most difficult is in Research and Marketing areas as these areas tend to resist being structured.

The steps involved in carrying out a business review are as follows:

1. Assess the Organisation to identify opportunities for improvement - use VSM or similar approach
2. Estimate financial values for the opportunities
3. Determine what would be required to realise the improvements
4. Assess the Infrastructure that already exists for improvement
5. Estimate the costs associated with making the changes
6. Make the Business Case!

Once the business case has been established, and assuming that it makes financial sense to progress it, then support for the changes required needs to be built. Getting support within the business requires concentration on the people side of things. It should be remembered that in almost every organisations the financials are important, even non-profit making organisations need to demonstrate best value and balance the financial books. The language of organisations is therefore money, and the business case needs to be expressed in monetary terms.

Having said this, it is important that the vision for the future means something for the people as well, and paints a picture of a better working environment for example, or better customer satisfaction.

Thinking further about how to implement, sometimes people are reluctant to accept ideas from others, but most people will think about what any change in the business means to them as well as what it means for the organisation as a whole. It is a good idea to work out in advance what benefits there will be for different groups of people within the organisation, for example if customer service is better it will make it easier for sales to generate repeat business. If this benefit is communicated to sales management they will want to support it.

In summary, building the business case can be done at a generic level first and if this makes sense, a more detailed study can be done. Once this shows a good indication of the benefits support can be sought to start the process of implementation. 

“Our plans miscarry because they have no aim.  When a man does not know what harbour he is making for, no wind is the right wind.” Seneca (4BC – AD65)

Many people have heard of Lean, Six Sigma and Lean Six Sigma. Perhaps fewer have actually worked with Lean Six Sigma methods and tools such as DMAIC and DMADV, worked to identify and eliminate leans 7 wastes. Even fewer will have achieved some fantastic project successes for their organisations, and a small number of the above will have become certified Green, Black or Master Black Belts.

But very few will have worked on developing a sustainable approach to improvement within an organization, taking several years to put in place the building blocks needed to develop the required competences and achieve the culture required.

So, how does one go about achieving sustainable performance improvement? At Sigma we have studied this topic for many years, both first hand as practitioners ourselves, working with organisations directly to help them improve and change, and researching the topic in partnership with a leading business school. We see 6 key areas that need consideration when planning a Lean Six Sigma deployment.

Work out why

The first phase is to work out why. Why do you want to do achieve sustainable performance improvement? If you can’t answer this question, if you don’t know what harbour you are making for, then like Seneca’s quote, no wind is the right wind. In other words the direction you go in won’t matter, it makes no difference.

If we consider a conceptual model of organisation improvement, it has 3 components, where you are now, where you want to be, and the direction you must go in to get from where you are to where you want to be.

This simple model is at the heart of most strategic approaches.

From a performance improvement perspective, the Sigma research has identified 4 organisational competences required to create a sustainable approach to improvement. These are strategic thinking, operational excellence, data driven decision making and continual improvement. The current and future level of competence can be defined, the culture mapped, and in conceptual terms a plan can be built to move from current state to future state, in the same way as Lean teaches us to model the future state of our operation. A plan can then be created of the who, what, where and how, with milestones along the way.

If organisations develop these competences, and have suitable culture and values, sustainable performance improvement is achievable.

Establish fit to strategy

So, having done this work it should be straightforward to move easily along the chosen path, developing the competences, creating the culture and achieving a sustainable approach to performance improvement? Well, actually no. Like any voyage, it is not just a question of having a map, a destination, a compass and setting sail.

Before setting sail there a number of factors to consider.

Firstly, what is the overall strategy for the business? If this is to build the business up over the next 3 years and then sell it to a competitor then a long term plan to develop an improvement culture may not be a sensible option, and is unlikely to get a great deal of support. Better to focus on building value in the short term. On the other hand, if the business is well established, and the strategy is to improve market share through satisfying customers then continuous improvement is a very sensible and indeed necessary approach. It is likely to get good support. Any initiative to create a sustainable improvement approach must alignment with existing business objectives.

If considering using Lean Six Sigma approaches, then there are two ways of looking at things, the approaches can either be used strategically or tactically. A tactical approach means using lean six Sigma approaches to solve a particular problem, for example high scrap, long lead times or poor customer service. A strategic approach means developing people in the organisation whilst at the same time improving important areas of performance, and building the required culture over time.

It is also important con consider how Lean Six Sigma integrates with any other initiatives within the organisation if these exist. Human Resources may already be working on developing the required culture, and if this is being done successfully then Lean Six Sigma approaches can be integrated to become part of the overall culture change programme.

Decide on the resources

When considering Belts, there are two basic options, to have them full time or part time. Larger organisations may decide to have full time Black Belts, but for smaller organisations this may provide difficult to justify. Bear in mind the resource in results out equation, research proves that full time belts will get better results faster. It is also important to consider whether to recruit people with the right skills (Green or Black Belt), or develop them internally.

The original Six Sigma approach was a strategic development programme, take the best people and train them in advanced tools and methods, and get them to work on important improvement projects. Of course there is also a case for bringing in new people with fresh ideas and new skills.

Decide deployment approach

The final choice revolves around how the deployment is to be started. There are two basic choices, “Big Bang”, or “Pilot”.

Create the plan

Having decided on the big questions, it is then time to get into the details and create the plan.

Our first recommendation is to involve people within the organisation, and do not try and do it alone. It is easier to sell if more people are behind it, and the workload can be spread.

Remember that the organisation needs to build maturity over time; it will be hard to promote some of the more advanced methods such as Design for Six Sigma or Design of Experiments if the basics such as performance measurement are not in place. The plan should have a series of milestones over time that reflect progression through different stages of maturity.

Do not forget the business case, there has to be a return for the organisation in doing the work involved to train, coach people and run projects. Fundamentally these initiatives are about making the organisation more competitive, and driving better financial performance, if you can’t work out how what is being proposed will contribute to this, don’t be surprised when people question it.

When planning out the actions required to start implementing new methods and tools, if there are already processes in place to do some of the things required, for example communication within the organisation, then use these processes, do not throw them away and start again. Don’t “throw the baby out with the bath water”.

When setting targets for improvement do aim high, provide a challenge for the organisation. Six Sigma in particular was always about breakthrough improvement, not about making small changes.

Lean Six Sigma promotes a structured, data driven approach, so the approach to implementation should be structured and data driven, it must use the behaviours being promoted. Plan the approach with this in mind.

Remember it’s about people

The last piece of advice however, is to never forget it’s about people. People at all levels of the organisation will be required to make the initiative work, select the projects, run the projects, approve the spend, sign off the benefits.

The Q x A= E equation tells us that overall effectiveness will be a function of the technical quality of the programme we put together, and the cultural acceptance for the programme from the people in the organisation. So take as much time on the people aspects as on the technical aspects. Communication, seeking support, involving people in key decisions will take time but pay back enormously in the long term.

If your deployment planning considers the six key areas above then it will stand a great deal more chance of succeeding.