1. Identify the constraint.
Data helps you identify the bottlenecks in your processes, of course, but you should be able to see them fairly easily, too. Look for backlogs and a build-up of work in progress, or take note of where people are waiting for work to come through to them. These are pretty good clues that demand is exceeding capability and you have a bottleneck.
2. Exploit the constraint.
Look for ways to maximise the processing capability at this point in the process flow. For example, you may minimise downtime for machine maintenance by scheduling maintenance outside of normal hours.
3. Subordinate the other steps to the constraint.
You need to understand just what the bottleneck is capable of – how much it can produce, and how quickly it can do it. Whatever the answer is, in effect, that’s the pace at which the whole process is working. The downstream processes know what to expect and when, and having upstream processes working faster is pointless; their output simply builds up as a backlog at the bottleneck. So, use the bottleneck to dictate the pace at which the upstream activities operate, and to signal to the downstream activities what to expect, even if that means these various activities are not working at capacity.
4. Elevate the constraint.
Introduce improvements that remove this particular bottleneck, possibly by using a DMAIC (Define, Measure, Analyse, Improve and Control) project (we delve into DMAIC in Chapter 2).
5. Go back to Step 1 and repeat the process.
After you complete Steps 1–4, a new constraint will exist somewhere else in the process flow, so start the improvement process again.
The customer, not the organisation, specifies value. Value is what your customer is willing to pay for. To satisfy your customer, your organisation has to provide the right products and services, at the right time, at the right price and at the right quality. To do this, and to do so consistently, you need to identify and understand how your processes work, improve and smooth the flow, eliminate unnecessary steps in the process, and reduce or prevent waste such as rework.
Imagine the processes involved in your own organisation, beginning with a customer order (market demand) and ending with cash in the bank (invoice or bill paid). Ask yourself the following questions:
✔ How many steps are involved?
✔ Do you need all the steps?
✔ Are you sure?
✔ How can you reduce the number of steps and the time involved from start to finish?
Lean thinking has five key principles:
✔ Understand the customer and his perception of value.
✔ Identify and understand the value stream for each process and the waste within it.
✔ Enable the value to flow.
✔ Let the customer pull the value through the processes, according to his needs.
✔ Continuously pursue perfection (continuous improvement).
We’ve covered these briefly in the preceding pages, but look at them again in more detail in Chapter 2, when we see how they combine with the key principles of Six Sigma to form Lean Six Sigma.
Sussing Six Sigma
Six Sigma is a systematic and robust approach to improvement, which focuses on the customer and other key stakeholders. Six Sigma calls for a change of thinking. When Jack Welch, former General Electric CEO, introduced Six Sigma, he said:
We are going to shift the paradigm from fixing products to fixing and developing processes, so they produce nothing but perfection or close to it.
A sigma, or standard deviation, is a measure of variation that reveals the average difference between any one item and the overall average of a larger population of items. Sigma is represented by the lower-case Greek letter σ.
Suppose you want to estimate the height of people in your organisation. Measuring everyone isn’t practical, so you take a representative sample of 30 people’s heights. You work out the mean average height for the group – as an example – say this is 5 foot, 7 inches. You then calculate the difference between each person’s height and the mean average height. In broad terms, one sigma, or standard deviation, is the average of those differences. The smaller the number, the less variation there is in the population of things you are measuring. Conversely, the larger the number, the more variation. In our example, imagine the standard deviation is one inch, though it might be any number in theory.
Figure 1-2 shows the likely percentage of the population within plus one and minus one standard deviation from the mean, plus two and minus two standard deviations from the mean, and so on. Assuming your sample is representative, you can see how your information provides a good picture of the heights of all the people in your organisation. You find that approximately two-thirds of them are between 5 foot 6 inches and 5 foot 8 inches tall, about 95 per cent are in the range 5 foot 5 inches to 5 foot 9 inches, and about 99.73 per cent are between 5 foot 4 inches and 5 foot 10 inches.
© John Morgan and Martin Brenig-Jones
Figure 1-2: Standard deviation.
In reality, the calculation is a little more involved and uses a rather forbidding formula – as shown in Figure 1-3.
© John Morgan and Martin Brenig-Jones
Figure 1-3: Standard deviation formula.
Using n – 1 makes an allowance for the fact that we’re looking at a sample and not the whole population. In practice, though, when the sample size is over 30, there’s little difference between using n or n – 1. When we refer to a ‘population’ this could relate to people or things that have already been processed, for example a population of completed and despatched insurance policies or hairdryers.
The process sigma values are calculated by looking at our performance against the customer requirements – see the next section.
In the real world you probably don’t measure the height of your colleagues. Imagine instead that in your organisation you issue products that have been requested by your customers. You take a representative sample of fulfilled orders and measure the cycle time for each order – the time taken from receiving the order to issuing the product (in some organisations this is referred to as lead time). Figure 1-4 shows the cycle times for your company’s orders.
© John Morgan and Martin Brenig-Jones
Figure 1-4: Histogram showing the time taken to process