0:15

So let's take this idea of defects per million opportunities and

Â sigma levels and apply it to a very simple situation.

Â So here we have a medical center pharmacy that is concerned about the amount of time

Â pharmacists are spending calling doctors to confirm prescription information.

Â So what is happening here?

Â The pharmacy gets a prescription, and what they're finding is there's a problem with

Â the prescription, they have to call and they have to get some things clarified.

Â The number one reason for such cases is illegible handwriting.

Â The pharmacist keeps a prescription log for two weeks, records for

Â each prescription whether there was illegible handwriting-related to the drug,

Â the dosage, the number of refills, and the doctor name.

Â So we have four pieces of information, four what we call critical to quality

Â characteristics from this process, four things that are critical to performing

Â this process correctly that are being looked at.

Â So those become the four opportunities that we're focusing on in

Â this particular example.

Â Now, we have a 1,200 prescriptions,

Â the sample that was taken was 1,200 prescriptions, and

Â there were 605 defects that were found in these 1,200 prescriptions.

Â Now, what you want to have clear in your mind is that these 605 defects

Â could mean that there where 605 prescriptions each with 1 defect each,

Â or there were many prescriptions that have several defects.

Â So the, actually, number of prescriptions that had defects was much less than 605

Â because there were prescriptions that had multiple defects.

Â So you can have almost as low as, in this particular case, we're going to be

Â talking about 151, 152 prescriptions that might have 4 defects each,

Â in which case you would get 605 defects, but

Â we don't know that just from looking at this information.

Â All right, so now that we have the data clear, we have 1,200 prescriptions,

Â we have 605 defects, what is the total number of opportunities?

Â Well, each prescription has 4 opportunities, so therefore our total

Â number of opportunities are going to be based on 1,200 multiplied by 4.

Â So to calculate your defects per million opportunities,

Â we're going to take 605 divide by 1,200 times 4, which is 4,800.

Â And, in order to scale it up to defects per million opportunities,

Â you're going to multiply by 1 million.

Â That gives you 126,042 defects per million opportunities,

Â so that's 126,042 defects per million opportunities.

Â Now, you can use the Excel formula to get the sigma level,

Â the Excel formula that you use for the standard normal distribution.

Â And what you're doing there essentially is you're saying give me the z-score for

Â when I have 126,042 defects per million opportunities in the right-tail.

Â So you're putting that in the tail of standard normal distribution,

Â and then you are trying to get to the z-score for it.

Â So, going through that in Excel you can see that 126,042 defects per million

Â opportunities translates into a sigma level of 1.15, so what this is telling us?

Â It's telling us that this is way far off from a six sigma level of performance,

Â and it's also telling us the current sigma level is 1.15.

Â So, you can have a target of two or

Â three sigma level as a first cycle of improvement,

Â trying to get better from this baseline that you have in this particular process.

Â Now, we talked earlier about Six Sigma not exactly resulting

Â in 3.4 defects per million opportunities.

Â We said that 3.4 defects per million opportunities does not exactly translate

Â to a Six Sigma level of performance, and why is that the case?

Â And then, again, you can trace this back to something that Motorola did, just as

Â you can trace the idea of six standard deviations, Six Sigma to Motorola.

Â There's also this adjustment that Motorola did

Â in order to compute their sigma levels.

Â So what they said was, well,

Â any process is going to have some natural drift over time, and

Â we are going to cut some slack to the process in order to calculate z-scores.

Â So what we're saying is that we're going to take a smaller

Â level of sigma and we're going to call it 3.4 defects per million opportunities.

Â We're not going to quite have it all the way to Six Sigma.

Â So what was the adjustment?

Â They gave it an adjustment of 1.5 sigma.

Â So when we say 3.4 defects per million opportunities, what we are actually

Â saying is that the process is at 4.5 sigma when

Â you're talking about it from the point of view of pure statistics, all right?

Â When you're talking about it from looking at a z-score, and

Â we're talking about it from looking at it from putting the formula into Excel and

Â trying to get a sigma level.

Â When you're talking about 3.4 defects per million opportunities,

Â it'll translate into a sigma level of 4.5.

Â So put this in the form of a picture, what you can see here is.

Â What you have on top is the actual distribution and

Â the level of defects if you're basing it on Six Sigma.

Â And what you have on the bottom is that you have a shifted distribution,

Â shifted by 1.5 sigma or 1.5 standard deviations.

Â And so you're using the one at the bottom, which is at actually 4.5,

Â but you're calling it 6 based on the picture on top because you're

Â saying that if it's at 4.5, we're going to call it Six Sigma.

Â So how do we do this calculation?

Â It's nothing more than simply taking what you get from Excel, taking what you get

Â from a purely statistical perspective and adding 1.5 to it.

Â So, you get your sigma level, it's 1.15.

Â If you remember from our example for that pharmacy and

Â the errors in prescriptions, we had a sigma level of 1.15,

Â you add 1.5 to that and you have a sigma level of 2.65.

Â So in other words, whenever you have a sigma level,

Â you just have to have this question of was this adjusted for the Motorola shift?

Â We even call it the Motorola shift because that's the company that made

Â this something that has become popular since then.

Â So, has it included the Motorola shift or

Â not is the question that you should be asking when you see a sigma level.

Â Now, just to make comparison, just to clarify this a bit more.

Â Here's a comparison of computing sigma levels with the shift and

Â computing sigma levels without the shift.

Â So with a shift of 1.5

Â Six Sigma translates to 3.4 defects per million opportunities.

Â But when you're talking about without the shift, what you can see it

Â is at 4.5 sigma, you have 3.4 defects per million opportunities.

Â In fact, without the shift, if you're talking about a Six Sigma process here,

Â it is going to be closer to 0 defects per million opportunities.

Â It's going to be, even if you go to two decimal points,

Â you're not going to get a number.

Â So you have to go to four decimal points to find a defects per million

Â opportunities number when you're talking about it without the shift.

Â 7:37

Now, all this is about the metric of Six Sigma, right?

Â We're talking about this from the point of where did 3.4 defects per million

Â opportunities come from and where did the idea of 6 come from.

Â But Six Sigma as a process improvement initiative, Six Sigma as a continuous

Â improvement initiative is much more than simply the metric.

Â It's a methodology, it's a methodology that is used by companies to implement

Â continuous improvements.

Â So here you have a definition of Six Sigma which takes all of that into account.

Â So going partially into this definition,

Â Six Sigma is uniquely driven by understanding of customer needs,

Â disciplined user facts, data and statistical analysis.

Â So what are we talking about here?

Â We're talking about taking some problem, taking some improvement

Â opportunity based on customer needs, and that could be a process customer.

Â Using facts, using metrics as much as possible, using numbers,

Â and trying to use statistical analysis to improving that process.

Â Going back to the first line in the definition, it's a comprehensive and

Â flexible system for achieving, sustaining, and maximizing business success.

Â So it's a system for putting continuous improvement in place, it's a system for

Â having the idea of continuous improvement in place.

Â So what is it beyond 3.4 defects per million opportunities,

Â what is it beyond the metric?

Â So, Six Sigma is not only about reducing defects,

Â it's also about reducing cycle times.

Â We could be talking about a project that not only focuses on defects in

Â the conventional sense, in terms of a product not working, or

Â a service having a defect, but in terms of saying we want to reduce the cycle times.

Â We have certain cycle time in mind, we want to reduce the cycle time, we want to

Â reduce the lead time for when somebody places an order and receives a product.

Â We want to target higher levels of customer satisfaction or

Â even employee satisfaction, we want to target higher levels of that.

Â So that could be an objective.

Â So it's not just about looking at defects in

Â the very common sense of looking at defects in a product.

Â It could be about reducing work-in-progress inventory, or

Â it could be a longer process in talking about decreasing time to market from

Â conceptualization of a product idea to actual production and

Â bringing it into the market for customers.

Â So it could be any of these things.

Â Different elements of Six Sigma that go beyond the metric, different

Â aspects of Six Sigma that go beyond the metric are cross-functional teams.

Â So we're talking about teams that are made up of people from

Â different parts of the process.

Â You have people that are related to the actual process that is being improved or

Â they could be support staff or support employees.

Â So if you're talking about a process for reducing cycle time, you

Â may have somebody from information systems because if there's an IT solution to that,

Â you want them involved.

Â Although they're not directly in the process, they're somebody that can help

Â with the improvements, so it's cross-functional teams.

Â Six Sigma relies a lot on the idea of project leaders,

Â it has this concept of black belts and green belts.

Â And these are some full-time project leaders, some part-time project leaders

Â that lead continuous improvement projects, that are trained in the methodology for

Â conducting a process improvement project using Six Sigma methodology.

Â So there's a specific methodology for using Six Sigma in a project.

Â Systematic project selection, it's about getting projects that are going

Â after organizational objectives and making sure that we prioritize projects

Â based on how much they're going towards particular organizational objectives.

Â So which ones should we be focusing our attention on more, and how

Â does that translate into something that the organization will be able to achieve?

Â Six Sigma has this idea of having very specified project goals, so

Â the notion that even before you start a project there should be specific goals

Â and, to the extent possible, enumerated in terms of money,

Â in terms of dollars, euros, rupees, whatever the case may be.

Â But in terms of what is this project going to get us in terms of top line and

Â bottom line, that's what there should be some specification of that before you

Â start the project.

Â It's about structure, project execution,

Â using the DMAIC way of executing a project, define, measure,

Â analyze, improve, control, that's the most popular framework under Six Sigma.

Â It has emphasis on data and measurement and

Â the idea of we're focusing on making improvements based on root cause analysis.

Â We're trying to find the causes for the effect.

Â We're trying to find the ys,

Â we're trying to find the xs that have an impact on the y.

Â If y is the outcome, we should be looking at what are the different xs

Â that are affecting that why and focusing on those to make improvements.

Â So here we can see that Six Sigma is much more than simply the idea

Â of 3.4 defects per million opportunities.

Â