Archive for the ‘Six Sigma Projects’ Category

Lean Six Sigma Improvement and Work Design

Monday, June 14th, 2010

This article is an excerpt from a lesson in Pyzdek Institute Lean Six Sigma Black Belt training. Future posts will continue the topic.

In previous lessons you learned how to change a traditional batch-and-queue value stream into a lean value stream. Now we will discuss the design of the actual work that will take place within the processes of the value stream. By going a level deeper we will be able to improve the flow of work within the different processes in the value stream. Specifically, you will learn how to design continuous flow work cells. While the discussion here focuses mainly on manufacturing work cells, the lean principles described apply to any work, including that done in administrative, transaction, or services such as healthcare, retail, and so on.

Selecting Subprojects

The first step is to identify subprojects within the value stream. Subprojects, sometimes called project “loops,” are determined by looking at the future state value stream map and choosing groups of related processes in the value stream for improvement analysis. Each subproject will require a different team with its own set of knowledge, skills, and abilities. However, it is desirable to have at least one member of the Lean Six Sigma team who participates on all of the subproject teams. Figure 1 shows a future state value stream maps with subprojects identified.

Figure 1-Subproject “Loops”

Subproject Loops

Once subprojects are identified, the Lean Six Sigma team must decide which to pursue first, second, and so on. As a general rule it is a good idea to begin at the customer end of the value stream and work backwards. This provides the customer with improved service that they can see and feel quickly. Another criterion is that the pacemaker process should be improved early, since it sets the pace for the rest of the value stream. The “Inside-Out Rule” should be observed: get your own house in order before extending your improvement efforts to include the value streams of outside customers and suppliers. Of course, your decision regarding the starting point should also take into account the likelihood that the subproject will have a big impact on the business and its customers.

Don’t think of the future state value stream map as untouchable. If, as you go through the exercise of selecting and prioritizing subprojects, you see an obvious improvement that’s not on the map, revise the map. Remember, the goal is to improve as much and as quickly as possible.

Once the subprojects have been identified and prioritized, treat each of them as you would any project. You may want to review the modules covering project management in the Define phase at this time. For each project find a sponsor (the value stream owner is a good candidate,)  write a charter, select a team, develop a schedule, identify stakeholders, etc..  By now these things will be second nature to you.

Elements of Work

Figure 2 shows the relationship between value streams, processes, operations, workplaces and procedures in the creation of value. The relationship is hierarchical. To implement Lean all levels of the hierarchy are considered. In previous lessons we discussed ways to change value streams by replacing batch-and-queue push scheduling systems with lean value streams where work is scheduled to maximize flow. Several other lessons focus on ways to improve processes, the next level of the hierarchy. For example, by using process maps to see how work flows through processes or by identifying non-value-added work. In designing work cells we will go deeper than the process level and look at the design of operations, including the layout of workplaces and the standard procedures followed to perform the work in each operation. Such operations are known as standard operations, because the way work is performed follows strict standard procedures.

Figure 2-Value Creation  Hierarchy



Value Creation Hierarchy

Value Creation Hierarchy



Processes are distinct sets of operations nested within a value stream. Process improvement has been the topic of numerous lessons in this course and it requires knowledge of the root causes creating process problems. In the context of designing continuous flow work cells in Lean Six Sigma, we focus primarily on the things in a process that inhibit flow, such as

  • Non-value added process steps on the opportunity map
  • The distance people, materials, or WIP travel between process steps (from the spaghetti chart)
  • Changeover, setup and adjustment time (discussed below)
  • Identify the root causes that are creating quality issues that are responsible for scrap, rework, or problems downstream (discussed in later modules)

In Lean Six Sigma we design work cells that improve the process as well as the specific operations within a cell. We get into “nitty-gritty” details of the work itself, considering how materials are handled and moved, fixtures, workplace layout, movement of various workers, etc. The transfer of work elements  (small units of work) between workers is carefully considered. “Work” is the sum of all of the work elements required to create one complete unit through the entire value stream.

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The Roadmap to a Successful Six Sigma Project

Tuesday, October 27th, 2009

There are a lot of reasons that Six Sigma projects fail but they do not have to IF you can stick to the roadmap. I have done lots of projects most very successful but some have failed. In every case we stepped off of the tried and true path to success, the DMAIC roadmap. As simple and easy as these five steps seem to be, you will many times find them difficult to complete. But if that is happening, my advice to you is to “stay on the path”. Don’t skip a single step. If you stay on the path, you will find success.

DMAIC The five step process

So what are the five steps of this DMAIC roadmap? They are Define the issue, Measure the current state, Analyze and identify opportunities, Improve by implementing the best opportunities, and Control the new process to maintain the gains. You start every project at Define working your way through each step until you have put in place Controls to maintain your gains. What many of us do without thinking is we see a problem (Define)and go solve (improve) it. Most of the times you will find that within a year or maybe even a month or week the problem is back. What went wrong? We missed the other steps of the DMAIC roadmap. So let me spend some time talking about each step.

D – Define

The objective of Define is to define the issue (problem) and the real NEED to improve it. I call this need “the burning platform”. It can not be a nice thing to do, it has to be something that will have an impact on the bottom line of the company.

The second part of the define objective is to get alignment and commitment to solve this issue from the project sponsor and the project team. It also includes the team member’s supervision. We need them committed so they will not pull the team member for priorities lower than this project.

M – Measure

The objective of Measure is to go as a team, to where this process is physically and factually understand the existing process. This means collect facts and data not opinions. Everyone has an opinion but few have the facts to back up the opinion. I am not discounting opinions because most folks down in the trenches (and that is where you have to go) are the experts and have excellent idea of what is happening. The thing they lack is the data to prove it. So we listen to them carefully and then collect the data to prove what is happening. Note I said happening, that is not always what the expert says. But with the facts and data we can now go back to the expert and see if they now agree with what we found. Usually they do and are surprised by the findings.

The second part of the Measure objective is to then compile that data you have collected into a characterization of the current state of the process (the baseline for your project). This will show how bad things are or are not. Most of the time things will be worse than they first thought. In some cases, you may find that things are not bad at all. Then you need to explain your results to the sponsor and if the sponsor agrees close the project. You see sometimes even sponsors opinion of what is wrong is not backed by facts and data. So when you collect them it becomes obvious that this was not an issue.

A – Analyze

The objective of Analyze is to take the current state data and analyze it to determine the root causes of the issue. These root causes become opportunities to improve. Measure data shows you the “surface effects” or “pain” the company feels but not usually the deeper root of the issue. Because of that, you will usually find that you need to collect more data related to the measure data that validates the teams opinion of what is causing the current state issue to exist. So here in Analyze we have to take a “Deep Dive” into areas that measure pointed out as really needing improvement.

I – Improve

The objective of Improve is to develop and implement the best plan for improvement of the opportunities (root causes) identified in the Analyze step. There are two key phrases in this objective. “Develop the best plan” and “implement the best plan”. Develop takes some brainstorming and then some experimenting to validate that what you came up with would work. Second in develop is a plan. In the plan you will need several options so that when the time comes for getting an OK to implement it is not one or done (no action taken). Give the sponsor options to choose from but pick your best set and pitch it to them with a why it is best (remember facts and data).

The second key phrase is “implement the best plan. Whatever is picked, you need to create a detailed implementation plan. Create a time line and stick to it.

C – Control

Note: this is the most forgotten step. The objective of Control is to develop and implement the best controls to maintain the gains that the new process is producing. With anything new, things never work perfect. When things go wrong, as they will, you need a plan/ controls that will guide everyone as to what to do. If you do not do this when things go wrong, those involved will revert back to what they know and have done for years. A control plan can be as simple as a log of what happened, or as complex as a statistical control chart. What ever it is it needs to help the people working the new process continue to follow it.

There is a second part to control that has nothing to do with control but has everything to do with recognition. People on and off the team have worked very hard during the project to solve the issue and to keep things going while the team has worked to solve the issue. There needs to be a celebration and rewards for everyone involved to celebrate the success and their contribution to the solution. In today’s business world, we are faster to tell folks what is wrong than what is right so make sure you celebrate your success.

This is just a quick look at the DMAIC process and has not even address questions that should be answered in each step. My plan is to write five more articles each one addressing one of the steps in the DMAIC process in more detail. If you don’t see them at this blog, you will find them at the Six Sigma Knowledge Center.

Peter Bersbach
Six Sigma Master Black Belt
Bersbach Consulting LLC
(520) 829-0090

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Getting Your First Six Sigma Gig

Monday, September 21st, 2009

Since I started teaching students online a year ago I’ve encountered something new: students trying to get into Six Sigma for the first time. This obviously wasn’t a problem when I was training clients whose employers were getting them trained specifically to use the approach in their organizations. I write this for those of you who are trained in Lean Six Sigma and are in the situation that you are not working for an employer who gives you the chance to practice your newly acquired skills.

Newly trained Six Sigma Belts without experience face a situation similar to that of newly graduated college students. This site contains some great tips on writing a resume when you have no experience.

However, nearly all of you have a big advantage: you have a lot of job experience. And much of your experience is closely related to Six Sigma. Many of you have led project teams, quality improvement teams, or other work teams. This is, of course, a big part of Six Sigma work. Play it up in your resumes!

You can also try finding projects where you can enhance your resumes by actually applying what you’re learning in your training. I’ve done pro bono work for community hospitals and charities such as Goodwill and Red Cross. Some of my self-study students are working with their physicians offices to reduce errors and improve efficiency. Others are working with church groups to help improve attendance at churches or church events, lower costs, or improve the satisfaction of those who attend. My guess is that few churches wouldn’t be interested in Six Sigma projects to increase collections!

The most important thing to realize is that you have an extremely useful skill set. Be bold and confident when you approach your prospective “client” for a project. Six Sigma has been proclaimed by management guru Jack Welch as the most significant management innovation in the past quarter century. You’re learning about an approach that few know and nearly everyone can benefit from. You’ll be surprised at how much fun it can be, and how productive. Finally, this stuff really works! You’ll soon find that your skills are soon in more and more demand. After all, the supply of processes that need to be improved is infinite!

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Selecting Winning Projects

Wednesday, September 16th, 2009

Software helps select the best projects.

In a previous column I discussed how Six Sigma projects should be selected using the theory of constraints (TOC). After attempting to do so, most discover yet another constraint: money. In most organizations there are more opportunities for improvement than one can afford to pursue. If it isn’t money, some other resource will be in short supply, such as talent. And as if that weren’t bad enough, the task is further complicated by uncertainty of the payoff from the projects and their probability of success.

An exciting computer software product known as Crystal Ball Pro by Decisioneering makes it possible to select winning projects by factoring in all of the relevant factors. It does so by simulating various scenarios thousands of times, then choosing those that perform best.

For example, the research and development group of a major public utility has identified eight possible Six Sigma projects. A net present value analysis has computed:

  • The expected revenue for each project, if it’s successful
  • Its estimated probability of success
  • Its required initial investment

Using these figures, the finance manager has computed the expected return and the expected profit for each project. Unfortunately, the available budget is only $2 million, and selecting all projects would require a total initial investment of $2.8 million. Thus, the objective is to determine which projects will maximize the total expected profit while staying within the budget limitation. Complicating this decision is the fact that both the expected revenue and success rates are highly uncertain. Figure 1 shows a spreadsheet model for this problem.

Figure 1: Project Selection Spreadsheet

The decision variables in column H are binary; that is, they can only assume the values zero (do not fund the project) and one (fund the project.) The assumption variables are in the “Expected Revenue” and “Success Rate” columns. Crystal Ball Pro will use simulation to evaluate a range of values for these two columns. The total profit, shown in cell G19, is a forecast variable whose values depend on the assumption and decision variables. The idea is to find the combination of projects (determined by the decision variables) that maximize total profit, taking into account the variation in expected revenue and the probability of success.

The project selection spreadsheet isn’t quite good enough given that the number of possible sets of projects is too large to identify by trial-and-error. Crystal Ball Pro can help here too. It includes a
program, called OptQuest, which will perform a search to find the optimal package of projects (see Figure 2).

Figure 2: Progress Toward a Solution

The best solution OptQuest found (in a search that I limited to 10 minutes) is to fund all projects except 3 and 5 (see Figure 3). The expected net profit is $1.54 million. Note that the distribution of total profit includes a number of scenarios that would result in a net loss. This occurs because OptQuest was asked to find the solution that maximized expected (average) total profit, but it can limit searches to profitable software solutions too.

Figure 3: Results

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Project Selection – Getting a good one!

Thursday, September 3rd, 2009

Bersbach Consulting LLC provides Six Sigma training coaching and support across Arizona, including the Tucson, Phoenix, Scottsdale, and Glendale areas. At this time we would like to thank our friends and clients for their support. If you have landed here looking for our Six Sigma training, coaching or support services in Tucson, then please follow this Six Sigma Training link.


Project selection is critical to project success.  To insure you have the right project let me give you nine areas that you should think about and if any you do not have then I’d find another that has all nine as they ALL are important.

  1. Project Sponsorship – The project needs a High Level individual that is committed to seeing this project completed. Not just interested but a real need for him/her to see success.
  2. Benefits – You need to make sure you have well defined and measurable benefits agreed upon by you your team and your sponsor.
  3. Available Resources – You do not have a crystal ball so at this point you will not know all the resources that you will use but you do have an idea of some of the resource that it will take. Make sure that they will be available during the project when you need them.
  4. Scope in terms of your (the black belt) effort – Do you have the time to do the project and will it return a big enough benefit for your level of expertise.  This is really asking will it take to much of your time and you will need other Black Belt help or is it something that is a “go do project” that really does not need your Six Sigma Expertise to accomplish.
  5. Deliverables – Have the things that you need to accomplish well defined. This is not the benefits but the things you have to put in place to get the benefits. Think of this as the vision of the state you are trying to achieve.
  6. Time to Complete defined – Usually for a Black Belt project it should take more than 3 months but less than 12. Like some else said if the project is to big, break into pieces and make your first project one piece. BUT avoid making the problem a “Job”. You have to complete hand off and move on.
  7. Team – Do you have a true cross functional team? What I mean is do you have someone from every function that works the process you are trying to improve.
  8. Project Charter – This is where you have the project well defined. As mentioned by other if you do not have this you will not succeed.
  9. Approach Value – Like the Scope in terms of your effort ask yourself if this project really needs a Six Sigma approach to solve? Or can a group just go do it. Usually if the project has been suggested by someone who understands Six Sigma it will be and will need the DMAIC process to solve. But I have projects given to me to “Clean the lab”. In reality they just did not have time themselves to clean it. So hire someone to do that for less than you make and you use your talents on a project fitting them.

Well I hope that is help.

Good luck! Let me know if I can help any more.

Peter Bersbach

If your business is located anywhere in the World including the US, Tucson, Oro Valley , Oracle, Phoenix, Glendale, and Scottsdale, Marana, Green Valley Arizona or beyond and you would like to learn more about our Six Sigma training, coaching and support services please call  Bersbach Consulting LLC at 1-520-829-0090  Now!

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Selecting Six Sigma Projects

Thursday, August 6th, 2009

Sometimes just determining which projects to undertake isn’t enough.

Six Sigma is project-intensive. Large firms, such as General Electric, report completing as many as 7,000 Six Sigma projects in a single year. Even much smaller companies can complete several hundred projects per year. But this should come as no surprise, as projects are the means by which Six Sigma converts knowledge into bottom-line results.

However, not all Six Sigma projects produce bottom-line benefits; many produce only local improvements. In my June column I described how to use the theory of constraints (TOC) to decide where in the process to conduct Six Sigma projects. But we need to go even further. In addition to telling us where to conduct Six Sigma projects, knowing the process constraints also helps us determine what the focus of the project should be.

Six Sigma projects address three different areas of potential improvement: quality, cost and schedule. Critical characteristics in the product, process or service are identified using CTx notation: Critical-to-quality characteristics are designated CTQ; critical-to-cost, CTC; and critical-to-schedule, CTS. This classification scheme, combined with the TOC, can help focus Six Sigma projects by defining project deliverables in terms of their impact on one or more CTx characteristics.

Figure 1: A Simple Process with a Constraint

Consider the simple process in Figure 1. The process is producing a product for which there is a market demand of 20 units per week. However, the best this process can deliver is seven units per week because that’s the best step C can do.

Applying the TOC strategy described in another post, we know that Six Sigma projects that affect step C should be given priority, those affecting steps D and E second priority, and those affecting A and B third priority. This tells us where to focus our efforts. The CTx information can help us determine what to focus on.

Assume that you have three Six Sigma candidate projects all focusing on process step C, the constraint. The area addressed is correct, but which project should you pursue first? Assume that one project will improve quality, another cost, and another schedule. Does this new information help? Definitely! Table 1 shows how this information can be used.

Table 1: Throughput Priority of CTx Projects That Affect the Constraint

Projects in the same priority group are ranked according to their impact on throughput. The same thought process can be applied to process steps before and after the constraint. The results are shown in Table 2. (Note that Table 2 assumes that projects before the constraint don’t result in problems at the constraint.) Remember, impact should be measured in terms of throughput.

Knowing the project’s throughput priority will help you make better project selections among project candidates. Of course, the throughput priority is just one input into the project selection process; other factors–for example, integration with other projects, a regulatory requirement or a better payoff in the long-term–may lead to a different decision.

Table 2: Project Throughput Priority vs. Project Focus

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Why Six Sigma is an All-Or-None Proposition

Monday, July 27th, 2009

A “toe in the water” approach won’t always tell you if six sigma will work.

Recently some prospective clients asked me for a demonstration project to help them determine if six sigma would be a good idea at their company. I advised them against it. Such a demonstration only shows management’s lack of commitment to the success of six sigma. Although philosophical issues are important, there are more concrete problems with such “toe in the water” projects. In particular, major quality improvements can sometimes yield little or no bottom-line cost impact. The result of such projects is to convince management that six sigma adds cost without adding value. This belief is, of course, totally wrong. But it’s also a logical result of the demonstration approach itself.

For example, Sam was a six sigma enthusiast. He’d studied its use at several major companies and was convinced that it would save his company, which we’ll call Acme, millions of dollars. The hype had also caught the attention of the senior leadership at Sam’s company. But before diving headlong into six sigma, they wanted Sam to conduct a demonstration project to see if the savings reported by the press could actually be obtained at Acme.

The company’s main product was a complex assembly, which Acme sold to a large aerospace customer. The assembly- manufacturing process was in statistical control and producing an average of 10 defects per assembly. With management’s support, Sam documented the cost of noncompliance to be about $1,000 per assembly. After months of diligent effort, Sam’s six sigma team was able to redesign the process. To their delight, they were able to reduce the number of defects per assembly by a full 50 percent, from 10 defects per assembly to five.

Management was also interested in the project. But the accounting department had carefully monitored the costs for the assemblies, and to everyone’s surprise, accounting found only a minuscule 0.7-percent cost savings.

Based on these results, leadership’s conclusion was simple: Quality doesn’t pay. The company won’t pursue six sigma any further.

Did accounting make a mistake? In a word, no. The problem arose because Sam measured quality as defects. The truth is that most costs are incurred because of defectives rather than because of defects. (Thanks to Mikel Harry of the Six Sigma Academy for this insight.) A defective is a unit of product or service that contains one or more defects. Whether a unit contains one defect or several is irrelevant. Customers generally react to defective units by returning them for warranty repair, refunds or other options. Internally, defective units must be identified through costly inspection and then routed through equally costly rework processes, or else scrapped entirely. A unit with one defect costs nearly as much as one with several.

Mathematically, the Poisson distribution describes the relationship between defects and defectives. The equation for the Poisson distribution is

In the equation, x represents the number of defects in the sample, and P(x) means the probability of finding x defects. For example, P(1) is the probability of finding one defect. The symbol μ is the average number of defects per unit of product or service. For Sam’s project, the average assembly had 10 defects before six sigma was applied, so μ = 10. The efforts of the six sigma team reduced the average number of defects per assembly to 5, for a 50 percent improvement in quality.

Let’s plug these numbers into the equation and see what happens. Because we are interested in the probability of an assembly being defect-free, we want to know P(0) for each of the two quality levels. Before six sigma, with μ = 10 we get

In other words, there were virtually no defect-free circuit assemblies before applying six sigma methodologies. After applying six sigma, the probability of getting a defect-free assembly at Acme was

Thus, a 50-percent improvement in the quality level as measured in defects produces only a 0.7-percent improvement in the number of defect-free circuit assemblies. A complete graph of this relationship is shown in Figure 1.

Figure 1-Quality Improvement vs. Cost Savings

The real cost-reduction benefits only start to appear when quality reaches very high levels. This relationship explains the commonly observed phenomenon of quality programs not paying off in the short term. Only when companies stick with it long enough to begin to approach six sigma quality levels do they get the desired results. Too often, “toe in the water” projects scare companies out of the pool before they even start to swim.

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Using the Theory of Constraints to Choose Six Sigma Projects

Monday, July 13th, 2009

The theory of constraints helps pick winning projects.

If you choose the wrong projects it’s possible to make big “improvements” in quality and productivity that have absolutely no impact on net profit. One approach uses the theory of constraints (TOC) to determine which project(s) to pursue.

Every organization has constraints, which come in many forms. When a production or service process has a resource constraint, the sequence of improvement projects should be identified using very specific rules. According to Eliyahu M. Goldratt, the rules are:

Figure 1: A Simple Process with a Constraint

1. Identify the system’s constraint(s). See if you can identify the system constraint in Figure 1. The answer is printed at the end of this column. This fictitious company produces only two products, P and Q. The market demand for P is 100 units per week, and P sells for $90 per unit. The market demand for Q is 50 units per week, and Q sells for $100 per unit. Assume that A, B, C and D are workers who have different, noninterchangeable skills and that each worker is available for only 2,400 minutes per week (8 hours per day, 5 days per week). For simplicity, assume there’s no variation, waste or similar problems in the process.

2. Decide how to exploit the system’s constraint(s). Look for Six Sigma projects that minimize waste of the constraints. For example, if the constraint is the market demand, we should look for Six Sigma projects that provide 100-percent on-time delivery. If the constraint is a machine, focus on reducing setup time, eliminating scrap and keeping the machine running as much as possible.

3. Subordinate everything else to the decision made in step 2. Choose Six Sigma projects that maximize throughput of the constraint. First choose projects to eliminate waste from downstream processes; once the constraint has been utilized to create something, we don’t want to lose it to some blunder downstream. Then choose projects to ensure that the constraint is always supplied with adequate nondefective resources from upstream processes. We pursue upstream processes last because they have slack resources, so small amounts of waste upstream that are detected before reaching the constraint are not damaging to throughput.

4. Elevate the system’s constraint(s) . Elevate means “Lift the restriction.” Often the projects pursued in steps 2 and 3 will eliminate the constraint. If the constraint continues to exist after performing steps 2 and 3, look for Six Sigma projects that provide additional resources to the constraint. These might involve, for example, purchasing additional equipment or hiring additional workers with particular skills.

5. If, in the previous steps, a constraint has been broken, go back to step 1. If the constraint has been lifted, you must rethink the entire process. Returning to step 1 takes you back to the beginning of the cycle.

Table 1: Process Scrap rates

The TOC approach is superior to traditional total quality management project selection. For example, consider the data in Table 1. If you apply Pareto analysis to scrap rates, you would begin with Six Sigma projects that reduced the scrap produced by Worker A. In fact, assuming the optimum product mix, Worker A has about 25-percent slack time, so the scrap loss can be made up without shutting down Worker B, who is the constraint. The TOC would suggest that the scrap loss of Worker B and the downstream processes C and D be addressed first, the exact opposite of what Pareto analysis recommends.

Of course, you’ll still need to perform cost-benefit analyses, and you should estimate the probability of the project’s success. But by using the TOC you’ll at least know where to look first for opportunities. I’ll discuss how to select an optimum set of projects from these opportunities in a future column.

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How To Calculate Process Yields

Thursday, July 2nd, 2009

Unit yields are a misunderstood tradition.

Sam handed Peter a computer printout and asked, “If the yields are so high, why is my efficiency so low?”

Peter studied the report for a moment and then nodded. “Let me show you what’s going on,” he said as he picked up a marker and drew a diagram (see Figure 1).

Figure 1: Process with 10 Steps


“This process has 10 separate steps,” Peter began. “Each step has a yield of about 90 percent. This is the unit yield for that process step.”

“Right,” Sam interjected. “And all of them are about 90 percent, so the average yield for the whole process should be about 90 percent.”

“Yes, but that isn’t the number you need if you’re trying to determine the final yield for the process,” Peter responded. “Final yield is the proportion of defect-free units out of the final process step relative to what you started with at the first process step.”

Sam nodded. “Yeah, but even though the average yield is nearly 90 percent, our final yield is nowhere near that high.”

Peter turned back to the board. “Here’s a mathematical model of what happens when all process steps have the same unit yield.” He wrote an equation:

Yoverall = (Ystep)number of steps

“The unit yield at every step is about 0.9, but you have to multiply the step unit yields together to get the final unit yield. You can’t just average them,” Peter explained. “Think of a simple two-stage process. You start 100 units at the first step and 90 pass. These 90 start the second step and 90 percent of them pass, leaving 81. The average unit yield is 90 percent, but the final unit yield is only 81 percent.”

“So for our 10-step process,” Sam began.

Peter punched his calculator keys. “0.9 raised to the 10th power is about 0.35. So 35 percent is your predicted final yield.”

“And that’s pretty close to what we’re getting,” Sam said.

Peter knew that misunderstandings on yields lead to a variety of poor management decisions. He was pleased that Sam had asked for clarification. But, Peter knew, Sam still didn’t know the whole picture. Six sigma requires an entirely different mental model of yields.

“That’s not all,” Peter said. “So far we’ve been talking about unit yields. That’s the customary way of doing it around here, but there’s a better way.”

“Unit yields often have very little to do with costs,” Peter continued. “Who knows how we got those 350 good units? Maybe they were reworked several times. There can be a lot of cost hidden in the numbers. If you want an accurate picture of process performance, use rolled throughput yields.”

Peter sketched another picture on the board (see Figure 2).

Figure 2: Unit Yields vs. Rolled-Throughput Yield

“Let’s assume that we have two lines making the same product. If we only look at unit yields, they look much different. One process has a 50-percent yield, the other a 90-percent yield. But assume that each unit had 10 critical-to-quality characteristics. If we look at characteristics, we see that both have produced five defects in 100 defect opportunities. In terms of the ability to produce defect-free quality characteristics, they’re actually the same.”

“So if it costs $100 to fix a defect, the two processes have about the same rework cost, even though the unit yields would make the first process look a lot better,” Sam replied, nodding.

“This is exactly why we use rolled throughput yields in six sigma,” Peter responded. “They correlate much more closely with labor, cycle time, rework costs and other important management metrics.”

Sam frowned. “That means that our efficiency reports are worse than useless–they’re misleading!”

Peter smiled.

“Thanks, Peter!” Sam exclaimed. “I think you’re just the man to head a project to fix them!”

Yields: A Glossary

Yield, First-time Yield (unit-based)–the number of units that pass a particular inspection compared to the total number of units that pass through that point in the process.

Final Yield (unit-based)–the number of units that pass the last step in a series of steps in a process compared to the number of units the entire process started with.

Throughput Yield (defect-based)–the probability that all defect opportunities produced at a particular step in the process will conform to their respective performance standards.

Rolled Throughput Yield (defect-based)–the probability of being able to pass a unit of product or service through the entire process defect-free.

Normalized Yield (defect-based)–the geometric average throughput yield one would expect at any given step in the process. Analogous to the “typical” yield. For a k -step process, the normalized yield would be the kth root of the rolled throughput yield. A note of caution: This metric can be misleading if the throughput yields of the process steps vary a great deal.

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Six Sigma Project Guidelines in Plain English

Thursday, April 30th, 2009

Define the project

In this phase you will select a good project and describe it in detail. A good project is one that will have an impact on something important to the organization, requires the Six Sigma skill set, and has a good chance of succeeding. To determine this you need to link your project to the goals of your leadership; make sure the project isn’t too large to be manageable or too small to be meaningful; is authorized by an appropriate sponsor; and is well planned. You will form a team to work with you. (From this point on whenever the word “you” is used, it refers to your team.) To describe your project you will draw a picture of the process your project will address, identify the customers for your project and determine what they want from the project, and qualitatively determine what will drive the project’s results.
Validate the measurement system and get the baseline

In this phase you will make sure that you can measure the process and the project outcomes. You will operationally define the drivers by identifying how to measure them and you will gather data to determine the process baseline. The baseline is how the key project outcome metrics and drivers have performed in the past and are performing now. You will link the driver data to the outcomes to help you determine which drivers are likely to be the most important (this is called stratifying the data.) You will look at how well other organizations do on your project outcome measures and you will use this information to set goals for the outcomes.

Identify key levers (Xs) that drive outcomes

You will sharpen your focus by drawing a detailed picture of the process. Using the map and the information from the previous phase you will think about what causes the outcomes and the drivers to vary. You will convert your ideas into hypotheses that can be tested scientifically. You will collect data and analyze the data to test your hypotheses and to create mathematical models of cause and effect. You will use the models to determine which drivers need to change to achieve your goals for the outcomes. You will analyze the cost of changing the drivers.

Determine improvement strategy

Using the cost analysis and performance the models you will set goals for the drivers. You will come up with creative ways to achieve these goals and create plans for implementing these changes. You will look at how the plans could fail and take action to reduce the risk of failure. You will try your plan on a small scale to test your plan. For the risks that can’t be eliminated, you will develop contingency plans.

Make permanent improvements

You will create standard operating procedures for the new process and you will work with the process owner to implement the changes. You will create a set of measurements that the new owner will use to monitor the new process. You will hand the process over to the owner. Periodically you will check back with the owner to provide assistance and to confirm that the project’s goals continue to be met.

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Resources for Six Sigma


Introduction to Six Sigma
Six Sigma Projects
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Leading Six Sigma
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Good books on Six Sigma and other topics

What is Six Sigma?

By Thomas Pyzdek, Author of The Six Sigma Handbook

For Motorola, the originator of Six Sigma, the answer to the question "Why Six Sigma?" was simple: survival. Motorola came to Six Sigma because it was being consistently beaten in the competitive marketplace by foreign firms that were able to produce higher quality products at a lower cost. When a Japanese firm took over a Motorola factory that manufactured Quasar television sets in the United States in the 1970s, they promptly set about making drastic changes in the way the factory operated. Under Japanese management, the factory was soon producing TV sets with 1/20th the number of defects they had produced under Motorola management. They did this using the same workforce, technology, and designs, making it clear that the problem was Motorola's management. Eventually, even Motorola's own executives had to admit "our quality stinks." Read More...

 
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