Archive for the ‘Six Sigma Projects’ Category

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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Project Selection for DMAIC | Quality Digest

Wednesday, April 22nd, 2009

Project Selection for DMAIC | Quality Digest.

As a Quality Digest columnist I tend to be a reader of their other authors, too. This month’s Inside Six Sigma article from Steven Ouellette, The Six Sigma Heretic, provides a pretty good overview of important things to consider when choosing a Six Sigma project. But there’s one big oversight that I noticed. Steven fails to include as a criteria that the project should address a problem or opportunity where the connection between the desired outcome and the causes driving it are unclear. Projects can have every other attribute mentioned in the article and still not be good Six Sigma projects if they’re missing this vital attribute. The reason is simple: if the causes of the outcome are known, you don’t need the Six Sigma skill set to successfully complete the project. It is, essentially, a “Just Do” project. The training provided to Six Sigma Black Belts or Six Sigma Green Belts will be wasted. There are probably better projects for them to undertake.

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Project Selection for DMAIC | Quality Digest

Wednesday, April 22nd, 2009

Project Selection for DMAIC | Quality Digest.

As a Quality Digest columnist I tend to be a reader of their other authors, too. This month’s Inside Six Sigma article from Steven Ouellette, The Six Sigma Heretic, provides a pretty good overview of important things to consider when choosing a Six Sigma project. But there’s one big oversight that I noticed. Steven fails to include as a criteria that the project should address a problem or opportunity where the connection between the desired outcome and the causes driving it are unclear. Projects can have every other attribute mentioned in the article and still not be good Six Sigma projects if they’re missing this vital attribute. The reason is simple: if the causes of the outcome are known, you don’t need the Six Sigma skill set to successfully complete the project. It is, essentially, a “Just Do” project. The training provided to Six Sigma Black Belts or Six Sigma Green Belts will be wasted. There are probably better projects for them to undertake.

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Six Sigma Projects Management Support

Tuesday, March 31st, 2009

Author:  Peter L. Bersbach

The most critical item to the success of a Six Sigma project is management support. We always check and ask for it. But is what we ask for what we really want?

SUPPORT  YOU DON’T WANT

There are four types of management support that you really do not want to have. They are support by command, decreed rules, authorized overrides, and redirection of resources. With these employees tend, luckily, to use their best judgment to “adjust” the dictates to make things work. But doing that adds to the confusion and never really solves the problem. Many times it make matters worse. Plus if YOU are the one these are suppose to help, these support type have just done the opposite. Think about it when a manager commands you to do something, changes the rules, gives someone the authority to NOT follow the standard procedure, or pull resources from you for a “Pet Project”; how do you feel? Frustrated, confused, and angry NOT a good way to start a project.

SUPPORT YOU DO WANT

Lucky for us there are four OTHER ways for management to support our projects. They  Are cultural change, mentoring, identifing informal leaders, and legitimate ways around roadblocks. Of these four by far the best one is cultrual change.

Support through cultural change happens when the managers uses their persuasive power to create a company culture that embraces change instead of fighting it. Where they show employees the benefits of being evolved in solving the companies issues and problems. This is empowerment to help make the change NOT “My way or the Highway”

Support through mentoring – Today’s companies are complex and sometimes confusing as to who or where to go for help in solutions to roadblocks. A mentor is a wise and trusted counselor or teacher. This should be Management ( your project sponsor).  Management has the top level birdseye view of the company that allows them to guide you through that company maze identifing who can help you solve your roadblocks.

Support through Informal Leaders – Many times it is not a manager that is the expert but some individual engineer, supervisor, or lead technician that has the answers to a problem. BUT I can assure you management knows who these “informal leaders” are and can guide you to them.

Support through Legitimate ways around a roadblock – There maybe way to get issues solved through resource not known to you as the project leader. Here again management with their birds eye view of the company may know just where to find that resource. For example you may need a mechanical engineer for your project but engineering can not part with one due to work loads. Management may know a place, like a temp organization that they could hire one to do the job for this project.

So as you can see when you ask for management support think about what you will need and let them know what that is. Both you and management will be much happier with the results.

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Selecting Winning Project Portfolios

Friday, March 6th, 2009

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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Selecting Six Sigma Projects

Friday, March 6th, 2009

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 the June column, 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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DMAIC and project plans

Friday, March 6th, 2009

Six Sigma’s magic doesn’t lie in statistical or high-tech razzle-dazzle. Six Sigma relies on tried-and-true methods that have been around for decades. In fact, Six Sigma discards a great deal of the complexity that characterizes total quality management (TQM). By one expert’s count, there are more than 400 TQM tools and techniques. Six Sigma takes a handful of these methods and trains a small cadre of in-house technical leaders, known as Six Sigma Black Belts, to a high level of proficiency in the application of these techniques. To be sure, some of the methods used by Black Belts, including up-to-date computer technology, are highly advanced. But the tools are applied within a simple performance-improvement framework known as DMAIC, or define-measure-analyze-improve-control, which is analogous to the older TQM model known as plan-do-study-act. Anyone with more than the most cursory exposure to Six Sigma is familiar with the DMAIC cycle (see Table 1.)

Table 1: DMAIC Framework

DMAIC is almost universally used to guide Six Sigma process-improvement projects. Although truly dramatic improvement in quality requires transforming the management philosophy and organizational culture, the fact is that projects must be undertaken sooner or later to make things happen. Projects are the means through which processes are systematically changed; they are the bridge between the planning and the doing. However, DMAIC is not a method of planning projects. Project planning is a subject in its own right. Although projects and plans are closely related, they also differ in many respects.

The dictionary defines the word “project” as “a plan or proposal, a scheme, or an undertaking requiring concerted effort.” Under the synonym “plan,” we find “a scheme, program or method worked out beforehand for the accomplishment of an objective: a plan of attack; a proposed or tentative project or course of action; or a systematic arrangement of important parts.”

In other words, the project describes what will be done while the plan describes, in advance and in detail, how it will be done. Both elements can be integrated under the DMAIC umbrella. (A graphic representation of the DMAIC cycle can be found in the online version of my column in this month’s issue at www.qualitydigest.com or at www.pyzdek.com.)

I use the DMAIC cycle when training Black Belts, Green Belts and management. The complete approach integrates Six Sigma tools, financial analysis, project schedule development and many other topics all vital to ultimate success. However, I have observed that many trainees copy and carry around this single page from the 240-page Six Sigma Project Planners I give them. They use it for initial project planning, to be sure their projects are on track, to explain projects to others and for other day-to-day project activities. In short, my clients have found it useful in helping themselves and others understand the overall structure of a Six Sigma project. I hope you will also.

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