Posts Tagged ‘six-sigma’
Monday, October 12th, 2009
A while ago I was asked by a colleague to recommend a Lean Six Sigma benchmark partner for a large aerospace firm that had been using Six Sigma for quite some time. Upon calling some of my favorite clients I learned that their Lean Six Sigma initiatives had been phased out. I was dismayed to hear of this and arranged to meet with one of the senior leaders to discuss why this had occurred in his organization. I learned that what had disappeared was not the Lean Six Sigma approach. Indeed, the senior leader said he knew of no other way to manage his operations. What had gone was the Lean Six Sigma bureaucracy. The personnel devoted to coaching senior leaders, providing Lean Six Sigma courses for training, etc. were gone.
It doesn’t take a lot of thought to understand why this would occur. Lean Six Sigma has been around in one form or another since 1986. That’s a pretty long run. It has evolved into a complete system for leading organizations to operational excellence. If an organization is still using Lean Six Sigma solely to execute projects, then it is missing the benefit to be had from applying the approach in its normal day-to-day operations.
If the organization has been using Lean Six Sigma for several years, it is also wasting a lot of talent by relying too much on Belts. The nature of Lean Six Sigma’s change agent infrastructure is such that the personnel involved in the program full time are routinely cycled back into the organization. These people are “damaged goods” in the sense that they can no longer function as traditional managers. Lean Six Sigma is based on principles such as root cause identification, value flow, defect prevention, etc.. Traditional management is based on command-and-control, not process; it focuses on results, not on causes. Traditional managers manage via feedback, Lean Six Sigma Leaders manage using feed-forward models.
In short, Lean Six Sigma is at its best after it has all but disappeared from the organization chart. It is still there, embedded in everything the organization is doing in its operations. It won’t go away because its practitioners realize that old-fashioned management is horribly flawed and a terrible way to run an organization. Traditional management is a disease; the Lean Six Sigma approach, done properly, is the cure.
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Tags: aerospace firm, agent infrastructure, bureaucracy, lean-six-sigma, operational excellence, organization chart, root cause, six-sigma, traditional management, traditional managers, value flow
Posted in Introduction to Six Sigma, Leading Six Sigma | No Comments »
Wednesday, September 16th, 2009
Software helps select the best projects.
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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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Tags: assumption, computer software product, constraint, crystal ball, decision variables, development group, figure 1, finance manager, initial investment, present value analysis, probability, project selection, relevant factors, research and development, sigma projects, six-sigma, spreadsheet model, success rate, success rates, theory of constraints
Posted in Introduction to Six Sigma, Leading Six Sigma, Six Sigma Projects | No Comments »
Tuesday, September 8th, 2009
Have you attributed your results to the right base data?
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It may come as a surprise that the biggest challenge facing black belts and master black belts is usually not in selecting the best statistical technique for analyzing a particular data set. Most statistical techniques work fairly well even if the underlying assumptions are not precisely correct. If a black belt supplements the numerical analysis with graphical evaluation, the chance of making grossly erroneous decisions is negligible.
A mistake that is far more serious–but far more common–is comparing the results of a study to the wrong base data. These “apples to oranges” comparisons often lead to poor decisions and, worse still, to inaccurate beliefs that can derail faith in the Six Sigma approach itself. A recent incident with a client brought this point home for me.
The situation involved a project in the sales organization of a software company. The company had several sales teams and wanted to know if a new approach to closing the sale would improve the rate of closing sales. The company didn’t have a Six Sigma program, and the project was planned and carried out without black belts. The results were presented to management in a classic form: a bar chart (see Figure 1). The team had declared victory, and management–convinced by the “data”–prepared to revamp the sales training to incorporate the new approach companywide. All of the leaders looked forward to the bottom-line improvement they’d see from a 29-percent improvement in the sales closing rate.
Figure 1: Sales Closing Rate Improved by New Approach
All of the leaders, that is, except Lorraine. She’d received green belt training from her previous employer, and she’d seen enough black belt presentations to know that the analysis of the sales team was seriously flawed. It was undeniable that the project team’s sales close rate was 2.53 percent higher than the sales close rate for the rest of the sales department during the 16 weeks of the test, and, yes, the 2.53 percent did represent a 29-percent improvement over the 8.83-percent rate for the rest of the team. Despite these “facts” and the air of scientific objectivity surrounding the analysis, Lorraine had many unanswered questions. She asked management to delay any decision until she could explore these questions with a Six Sigma consultant. That’s where things stood when I entered the picture.
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Table 1: Old vs.
New Closing Rates |
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Lorraine viewed the analysis as important because it would demonstrate that the Six Sigma approach could be applied in this service company, something that skeptical managers didn’t believe. In a meeting with the sales team leader, I was presented with the data shown in Table 1. As often happens, this summary data was all that was available; for a variety of reasons (but chiefly due to a time constraint) the number of sales calls used to compute these rates could not be obtained.
If you are a black belt or master black belt, or just statistically inclined, please take a couple of minutes before reading the remainder of this column to think about the data and jot down how you’d proceed from here.
When dealing with the data in Table 1, it’s tempting to apply a statistical technique such as a paired t-test to it. Using Microsoft Excel, it’s a simple matter to compute the t-statistic, which is 4.55, a highly significant result. Statistical purists would ask if the data are approximately normal and an endless variety of other technical questions about the data. I would argue, however, that all of this is premature and, ultimately, beside the point. The first order of business is to determine if we are comparing apples to apples.
Table 2: Apples-to-Apples Comparison
Further discussion revealed that the company had not two but nine sales teams, all of the same size. A further complication was that the teams sold different products. More probing uncovered the fact that four of the eight other teams sold a product mix similar to that of the team using the new closing method. At this point it appeared that, to make an apples-to-apples comparison, you would assess the results of these five teams for the 16-week project. Descriptive statistics are shown in Table 2.
| Table 3: Data Groups |
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Further analysis using nonparametric methods indicated that there are three distinct groups in these data (see Table 3).
Table 3 presents a decidedly different picture than was originally given to management. The new closing method now appears to be no better than normal. Still, there are bright spots. Assuming that teams 5 and 8 aren’t oranges being compared to apples, potential gains should be possible from discovering why team 5 performs under the norm, and why team 8 outperforms the norm. More information might also be obtained by plotting the 16 weeks over time to identify trends and other patterns. Using the Six Sigma approach, the information can be converted to knowledge, the knowledge to action, and the action to an improved bottom line. It’s more work than the old standby, the bar chart, but it’s worth it.
The complete data file used in this article is posted at www.pyzdek.com/2000-05.xls . The challenge is to analyze the data in a number of different ways to determine how the different analyses would affect management decisions. Send your results to me for inclusion in a future column.
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Tags: assumptions, black belt, bottom line improvement, graphical evaluation, numerical analysis, poor decisions, sales organization, six-sigma, statistical technique
Posted in Six Sigma Tools, Statistical Tools for Six Sigma | No Comments »
Monday, August 31st, 2009
A Black Belt steps up to the plate with Six Sigma confidence.
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Bill had a problem. His company’s baseball team wasn’t doing that well, and he was part of the reason. Bill was in a long slump. Frankly, he stunk at the plate.
But Bill is a Six Sigma Black Belt. He decided to approach his batting problem just like he would approach any process problem at work–by conducting a designed experiment. First, Bill determined which factors are important. He wrote up a lengthy list and then winnowed it down to four experimental variables (see Table 1).
Table 1: Experimental Variables for Hitting
Bill decided to spend a few evenings and weekends on the practice field swinging at 100 pitches for each of the 16 combinations of the four variables needed to conduct a full-factorial experiment. The field was equipped with a pitching machine that could be programmed to throw pitches at either 60 mph or 80 mph. Bill decided to count any ball that went past the infield in fair territory as a hit. Over a two-week period Bill was able to complete the experiment, producing the results shown in Table 2.
Table 2: Bill’s Batting Experiment
The analysis indicates that factors B and D, and especially the C-D interaction, make big differences in Bill’s performance. Factors A and C do not have a significant effect on Bill’s batting average. The analysis in Table 3 shows the details.
Table 3: Significant Factor Effects
The 95-percent confidence interval for C (position in the batter’s box) includes zero, meaning that C is not statistically significant as a main effect. (C is included because the significant C-D interaction term requires it for statistical reasons.) However, the other factors in the table–B (choke on the bat) and D (speed of the pitch)–are statistically significant. The most important factor is the C-D interaction, which has an impressive effect of more than 9 percent. The coefficient estimate tells us what happens to Bill’s batting average as we go from one level of the variable to another. For example, when B is at the high level (choke up on the bat two inches), Bill’s batting average improves by about four percentage points.
The analysis indicates that when Bill is facing a pitcher with real heat (80 mph isn’t too bad for an amateur pitcher), he can improve his batting average from 8 percent to 28.75 percent by standing near the back of the batter’s box (see Table 4). Conversely, when Bill is up against a 60-mph hurler, he’s better off in the front of the batter’s box (38.75 percent in front hits vs. 15 percent in back). Combining all of these results, Bill’s strategy is to always choke up on the bat and position himself in the batter’s box depending on the expected speed of the pitch.
Table 4: Bill’s Results
Bill may not be ready for the majors with this strategy, but he’s hitting a lot better than the .206 (20.6%) he’d been getting without a strategy. In the meantime, Bill, work on hitting that fast ball!
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Tags: batting average, combinations, confidence interval, evenings, factorial experiment, interaction, performance factors, sigma black belt, six-sigma, slump
Posted in Six Sigma Tools, Statistical Tools for Six Sigma | 1 Comment »
Monday, August 17th, 2009
Who are they and what do they do?
I‘m often asked about the term “black belt” as it relates to six sigma. What, precisely, is a black belt? Where did the term originate? For that matter, where did the term “six sigma” originate? And, while we’re on the subject, what’s a green belt or master black belt?
Let’s start with the term “six sigma.” In a conversation with Ed Bales of Motorola University, I learned that Motorola coined the term in 1986. As those who have worked in quality for a while know, this term has statistical roots in the technique known as process capability analysis. Prior to the Japanese industrial invasion of U.S. markets, quality practitioners were happy with three sigma quality, which translates to about three errors or defects per 1,000 items for processes in a state of statistical control. Motorola discovered that its processes weren’t in statistical control–estimates based on field failure data indicated that Motorola’s processes apparently drifted by an average of 1.5 standard deviations. In a conversation with ex-Motorola trainer Mikel Harry, I learned that he considers the Cpk index–which measures short-term process variability under statistical control–worthless. Harry prefers the Ppk index, which measures actual performance rather than process capability. (Note that many experts, including me, disagree strongly with Harry on this issue.) In any case, before computing expected process failures, Motorola adds this 1.5 standard deviation. Thus, when we hear that a six sigma process will produce 3.4 parts-per-million (PPM) failures, we find that this PPM corresponds to the area in the tail beyond 4.5 standard deviations above the mean for a normal distribution.
Motorola also adopted the terms “black belt” and “green belt.” For my book The Six Sigma Handbook
, I did extensive research into what employers expect of people with these titles. Here is a summary of these various responsibilities:
- Master black belt–This is the highest level of technical and organizational proficiency. Because master black belts train black belts, they must know everything the black belts know, as well as understand the mathematical theory on which the statistical methods are based. Masters must be able to assist black belts in applying the methods correctly in unusual situations. Whenever possible, statistical training should be conducted only by master black belts. If it’s necessary for black belts and green belts to provide training, they should only do so under the guidance of master black belts. Because of the nature of the master’s duties, communications and teaching skills should be judged as important as technical competence in selecting candidates.
- Black belt–Candidates for technical leader (black belt) status are technically oriented individuals held in high regard by their peers. They should be actively involved in the organizational change and development process. Candidates may come from a wide range of disciplines and need not be formally trained statisticians or engineers. However, because they are expected to master a wide variety of technical tools in a relatively short period of time, technical leader candidates will probably possess a background in college-level mathematics, the basic tool of quantitative analysis. College-level course work in statistical methods should be a prerequisite.
Six sigma technical leaders work to extract actionable knowledge from an organization’s information warehouse. Successful candidates should understand one or more operating systems, spreadsheets, database managers, presentation programs and word processors. As part of their training they will be required to become proficient in the use of one or more advanced statistical analysis software packages.
- Green belt –Green belts are six sigma team leaders capable of forming and facilitating six sigma teams and managing six sigma projects from concept to completion. Typically, green-belt training consists of five days of classroom training and is conducted in conjunction with six sigma team projects. Training covers facilitation techniques and meeting management, project management, quality management tools, quality control tools, problem solving, and exploratory data analysis. Usually, six sigma black belts help green belts choose their projects prior to the training, attend training with their green belts and assist them with their projects after the training.
Although the martial arts terms described above are common, they are by no means universal. Companies and consulting firms often create their own titles to describe the work done by these technical leaders.
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Tags: black belt, black belts, certification, green belts, master black belt, Projects, quality engineers, quality profession, sigma black belt, Six Sigma Tools, six-sigma, statistical methods
Posted in Introduction to Six Sigma | No Comments »
Monday, August 17th, 2009
Marketing is a process. Six Sigma is an approach for achieving process excellence. It will help you improve the marketing process by providing tools & techniques for identifying what the marketing process is, including suppliers, inputs, process steps, outputs, and customers. Six Sigma helps you understand the need to determine who owns the process and helps the process owner determine how to improve it. It provides a framework for improving all aspects of this process. It does much more as well. I recommend you enroll and take a week to look around the training site. If it looks like a good value to you, stay in the course and become a Certified Six Sigma Black Belt or Green Belt.
The converse is also true, marketing can help Six Sigma. Both marketing and Six Sigma focus on customers. Marketing is a management discipline dedicated to understanding customer demands, how to design products meet them, and how to let potential customers know what’s available. In Six Sigma training for Black Belts and Green Belts we teach a number of tools that are borrowed directly from marketing, such as the analytic hierarchical process, quality function deployment and Pugh matrices. Master Blacks use conjoint analysis, a quasi-designed experiment approach to measuring customer importance weights. Design for Six Sigma is all about integrating the design process across marketing, engineering, and production to better meet implicit and explicit customer demands.
Beyond the technical tools, when Six Sigma or Lean Six Sigma is well done it begins with understanding what customers are solving for, then helping them achieve their goals by improving the processes you use to provide them with service. This is truly an integration of marketing and Six Sigma.
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Tags: black belts, certified six sigma black belt, green belts, lean-six-sigma, management discipline, marketing engineering, marketing process, quality function deployment, sigma black belt, six sigma black belt, six-sigma
Posted in Introduction to Six Sigma, Leading Six Sigma | 4 Comments »
Thursday, August 13th, 2009
Last night I watched a great movie, Living Proof, which documented a true story about a doctor struggling to get a promising breast cancer treatment drug approved. In one of the scenes the doctor has just completed a Phase I clinical trial and has to explain to one of the patients why she won’t be allowed to move on to the next phase. Essentially, the reason is FDA rules. For all practical purposes the woman is sentenced to death. She had responded favorably to the experimental drug, but not favorably enough to move to the next phase.
Ok, you might say. But surely she could be given the drug outside of the clinical trial, right? Wrong. She is denied access to the only medicine that could possibly save her, presumably in the name of safety.
This isn’t an isolated case. Because I’ve rented similar movies in the past Netflix recommends a host of other movies about people fighting heroic battles to get potential cures through the FDA’s approval process. In the article, Whose Life is it Anyway? former FDA commissioner Scott Gottlieb is quoted as saying that the FDA is failing to use its authority to strike a balance on this issue. Gottlieb suggests a number of process improvements. Too bad his suggestions probably won’t be taken seriously.
I’m one of the lucky ones. When I turned 50 I was diagnosed with severe Barrett’s esophagus. The standard of care is what could be termed “watchful waiting.” It involves periodic endoscopies and drug treatment. In my case, the drugs did no good and my condition got steadily worse. My checkups went from every two years, to every year, to every six months. The biopsies looked more and more like cancer, putting me and my family through periodic nightmares as we awaited the biopsy results. Eventually, I was sure, my condition would progress to esophageal cancer. Like most cancer treatments, the treatments for esophageal cancer are expensive, gruesome and ineffective.
Finally, after eight years of this, I spent my own money to buy 30 minutes of time with a physician at Mayo clinic in Scottsdale. As luck would have it, he had a clinical trial starting. I qualified, received the treatment, and am now completely free of Barrett’s esophagus. While I was blessed, my nephew’s father-in-law was less fortunate. His Barrett’s degenerated into cancer and he died during my clinical trail. It will probably be several years before the treatment is approved and made available to the public. In the meantime, more people will die.
The FDA’s drug approval process is over 50 years old. It takes years and costs hundreds of millions of dollars. Thousands die while the FDA slogs along. It doesn’t take Six Sigma or Lean training to see that this process is screaming for improvement. It just takes a heart.
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Tags: breast cancer, breast cancer treatment, clinical trial, drug approval process, experimental drug, fda rules, living proof, six-sigma
Posted in Healthcare Quality, News | 1 Comment »
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
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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
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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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Tags: bottom line benefits, bottom line results, classification scheme, constraint, converts, critical characteristics, general electric, improvements, ix sigma, priority, project deliverables, quality characteristics, quality cost, sigma projects, six-sigma, smaller companies, step c, surprise, theory of constraints
Posted in Six Sigma Projects | 2 Comments »
Monday, August 3rd, 2009
To make your customers truly happy, you must go beyond six sigma.
Some people, including me, believe that garden variety six sigma doesn’t go far enough. In fact, even zero defects falls short. Defining quality as only the lack of nonconforming product reflects a limited view of quality. Of course, that was never Motorola’s intent when it invented the Six Sigma program. However, the misinterpretation prevails.
Progressive people in the six sigma camp move beyond defining quality in terms of defects and defectives. This group looks for critical-to-quality (CTQ) characteristics in a product or service. CTQ features are those that customers expect and consider explicitly when evaluating product or service quality. A product or service that doesn’t provide the CTQ features that customers expect suffers lower customer satisfaction. But even this definition isn’t enough. The problem is illustrated by the Kano model of customer satisfaction (see Figure).
Garden variety six sigma only addresses two-thirds of the Kano model: Basic Quality features and Expected Quality features. When six sigma addresses nonconformances and defects, it’s focusing on the Basic Quality curve in the Kano model. When these items are handled perfectly, the result is a customer who is not dissatisfied. This is certainly important, but “not dissatisfied” is hardly a rousing endorsement of a product or service.
Six sigma activities that seek to identify CTQ characteristics address the portion of the Kano model on and below the line labeled “Expected Quality.” If all CTQ characteristics are properly produced, the result will be a satisfied customer. Important, of course, but is it enough to simply satisfy the customer?
Even perfection in these areas won’t ensure that the organization remains viable in the long term. The Competitive Pressure curve on the Kano model indicates that market forces will make today’s expected quality features tomorrow’s basic quality features. Long-term success requires the customer to be excited by unexpected innovations provided by a company’s products and services: Continued survival requires that your organization continuously innovate. Innovation is the result of creative activity, not of analysis. Creativity can’t be achieved “by the numbers.” In fact, excessive attention to a rigorous process such as six sigma actually detracts from creativity. The creative organization is one that exhibits variability, resource redundancy, quirky design and slack. It’s vital that the organization keep the Six Sigma Management Paradox in mind: To attain six sigma performance, we must minimize process variability, slack and redundancy by building variability, slack and redundancy into our organizations.
The key is to keep human enterprises and processes separate. You can encourage creativity in your company if you:
- Celebrate failure. Most innovations fail to produce the hoped-for progress. Management must not only tolerate valiant efforts that fail, but they must also make it clear that such efforts are valued.
- Create quality time. Set aside a specified block of time each day or each week for creative activity. During this quality time, people aren’t allowed to spend time on routine work; they must focus instead on how they can improve products, processes or service. When I owned Quality America a number of years ago, I designated the last hour of each day “Quality Hour.” I believe that Quality Hour helped us more than double our sales without the need for additional personnel, not a bad return on investment for an investment equal to 12.5 percent of payroll!
- Reduce procedure protocols . Although process control and quality control bring better products at lower costs, these control systems also inhibit experimentation and innovation. Quality professionals should study existing systems to determine how little control is absolutely necessary to protect the customer and the brand.
- Mass DOE education. Statistical design of experiments (DOE) is a complex and advanced subject area. But it is possible to develop easy-to-use DOE systems that everyone can use. For example, quality engineers can develop spreadsheets that allow employees to easily evaluate two-level experiments for three or four factors simultaneously. By working with information systems departments, we can help everyone get access to the data they need to determine which areas require improvement and to monitor the results of their experiments.
- Utilize undesigned experiments. Despite the fact that DOE is the method of choice, we can learn a lot from ad hoc changes to processes. By allowing people to experiment without getting permission, we increase variability and increase the chance that we’ll learn something. We must establish guidelines to protect the customer and to protect the employee from reprisal should things not go as hoped.
These ideas can work in practice. Send me e-mail to let me know what suggestions you have for helping organizations become more innovative. If I get a sufficient number of responses, I will print them in a future column.
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Tags: creativity, customer satisfaction, Innovation, kano model, motorola, quality features, service quality, six-sigma
Posted in Introduction to Six Sigma, Leading Six Sigma | 3 Comments »
Thursday, July 30th, 2009
Today I received a call from a person interested in becoming a Certified Six Sigma Black Belt. Of course, we value him as a customer and he will learn a great deal if he decides to enroll in our Six Sigma training. Among the things he’ll learn are both “hard skills” involving statistics and data analysis techniques, and “soft skills” such as conflict management, team dynamics, and stakeholder analysis. Still, I have my doubts about his chances of becoming a successful Six Sigma Black Belt. He has what I call a “Can’t Do” personality. This is the diametric opposite of the Can Do person. This type of individual looks for reasons why a particular thing can’t be done. How about a project in the sales department? No way, sales people won’t go for it, sales isn’t a process anyway, management won’t let us touch the sales area, etc. etc. etc.
Successful change agents are invariably Can Do people. To be sure they spend a lot of time planning to avoid obstacles, but when they encounter the inevitable obstacle, they don’t shrink from the challenge. They found ways over, under, around, or through the obstacle. They are not to be stopped. They are relentless pursuers of change.
I once had the opportunity to work with a major aerospace client to study the success factors for their Six Sigma Black Belts. We reviewed the histories of a number of Black Belts who had success levels that varied from poor to excellent. After coming up with a list of the factors that seemed to have an impact on success we went through an exercise to determine the importance weights. Using the Analytic Hierarchical Process (AHP) the Six Sigma Champion, Master Black Belts, and me came up with the weights shown in Figure 1.
Figure 1-Black Belt Success Factor Weights
The weights are, of course, subjective and only approximate. You may feel free to modify them if you feel strongly that they’re incorrect. Better yet, you may want to identify your own set of criteria and weights. The important thing is to determine the criteria and then develop a method of evaluating candidates on each criterion. The sum of the candidate’s criterion score times the criterion weight will give you an overall numerical assessment that can be useful in sorting out those candidates with high potential from those less likely to succeed as Black Belts. Of course, the numerical assessment is not the only input into the selection decision, but it is a very useful one.
You may be surprised to see the low weight given to math skills. The rationale is that Black Belts will receive 200 hours of training, much of it focused on the practical application of statistical techniques using computer software and requiring very little actual mathematics. Software automates the analysis, making math skills less necessary. The mathematical theory underlying a technique is not discussed beyond the level necessary to help the Black Belt properly apply the tool. Black Belts who need help with a particular tool have access to Master Black Belts, other Black Belts, consultants, professors, and a wealth of other resources. Most statistical techniques used in Six Sigma are relatively straightforward and often graphical; spotting obvious errors is usually not too difficult for trained Black Belts. Projects seldom fail due to a lack of mathematical expertise. In contrast, the Black Belt will often have to rely on his or her own abilities to deal with the obstacles to change they will inevitably encounter. Failure to overcome the obstacle will often spell failure of the entire project.
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Tags: aerospace, certified six sigma black belt, champion, obstacles, personality, sigma black belt, six-sigma, success factor, weights
Posted in Uncategorized | No Comments »
