Tuesday, August 5, 2008
Monday, August 4, 2008
Analysing experiments
Analyzing Experiments with Ordered Categorical Data
Six Sigma projects in various industries often deal with experiments whose outcomes are not continuous variable data, but ordered categorical data. Analysis of variables (ANOVA) is a technique used to analyze continuously experimental data, but is not adequate for analyzing categorical experimental outcomes. Fortunately, many other methods have been developed to deal with categorical experiments, such as Jeng and Guo’s weighted probability-scoring scheme (WPSS).
The WPSS technique is interpretable and easy to implement in a spreadsheet software program. The following case study, which involves medical devices, serves as an example of how a modified WPSS technique can be used to analyze experiments with ordered categorical data.
Determining the Best Factors
This study explores the influence of contact lens design factors on outcomes related to ease of lens insertion, meaning how easy it is to put patients’ contact lenses in their eyes. Soft contact lenses are thin pieces of plastic or glass that float on the tear film on the surface of the cornea. They are shaped to fit the user's eye and are used to correct refractive errors such as nearsightedness, farsightedness and unequal curvature of the cornea (astigmatism). For this example, only three lens design factors of a certain lens type with fixed material properties are considered: lens thickness profile (3 levels), base curve dimension (3 levels) and base curve profile (2 levels). Determining the ease of insertion is a five-step process.
Step 1: Design an Experiment
Because this is an exploratory experiment, an L9 orthogonal matrix is used. The design matrix with the three lens design factors is shown in Table 1.
| Table 1: L9 Orthogonal Matrix of Three Lens Design Factors | |||
Design Factors | |||
| Experiment Number | Thickness profile | Base curve dimension | Base curve profile |
| 1 | 1 | 1 | 1 |
| 2 | 1 | 2 | 2 |
| 3 | 1 | 3 | 1 |
| 4 | 2 | 1 | 2 |
| 5 | 2 | 2 | 1 |
| 6 | 2 | 3 | 1 |
| 7 | 3 | 1 | 1 |
| 8 | 3 | 2 | 1 |
| 9 | 3 | 3 | 2 |
Step 2: Plan Number of Samples and Data Categorization
In small clinical trials, nine trained contact lens wearers are asked to try each of the nine lens designs from the L9 matrix and give their opinion on the ease of insertion. Each time a patient inserts a lens in their eye, they are asked to rate how easy it was to do. Their responses are integer numbers from 1 to 10, with the worst condition rated 1 (the patient cannot insert the lens) to the best condition rated 10 (the patient needs only one trial and the lens immediately sits on the right location of the eye). The ratings are grouped into four categories of ease of insertion:
- Category I (very easy to insert): Ratings 9 – 10
- Category II (easy to insert): Ratings 7 – 8
- Category III (moderate to insert): Ratings 5 – 6
- Category IV (difficult to insert): Ratings 1- 4
The design matrix with the outcomes for each run is shown in Table 2.
| Table 2: Insertion Ratings Grouped By Category | ||||||||
Design Factors | Number of Observation By Category | |||||||
| Experiment Number | Thickness profile | Base curve dimension | Base curve profile | I | II | III | IV | Total |
| 1 | 1 | 1 | 1 | 1 | 2 | 5 | 1 | 9 |
| 2 | 1 | 2 | 2 | 3 | 3 | 3 | 0 | 9 |
| 3 | 1 | 3 | 1 | 4 | 2 | 2 | 1 | 9 |
| 4 | 2 | 1 | 2 | 2 | 2 | 3 | 2 | 9 |
| 5 | 2 | 2 | 1 | 4 | 4 | 1 | 0 | 9 |
| 6 | 2 | 3 | 1 | 1 | 3 | 1 | 4 | 9 |
| 7 | 3 | 1 | 1 | 5 | 3 | 1 | 0 | 9 |
| 8 | 3 | 2 | 1 | 2 | 5 | 1 | 1 | 9 |
| 9 | 3 | 3 | 2 | 4 | 1 | 4 | 0 | 9 |
Step 3: Calculate Probability of the Outcomes Per Category and Run
In order to estimate the location and dispersion effects of each run, the scores of each category of each run must be transformed into probability values. Let i be an experiment run, for i = 1, 2,…I (in this example, I = 9) and j be a category of experimental outcomes, for j = I, II,…J (in this example J = IV). Then it is possible to calculate the probability (proportion) that an outcome is placed in j-th category of i-th run, i.e. pij, as the following:
pij = nij/si
where nij is the number of outcomes in j-th category of i-th run and si is the total outcomes of all categories in the i-th run.
For example, the probability of an outcome being placed in the III-th category of the 1st run is p1III = n1III/s1 = 5/9 = 0.56. The probability of the outcome in each category of each run is shown in Table 3.
| Table 3: Probability of Outcomes | |||||||||
Number of Observation | Probabilities for Each Category | ||||||||
| Experiment Number | I | II | III | IV | Total | (I) | (II) | (III) | (IV) |
| 1 | 1 | 2 | 5 | 1 | 9 | 0.11 | 0.22 | 0.56 | 0.11 |
| 2 | 3 | 3 | 3 | 0 | 9 | 0.33 | 0.33 | 0.33 | 0.00 |
| 3 | 4 | 2 | 2 | 1 | 9 | 0.44 | 0.22 | 0.22 | 0.11 |
| 4 | 2 | 2 | 3 | 2 | 9 | 0.22 | 0.22 | 0.33 | 0.22 |
| 5 | 4 | 4 | 1 | 0 | 9 | 0.44 | 0.44 | 0.11 | 0.00 |
| 6 | 1 | 3 | 1 | 4 | 9 | 0.11 | 0.33 | 0.11 | 0.44 |
| 7 | 5 | 3 | 1 | 0 | 9 | 0.56 | 0.33 | 0.11 | 0.00 |
| 8 | 2 | 5 | 1 | 1 | 9 | 0.22 | 0.56 | 0.11 | 0.11 |
| 9 | 4 | 1 | 4 | 0 | 9 | 0.44 | 0.11 | 0.44 | 0.00 |
Step 4: Estimate Location and Dispersion Effects of Each Run
Given each category j has a weight wj, which is the upper limit of the j-th category rate, the location scores Wi for the i-th run is defined by
The rationale for using the upper limit of the category rate is that the weight should reflect the rating values. The dispersion score di2 is defined by
where the target values are defined as {The upper limit of the I-st category rate, 0, 0, …, 0} for categories {I, II, III, … ,J}, respectively.
The rationale of setting the target values is that only outcomes that fall in the best category are rewarded. For example, the location and dispersion scores for the 1st run are W1 = 10*0.11 + 8*0.22 + 6*0.56 + 4*0.11 = 6.7 and d12 = [10*0.11 – 10]2 + [8*0.22 – 0]2 + [6*0.56 – 0]2+ [4*0.11 – 0]2 = 93.48. The location and dispersion scores of the outcomes of each run are shown in Table 4.
| Table 4: Location, Dispersion and Mean Square Deviation Scores | ||||||
| Experiment Number | Design Factor – Thickness Profile | Design Factor – Base Curve Dimension | Design Factor – Base Curve Profile | Location Scores (Wi) | Dispersion Scores (di2) | MSD |
| 1 | 1 | 1 | 1 | 6.7 | 93.5 | 0.16 |
| 2 | 1 | 2 | 2 | 8.0 | 55.6 | 0.06 |
| 3 | 1 | 3 | 1 | 8.0 | 36.0 | 0.04 |
| 4 | 2 | 1 | 2 | 6.9 | 68.4 | 0.11 |
| 5 | 2 | 2 | 1 | 8.7 | 44.0 | 0.04 |
| 6 | 2 | 3 | 1 | 6.2 | 89.7 | 0.21 |
| 7 | 3 | 1 | 1 | 8.9 | 27.3 | 0.03 |
| 8 | 3 | 2 | 1 | 7.8 | 80.9 | 0.08 |
| 9 | 3 | 3 | 2 | 8.0 | 38.8 | 0.04 |
One performance measure to combine location and dispersion effects is mean square deviation (MSD), which allows practitioners to make judgments in one step. If any outcome is the larger-the-better characteristic, then its expected MSD can be approximately expressed in terms of location and dispersion effects as follows:
For example, the expected MSD for 1st run is E[MSD]1 = 1/(6.67)2 (1+ (3*93.5)/(6.67)2) = 0.16. The MSD scores for all runs are given in Table 4.
The location, dispersion and expected MSD effects for each design factors are shown as Tmax-Tmin (Figures 1, 2, 3). Higher Tmax-Tmin values or steeper main effects curves indicate a stronger influence of that design factor on the outcomes.
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Step 5: Determine Optimal Solutions
The level of a particular design factor with the highest location value, the lowest dispersion value or the lowest expected MSD value is the optimal solution for each of those factors, respectively. The optimal solution based on the expected MSD criteria is the optimal trade-off between maximal location and minimal dispersion scores.
The predicted optimal solution based on the expected MSD criteria is thickness profile at level 3, base curve dimension at level 2 and base curve profile at level 2. But if practitioners know there are interaction effects among design factors, they cannot depend solely on the main effect values or plots to choose the settings of design factors. The interaction plot for the expected MSD effects shows that thickness profile heavily interacts with base curve level/dimension (Figure 4). A small interaction also exists between base curve dimension and base curve profile. After taking interaction effects into consideration, practitioners need to examine whether the chosen optimal design factor levels still give optimal effects to the experiment outcomes.
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In this case, thickness profile at level 3 gives almost consistently the lowest MSD scores for different levels of base curve dimension and also consistently gives the lowest MSD scores for different levels of base curve profile. Thus, it gives the optimal effect to the experiment outcomes. Base curve dimension at level 2 almost consistently gives the lowest MSD scores for different levels of thickness profile and also consistently gives the lowest MSD score for different levels of base curve profile. Thus, it too gives the optimal effect to the experiment outcomes. The Tmax-Tmin value of the base curve profile is the lowest and its curve is flat. Thus, base curve profile has insignificant influence on the outcomes, and can be set at either level 1 or 2. Therefore, the expected MSD predicts that lens design with thickness profile at level 3, base curve dimension at level 2 and base curve profile at either level 1 or 2 would give the optimal ease of insertion.
Easy to Implement Optimization Method
A modified WPSS is a simple and straightforward method for dealing with ordered categorical data. This case study shows that a single performance measure MSD derived from WPSS can provide insight to a system through experiments and can direct practitioners to the optimal solution.
About the Author: Liem Ferryanto, Ph.D., is project director and Six Sigma Champion of global research, development and engineering at CIBA Vision Corp., a Novartis company, in Duluth, Ga., USA. He can be reached at lferryanto@gmail.com.
Wednesday, July 30, 2008
Statistical Definition
Statistical Six Sigma Definition
What does it mean to be "Six Sigma"? Six Sigma at many organizations simply means a measure of quality that strives for near perfection. But the statistical implications of a Six Sigma program go well beyond the qualitative eradication of customer-perceptible defects. It's a methodology that is well rooted in mathematics and statistics.
The objective of Six Sigma Quality is to reduce process output variation so that on a long term basis, which is the customer's aggregate experience with our process over time, this will result in no more than 3.4 defect Parts Per Million (PPM) opportunities (or 3.4 Defects Per Million Opportunities – DPMO). For a process with only one specification limit (Upper or Lower), this results in six process standard deviations between the mean of the process and the customer's specification limit (hence, 6 Sigma). For a process with two specification limits (Upper and Lower), this translates to slightly more than six process standard deviations between the mean and each specification limit such that the total defect rate corresponds to equivalent of six process standard deviations.
Many processes are prone to being influenced by special and/or assignable causes that impact the overall performance of the process relative to the customer's specification. That is, the overall performance of our process as the customer views it might be 3.4 DPMO (corresponding to Long Term performance of 4.5 Sigma). However, our process could indeed be capable of producing a near perfect output (Short Term capability – also known as process entitlement – of 6 Sigma). The difference between the "best" a process can be, measured by Short Term process capability, and the customer's aggregate experience (Long Term capability) is known as Shift depicted as Zshift or sshift. For a "typical" process, the value of shift is 1.5; therefore, when one hears about "6 Sigma," inherent in that statement is that the short term capability of the process is 6, the long term capability is 4.5 (3.4 DPMO – what the customer sees) with an assumed shift of 1.5. Typically, when reference is given using DPMO, it denotes the Long Term capability of the process, which is the customer's experience. The role of the Six Sigma professional is to quantify the process performance (Short Term and Long Term capability) and based on the true process entitlement and process shift, establish the right strategy to reach the established performance objective
As the process sigma value increases from zero to six, the variation of the process around the mean value decreases. With a high enough value of process sigma, the process approaches zero variation and is known as 'zero defects.'
Statistical Take Away
Decrease your process variation (remember variance is the square of your process standard deviation) in order to increase your process sigma. The end result is greater customer satisfaction and lower costs.
Article -what is six sigman
Six Sigma - What is Six Sigma?
Six Sigma at many organizations simply means a measure of quality that strives for near perfection. Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects (driving towards six standard deviations between the mean and the nearest specification limit) in any process -- from manufacturing to transactional and from product to service.
The statistical representation of Six Sigma describes quantitatively how a process is performing. To achieve Six Sigma, a process must not produce more than 3.4 defects per million opportunities. A Six Sigma defect is defined as anything outside of customer specifications. A Six Sigma opportunity is then the total quantity of chances for a defect. Process sigma can easily be calculated using a Six Sigma calculator.
The fundamental objective of the Six Sigma methodology is the implementation of a measurement-based strategy that focuses on process improvement and variation reduction through the application of Six Sigma improvement projects. This is accomplished through the use of two Six Sigma sub-methodologies: DMAIC and DMADV. The Six Sigma DMAIC process (define, measure, analyze, improve, control) is an improvement system for existing processes falling below specification and looking for incremental improvement. The Six Sigma DMADV process (define, measure, analyze, design, verify) is an improvement system used to develop new processes or products at Six Sigma quality levels. It can also be employed if a current process requires more than just incremental improvement. Both Six Sigma processes are executed by Six Sigma Green Belts and Six Sigma Black Belts, and are overseen by Six Sigma Master Black Belts.
According to the Six Sigma Academy, Black Belts save companies approximately $230,000 per project and can complete four to 6 projects per year. General Electric, one of the most successful companies implementing Six Sigma, has estimated benefits on the order of $10 billion during the first five years of implementation. GE first began Six Sigma in 1995 after Motorola and Allied Signal blazed the Six Sigma trail. Since then, thousands of companies around the world have discovered the far reaching benefits of Six Sigma.
What is Six Sigma
Six Sigma
The goal of Six Sigma is to increase profits by eliminating variability, defects and waste that undermine customer loyalty.Six Sigma can be understood/perceived at three levels:
- Metric: 3.4 Defects Per Million Opportunities. DPMO allows you to take complexity of product/process into account. Rule of thumb is to consider at least three opportunities for a physical part/component - one for form, one for fit and one for function, in absence of better considerations. Also you want to be Six Sigma in the Critical to Quality characteristics and not the whole unit/characteristics.
- Methodology: DMAIC/DFSS structured problem solving roadmap and tools.
- Philosophy: Reduce variation in your business and take customer-focused, data driven decisions.
Here's an article with more detail on defining Six Sigma: What is Six Sigma?
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Six Sigma is a rigorous and a systematic methodology that utilizes information (management by facts) and statistical analysis to measure and improve a company's operational performance, practices and systems by identifying and preventing 'defects' in manufacturing and service-related processes in order to anticipate and exceed expectations of all stakeholders to accomplish effectiveness.
Posted By: Craig Tonner
Modified By: pradeep patra
Last Modified: Sep. 3, 2003
Value Stream Mapping to Identify Improvement Projects
Value Stream Mapping to Identify Improvement Projects
With many companies integrating Lean and Six Sigma methodologies into a single improvement tool kit, value stream mapping has emerged as a preferred tool to identify process improvement opportunities. A number of valuable points can be made about applying value stream thinking to project selection across a range of industries and processes both in the United States and in Europe. They include:
Understand the Overall Goal
The power of value stream thinking lies in looking at an entire business process. With the typical Lean or Six Sigma project focusing on fixing a specific issue in a narrowly defined process, it is critical to have an overall perspective for selecting what projects to tackle. Even more important, value stream mapping not only includes defining the current state, but also includes defining the future state and the gaps between the two.
With a clear picture of how the entire process should operate in the future, it is relatively easy to identify the projects that will close the gap. For discrete manufacturing processes, the ideal of continuous flow often drives the design of the future state. For process manufacturing and administrative processes, the characteristics of the future state are often less well-defined and require a strategic perspective. For example, using value stream mapping for mapping core human resources (HR) processes forces the business leadership team to decide on the future service delivery model – to what extent should the business adopt a self-service approach, what tasks will be performed by HR specialists versus local generalists, etc.
When developing the future state, it is crucial to define the overarching goal for the process that will guide the design. For a staffing process, the goal might be filling a position in less than two months. For a pharmaceutical filling operation, the goal might be to achieve a higher number of fills. A clearly defined business goal for the process provides the perspective that drives the design.
Understand the Real Constraints
When developing the future state, it is critical to understand the real process constraints of the current state and to evaluate to what extent these constraints will remain in the future state. For example, developing the future state for pharmaceuticals manufacturing needs to consider the time required to validate new equipment. A typical future state map describes the state of the process 12 or 18 months from now. If it takes 30 months to get a new piece of equipment in place and validated, the current equipment becomes a constraint for the future state map. Some constraints are real, others are only imagined. For example, headcount should never be a constraint.
Focus on Projects That Help Achieve the Goal
When analyzing the gap between the current and future states, one should focus only on those projects that will help achieve the overall goal. In many instances, the improvement plan is filled with projects that have no clear link to the overall objective. Most companies have only limited resources at their disposal; therefore the available resources should be concentrated on those projects that really need to be done.
Define the Options
In almost every instance, there are several different paths to achieve the future state and meet the overall process goal. For example, when focusing on capacity increase at a bottleneck machine, this goal could be accomplished by reducing process cycle time, unplanned downtime, changeover times or process yield. Defining the alternative "project packages" is helpful to understand the trade-offs and make smart resource allocation decisions.
Integrate Existing Initiatives into the Plan
Initiatives and projects already under way or planned for the foreseeable future need to be integrated into the overall plan to the extent that they impact the future state. However, one needs to be careful and review whether the deliverables for these initiatives are realistic.
For example, a company which was mapping its manufacturing process identified two projects that were expected to yield a substantial reduction in process time. However, when the team reviewed these projects it became obvious that the impact expectations were very unrealistic. When putting together an inventory of these current or planned projects or initiatives, the team should ask itself: Are the objectives for these projects clearly defined? Are these initiatives on track to deliver the expected results? What is the risk of these projects failing? Especially when it comes to technology projects, reviewing the track record of similar projects in the company can help to understand whether the team should count on successful completion or not.
Be Creative and Adapt the Approach to the Situation
Value stream mapping typically focuses on a product family. However, in many instances the concept of product family is limiting. In many process industries, the equipment is not dedicated to a certain product or family, and processing paths can vary from run to run. Similar issues arise in many service processes – for example, when customers can choose between various channels (internet, phone, e-mail, etc). Focusing narrowly on a product family does not really provide much insight into the improvement opportunities available. In such cases, the value stream perspective can be enhanced by combining the mapping with other tools such as bottleneck analysis.
Value stream mapping is a powerful tool that helps to identify the vital few Lean and Six Sigma projects that will yield the highest value to the process in question.
About the Author
Thomas Bertels is a partner of Valeocon Management Consulting, and serves as the global firm's regional director for the Americas. He has worked with clients such as TRW, Siemens, Vanguard and Johnson & Johnson, and also served as the editor of and main contributor to a Six Sigma leadership handbook. Mr. Bertels started his career at ABB (Asea Brown Boveri), one of the early adopters of Six Sigma. Fluent in German and English, he is based in New York, N.Y., USA, and can be reached at thomas.bertels@valeocon.com.
Learning to Recognize Process Waste in Financial Services
Learning to Recognize Process Waste in Financial Services
By Bill Kastle
One of the biggest challenges for Six Sigma practitioners in financial services is developing the ability to recognize waste.
Imagine an "overnight pack" entering Bank One's wholesale lockbox process for processing remittance payments. By the time it has been through every step, up and down the elevators, back and forth between departments, it would have traveled one-and-a-half miles. Hard to believe? The lockbox staff also thought so, at first. But as they traced the physical flow of the value stream, everyone was floored. "Well, I guess maybe it could travel that far!"
What is even more astonishing is how much that distance could be shortened. Bank One's team came up with a workspace design that required just 386 walking steps to complete the entire process – an 80 percent reduction in transportation.
Most departments or companies that provide financial services are in the same position as Bank One. They accept things like traipsing up and down hallways as simply part of "how work is done around here." But success with Six Sigma means developing new eyes, then critically and regularly re-examining what is being done and how it is being done. The goal is to identify the steps in processes that are value-added in the eyes of customers. That is, steps which customers would value and be willing to pay for if they knew about them. Everything else is waste. A company will never be able to recoup the time, resources and dollars spent on waste.
To help Six Sigma practitioners in financial services begin developing a "waste-sensing" ability, here are seven types of process waste that someone is doing right now somewhere in virtually every company:
Waste No. 1: Over-Processing
Adding more value to a service or product than customers want or will pay for - The basic theme of over-processing is doing more work than is absolutely necessary to satisfy or delight customers. There are two elements to over-processing:
- Not knowing what customers want. For example, including return envelopes for loan payments is seen as value-added by customers who pay by check, but waste by customers who pay through automatic transfer.
- Redundancy. Consider a process that involves a number of approval steps or handoffs. Would customers think that each of those steps is adding value? Rather than requiring five managers to sign off on a decision, why not develop a process and guidelines so one manager can make the call?
Waste No. 2: Transportation
Unnecessary movement of materials, products or information - Too much physical back-and-forth movement is one of the problems that plagued Bank One's original lockbox process. Excess transportation is important because every move from one activity to another adds time to a process – and world-class organizations are passionate about reducing time.
Yet in many service processes, it is not uncommon for paperwork to loop back several times…waiting in queues in a virtual or actual in-box every time it goes through again. Transportation in service processes almost always manifests itself as materials constantly being collected or delivered, or the actual or virtual chasing of information ("Who has that expense figure? Marcy? Okay, I'll ask Marcy…. Marcy says Hector has it…"). At one end of the spectrum, eliminating excess transportation can involve combining steps to eliminate loops. Cutting the hand-offs in half generally cuts the queue time in half. At the other end is the option to rearrange the workspace to match the flow of the process.
Waste No. 3: Motion
Needless movement of people - While "transportation" refers to the movement of the work, "motion" involves movement of workers. Both are much harder to see in service environments than in manufacturing. Motion may show up as people constantly switching between different computer domains or drives, or simply having to perform too many keystrokes to accomplish a computerized task. Solutions can involve everything from rearranging people's desks, to purchasing ergonomic furniture and equipment, to using software that performs tasks offline so information is waiting for the staff rather than vice versa.
Waste No. 4: Inventory
Any work-in-process that is in excess of what is required to produce for the customer - The evils of inventory were first recognized in manufacturing because that is where the inventory itself is most visible. It is hard to ignore a room full of half-completed assemblies – a very visible reminder of thousands of dollars the company could be putting to better use.
Inventory in service areas is just as big a problem, but more insidious because it is not as readily apparent. Look for physical piles of forms (in in-boxes, for example), a list of pending requests in a computerized email program, callers on hold, people standing in line at a branch, and the like. This excess inventory is often the result of overproduction. (See Waste No. 7) The goal, from a Lean standpoint, is to have on hand only what is needed immediately or in the short-term. (To find solutions to inventory problems, read up on Lean practices such as pull systems and triaging.)
Waste No. 5: Waiting
Any delay between when one process step/activity ends and the next step/activity begins - One of the biggest evils in today's marketplace is to make customers wait for delivery of a product or service – because chances are a competitor will be able to get it to them quicker. Anything in a process that makes a work item wait to be processed should be eliminated. Because so much of the work in a service process is invisible to the naked eye, process-mapping techniques (flow charting, value-stream mapping) are essential for identifying delays in a process.
Waste No. 6: Defects
Any aspect of the service that does not conform to customer needs - Producing work that customers are not going to pay for – or that makes them seek out other companies to do business with – is one of the more obvious forms of waste. Six Sigma practices have long been structured around minimizing the possibility of producing defects. In services, that translates to preventing the possibility of missing information, thus improving the chance of making deadlines.
One clue to studying defects is to recognize that their impact is usually felt far downstream from where they occurred. A customer service staff, for example, is likely to receive the complaint calls from customers upset about something that happened in an entirely different part of the process. The defect has to be traced back to where it happened – where the incorrect information was put into the computer system, for example – in order to find a solution that will last.
Waste No. 7: Overproduction
Production of service outputs or products beyond what is needed for immediate use - In one of Lockheed Martin's procurement centers, buyers purchased items for 14 or more different facilities. The way the computer system was initially set up, it was incredibly cumbersome for the buyers to switch from one facility to another. So they naturally processed all the requests from one center before moving on to the next, even if there were urgent or priority requests in queue from other facilities. As a result, non-priority requests from one center would be processed before priority requests from another facility. This batch processing and delivering a service before it is needed by the customer is a type of overproduction common in services. The solution to overproduction is to examine the process and see why the staff does not work in a way that reflects actual customer needs, then make changes accordingly. (At Lockheed Martin, the solution was to change the computer system so buyers could see priority requests from all facilities simultaneously.)
The better Six Sigma practitioners in financial services are at recognizing these forms of waste, the more effective improvement efforts will be.
About the Author: Bill Kastle is a vice president at George Group and has helped guide Lean Six Sigma initiatives at major corporations. He is co-author of the book What Is Lean Six Sigma? (McGraw-Hill, 2003). He has conducted executive training at Fortune 500 companies such as Alcan, Geico, Xerox, ITT Industries and DuPont-Merck. For more than 15 years, he has helped teams at all levels apply Lean and Six Sigma tools to respond to their customer needs. Mr. Kastle can be reached at bkastle@georgegroup.com.
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