Part II: Organizational Prerequisites for Smart Materials, Automatic Identification, and Quality
Chapter 12: A Sound Methodology for Enterprise Management: General Electric's Six Sigma
12.4 Using Six Sigma to Analyze Manufacturing Processes and Operations in the Back-Office
Significant benefits can be derived from implementing a rigorous statistical method, in locating defects, and in breaking a customer's requirements into manageable tasks; for example, setting optimum specifications for each part of the process and accounting for how the parts interact. This lays the ground for accurate responses to customer wishes through technical specifications that are studied analytically. There is a wealth of statistical methods and tools whose benefits companies forego because they do not have the necessary know-how, and their management is afraid to take bold steps.
A good example of Six Sigma in action comes from Camco, GE's Canadian appliance subsidiary.[7] The company had tried conventional approaches to solve a problem connected to lack of rigidity in some of
its cooktops, a problem that was leading to high scrap rates. Industrial engineers tried different
approaches during assembly, that seemed logical, but the problem persisted. Then, using experimental design, engineers tested ten possible causes in various combinations. These ten causes were analyzed in 14 versions of an experiment.
Subsequently, graphical presentation allowed them to pinpoint the source of variation. The pattern was detected by hanging the parts in the oven during the enamel-baking process and analyzing the ratio of enamel on the top and underside. By tightly controlling these processes, it was possible to solve some outstanding quality issues, reduce costs, and improve yields. This is a good example of how
experimental design gets people away from thinking that there is only one good method — or that 99 percent assurance is everything.
Many companies work on the false premise that their customers do not see the quality defects. They are utterly wrong on this account. Customers are not stupid. Industry leaders know that and do their utmost to increase customer satisfaction while keeping costs under control.
"Quality, as measured by our customers, could be the biggest differentiator we have going forward into the next century," according to Jeff Immelt, president and CEO of GE Medical Systems (GEMS).[8] The concept behind this statement is admirably exemplified in Exhibit 12.7, which visualizes how the concept of Six Sigma allows one to meet customer specification requirements in an able manner.
Exhibit 12.7: The Measure of Process Capability Is a Good Proxy of the Probability of Defect (By permission of General Electric.)
The smaller the standard deviation of products from a production process, the higher the quality being assured.
The challenge is to fit six or more standard deviations between mean value (or target) and customer specifications.
In the upper part of Exhibit 12.7, high quality is exemplified by 6 σ. The lower part depicts the usual case of 3 σ between target and customer specs. Much more than the company's reputation and its profit figures are at stake. The market is changing rapidly and it demands higher and higher product quality.
The more impersonal Internet commerce becomes, the more the brand and reputation of a company will depend on the quality of its product. Ironically, this impersonal characteristic will increase with the customization of products in I-commerce, and most companies have yet to face up to this challenge.
At the same time, quality cannot be effectively controlled with due care about the bottom line. By all indications, variable cost productivity in most firms is lagging price decline and this squeezes margins.
Vendors can no longer afford to be complacent. That is why this GE example is so valuable to the reader.
To turn the tide in high quality at low cost, one needs both a rigorous methodology and metrics — something one can measure and compute for any product, service, or process, at any time. One must also ensure that the problem does not come back again and again. This is what GEMS and the other GE divisions have done during the past five years.
The aftermath of the Six Sigma initiatives discussed above is impressive. Take GE Medical Systems' new Performix CT X-ray tube as an example. This is a process-intensive, high-technology industry with
$20 million per year of scrap and rework; hence, the possibility of important savings. The chosen solution led to 100 Six Sigma projects on improving plant yield, and 200 Six Sigma projects on increasing current tube life.
One of the goals has been 0 percent dead on arrival (DOA). Reaching the objective essentially means no patient rescheduling at the hospital end. Other goals have been guaranteed tube availability and an order of magnitude reduction in unquality cost. Each of these aims is food for thought to practically every industry.
The sequential steps in reaching such goals are dramatized by the torrent of normal distributions in Exhibit 12.8 (from a practical Six Sigma implementation, with the permission of GE). It is not possible to go from 6.6 percent defects to 0.0 percent defects overnight. Such improvement is doable over a period of time, after:
Identifying, qualifying, and quantifying all the factors that are critical to quality (CTQ)
Co-involving the customers in pinpointing those aspects of product or service deemed critical from a quality perspective
Establishing a process that permits steady quality improvements until the goal of no defects is reached
Exhibit 12.8: Three Standard Deviations Usually Fit Between Quality Control Target and Customer Specifications, but this Is Not Enough
These three basic steps to Six Sigma deliverables are valid for the factory floor, the sales outlets, and the back-office. One of the back-office applications of Six Sigma concerns invoice processing time. At GE's Ft. Myers, Florida operation, a Six Sigma project concentrated on time spent on handling invoices.
Its aim has been to collect meaningful data and then use appropriate statistical measures to analyze the process. Input data came from evaluating time required to process an invoice for ten specific invoices (sample size = 10). The statistics are shown in Exhibit 12.9. The mean of this distribution is x = 22.6; the variance is s2 = 35.8 and standard deviation is s = 6. The crucial element in connection with this
distribution is customer specifications relative to the mean and the effect of s on specifications.
(in days) 23.4 16.9 25.0 22.2 28.6 18.5 24.1 17.6 14.9 34.6 x = 22.6 s = 6.0
Note: These statistics are used as proxy of the µ and σ parameters (see Equation 12.3).
Exhibit 12.9: Statistics in Processing Ten Invoices
The ten values in Exhibit 12.9 are a sample. Samples taken over successive days suggest that the mean itself has a distribution, whose expected value indicates the long-term capability of the process to meet objectives. This long-term capability also has a variance. As long as the short-term capability varies in comparison to the long term, the invoice handling process has shift and drift. Typically, the short-term variation fits within the long-term distribution (as seen in Exhibit 12.6).
While the potential performance of a process over time is defined by long-term capability, actual
performance is indicated by short-term statistics that represent ongoing results. The difference between long term and short term, also known as a sigma process, serves in exercising management control. A process is considered to be in control if it fits within longer term boundaries or tolerance limits.
These principles apply hand-in-glove to the implementation of enterprise resource planning and customer relationship management. Both ERP and CRM are processes that must be introduced and administered to obtain commendable results. They are both office processes and GE's Ft. Myers example is a good lesson on what is necessary to get the most out of these applications. Any office process obeys the inverse relationship rule: as the variance decreases, process capability increases.
This, in turn, swamps the probability of defect.
The probability of defect is given by defects per opportunity (DPO) (see Chapter 12.2). Using Six Sigma methodology based on statistical rules, what is the likelihood that an invoice that enters the system 35 days before it is due will get paid in time? The answer is (see Equation (12.1) and Exhibit 12.9):
(12.3)
Looking up the z statistical tables, for x = 2.05s, the probability is 95.96; by extrapolation for x = 2.066, the probability is about 96.12 percent. That is, 96.12 percent will be on time and 3.88 percent will be late. The data used with this example is sample-based, but Six Sigma methodology uses x and s as proxy to µ and σ, the population parameters.
Statistics vary from one sample to another. With the GE methodology, z is computed through Equation (12.1) with long-term data. If one does not know the actual shift characterizing the sample, the
convention is to use 1.5 Sigma shift added to the long-term z to get the reported short-term Sigma of the process.
While it is difficult to find a theoretical justification for this convention, in practical terms its effect is neutral, particularly in cases where the results of one period are compared to those of another. As Piet C. van Abeelen has noted, "At GE, we have placed more emphasis on comparing the Sigma Level at the start of the project with the Sigma Level at the completion of the project."
[7]Report on Business Magazine, October 1997.
[8]From an internal GE Instruction Manual, with the permission of General Electric.