BI's Promised LandPerformance management's value transcends that of business intelligence. Can Six Sigma techniques extend that value even further?
by Erik Thomsen Continued from Page 1 Framework For Representing ProcessesThere are two key steps to the representation of processes: representing the boundaries of acceptable and unacceptable process quality, and representing the inherent variability in the quality of those same processes. Step one: Define what's acceptable. Where's the line between acceptable and unacceptable? And, is there one line or two? Some (unidimensional) process measurements, such as baking temperature for a ceramic process or driving speed in the curves for a race car at the Indy 500, have two boundaries: an upper and a lower. Other processes, such as customer service, may have just one. Some acceptability boundaries, as is frequent in manufacturing, may be inherent in the process; others may be drawn by the customers (which is common among services-based businesses): How long is a customer willing to be kept on hold? How often is a customer willing to find a bug in your software, or your pizza? Figure 1 shows the output of a sales forecasting process in the form of a graph where the upper and lower quality boundaries are defined to be where Plan is more than 10 percent above or below Actual. Because these forecasts are for consumption by the organization (as opposed to the financial markets), "beating the estimates" by a big margin isn't a good thing. That's because the estimates are used for capital budgeting, human resource planning, and other planning purposes, and it can be just as damaging to the organization to underplan as to overplan. Step two: Measure how you're doing. Figure 2 represents a filling-in of the graph in Figure 1 where each point on the graph represents a monthly forecast. It's easy to see for each forecast point (and in hindsight) whether it was an acceptable or unacceptable forecast. Now, imagine you're the new CFO and you just asked one of your analysts how recent sales forecasts compared with their respective actuals. And let's say your analyst told you the last two plans averaged about 15 percent above actuals. What could you do with that information aside from grumble? The answer is: not too much. Here's where Six Sigma is a great approach. To be able to do something with the knowledge that your most recent, and reconciled, forecasts were averaging about 15 percent over actuals, you need to know whether it was a random fluke caused by chance or whether it's a sign of a systematic problem. And to know that, you have to calculate the inherent variability in the sales forecasting process, which can be done either by measuring every sales forecasting instance (called the population), or by measuring a sample drawn from the population. For example, if the inherent variability in your sales forecasting process were very small (see Figure 3), the chance that two forecasts in a row differ from actuals by an average of 15 percent would be about one in 5,000! Thanks to your knowledge of the historical variability in your forecasting process, you can be pretty sure that some extra-systemic thing is affecting your otherwise high-quality sales forecasting process. Searching for whatever is affecting your sales forecasting process would be a rational response. In contrast, if the inherent variability in your sales forecasting process were large (see Figure 4), the likelihood that two forecasts in a row differ from actuals by an average of 15 percent would be about one in two! In other words, your forecasting process is so poor that a series of two poor forecasts in a row had a 50 percent chance of occurring naturally. Here, a rational response would be to look for ways to reduce the variability in your sales forecasting process. Most Six Sigma implementations provide a number of basic process scorecarding techniques that work within an event-based triggering system to alert the user to combinations of events (single events or series of events), that have an unusually low risk of occurring purely by chance. For example, the occurrence of four out of five consecutive observations occurring two or more standard deviations above the mean, or the occurrence of six of more consecutive observations either all increasing or decreasing, is an indicator that something systematic is happening and needs to be investigated. This stuff is useful for any OPM system! Regardless of whether the inherent variability in your organizational processes are large or small, knowing that inherent variability, which can only result from an ongoing process of measurement, is the key to understanding the meaning of real-time events and the appropriate decisions that need to be taken as a result. As John Wilkes (in his oft-quoted presidential address to the American Statistical Association in 1951) incorrectly attributed to H.G. Wells: "Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write." OPM's process-centric approach is a great step forward for the BI community. But if OPM is going to lead BI consumers all the way to the Promised Land, it needs to incorporate the statistically grounded process representation techniques of its sister tribe, Six Sigma. Erik Thomsen [ethomsen@dsslab.com] is a researcher and consultant for DSS Lab Inc. and focuses on integrated multitechnology analytic solutions. He is the author of OLAP Solutions, Second Edition (John Wiley & Sons, 2002) and coauthor of Microsoft OLAP Solutions (John Wiley & Sons, 1999). RESOURCESCochran, William G., and G.M. Cox. Experimental Designs: Second Edition. John Wiley & Sons, 1957. Johnson, Richard A. Miller & Freund's Probability and Statistics for Engineers: Sixth Edition. Prentice-Hall, 2000. Pande, Peter S., R.P. Neuman, and R.R. Cavanagh. The Six Sigma Way: How GE, Motorola, and Other Top Companies are Honing Their Performance. McGraw-Hill, 2000. Taylor, Frederick W. The Principles of Scientific Management. W.W. Norton & Co., 1967. Geishecker, L., and N. Rayner. "Corporate Performance Management: BI Collides with ERP." Gartner Research, Dec. 17, 2001. Blumstein, Robert, and H. Morris. "Analytic Applications for Business Performance Management: Worldwide Financial/Business Performance Management — Software Forecast and Analysis, 2002-2006." IDC, June 2002. S.G. Cowen Securities Corp. Business Performance Management, January 2002. ASQ: www.asq.org Juran Institute: www.juran.com Six Sigma Academy: www.6-sigma.com W. Edwards Deming Institute: www.deming.org
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