BI's Promised LandPerformance management's value transcends that of business intelligence. Can Six Sigma techniques extend that value even further?
by Erik Thomsen Like Moses leading his tribe out of Egypt toward the land of milk and honey, business intelligence (BI) analysts, associations, and vendors over the last year have attempted — once again, after many tries — to lead the tribes of BI consumers toward their version of the Promised Land. Instead of the original milk and honey, BI consumers are promised the representation and management of processes, not just data. IDC, S.G. Cowen Securities, and Meta Group use the term business performance management (BPM) to describe the process of managing organizational performance, whereas Gartner Group uses the term corporate performance management (CPM) for the same thing. Not surprisingly, vendors have begun to adopt the same slogans. Cognos Inc., for example, falls into the CPM camp, while Hyperion Solutions Corp. has positioned itself as a BPM provider. However, because the underlying concepts, technology, and methods are relevant to any organization, I'll use the term organizational performance management (OPM). OPM's emphasis on process does legitimately differentiate it from traditional BI, which emphasizes data and the querying of data. I can't imagine any members of the BI tribe arguing against the need for managing organizational performance. And in the months and years to come, I expect the operational definition of OPM to mature along with the available technology and solutions. But is anybody else already delivering on these promises? Are there any other tribes in BI's Promised Land? In this article — the first of two installments — I'll describe how one tribe, which calls itself "Six Sigma," is at least close to this Promised Land (if not already in it), and why and how the essence of Six Sigma should be incorporated into OPM. A Tale Of Two TribesThe term Six Sigma is a buzzword meant to evoke images of BI's Promised Land. Specifically, "Six Sigma" is shorthand for an organizational process that's performing at such a high level of quality that six standard deviations of variance about the mean outcome of the process fall within customer-defined ranges of acceptability for the result of that process. This translates into only 3.4 unacceptable outputs, or defects, per million repetitions of the process — meaning that the process is operating at 99.9996 percent of perfection. (See Table 1) Business intelligence, as a destination or purpose term, replaced decision support, executive information systems, and management information systems (and is itself on its way to being replaced by BPM, CPM, or OPM). Similarly, the term Six Sigma, which was coined at Motorola in the 1980s, publicly adopted by such companies as Allied Signal and Cisco Systems, and made famous by Jack Welch at General Electric, has replaced — and to some degree competes with — other tribe-defining terms such as total quality management and continuous improvement. In the same way that the real substance behind BI comprises differently named things such as relational and multidimensional databases, query optimization, and visualization, the substance behind Six Sigma derives from statistical process control and methods for organizational improvement that trace back to the early parts of the 20th century. (Key contributors included F.W. Taylor, who spearheaded the move for organizational efficiency by attempting to scientifically measure and improve all aspects of a process; R.A. Fisher, the leading statistical thinker behind both statistical process control and the design of experiments; and W.E. Deming and J.M. Juran, who applied Fisher's techniques to industrial settings, most notably post-WWII Japan.) The difference between the BI tribe and the Six Sigma tribe is less about the destination than the origin. BI (and now OPM) began with finance and sales and marketing managers in financial and retail industries and is slowly moving to encompass at least the top third of employees across all organizational functions, including manufacturing, through ideas such as activity-based management. In contrast, Six Sigma began with statisticians and engineers in manufacturing firms and is slowly moving to encompass all the functions of an organization including senior management, finance, and sales and marketing — as well as moving beyond manufacturing firms to encompass a range of industries. According to Michael Edwards, senior statistician at Rexam (www.rexam.com), a multinational consumer packaging company with more than 20,000 employees, the company's initial successes using Six Sigma for the management of its manufacturing has led to the adoption of Six Sigma techniques for management, as well as the design of information systems. Edwards says that Six Sigma techniques are so helpful to organizational management that if it could do so, Rexam would start over by training its management team and then pushing the techniques out to the rest of the company. Dan Thorpe, director of statistical resources at W.L. Gore & Associates (www.gore.com), the makers of Gore-Tex fabrics among many other innovative products, says that his company uses Six Sigma techniques in forecasting and financial performance, where the emphasis is the same as in manufacturing: separating the noise from what's real in a process. Key Concepts And MethodsThere are four key concepts and methods behind Six Sigma, but I'll only discuss the first two here (saving the rest for the next installment):
Thinking In Terms Of ProcessesManufacturing products is a process; developing software or producing a news program is a process; selling shoes, cars, insurance, or software is a process; creating a brand is a process; managing people is a process; predicting sales, costs, and operating profit is a process; improving a customer's happiness or value function is a process; deciding whether to buy a company is a process. Anything that can be described using action, motion, or activity terms within a sequence of steps that occupies some space(s) can be thought of as a process. Thinking in terms of processes sets the stage for you to look at entire sets of interconnected events with an emphasis on the factors that explain, drive, or cause outcomes rather than just the outcomes themselves. Take sales, for example. Within a BI context, organizations typically measure and report on how many products are sold for any time period (and location, channel, brand, and so on). In contrast, within a Six Sigma context, an organization would think about sales as the outcome of a sales process. Even if the organization is initially only tracking measurable attributes of the outcome of the process (how well sales are doing, for example), if it wants to improve product sales (whether by increasing the number of products sold or by lowering the cost of sales), it can't (without cooking the books) directly manipulate the outcome or the fact that sales were below forecast. Rather, it can only act on aspects of the sales process — whether it be the way the product is marketed, the way sales calls are followed up, the way demos are given, or the way the company works with its channel partners. Within a process-centric framework, Six Sigma combines a statistically informed way of representing processes and a method for improving them in an iterative loop.
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