Sales Forecasting as a Knowledge Management Process

Article excerpt

Business intelligence is data plus information plus knowledge ... a knowledge based management process is not complete until data, information and knowledge are exchanged among business participants... the choice of intelligence form and exchange strategy has unique implications for sales forecasting managers.

While knowledge management is a growing buzzword in business, very little attention has been paid to knowledge management within the context of sales forecasting. Such attention is warranted given that sales forecasting is predicated on "company knowledge" or what we prefer to call "company intelligence," as will be discussed later. In fact, sales forecasting - from both a theoretical and practical perspective - is inherently a company intelligence management process.

By failing to equate sales forecasting to a process of intelligence management, companies inadvertently fail to capture key forecasting intelligence. Personal experience, "learning" from month-to-- month business activities, sales leads, and customer-specific history (among other items), which are all key elements that could facilitate the sales forecasting effort, are often not tracked nor collected in many companies. Indeed, it is conceivable that companies' difficulties in forecasting may not result from poor techniques per se, but rather from spoor intelligence management infrastructure that would otherwise support the forecasting function.

To highlight this issue, this article discusses how companies might envision sales forecasting as a knowledge management process. We specifically present knowledge management from the perspective of managing intelligence exchange networks to support the sales forecasting function. Such networks can carry various forms of intelligence by way of two predominant forms of intelligence exchange strategies. The choice of intelligence form and exchange strategy has unique implications for sales forecasting managers.


'Intelligence' is comprised of data, information, and knowledge. Each of these intelligence forms is unique, and has particular implications for the sales forecasting process:

Data: They are a collection of facts. Raw numbers corresponding to sales, invoices, returns, etc. represent data. Even if downloaded from the company computer system into an Excel spreadsheet, unanalyzed numbers are still just data.

Information: When data are organized, summarized, and analyzed, they become information. For example, trend analysis of sales data provides information about a company's performance.

Knowledge: When information is combined with experience, context, and reflections, it provides implications, and presents strategies and tactics on which to base decisions. In the forecasting context, knowledge is created when the forecasting analyst takes the results of trend analysis, draws inferences and implications, and develops an action plan, based on intuition and experience from similar trend statistics with other product lines. In other words, trend analysis (information) is enhanced when experience and intuition are added to it.

Recognizing the distinction between data, information, and knowledge does not mean that a company has a knowledge management process in place, however. Simply generating intelligence does not complete the process. To complete it, it is necessary to exchange the data, information, and/or knowledge between individuals and departments.


Two strategies for exchanging intelligence (i.e., data, information, and/or knowledge) are codification and personalization. A codification strategy is a document-centered strategy, which means that data, information, and/or knowledge are put into a written form by way of memorandums, reports, faxes, and/or forms. A personalization strategy is a communication-based strategy, where data, information, and/or knowledge are exchanged verbally via telephone calls and/ or face-to-face meetings. …


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