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    What is Demand Planning in BI

    This blog post explains what about demand planning in business intelligence

    When it comes to forecasting demand, individual stakeholders in business have conflicting interests. The retailers lose most from loss of sales opportunities and they would rather overestimate demand than face disappointed consumers. They have a vested interest in placing larger orders, especially when sales growth is brisk and vendors have limited capacity, than their needs warrant. The manufacturers, on the other hand, prefer to bunch orders so that per unit costs of transportation and administration remains low. Sales personnel make deals with customers to book orders, later returned, to ensure that they earn their annual bonuses. Academics call this the “bull whip” effect or swings in demand triggered by pressure rather than actual needs.

    Business intelligence systems, specifically Collaborative Planning, Forecasting and Replenishment (CPFR), enable each party to share information about the entire business activity and estimate the demand more accurately. Wal-Mart took one of the first initiatives in this direction by involving Proctor and Gamble in such a project.

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    In the past, a lack of collaboration stymied the co-ordination of promotions and supply; vendors were unprepared for surges in demand caused by incentives. Nabisco increased visibility to variations in demand by letting Wegmans, a grocery, access to its information. Its Planters sales vaulted 40% while the fill rate for its warehouse increased from 93% to 97%, and inventory dropped by 18%. Cisco used to similar system to share information with the flexible manufactures who could then directly supply goods to customers eliminating the need for its own distribution system.

    Demand forecasting failures are the bargain hunters dream; scarce space compels retailers to dispose inventory at a loss. Manufacturers’ ability to estimate market size is confounded by a growing list of unknowns; competition and price changes, creative destruction by innovative new products, productivity growth besides the familiar factors like seasonal changes, quirky weather and misjudgment.

    Business intelligence and forecasting tools has brought about a paradigm shift in the management of demand. Analysts find in the data early indicators of the demand for their products; game producers, for example, have learned to anticipate the demand from the early reservations of their products.

    Forecasting tools, to be sure, are imperfect or make predictions with unacceptable margins of error. The availability of real time data allows analysts to continually modify their models and reduce the margin of error by taking into account the impact of events as they happen.

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    Sara Lee’s household division needed to considerably improve its forecasting accuracies as it increasingly it sought to introduce its European brands into the USA. The lead time for importing goods from Europe is 12 to 14 weeks; the company had to increase its safety stocks to ensure that it had enough to meet unexpected changes in demand. Its software tool was able to generate forecasts at the brand and SKU level; the detailed forecasts elicited interest from sales staff and their commitment to feed data to the system. The accuracy of the forecasts increased from 51 percent in 1998 to 73 percent in 2002. The order fill rate is as high as 93 percent considerably lowering the need to hold inventories.

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