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Demand modeling
Demand modeling uses statistical methods and business intelligence inputs to generate accurate demand forecasts and effectively address demand variability. Demand modeling is becoming more important because forecasting and inventory management are being complicated by the increasing number of slow moving items, the so–called “long-tail” of the product range, many of which have unpredictable demand patterns in which the typical “normal distribution” assumption used by traditional models is totally inadequate.〔Lora Cecere, “Of Long Tails and Supply Chains,” ''Supply Chain Brain'', January 9, 2008.〕 In these scenarios, successfully managing forecasts and inventories requires advanced demand and inventory modeling technologies in order to reliably support high service levels.〔Kevin Perment, “(Trade Promotion Management: An Excess of Excel! ),” ''AberdeenGroup blog'', August 21, 2012.〕
== Challenges of demand modeling ==
Product proliferation has resulted in an increasing number of products targeting niche markets with difficult-to-predict demand. These “long tail” products are becoming an increasingly important fraction of the product range, so companies are forced to deliver higher service levels even for these products to achieve overall service levels in the 98% to 99% range.〔Lora Cecere, “Of Long Tails and Supply Chains,” ''Supply Chain Brain'', January 9, 2008.〕
Traditional demand modeling methods suggest safety stock levels which fail to achieve these service levels. These methods instead lead to manual corrections which often overcompensate, leading to uncontrolled overstocks.〔Lora Cecere, “Of Long Tails and Supply Chains,” ''Supply Chain Brain'', January 9, 2008.〕
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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