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Demand Sensing is a next generation forecasting method that leverages new mathematical techniques and near real-time information to create an accurate forecast of demand, based on the current realities of the supply chain. The typical performance of demand sensing systems reduces near-term forecast error by 30% or more compared to traditional time-series forecasting techniques. The jump in forecast accuracy helps companies manage the effects of market volatility and gain the benefits of a demand-driven supply chain, including more efficient operations, increased service levels, and a range of financial benefits including higher revenue, better profit margins, less inventory, better perfect order performance and a shorter cash-to-cash cycle time. Gartner, Inc. insight on demand sensing can be found in its report, "Supply Chain Strategy for Manufacturing Leaders: The Handbook for Becoming Demand Driven." 〔Jane Barrett, Michael Burkett, Hussain Mooraj, Gartner, July 15, 2010 “(Supply Chain Strategy for Manufacturing Leaders: The Handbook for Becoming Demand Driven. )”〕 The principles of demand sensing apply across industries and to any large company in the supply chain, including manufacturers, retailers or suppliers. Well-known companies that have implemented demand sensing strategies and technologies include Procter & Gamble, Unilever, Kraft Foods, Kimberly-Clark and General Mills. Traditionally, forecasting accuracy was based on time series techniques which create a forecast based on prior sales history and draws on several years of data to provide insights into predictable seasonal patterns. However, past sales are frequently a poor predictor of future sales. Demand sensing is fundamentally different in that it uses a much broader range of demand signals (including current data from the supply chain) and different mathematics to create a more accurate forecast that responds to real-world events such as market shifts, weather changes, natural disasters, consumer buying behavior etc. Companies with large global supply chains tend to benefit most from demand sensing. As such, demand sensing systems must scale to process masses of data associated with hundreds of thousands of items and location combinations every day. The sheer volume, frequency and small processing window require automation and the application of mathematics in a structured way to ensure that results published daily to Supply Chain Planning systems to build, distribute and order products or components are accurate and consistent. == Market Forces Behind Demand Sensing == Demand sensing emerged from the need to improve demand forecast accuracy in supply chain planning, decrease inventory costs and increase profits. As part of a drive to increase flexibility and responsiveness from suppliers, retailers (and, in turn, manufacturers) have reduced product order times and lowered their inventories, shifting the inventory burden upstream to suppliers. In parallel, manufacturers, retailers and suppliers are all are under pressure from investors to free up working capital by reducing inventory levels. These events, along with changing consumer behavior and rising market volatility, have underscored the opportunity to sense and react in near real-time to changes in the supply chain and exposed the limitations of traditional forecasting techniques. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Demand sensing」の詳細全文を読む スポンサード リンク
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