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techniques which create a forecast based on prior sales history and draws on several years of data to provide insights into predictable seasonal patterns. Demand sensing uses a broader range of demand signals, (including current data from the supply chain) and different mathematics to create a
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forecast that responds to real-world events such as market shifts, weather changes, natural disasters and changes in consumer buying behavior.
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48:
Byrne, Robert F. (Summer 2012). "Beyond
Traditional Time-Series: Using Demand Sensing to Improve Forecasts in Volatile Times".
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and real-time data capture to create a forecast of demand based on the current realities of the
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69:"Estimating benefits of Demand Sensing for consumer goods organisations"
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Journal of
Database Marketing & Customer Strategy Management
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67:Folinas, Dimitris; Rabi, Samuel (2012-12-01).
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19:is a forecasting method that uses
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50:Journal of Business Forecasting
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21:artificial intelligence
119:Supply chain analytics
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56:(2): 13–19.
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