Demand sensing
Demand sensing is a demand forecasting method that uses data mining and real-time data capture to create a forecast of demand based on the current realities of the supply chain.[1][2]
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. Demand sensing uses a broader range of demand signals, (including current data from the supply chain) and different mathematics to create a forecast that responds to real-world events such as market shifts, weather changes, natural disasters and changes in consumer buying behavior.
References
[edit]- ↑ Byrne, Robert F. (Summer 2012). "Beyond Traditional Time-Series: Using Demand Sensing to Improve Forecasts in Volatile Times". Journal of Business Forecasting. 31 (2): 13–19.
- ↑ Folinas, Dimitris; Rabi, Samuel (2012-12-01). "Estimating benefits of Demand Sensing for consumer goods organisations". Journal of Database Marketing & Customer Strategy Management. 19 (4): 245–261. doi:10.1057/dbm.2012.22. ISSN 1741-2447.
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