Demand Sensing

Description

From the Wikipedia:

"Demand sensing is a forecasting method that uses artificial intelligence and real-time data capture to create a forecast of demand based on the current realities of the supply chain. 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."

(https://en.wikipedia.org/wiki/Demand_sensing)

Discussion

Bob Haugen:

"With the rush of so-called “big data” - notably the escalation of real-time data - now available for demand forecasting, new toolsets are required to drive advanced inventory planning. Such tools are now available: They synthesize massive amounts of data - much of it real-time - such as multiple customer point-of-sale (POS) data streams, variables related to weather conditions, economic indicators, sales of competing products, social media hype, and a host of additional indicators. The Journal of Business Forecasting notes that demand sensing sorts out the flood of data in a structured way to recognize complex patterns and to separate actionable demand signals from a sea of “noise.”

Demand sensing technology has already been adopted by companies that are recognized as having the most progressively managed supply chains. Indeed, investments in demand sensing solutions are growing more rapidly than supply chain spending in general.

According to a recent IDC Marketscape assessment of sensing and planning vendors published in September 2013, demand sensing initiatives accounted for 8.5 percent of supply chain spending in 2013, and are expected to reach 8.7 percent in 2015 [1]."

([2])

More Information

• Need Sensing

References from the Wikipedia article:

• 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.