The point of consumption (POC) is like the head of a mountain spring. It is the purest and most reliable point in the demand stream. The retail point-of-sale (POS), scanning products into a hospital room off a delivery cart at the hospital bedside, the order commitment in internet shopping, and the placement of a repair part on a broken machine are all examples of points of consumption. In some cases it is possible to anticipate the consumption. For example, probability of failure data helps us anticipate the consumption of a service part in a broken machine. Ideally, as much of this information is used as possible in data mining for customer and sales activity; and for demand forecasting.
Data freshness is a term we coined to assess how close to the point of consumption demand data really is. A healthy assessment for the quality of data is to monitor just how close to the point of consumption your data really is?
Scanning at retail, the “point of sale” is an example of a supply chain point of consumption.
Self checkout is an increasing popular “point of sale” in retail supply chains.
Point of consumption data is the basis for demand forecasting.