Is the recently stated supply glut at retailers such as Target and Walmart due to the Bullwhip Effect? What goods are experiencing less demand than ancticipated? What effect is this having on retailers' business? And why factors caused this seemingly sudden inventory glut in a period of supply shortages? Finally, what does this indicate for inflation and future demand?
There are a lot more demand forecasts being made today at companies then their used to be. Many of these forecasts depend upon having more data. Traditionally, demand management applications used order shipment history to make forecasts. But gradually new data sets were added that improved the forecasting accuracy. A demand management application can truly be a Big Data solution today.
Machine learning engines can take data on forecast accuracy and use that data to automatically improve the forecast model. However, three is a rub. Lost sales is a key piece of data on the accuracy of the forecast, but lost sales is generated with a demand forecast. This is circular reasoning. But that does not mean this analysis is without value.
Artificial intelligence (AI) is hot. Over $4 billion in venture capital has been invested in AI firms just in the US. But supply chain planning software companies, with their cadre of operations research Ph.Ds who have been modeling complex problems for decades, may be better poised to solve many complex business problems than the hot new Silicon Valley firms.
A water purification company in Brazil can have the demand for their water treatment and sewage services increase as much as four times on a sunny day on a weekend. To more accurately forecast demand, they count the number of vehicles driving into the community. Demand analysis also relies on a forecast of weather conditions and historical tourism data. They are also prototyping IoT, Big Data, machine learning, and predictive analytics for process optimization.
Robust benchmarking is difficult. It is now possible to do robust benchmarking of the demand management process. That is much harder to do than you might think.
