Using this information, supply chain leaders can address supply chain challenges, reduce costs and at the same time improve service levels. Predictive analytics techniques allow organizations to identify patterns and trends hidden in their data to understand market trends, identify demand and establish appropriate pricing strategies.
To illustrate the point, a recent paper from Ventana Research on supply chain planning revealed issues that companies using spreadsheet planning have experienced, including:
According to Supply Chain Digital Magazine, many manufacturers are falling behind with more than two-thirds of them being unable to provide data-driven analytical information to support their business decisions.
Myth #1: Companies believe they already have/use prescriptive analytics. Just mention advanced analytics or its subsets like machine learning, AI, simulation, etc., and you’ll find many people use these terms interchangeably. Advanced analytics is an umbrella term for the following:
Predictive and prescriptive analytics are two forward-looking tools used by business leaders which overcome these limitations. Using a combination of historical data (descriptive analytics), rules and a knowledge of the business, they more accurately predict the future, and, in the case of prescriptive analytics, guide leaders to the best overall
Let’s use a very simple example. You take your car to the shop and they determine that two of your tires have a tire tread below 2/32″. That’s the legal limit for tires. Past that point, it’s been determined that the risk of accidents, blowouts, hydroplaning, etc. are too high.
