How to Improve Decision Making in Supply Chains
David Simchi-Levi, MIT Professor, INFORMS Fellow and Former Editor-in-Chief of Operations Research (INFORMS Journal)
The ability to understand a combination of historical behavior, market conditions and future needs drives decision making. New analytic capabilities that combine machine learning and optimization can take into account historical characteristics and competitor behavior to determine future demand that will allow optimization for the best results - such as profit, market share or revenue.
Examples of decisions where this approach can be used is assortment, pricing, sourcing strategies for new products, predictive maintenance using process sensors.