Energy Supply Chain Optimization and Prescriptive Analytics

This is a re-blog of a post by Chris McManaman, Director of Commodity Trading and Risk Management at CGI, that discusses how optimization models (prescriptive analytics) are successfully being applied in the Energy supply chain.

The ever-increasing complexity of the global marketplace is making optimization even more important to organizations. With easier access to such powerful tools, more companies are turning to optimization models to help make crucial decisions including tasks such as hedging strategies, storage management, allocating inventory, selecting transportation routes, product optionality, blending and optimizing portfolios.

All the functions in a company are usually aimed at a single goal – to make more money by selling more products at a higher price than what the products cost. However, this goal is often forgotten because the way they operate is so myopic and siloed.

Each portion of the business has its own metrics and, all too often, narrowly pursues them to the detriment of the company. Supply chain strives for increasing demand forecast accuracy or reducing lead-times. Storage and processing aims to maximize its capacity utilization. Procurement wants to reduce the cost of raw materials.

Unfortunately, much optimization is done in excel and focused on solving a simple set of decision variables within a single functional silo. None of the models are integrated despite the fact that these critical planning decisions are most certainly inter-related in the real-world. After enough practice it is easy enough for an Excel expert to get one optimization model working but maintaining the model over time with all the changing variables becomes too costly. Any high level optimization requires a compilation of output from various spreadsheets and applications across the enterprise....

Read the full article here.

Below, you'll find a video by McMananaman that discusses the same topic in detail.  

 

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