Leveraging Predictive Analytics in the Supply Chain: Pairing Best of Breed and ERP

Making timely decisions based on relevant, available data is crucial to business success, yet a high number of companies still rely on historical information to steer their decision-making. Others are tapping into the value of predictive analytics - the use of statistics, modeling, machine learning, and data mining to analyze current and historical facts and make future predictions - to manage various aspects of their supply chains and logistics operations.


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About Bridget McCrea

Bridget McCrea covers business and technology topics for various publications. She can be reached at bridgetmc@earthlink.net.

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Next level predictive analytics

Interesting article. I agree with Ann that ERP systems “don’t help companies when it comes to new areas of inquiry.” That’s why most people turn to Excel to do any sort of analysis or decision support. However, as great a tool as Excel is, it is very difficult to model a global supply chain including all the data and analytics associated with the typical global network. This is where “best of breed” can add the technology and “best practice” enhancements to ERP.

In the referenced article, the author quotes, “Rarely in business does one come across a decision that doesn’t require some gut instinct and practical wisdom.” That is almost always the case. Taking predictive analytics to the next level would be the ability to simulate your decisions and understand with confidence how your response to what the data is telling you will impact the future. Imagine the ability to model multiple decisions and response options to see what course of action is best for the business. In some cases what is good for an individual department, site or business unit might be at the expense of a company’s overall goals. That combination of predictive analytics and simulation would give you the ability to model your “gut instinct and practical wisdom.”