Skip to main content

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

by Bridget McCrea
Contributing Writer,
April 04 2013

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.

Predictive analytics have already proven their worth on several fronts. In Revising an Outdated Business Model? Try Predictive Analytics, for example, the author discusses how sensors were monitoring a Swiss airliner's engines during a recent trans-Pacific flight. Those sensors were sending back data to the offices of an analytics provider that, at one point when the airliner was over the ocean, realized that there was an engine problem. Thanks to predictive analytics, the pilot was able to take fast action and landed the aircraft safely and without incident.

Not all instances of predictive analytics in the supply chain are as dramatic as this in-air example, but their usage is definitely growing across many industries. Ann Grackin, CEO at ChainLink Research, says that more companies are catching onto the value of predictive analytics in the supply chain. Armed with reams of historical data, ERP users are well positioned to begin using the information gathered to make better, more informed supply chain decisions in the future.

Unfortunately, the historical supply chain data generated by systems like Microsoft Dynamics AX don't tell companies what's impending or looming on the horizon. "They also don't help companies when it comes to new areas of inquiry," Grackin points out, "...

FREE Membership Required to View Full Content:

Joining gives you free, unlimited access to news, analysis, white papers, case studies, product brochures, and more. You can also receive periodic email newsletters with the latest relevant articles and content updates.
Learn more about us here

About Bridget McCrea

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

More about Bridget McCrea
Submitted by bdubois on Thu, 04/11/2013 - 13:38 Permalink

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.”

In reply to by anonymous_stub (not verified)