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SCDigest Expert Insight: Supply Chain by Design

About the Author

Dr. Michael Watson, one of the industry’s foremost experts on supply chain network design and advanced analytics, is a columnist and subject matter expert (SME) for Supply Chain Digest.

Dr. Watson, of Northwestern University, was the lead author of the just released book Supply Chain Network Design, co-authored with Sara Lewis, Peter Cacioppi, and Jay Jayaraman, all of IBM. (See Supply Chain Network Design – the Book.)

Prior to his current role at Northwestern, Watson was a key manager in IBM's network optimization group. In addition to his roles at IBM and now at Northwestern, Watson is director of The Optimization and Analytics Group.

By Dr. Michael Watson

May 27, 2014



Step Up Your Preventative Maintenance with Predictive Analytics

A Wealth of New Data and New Ways to Analyze the Data May Be able To Provide Much Better Preventative Maintenance


Dr. Watson Says:

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...With all this new technology, it may be time to step up your preventative maintenance program ...
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Preventative maintenance programs have been around a long time for good reason—they help drive down costs and prevent costly unexpected failures.

But, now, you have more like sensors on your equipment and in your facilities, you have better ways to store the data (like new open source Big Data storage systems), and you have better ways to analyze the data (new statistical methods and new machine learning algorithms).




Previous Columns by Dr. Watson

Supply Chain by Design: Nike's Phil Knight on the Importance of Supply Chain

Supply Chain by Design: Four Lessons from Hau Lee's Green Car Story Updated for the Era of Machine Learning

Supply Chain by Design: Profitability of Your Assets Depends on how you use Them

Supply Chain by Design: The Most Overlooked and Underestimated Data in Supply Chain Design

Supply Chain by Design: Self Driving Trucks May Create New Roles and New Types of Jobs

More


With all this new technology, it may be time to step up your preventative maintenance program.  Especially, if you have expensive failures or if you are not getting as much out of your equipment as you think you should.


At a high level, predictive analytics helps you uncover previously hidden patterns in the data that indicate that a machine is about to fail.  It can find correlations that can help you make predictions.  For example, it could predict based on the vibration sensor that something is not aligned and that this pattern leads to a failure within 20 hours. 

Besides predicting failures, predictive analytics can also help you determine how to get the most out of your equipment.  For trucks, predictive analytics could predict how you can minimize fuel consumption by better driving patters.  For your manufacturing equipment, it could predict how to adjust the setting to get the best quality products.

Final Thoughts:

If you are collecting data on your equipment—your trucks, your warehouse picking equipment, your manufacturing equipment, you should think about using new analytics technology to see if you can reduce failures, improve equipment performance, or improve quality.  



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