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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

December 15, 2015

Machine Learning and High Quality Potato Chips

Machine Learning Algorithms are Popping up in all Kinds of Places, Including a Cool Application in the Making of Potato Chips

Dr. Watson Says:

...One of the interesting stories he told was how they did quality control on their potato chips...
What Do You Say?

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I always like learning about new applications of machine learning algorithms to improve operations.  And, I especially like ones that are easy to explain. 

I ran across one such application in a podcast interview of Frito-Lay’s Brendan O’Donohoe where he was discussing the potato chip supply chain.  One of the interesting stories he told was how they did quality control on their potato chips. 

After the chips are produced, they are sent down a 60 mph conveyor belt to be packaged in bags for consumers.  This conveyor belt is the last chance to make sure that all the chips live up the Frito-Lay’s quality and size standards. 

Soon after falling on the belt, a high speed camera takes a picture of each chip.  Other technology then tracks that exact chip all the way down the line.

Later on the conveyer belt, there is a gap in the line.  The gap is large enough for a chip to fall through.    But, because the chips are moving at 60 mph, the laws of physics ensure that the chips safely fly over the gap.

Previous Columns by Dr. Watson

The Three Use Cases for Data Scientists

Learn Python, PuLP, Jupyter Notebooks, and Network Design

EOQ Model and the Hidden Costs of Fixed Costs

CSCMP Edge - Nike Quote: "It is All an Art Project Until you Get it on Someone's Feet"

Supply Chain by Design: Why Business Leaders should think of AI as an Umbrella Term


Now, here is the cool part.  Just above the gap is a series of air nozzles.  When a bad chip is just over the gap, the air nozzle fires a puff of air to send that chip down the gap into the waste bin. 

Although O’Donohoe didn’t discuss how the chip was identified as bad, we can guess that it was from a machine learning algorithm. 

Once the picture was taken of the chip, the image was likely turned into a big grid’s worth of data.  Each square in the grid would have information on whether there was chip in that part of the grid (to help tell how big the chip was) and the color of the chip (to look for quality) or some other characteristics.  Then, the image was compared to other chips of known quality or into a formula for quality.  The algorithm would then determine whether the chip met Frito-Lay’s quality standards or was destined to get a puff of air just when it was over the gap.

All of this has to happen in the time from when the picture was taken to when the chip reaches the gap.  And, it has to be done more presumably thousands of chips at a time.

Final Thoughts

Machine learning algorithms will continue to find industrial applications that help make products better or keep machines and equipment running.

Let me know your thoughts at the Feedback section below.

Recent Feedback

Mike - great post as always!  I agree - I am sure a ML algorithm of some type is involved.  This reminds me of a vision system that a liquor distributor installed - it took photos of cases of liquor travelling down a conveyor and compared them to a database that contained pictures of all of their products.  When it found a mismatch it diverted the case to QC where a supervisor would have to update the database with the new packaging photo or perform corrective action on the picker that mis-picked.  They used a slightly different type of vision system for open case (single bottle) picks by taking a picture from the top of the open box and counted 'rings'.  I was told that their cost of mis-picks/theft/etc. dropped from close to mid-single digits to almost 0.  (Surprisingly their inventory shrinkage was always on the high cost product and they had extra inventory on the rotgut).  They were dealing with rather expensive cases of liquor so they had a short payback on the project.  It's interesting to see that this type of quality control is being applied to something as low cost as a potato chip (and at such a velocity as 60mph!)

Did they mention what they did with the chips that were rejected?  This process may be why we're seeing potato chips in products like Ben & Jerry's "Late Night Snack".  

Bill Seliger
Director of Supply Chain & Project Management
Not Provided
Dec, 16 2015