A recent “Manufacturing Innovation Blog” from NIST (National Institute of Standards and Technology) described a supply chain issue and the ensuing problems a food processor faced when it was forced to use an alternative supplier for an ingredient contained in a white chocolate coating. The “new” ingredient wasn’t quite the same and resulted in an off-spec white chocolate coating that was different and not as reliable. The effects of the change rippled throughout: from recipe changes, operational temperature, processing equipment parameters and quality measurement methods to storage, equipment cleaning, traceability compliance, documentation and training.
Could an advanced supply chain system with machine learning have helped mitigate this unplanned supply chain disruption? Could such a system foresee a potential shortage of a key ingredient caused by a weather disruption, political conflict or transportation issue and suggested stocking additional product?