Don’t let incorrect design assumptions affect supply chain performance

Author: Alexa Cheater

by Alexa Cheater

Don’t let incorrect design assumptions affect supply chain performance

Wouldn’t it be nice if your supply chain actually performed the way you designed it to? That doesn’t have to be a pipe dream. Supply chain perfection just got one step closer with the Self-Healing Supply Chain and its ability to improve supply chain design assumptions.

Supply chains are complex and full of inter-dependencies. A problem in one area can wreak widespread havoc on others. Suppliers who are always late or one market consuming all available supply for globally required parts can destroy your ability to meet customer demands.

But it doesn’t have to be that way. Machine learning is the new frontier of supply chain analysis, planning and design.

Achieve supply chain excellence with the Self-Healing Supply Chain

According to Josh Greenbaum, Principal at Enterprise Applications Consulting, the Self-Healing Supply Chain is bringing supply chain excellence that much closer to reality. He says in an ideal world, planners would have the ability to compare their supply chain as it was designed, to how it’s actually performing in the real world and make adjustments to select variables to improve overall performance.

“In this ideal supply chain world, monitoring a supply chain’s behavior and identifying when certain elements of the supply chain are both outside expected performance levels and are significant enough to have an overall impact on revenue, customer satisfaction, overall inventory and other business metrics can help planners make adjustments and reduce the risk,” says Greenbaum. They’d be able to identify the root cause of the discrepancy between the reality and expected performance “so that

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