Victoria Ma, head of digital innovation at Miebach, discusses "self-healing" supply chains and other major trends in the industry.
Giving credit to a client for the coinage, Ma defines a self-healing supply chain as one that can detect signals predicting an event, and autonomously act to prevent the occurrence.
An example on the inbound side might involve concern over, say, a late ocean shipment. “We can leverage a visibility solution as well as advanced analytics to predict if my PO is going to be delayed even when it's at the supplier stage,” May says. “Or is my lead time still accurate?” She says a “threshold” can be set based on the importance of the goods, the level of delay and whether to place another order to protect strategically important stock.
“Do we want to expedite the existing order if it is still with the supplier, [or[ do we want to change suppliers if this is a problem that we cannot work through with them?” asks Ma. “These are decisions that take time, but we can build with technology some autonomous decision-making that accelerates the process.”
The outbound side is much the same. A backlog in the warehouse, coupled with a stringent cut-off time requirement, might mean a customer’s order will be delayed in getting out the door. Autonomous decision-making capability could set a different priority of the order, Ma says. “So we bump up the order importance in our day-to-day operation in our warehouses.”
Both are examples of self-healing supply chains that detect signals, predict what is to going to happen, then autonomously act to prevent it. The key, says Ma, is to focus on deploying data and developing advanced analytics in artificial intelligence, to support supply chain decision-making and process automation for companies.
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