David Simchi-Levi, professor of engineering systems at MIT, brings us up to date on the results of applying a supply-chain “stress test” to companies in multiple industries.
The stress test, a concept borrowed from the banking industry, involves a simulation created by way of a digital twin, which mirrors a physical supply chain. It focuses on two performance measures: time to survive (how long a company can continue to meet demand when a particular production facility fails) and time to recover (how long it takes that facility to get back to full operation). The exercise measures the impact of lost production on revenue and profit, and also identifies actions for improving supply-chain resiliency.
Application of the stress test to multiple companies reveals that risk can be found in unexpected places, such as an overlooked small supplier whose failure can bring an entire production line to a halt. It also has demonstrated that risk isn’t necessarily associated with a specific geography, meaning that companies won’t eliminate risk merely by consolidating manufacturing within a “safe” region. Finally, the test can help to identify cost-saving opportunities, often relating to inventory stocking practices.
Modern technology makes it possible. Prior to the use of artificial intelligence and data analytics, companies would not have been able to create the detailed digital representation of their supply chains that reveals hidden risk, Simchi-Levi says. “Over the last 12 months, company executives have started to realize that technology can help them address many of the challenges they have seen in the market during the pandemic.”
Simchi-Levi has seen companies take the results of their stress tests and use them to make concrete improvements in their supply chains, such as adjusting inventory levels as a buffer against disruption, and standardizing components to streamline production.
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