Supply chains breakdown in different ways, and each type of risk needs its own mitigation strategy.
Supply and demand shocks and shifts are one reason why supply chains break down. One example of a supply shock would be a supplier delivering late or not at all, which frequently happened in 2020. A demand shock would be a sudden surge or decline in demand for the goods being sold. For example, N95 masks experienced a demand shock last year. So did anything associated with air travel, but in the opposite direction.
Supply chains break down in the face of external shocks either because of an inability to foresee change, or an inability to adapt to it, depending on how quickly that change can harm their business. Those shocks can be mitigated with better predictions, which give you more time to prepare, and better responses, which depend on having a flexible system.
Using tools like simulation models to explore what-if scenarios, and predictive software to get better visibility into the future, are two ways to prepare for unusual events that may not be contained in historical data, or have been experienced by your team.
Many systems fail to adapt due to their own complexity, which makes them fragile. The complexity of software and physical operations makes those systems hard to understand, and therefore to debug. It also makes them hard to improve. Another cost of complexity is the rare expertise required to maintain it, which makes companies dependent on a few individuals or vendors.
LACK OF VISIBILITY
Another common flaw in supply chains is a lack of visibility up and down the chain. That is, retailers don’t know enough about what’s stocked in the distribution center, and distribution centers don’t know enough about patterns of demand at the retailer. Likewise, distribution centers suffer the same lack of visibility with upstream supply chain nodes like ports and factories. Better transparency up and down the supply chain can lead to better production and capacity planning.
That is essentially a data issue. The right data needs to be gathered, stored and made accessible to the right operators and analysts so that they can make better decisions to ensure they meet their own customers demands while keeping inventory costs low.