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Hanesbrands Inc., a manufacturer and marketer of basic apparel based in Winston-Salem, North Carolina, is using analytics to close the gap between insight and supply chain responsiveness. For example, the company recognizes that knowledge of a shortage of men’s T-shirts two weeks from now is of no benefit if the minimum lead time necessary to acquire more T-shirts is four weeks. So Hanesbrands is turning to machine learning to design predictive models to sense supply chain issues in time to execute prescriptive measures.
The predictive models incorporate supply chain data from external and internal sources to determine the likelihood of an inability to satisfy demand at a particular time. Once an impending supply chain issue is detected, a prescriptive action can be launched to mitigate it. For example, if an inventory outage is predicted, Hanesbrands will assess available options, depending on the time available for a response. The actions prescribed could involve a change in mode of transportation or a resequencing of manufacturing and purchase orders. Concurrently, the prescriptive model works to balance mitigation cost with the benefit to the company.
If that’s not how supply chain analytics works in your company, you’re not alone. Only a handful of companies have developed an ability to react quickly to supply chain signals. Despite analytical advances, many companies still do not fully understand the sources of their competitive advantage, even though they now have more data than ever about their operations.
The results of a recent study of supply chain professionals we undertook suggest that despite a growing interest in using analytics to better understand complex performance relationships, many companies still struggle to develop this capability as a competitive resource. (See “About the Study.”) Although the supply chain is recognized as a particularly rich area in which analytics could be used to improve performance, supply chain analytics is still in the early stages of development and implementation in many companies.
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