Jaguar, Land Rover and Colgate-Palmolive are among the many enterprises turning to machine learning software and other data-crunching tools to ensure supply can meet demand.
Technologies that can help companies choreograph how they ship goods around the world are having their moment. Battling shortfalls in parts and products in the wake of a coronavirus pandemic, enterprises are turning to machine learning (ML) and other data-crunching technologies to help clear supply chain hurdles.
Yes companies have mustered their way through disruptions, but nothing could have prepared them for the coronavirus pandemic, which has spurred shortages in everything from cleaning products and toilet paper to medical devices, such as ventilators critical for treating patients who have contracted COVID-19.
Thirty percent of 100 supply chain executives said their companies saw decreased market share during the outbreak, while 30 percent say they saw an uptick, says Matt Lekstutis, global lead for supply chain transformation at Tata Consultancy Services (TCS), which polled 100 executives in July. “What we considered the laws of physics of supply chain have been suspended,” Lekstutis tells CIO.com.
Organizations that relied on historical averages and trends to calibrate supply and demand are now seeing their data models drift. To get back on course, companies are embracing technologies such as graph databases and machine learning to recalibrate sales forecasts, anticipate and avoid machine breakdowns and make their supply chains more nimble and responsive.
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