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Source: ZDNet as of 23-07-2020

The recent Covid crisis and other calamities exposed a lack of nimbleness in supply chain networks. Can artificial intelligence make things right?

Clearly, building intelligence into supply chain networks is a hot thing. One market estimate puts AI in the supply chain on a 46%-a-year growth track, growing from $503 million in 2017 to more than $10 billion by 2025. As Denis Forget, CEO of Distribution Pharmaplus, a participant in the consortium, puts it, AI-powered supply chains “will allow us to unlock productivity by improving inventory management, limiting the impact of shortages and reducing administrative management-a typical win-win solution, as we reduce our costs, while increasing our sales and offering better service.”

What’s at stake? Today’s supply chain networks simply aren’t nimble enough to handle today’s and tomorrow’s challenges, says Ram Krishnan, CMO of Aera Technology. For example, “manufacturers are now looking to produce smaller batches quickly and efficiently to address demand spikes across geographies and channels. Yet many are set up in inflexible, slow-moving manufacturing-at-scale models that aren’t geared for agility and just-in-time batch production on demand. What was once a competitive advantage through economies of scale is now a barrier to nimble production.”  What’s conspicuously absent from today’s supply chains “is the ability to adapt to ever-changing supply chain constraints including raw material availability, product and shipping times, and budgetary limitations,” Krishnan continues. “Carefully planned lead times are upended when a disruption occurs at any link in the supply chain. If multiple disruptions occur simultaneously, the effects can cascade across the entire supply chain with potentially disastrous impact.”

Look no further than the recent Covid-19 crisis, he illustrates. “Some sectors are grappling with far-reaching disruptions in raw material availability and shipping times -even as demand for certain products soar. They lack the strategic agility to course-correct as constraints change.”  The issue that has been standing in the way is “monolithic transactional systems not suited to respond to rapid operational changes and enable informed decision-making at speed,” he says. “Despite major advances in cloud architectures and database scalability, underlying batch-oriented supply chain systems have largely remained unchanged since the 1990s. Such archaic infrastructures make virtually impossible for supply chain practitioners to quickly get the right data to make the right decisions as disruptions occur and constraints change.” As a result, enterprises “are faced with a massive load of manual data work to collect information from disparate systems for analysis in spreadsheets or a data lake. Ultimately, best-guess decisions may be made days or weeks after an unforeseen circumstance arose.”

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