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Economic activity is picking up across the globe now that pandemic-related restrictions have eased. This return to normal is not without some hiccups, most notably in supply chains emerging from year-long virtual shut-downs.
However, few organizations are willing to revert to the manual-driven operational frameworks of the previous decade. The shift to advanced automation and artificial intelligence in the management layer was already underway before the pandemic shut every thing down, and there are signs this change is accelerating the drive to simplify, streamline, and enhance operations in order to meet the needs of a fully digital economy.
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More than three-quarters of the business leaders reported deploying AI in pilot programs, in key business areas, or at full scale across the enterprise, according to a recent survey of more than 1,000 business leaders from NTT DATA and Oxford Economics. Cybersecurity remains the top challenge that these deployments are intended to address, and supply chain management comes in a close second. The most significant headwind to even greater adoption, however, is the sheer complexity of the technology, which must be deployed across a range of processes and throughout disparate infrastructure in order to produce the greatest benefit.
Nevertheless, most organizations view AI as the next step in supply chain management, not simply to recover from the pandemic but to maintain a competitive advantage going forward. Machine learning (ML) and other forms of AI are even finding homes in industries considered to be technical laggards, like trucking and transportation, Paul Beavers, CTO of AI-driven transportation management platform provider PCS Software, wrote recently for Supply & Demand Chain Executive. With its ability to find hidden patterns in data, AI can lower costs and improve productivity by reducing the number of empty miles incurred during return trips, identifying the optimal mode of transport for selected cargo and streamlining loading, fueling and other tasks.
Applying AI to the supply chain is not a simple matter of throwing it at various processes to see what sticks. Rather, a more strategic approach is needed — one that focuses on the value of data as the key driver of productivity, Mike Hubert, an AI developer at Noodle.ai wrote on SupplyChainBrain.
One way to do this is to use AI to assess risk. Today’s management stacks tend to flood workers with alerts without assigning any priority. AI has the ability to quantify risk so organizations gain broad visibility into the most crucial detriments to efficient operations. Even if the problem requires time and expertise to solve, it is still money well spent, and this in turn highlights the way in which AI and human intelligence can work together to produce the most desirable outcomes.
Most of the thinking about AI in the supply chain still tends to center on how it will enhance today’s processes. But as markets evolve into the new century, it will also help create and manage entirely new forms of multi-layered, dynamic chains serving highly virtualized and cloud-based business models. Even today’s emerging omnichannel environments require precise coordination between customer-facing infrastructure, warehousing, transportation, fulfillment and a range of other disparate functions, Global Trading Magazine’s Rumzz Bajwa noted recently. Much of this will have to be automated to accommodate the speed of business, and this can only be done through advanced, intelligent systems that can talk to each other with perhaps intermittent human oversight at best.
It should be noted that AI is not like technology developments of the past that began working their magic as soon as they were deployed. AI must be trained, refined, seasoned – just like any other employee. It’s a fast-learner, to be sure, but it makes its share of mistakes. So while the challenges of restarting crucial supply lines are significant in the post-pandemic transition, AI will not provide an instant solution.
Its true benefit to business operations will only become clear in the long term.
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