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HOW AI CAN SIMPLIFY, STREAMLINE, AND ENHANCE SUPPLY CHAIN OPERATIONS

Arthur Cole@acole602
June 4, 2021 10:20 AM
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Image Credit: chombosan/Shutterstock

We are excited to bring Transform 2022 back in-person July 19 and virtually July
20 - 28. Join AI and data leaders for insightful talks and exciting networking
opportunities. Register today!

--------------------------------------------------------------------------------



Global economic activity is picking up now that pandemic-related restrictions
are easing. But this return to normal has not been without hiccups, notably
including supply chains emerging from virtual shutdowns.

However, few organizations are eager 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 hit, and there are signs this change is accelerating the drive to
simplify, streamline, and enhance operations in order to meet the needs of an
increasingly digital economy.

1
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49
More Power to AI; How Duke Energy used computer vision and robots to improve
processes and cut costs by $74M 1
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5




More Power to AI; How Duke Energy used computer vision and robots to improve
processes and cut costs by $74M 1



READY FOR ACTION

More than three-quarters of business leaders reported deploying AI in pilot
programs, in key business areas, or at full scale across the enterprise, in a
recent survey of more than 1,000 executives from NTT DATA and Oxford Economics.
Cybersecurity remains the top challenge these deployments are intended to
address, with supply chain management a close second. The most significant
barrier to greater adoption, however, is the sheer complexity of the technology,
which must be deployed across a range of processes and throughout disparate
infrastructures 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 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.

But applying AI to the supply chain is not a simple matter of throwing it at
various processes to see what sticks. A more strategic approach that focuses on
the value of data as the key driver of productivity is needed, Noodle.ai
developer Mike Hulbert wrote on SupplyChainBrain.


MANAGING RISKS

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, that money is well spent, and solutions highlight the ways AI and
human intelligence can work together to produce the most desirable outcomes.

Most of the thinking around AI in the supply chain tends to center on how it
will enhance today’s processes. But as markets evolve into the new century, AI
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, something that can only be done through advanced intelligent systems
that talk to each other with perhaps intermittent human oversight.

It should be noted that AI is not like past technology developments that began
working their magic as soon as they were deployed. AI must be trained, refined,
and seasoned — just like any other employee. It’s a fast learner, to be sure,
but it makes its share of mistakes. When it comes to the challenges of
restarting crucial supply lines in the post-pandemic transition, AI will not
provide an instant solution, but its true benefit to business operations will
become clear in the long term.

VentureBeat's mission is to be a digital town square for technical
decision-makers to gain knowledge about transformative enterprise technology and
transact. Learn more about membership.

Author
Arthur Cole

Topics
AI

Big Data

Business

Cloud

Enterprise

Security





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