AI: the missing link in hyperconnected supply chains

Explore the role AI is playing to help deliver contextual insights and practical value to global supply chains.

AI The missing link in hyperconnected supply chains

Business priorities –  however well-defined – inevitably shift over time in response to new objectives, capabilities, market pressures, and customer expectations.

For global supply chains, such shifts have been driven by the need to create operations that are faster, smarter, and ultimately cheaper to run. As a result, we’ve seen the move from an ‘inside-out’ approach (with companies focused on making their supply chains more efficient) to an ‘outside-in’ mentality (designing operations around customer demands and expectations).

Then came the interconnected supply chain – with IoT devices integrated across the entire end-to-end process and providing unprecedented macro-level visibility into day-to-day conditions.

Now, both inside-out and outside-in strategies are blending together, inspired by the abundance of available data being generated, collected, and stored by the proverbial ‘bucket load.” In fact, there’s so much data coming out of supply chains that the key question quickly becomes: what to do with it?

Increasingly, the answer involves some form of AI, and in this article we’ll look at various ways technology is helping deliver the true potential of hyperconnected supply chains.

Big issues

Hyperconnected means just that: sensors, devices, and machines – etc. all internet-enabled and covering every step in a logistics operation. From vehicles and parcels to stock shelves and visual inspection cameras, the data being generated can certainly claim the title “big.”

The challenge of making sense of this data is enormous and no business wants to think it’s missing out on key insights due to a lack of analytical scope. Yet before AI started to become mainstream, such a situation was the norm rather than the exception.

Now, AI enables existing IT capabilities to serve up real-time information from the raw data made available from IoT devices and an array of operational inputs. The result is a clearer, more accurate picture of what’s going on inside global logistics networks, including:

  • The level of actual demand, viewed through the lens of real-time sales, weather patterns, seasonal fluctuations, etc.
  • The size of known and predicted risks, both short- and long-term, and the planning required for effective mitigation.
  • Customer and supplier experiences, and identifying opportunities to improve both to cement relationships.
  • Areas of inefficiency that include data on both cause and effect to support more intelligent and sustainable solutions.

Complementary capabilities

With the big breakthroughs from AI come from contextual intelligence. It’s able to self-learn, identify patterns, and correlate seemingly unrelated data into a coherent picture.

AI also offers proactive capabilities and insights that deliver fresh insights a human wouldn’t know was possible, versus the reactive quality of IoT devices that push data back to a central, cloud-based repository. Combining these capabilities makes for a powerful partnership:

  • Connected devices are ideal for tracking movements and gathering data across the supply chain.
  • AI has the computational power and “brains” necessary to make sense of it all, while also delivering practical, timely outcomes as a result.

Day-to-day value

There are many ways to deploy AI and many problems that it can solve. Here’s a brief f summary of the main use cases to date:

  • Supply chain planning: AI is tasked with defining the best possible demand scenarios to help optimize deliveries.
  • Improving delivery times: AI maps out optimal route schedules and responds in real-time to any traffic problems.
  • Enhancing warehouse productivity: AI informs automated sorting technologies, improves warehouse management, self-managing inventory systems, and even self-driving forklifts.
  • Refining supplier selection: AI is used to analyze data ranging from audits and delivery performance to credit scoring and evaluations that create a list of preferred suppliers.
  • Enriching the customer experience: AI introduces a deeper level of personalized engagement and voice-based services to help customers track shipments and submit queries.
  • Streamlining production planning: AI helps companies enhance scheduling and production planning, and introduce more sustainable “build to order” processes that can be reliably delivered.

Realize the Power of Two

AI offers much in the way of promise for global supply chains. Gaining this contextual insight, however, and using it to set new standards for efficiency and customer engagement, requires the Power of Two. That’s the power of NVIDIA® GPUs and the Google Cloud AI platform working in partnership to inspire breakthrough performance.

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