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When the Association for Supply Chain Management (ASCM) announced its top ten 2024 supply chain trends in September, it wasn’t surprising that the digital supply chain took the top spot for the first time in four years.

As we noted in our first post in this series, which focused on this topic, the COVID-19 era smacked a big exclamation point on the urgent need to shift from archaic practices to a digital framework that would help mitigate supply chain disruptions.

And last week, we discussed the second-place winner — big data and analytics — describing how organizations are applying the use of big data and analytics, aka BDA, to supply chain processes.

In this third post of our series, we’re going to dig into number 3 on the list, artificial intelligence (AI), to examine current trends regarding its use in supply chain management (SCM).

Over the eight weeks to follow, we’ll do something similar with each of the remaining categories in the top ten, which are:

  1. Supply Chain Investment (systems and people)

  2. Visibility, Traceability, Location Intelligence

  3. Disruption and Risk Management

  4. Agility and Resilience

  5. Cyber Security

  6. Green and Circular Supply Chains

  7. Geopolitics and Deglobalization of Supply Chains

AI is no stranger to SCM

Unless you’ve been living under a rock, you’ve probably heard a lot about AI — especially since generative AI (GenAI) exploded onto the scene late last year.

But before we get into the GenAI buzz, it’s important to remember that AI has been used effectively in SCM for many years.

As noted in an August 2018 CLN post, “Artificial Intelligence Set to Transform Traditional Freight Forwarders,” experts knew then what a big difference this technology could make to enhance supply chain (SC) efficiencies: “Just as the Industrial Revolution changed our social and living conditions, so too will the impending Digital Revolution. By effectively altering the way we work and communicate on a daily basis, AI will become as fluent to business as it already is to consumers.”

In our December 2020 post, “Is Artificial Intelligence the Answer to Efficient Supply Chains?”, we cited a 2019 survey of supply chain executives about “key innovation investments across retail, manufacturing and logistics.” In it, 77% of respondents cited “supply chain traceability and visibility” as their highest area of investment, with 82% planning to deploy or test cognitive analytics, and 62% planning to do the same with AI and machine learning (ML) technologies by 2021.

Fast forward to today, when we see AI making the ASCM’s top-ten list — and showing no signs of slowing down.

Defining AI

So, how is AI defined?

Although experts have various definitions, Accenture defines AI as “a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence. …Technologies like machine learning and natural language processing are all part of the AI landscape. Each one is evolving along its own path and, when applied in combination with data, analytics and automation, can help businesses achieve their goals, be it improving customer service or optimizing the supply chain.”

But how is traditional AI different from what everyone is talking about: GenAI?

We’re glad you asked.

Since GenAI is expected to make such a big impact on SCM, we did a three-part series on it in starting in May:

In Part I, we explained the difference between traditional and generative AI, leaning on two resources from McKinsey & Co to do it.

The global consulting firm says what’s creating so much buzz is GenAI’s unique ability to create new content based on the data it ingests — rather than just making predictions and recommendations, as is the case with traditional AI.

“This content can be delivered in multiple modalities, including text (such as articles or answers to questions), images that look like photos or paintings, videos, and 3-D representations (such as scenes and landscapes for video games),” the firm says. “Even in these early days of the technology’s development, generative AI outputs have been jaw-droppingly impressive, winning digital-art awards and scoring among or close to the top 10 percent of test takers in numerous tests, including the US bar exam for lawyers and the math, reading, and writing portions of the SATs, a college entrance exam used in the United States.”

Although GenAI models that produce content in a single format are the most common, McKinsey & Company says multimodal models are emerging that make it possible to create different types of content from the same GenAI offering.

“All of this is made possible by training neural networks (a type of deep learning algorithm) on enormous volumes of data and applying ‘attention mechanisms,’ a technique that helps AI models understand what to focus on,” the firm says. “With these mechanisms, a generative AI system can identify word patterns, relationships, and the context of a user’s prompt…”

Although traditional AI models may use the same tools, the firm says they aren’t designed with the ability to generate new content. Instead, they use content that already exists to do the work they do.

GenAI in SCM

In Part II of our series, we explored use cases and potential impacts of GenAI on SCM.

Describing the many challenges associated with an increasingly complex and evolving global supply chain, Cem Dilmegan, principal analyst at AIMultiple, says that generative AI offers “promising solutions” to tackle them.

“By leveraging the power of generative AI, supply chain stakeholders can analyze massive volumes of data, generate valuable insights, and facilitate better decision-making processes,” Dilmegan writes.

He also provides his list of the top 10 potential use cases for generative AI in SCM:

  1. Demand forecasting

  2. Supply chain optimization

  3. Supplier risk assessment

  4. Anomaly detection

  5. Product development

  6. Sales and operations planning

  7. Price optimization

  8. Transportation and routing optimization

  9. Inventory Management

  10. Financial optimization in supply chain

Ray Smith, VP, Resilient & Sustainable Supply for Microsoft concurs, noting in a March blog post describing a new offering — Microsoft Dynamics 365 Copilot — that supply chains are a “prime area for the application of AI,” due to the massive amounts of data and processes involved.

“The next generation of AI will transform the industry by making it more agile, efficient, and responsive to changes,” he says, describing several potential use cases for GenAI in SCM, including the ability to:

  • Mitigate risk

  • Optimize order fulfillment

  • Improve forecast accuracy

  • Use data Q&A to mitigate order delivery risks

  • Create autonomous supply chains

  • Provide “intelligent process automation”

  • Offer intelligent inventory visibility and optimization

  • Help “shorten warehouse inventory cycle times”

Smith says the list is just a starting point: “AI has the potential to revolutionize supply chains, offering new possibilities for improved efficiency, cost savings, and customer satisfaction.”

With GenAI surging onto the business landscape, references to AI increasingly include this emerging technology — which is rapidly evolving in terms of capabilities, use cases, and impacts on the workforce.

If you’d like to learn more about AI’s growing impact on SCM, here are a few recently published resources to check out:

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