AI’s Role in the Future of Your Supply Chain: Benefits and Risks

There’s been no shortage of media coverage on the generational bet corporations are making on the value proposition being dangled by artificial intelligence. What types of companies are directing their capital spending into these nascent platforms and for what specific business functions, however, have been less carefully parsed. While it’s clear that Big Tech juggernauts like Meta, Alphabet, and Microsoft are aggressively bullish on the technology, plowing billions into AI projects, platforms, and partnerships, gauging the growth of artificial intelligence beyond the Fortune 500 requires more nuanced probing. 

One business process that has seen a significant uptick in AI adoption in 2024 is supply chain risk management (SCRM). According to a Gartner survey from earlier this year, fully 40% of high-performing supply chain organizations are utilizing either AI or machine learning (ML) for demand forecasting, and nearly a third are deploying the technology to assist with supply planning. A report published by research firm Zero100 over the summer, meanwhile, evinced even broader implementation. The supply chain intelligence company found that roughly nine in 10 large businesses have already started experimenting with using AI in their supply chains in some capacity. 

Such heady figures notwithstanding, it remains too early to declare with any certainty that AI will become an essential aspect of supply chain risk management in the coming years. But just because a cohesive consensus has yet to emerge on the role of artificial intelligence in the future of supply chains doesn’t mean that manufacturers can’t start learning about these tools now. Because even if the corporate sector is still steeped in AI’s infancy—one yet to bear the wildly lucrative fruit many are rhapsodizing about—firms can still begin positioning themselves to gain a meaningful competitive edge two, three, or five years from now. And that kind of decisive advantage starts with learning how these dynamic technologies fit into your business model and strategic objectives, and grasping the full spectrum of outcomes they can generate.  

How AI Benefits Supply Chain Resilience 

Though artificial intelligence continues to be portrayed as an experimental product offering value that’s largely hypothetical, predictive AI, machine learning, and other AI tools have already established legitimate use cases for SCRM. As IBM puts it, “Supply chain systems powered by AI are helping companies optimize routes, streamline workflows, improve procurement, minimize shortages and automate tasks end-to-end.”

Because of their capacity to pore over staggering volumes of data and scan vast swaths of documents and files, these platforms can be an asset to professionals endeavoring to build supply chain visibility, evolve strategy, and mitigate risk. Under ideal circumstances, AI can add significant value to these processes, helping sourcing experts peer into supply chains with enhanced precision and make decisions informed by more data and additive historical context. 

Strengthens Supply Chain Visibility 

One of the most powerful mechanisms for achieving supply chain resilience is visibility. Sourcing and procurement teams that can map out both their direct suppliers and vendors operating at tier 2, 3, and beyond are able to more effectively vet manufacturers, evaluate risk, achieve transparency, and comply with regulations. Supply chain visibility, in other words, is an all but immeasurable resource in the world of risk management. 

But as we’ve covered extensively at Z2Data, carrying out the thorough supply chain mapping so inextricable from strong visibility has long eluded most organizations (only around 20 percent of manufacturers, for example, have consistent visibility into tier tier two suppliers). Artificial intelligence has the potential to change that, though, reversing a long history of manufacturers failing to keep up with supply chains of increasingly daunting depth and complexity. AI platforms can scour files, documents, and the internet more broadly to obtain key records, including transaction orders, receipts, freight bookings, and chain-of-custody forms. Once all this documentation is secured, these tools can then extract pertinent data and provide companies with the information necessary for comprehensive supply chain mapping. The resulting maps carve deeper into manufacturers’ supplier tiers, shedding light on previously unknown vendors. 

These capabilities remain raw, as artificial intelligence firms are still ushering platforms through their earliest stages of development. But smaller startups are already working to train specialized AI models to carry out supply chain mapping using public, private, structured, and unstructured data.

Enhances Supply Chain Diversification 

At first glance, the ability to effectively identify alternative suppliers might not seem like the kind of task an AI platform would excel at. After all, selecting from prospective vendors and establishing supply chain partnerships is a multifaceted process that requires research, analysis, assessment, and—arguably, at least—some measure of human subjectivity. 

As it turns out, though, artificial intelligence tools have been augmenting and accelerating supplier searches for several years now. Research conducted by McKinsey & Co. in 2021 found that machine learning algorithms could help companies carry out supplier searches that once took a month or longer in around a week. Sourcing experts started by feeding the AI tool search criteria, along with specific constraints (such as geographical location). Using a massive dataset of suppliers drawn from public and private sources and established databases, the ML algorithm then combed through the candidates before narrowing the list down to less than 100. As McKinsey explained, “This iterative approach allows search tools to operate with unprecedented speed and precision, finding a shortlist of possible suppliers from a database of millions in just a few hours.”

Taking advantage of an AI platform’s capacity to carry out extensive supplier searches in just a few days—or even a few hours—can bolster a company’s supply chain resilience. Due to the unparalleled efficiency of these tools, manufacturers can practice dual sourcing techniques throughout their supply chain in a way that’s not feasible when humans are tasked with identifying alternative vendors for dozens or even hundreds of direct suppliers. But because of the way these algorithms can execute lengthy, complex searches with maximum expediency, businesses are able to build out this essential SCRM measure without exhausting their resources. 

Executes Demand Forecasting 

While supply chain resilience experts have been exploring the value of AI models for a range of processes over the past few years, the technology has probably been most often deployed for demand forecasting. As we discussed in a previous post, demand forecasting has been a critical facet of supply chain management for centuries, as manufacturers have carefully gleaned historical and real-time sales data in an attempt to anticipate demand for specific products and inventory.

But demand forecasting carried out by individuals has always been limited by the breadth of information we can examine and interpret. Our finite bandwidths often lead to predictions that are compromised by incomplete datasets or flawed analytical methods. Predictive AI, however, is ideally suited to this type of task. These platforms can process vast quantities of sales data, highlight recurring patterns and trends, and render demand forecasts that are effective and actionable. With the aid of supply chain artificial intelligence, manufacturers are able to consistently maintain optimal inventory levels. This optimized approach reduces the risk of stockouts and surpluses, insulating operations from the costly whiplash triggered by unexpected fluctuations in demand. ]

The Risks AI Pose to Supply Chain Resilience 

The rising generation of AI tools are often glowingly portrayed as revolutionary technologies with the potential to dramatically increase productivity and make the firms who adopt them millions in additional revenue. The reality on the ground, however, is that incorporating artificial intelligence also ushers in new risks. These hazards are not lost on supply chain professionals. In Lehigh University’s latest Business Supply Chain Risk Management Index, recently released for the fourth quarter of 2024, AI was cited multiple times as a consequential emerging threat. Industry insiders expressed concerns about the ways AI and ML tools could introduce cybersecurity risks, trigger major upheaval in the field, and even lead to abuse by bad actors eluding regulatory scrutiny. All told, the report was just one exhibit speaking to the two-sided AI coin. While the technology can be effectively deployed to mitigate disruptions, the sweeping scale of implementation and lack of substantive oversight mean that it also introduces several new and undesirable variables. 

Models Trained on Poor Data

After nearly two years of widespread adoption and glaring media spotlight, the shortcomings of generative AI models like ChatGTP are well-established. For all their quantitative prowess and capacity to absorb endless volumes of data, these tools often betray a loose, undisciplined relationship to truth and factual accuracy. You don’t have to look very far to pinpoint the cause of their inaccuracy: because they’re trained on gigabytes of data culled from Wikipedia, online publications, and myriad other web sources, the tools reflect the same inconsistency and unreliability that characterizes the internet itself. 

These factual errors, falsehoods, and “hallucinations,” as they’re often referred to in the tech industry, may not have especially negative consequences for small-scale tasks like emails, summarizations, or school essays. But when AI is being utilized to carry out higher-order projects like mapping supply chains and identifying prospective vendors, the repercussions for inaccuracies can be more serious. Manufacturers and procurement professionals can’t afford to act on misinformation or hallucinatory fictions invented by AI software, and doing so could result in squandered time and resources. In a worst-case scenario, relying on AI-generated falsehoods could even create regulatory issues. For now, this means that much of the material produced by these tools—including written content, supply chain maps, sub-tier suppliers, and vendor lists—should be carefully vetted by experienced professionals.

Links to Rising Cybersecurity Risks 

In a Lehigh University press release accompanying the publication of a recent Supply Chain Risk Management Index, professor Zach. G. Zacharia noted how many industry insiders were worried about the link between AI and cybersecurity threats. “The supply chain professionals are apprehensive of cyber-attacks, data corruption, data theft, system viruses and especially how generative AI might increase their companies’ vulnerability,” he cautioned. 

Lehigh’s industry survey could be operating as the proverbial canary in the coal mine, signaling an impending crisis in the way AI empowers cyberattacks. Professionals surveyed by Lehigh fretted over how AI tools were offering new capabilities to cybercriminals, helping them orchestrate more potent, sophisticated attacks at increased frequencies. And as Zacharia alluded to, the growing prevalence of artificial intelligence use across the supply chain risk management industry could also introduce new internal susceptibilities, with the AI systems themselves becoming fresh targets for malicious actors intent on exploiting or contaminating data. 

Navigating a SCRM Landscape Transformed by AI 

Even if they’re committed to proceeding with caution, discretion, and methodical planning, supply chain firms are soon going to face increasing pressure to develop an internal strategy for adopting and implementing AI. The risks associated with falling behind in a rapidly evolving field are just too steep, the early-adopter windfalls too enticing to consistently rebuff. As one commenter in the Lehigh University Supply Chain Risk Management Index bluntly put it, “Adapting to the changing AI landscape and leveraging AI will determine the success of businesses going forward.”

Manufacturers interested in the capabilities offered by AI but not yet fully convinced the upfront costs will yield a worthwhile payoff can find much of the same value and functionality in a supply chain resilience tool. Industry-leading platform Z2Data offers a range of SCRM features, including part-to-site mapping, comprehensive data on tier 1, 2, and 3 suppliers, and even detailed risk assessments for individual manufacturing sites. Plus, Z2Data’s databases are maintained and reviewed by seasoned professionals with years of experience working in the electronic component supply chain—leading to near-negligible risks of inaccurate data or pernicious misinformation. 

To learn more about Z2Data and the suite of visibility features it provides to customers, schedule a free demo with one of our product experts.