As artificial intelligence and its raft of powerful new iterations continue to permeate businesses across a wide swath of industries, AI has steadily grown its profile in the world of supply chain management (SCM). A 2022 study found that over half of companies that had implemented an AI model for SCM were decreasing overhead costs, while close to 60% were increasing their revenue. Other reporting has indicated that by the end of 2024, at least half of supply chain organizations will have started utilizing AI software for one or more tasks. While AI in SCM is still in its infancy, the revolutionary technology is already transforming a number of essential supply chain functions. AI and machine learning (ML) models can spearhead efforts at supply chain mapping, conduct predictive maintenance, execute demand forecasting, and even provide comprehensive assessments of recent disruptions. Despite the technology’s unassailable potential, however, the implementation process often comes with complications that aren’t always apparent beforehand. Organizations looking to incorporate AI platforms should be aware of these drawbacks and the specific threats they pose to operations.
What Are the Key Types of AI?
Before supply chain firms seriously entertain bringing on an AI model to help them map their suppliers, forecast demand, or execute other critical functions, they should understand the different types of tools that currently make up the artificial intelligence landscape. There are at least four key types of AI at the forefront of this technology: machine learning (ML); predictive AI; large language models (LLMs); and generative AI.
While generative AI is currently the buzziest form of AI, with dazzling capabilities that encompass everything from generating text and computer code to rendering distinctive images based on a limitless array of inputs, it may not be the strongest fit for supply chain management. Machine learning and predictive AI, on the other hand, are capable of identifying emerging patterns in the marketplace, pinpointing supply chain bottlenecks, and anticipating equipment failures. Understanding exactly what you’re looking for in an AI platform—and finding the right fit for those requirements—is an essential first step in AI implementation.
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Are There Established Use Cases for AI in SCM?
Professionals in strategic sourcing, procurement, and component engineering are generally a grounded, practical group, less focused on the enthralling possibilities of a technology than how it can improve their costs and processes right now. Fortunately, AI has already established a myriad of legitimate use cases in supply chain management—functions and tasks that it can effectively perform in ways that benefit businesses today.
These uses cases include supply chain mapping, predictive analytics, demand forecasting, and supplier searches, among other capabilities. Surveys from consulting firms like McKinsey & Company and Gartner have shown that supply chain firms are already adopting AI for demand forecasting at scale, and are drawing on the software’s data processing muscle to discern shifts in consumer behavior as early as possible. While the use of AI for more complex tasks like supply chain mapping is not yet as widespread, the trajectory of implementation suggests that artificial intelligence is likely to become integrated into day-to-day supply chain management operations faster than many people realize.
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Benefits and Risks of Using AI in SCM
While the advantages AI can bring to supply chain management are increasingly well-known, they can also introduce risks that are decidedly less publicized. In Lehigh University’s Business Supply Chain Risk Management Index for the fourth quarter of 2024, AI was cited multiple times by manufacturers as a consequential threat that posed a number of different risks.
Chief among these hazards is the potential for AI models trained on inaccurate data to lead companies astray with misleading information or flawed guidance. Generative AI models, for example, have already developed a reputation for producing factual errors—often referred to as “hallucinations” in the tech industry—and supply chain maps or compliance information riddled with such falsehoods could have significant ramifications for businesses. Because of the incipient, experimental nature of many of these tools, supply chain organizations using AI should always vet the software’s outputs with trustworthy human expertise.
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The Most Effective Strategies for Implementing AI
Bringing on an AI tool often triggers major changes at a business. Leaders that want to maintain solidarity in their workforce and maximize the potential of their new technology should develop an AI strategy that’s both comprehensive and nuanced. Artificial intelligence models often require specialized expertise to be used effectively, and firms should allocate the appropriate resources to cultivating the requisite knowledge and skills in-house. (If this isn’t a viable option, however, companies can also recruit consultancies with backgrounds in digital transformation to spearhead effective implementation.)
In addition, executives can draw on the established business discipline of change management to steer their company through the transitional period that AI technology will inevitably usher in. Change management is a fundamental aspect of a thorough implementation strategy, allowing organizations to communicate the case for change, train staff in utilizing the new technology, and designate individuals and groups to manage and advocate on behalf of the project.
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