The Pros and Cons of an AI-Based Supply Chain

Icons representing AI concepts overlaid on image of smart factory

The fourth industrial revolution is in full swing. Advances in machine learning, artificial intelligence (AI), and Big Data have fueled the beginning of a new, algorithm-based era. Today, companies can automate any number of tasks, cutting down on errors and the downtime and expenditures associated with them. And supply chains, in particular, stand to benefit immensely from AI.

Before diving in, it’s important to remember that AI is frequently confused with machine learning. While they are interconnected and experts often refer to machine learning as a subset of AI, AI provides self-generated decision-making capabilities. In other words, AI isn’t just going to gather data, learn about the patterns in the data, and generate sophisticated recommendations; it’s also going to develop and execute a plan of action on its own.

But, as with any new technology, it comes with both benefits and drawbacks. To ensure you’re implementing the right tools for your company’s unique needs, it’s crucial to fully understand the pros and cons of AI in the supply chain.

The Benefits of an AI-Based Supply Chain

Robotics, smart warehouses, autonomous transportation vehicles, and automated predictive analytics (e.g., forecasting) can all enhance the safety of a working environment, drive down costs, and streamline systems and processes.

For example, AI can be used to gather comprehensive data that may affect delivery times — such as weather patterns, GPS information, and reroutes. This can help the sales team in predicting more accurate product delivery times, immediately notifying users of real-time inventory adjustments. As a result, companies are better able to provide both new and existing customers with optimal customer service.

The Drawbacks of an AI-Based Supply Chain

AI is still evolving, with countless research and development initiatives underway across the globe. But when algorithms begin to create other algorithms, which are then auto-executed, this presents a “black box” scenario. Researchers and AI engineers may not be able to quickly untangle the nuts and bolts of these AI-generated algorithms. To put this in perspective, imagine trying to understand as well as predict the “what,” “when,” “where,” and “how” of human creativity and behavior.

As an example, self-driving or autonomous delivery vehicles are powered by extremely complex systems, including sensors that feed into an AI algorithm, which, in turn, allows for monitoring of surrounding traffic while predicting and accounting for the behavior of nearby human drivers. In these situations, one incorrect prediction by the AI algorithm could be fatal for human drivers. And, of course, the various risks posed by weak cybersecurity protocols cannot be ignored.

Weighing the Options

AI comes with some inherent challenges and risks, but this doesn’t necessarily mean that it shouldn’t be adopted. Instead, AI within the supply chain should be carefully considered within a comprehensive risk, contingency, and mitigation matrix. And remember: AI is a tool, and is best used in conjunction with human skills and decision-making processes — not simply as a replacement for human labor.


Image credit: metamorworks/

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