Three ways GenAI could transform supply chain management 

GenAI in practice in the supply chain function

Where, then, might GenAI offer the greatest impact as it evolves? Three areas stand out.

1. Doing the job of narrow – but better

GenAI will prove a cheaper, more efficient replacement for some of the narrower AI tools. Tasks where narrow AI currently predominates, such as calculating optimal inventory levels, are often much less straightforward than they initially appear. For example, the classic news vendor challenge – how many newspapers to buy, given the inherently limited shelf life of the product – requires multiple inputs to reach a reliable answer.

Narrow AI solutions address this difficulty using complex algorithms that are run repeatedly until they produce the hoped-for results. This requires a substantial commitment of resources. GenAI tools are now matching the results achieved by algorithmic models and, in some cases, exceeding them. Critically, moreover, they do not demand the same level of resourcing, representing a much less costly route to inventory optimization.

2. Reducing reliance on Excel expertise

Excel is the supply chain industry’s dirty little secret: IMD’s research suggests supply chain professionals spend as much as 60% of their time on Excel spreadsheets. Not only does Excel hoover up huge amounts of time in the supply chain function, but it also creates undesirable dependencies. With few agreed standards on spreadsheet creation, individual approaches proliferate. If the individual in question leaves the business, faced with the idiosyncrasies of their handiwork, their successor is often obliged to start from scratch.

GenAI won’t replace Excel, but it does have the potential to take over much of the heavy lifting in spreadsheet development and analysis. That’s because tools such as Microsoft Copilot are now capable of programming Excel on the user’s behalf. This leaves scope for significant productivity gains, as the time devoted to Excel falls sharply and makes the loss of an individual spreadsheet author less of a disaster.

3. Asking new questions about the business

Outside established AI use cases such as demand forecasting, the supply chain function rarely asks other critical questions of the technology because, until now, it hasn’t been able to answer them. During the COVID-19 pandemic, when supply chain disruption left many businesses with limited amounts of raw materials, leaders found it very difficult to produce revised production schedules. All their forecasting tools were set up to answer questions about what inputs would be required for a given output; when the question was reversed, they had nothing.

GenAI, in contrast, can make a stronger attempt. Its flexibility and agility, in contrast with the narrow parameters of more traditional AI, enable supply chain leaders to deploy it in new areas, and it can offer meaningful answers to a wider range of questions.

It’s worth acknowledging that GenAI won’t necessarily provide the right answers, but it does give supply chain leaders access to a larger store of intelligence, which they can then make available to the rest of the business.