Artificial intelligence is becoming crucial to how large retail organisations manage and optimise supply chains. From predicting seasonal demand in goods to automating inventory ordering, AI is helping supply chain management system vendors gain new efficiencies for their clients.
In 2022, McKinsey reported that supply chain management was the top area where businesses reported AI-related cost reductions. At the time, large consumer packaged goods companies saw a 20% reduction in inventory, a 10% decrease in supply chain costs, and revenue increases of up to 4%.
AI for supply chains has only improved since 2022 and is accelerating with generative AI. A more recent report from McKinsey found that supply chain management was the function where businesses most commonly reported meaningful revenue increases of more than 5% due to investments in AI.
Laurence Brenig-Jones, vice president of product strategy at supply chain management and planning software provider RELEX Solutions, told TechRepublic the “number crunching” power of machine learning has been the dominant AI technology force used in supply chains to date.
“I think what we are seeing is there is a huge improvement in accuracy and automation [from machine learning capabilities] that can lead to very significant benefits in product availability, reduction in working capital, and if you’re a grocer, then a reduction in spoilage or wastage,” he said.
There are several use cases for which machine learning has been deployed in supply chains.
Predicting product demand is key in supply chain management. Brenig-Jones said this is “incredibly difficult” because it can involve predicting demand for a specific product, at a specific location, on a specific day or time of day — often up to 180 days or more in advance across an entire operation.
Over the last five years, machine learning algorithms have replaced previously used time series algorithms for this task. According to ERP vendor Oracle, AI can now use internal data such as sales pipelines and external signals like market trends, economic outlooks, and seasonal sales for forecasting.
Demand forecasting helps organisations optimise and automate inventory ordering. Though this includes ensuring sufficient stock is available to meet demand, retailers must also balance other factors, such as excessive working capital with too much stock, food spoilage, or capacity breaches.
Brenig-Jones said many optimization algorithms, with their ability to learn from the past through machine learning, can solve this complex problem and efficiently fulfill demand for the organisation’s supply chain, balancing all involved factors.
Machine learning is also embedded in logistics networks. According to Oracle, logistics companies use machine learning algorithms to “train models that optimise and manage the delivery routes by which components move along the supply chain,” ensuring more timely deliveries of goods.
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In one example, courier company UPS uses its dynamic road-integrated optimisation and navigation platform, ORION, to show drivers the most efficient route for deliveries and pickups on more than 66,000 roads in the U.S., Canada, and Europe, saving significant mileage and fuel costs annually.
Experts believe generative AI will become increasingly important in supply chain management and planning. Through natural language queries, the future will likely see an expanded role for generative AI.
Retailers will likely have much richer and more analytical natural-language interactions with their supply chain and retail planning data in the future. This could involve asking questions about the supply chain plans, what has happened in the past, or where there are opportunities to do better.
“You could ask: ‘What were my top five reasons for out-of-stocks last week?’ And it could tell you: ‘Number one was poor inventory accuracy in your stores, and these stores in particular. Number two was you had one big supply failure, and it caused this impact on your sales’, Brenig-Jones said.
Generative AI in supply chain management platforms could offer forward-looking recommendations for large retailers through natural language interactions. For example, a platform could advise an organisation on what to do next week to ensure everything is set up to hit its targets.
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“It might say: We recommend that you change this part of your configuration, or we recommend you go and talk to this supplier because there’s a risk based on our understanding of what happened last time.’ So it would be forward-looking and interacting in a natural language format,” Brenig-Jones said.
A further phase in the introduction of generative AI, and something RELEX is pursuing within its platform, is to turn AI into a “super user.” Like system users who are “real gurus in how the system is configured,” AI could become self-adaptive, helping organisations improve their systems over time.
“It would say: ‘I’ve come up with a better configuration for your solution based on what I’m seeing,’” Brenig-Jones explained. “So you would get into this kind of ability for the solution to self-adapt on the go. That is the direction we’re heading, and we’re working with our customers to understand how that would work best for them as well.”
