Generative AI will transform supply chain management

Given its capacity to analyse vast sets of complex data across multiple platforms at speeds no human could possibly match, generative AI has the potential to transform many industries, including supply chain management.

According to a recent KPMG study, AI can “help ensure procurement and regulatory compliance, streamline and enhance the efficiency of manufacturing production workflows or enable virtual logistics communication by using virtual assistants to handle routine inquiries and provide quick responses”.

AI is cited by the firm as a technology that can help companies anticipate market volatility, make complex network decisions, and achieve business objectives in the short term. Some 57 per cent of the executives who participated in the 2023 KPMG Global Tech survey stated that AI would help them to achieve their short-term goals, suggesting that AI is not an over-hyped pipe dream for them.

Neal Johnston, a business consulting partner at EY who specialises in supply chain and operations management, says organisations have been using the technology for demand planning and procurement for several years and are now looking at other uses such as process standardisation and last-mile-delivery optimisation. In the relatively new area of sustainability tracking, AI adoption is a high as 62 per cent, according to EY’s most recent study on supply chain sustainability, he says.

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One of the most powerful aspects of the AI technology is ease of use, Johnston notes.

“Organisations are training generative AI models on their own data sets and then asking them to find ways to improve efficiency and support future planning,” he says. “The power of the technology is enhanced by its simplicity of use. Users do not need to have any background in coding or software to operate it. They can use the ‘chat’ function to ask questions in natural language and receive responses in the same format.”

Generative AI can also be used to automate the tendering process and purchasing terms and conditions with suppliers.

“This brings structure and consistency to often complex processes while also taking the emotion out of face-to-face engagements,” says Johnston. “The technology can be directed to seek the best-value result, taking all variables into account and producing better outcomes in many cases. In fact, when a US retailer used a bot to negotiate terms, 65 per cent of the suppliers involved said they preferred interacting with it to negotiating with an employee.”

Meanwhile, Lorcan Sheehan of supply chain consultants PerformanSC says use of AI has been greatly facilitated by the increased levels of smart technology embedded within supply chain assets, which are producing useful data. In addition to internal supply chain data it is possible to analyse external data sources including complex regulatory information, weather conditions, traffic conditions and currency fluctuations, which also impact supply chains.

“There is a role for generative AI in helping to process and visualise large data sets in support of supply chain decisions, particularly in areas where scenario planning plays a role,” says Sheehan. “Areas like risk management, simulation and sustainability would be suited to testing the impact of changes in key variables in supply chain performance.”

While – for now, at least – AI won’t replace humans in supply chain management, it is making managers’ lives a lot easier. Take Unilever, for example, which has employed an AI application provided by the German-based firm Scoutbee to source alternative suppliers at short notice.

Procurement approval can be a notoriously slow process as companies need to assess new suppliers using a wide range of criteria. Scoutbee’s AI-enabled software cuts through the online data available on potential suppliers, looking at information such as the firm’s key financial metrics, customer reviews, patents and design issues, and even social media feeds that can be primed to analyse issues such as recruitment and termination.

The data produces a shortlist of the best matches which the procurement department can then zone in on and engage with the top prospective suppliers to validate and make final decisions. While the AI tool doesn’t make the final decision, its rapid screening work makes that decision much easier and faster to make.

However, Johnston cautions that, despite its potential, organisations need to be aware of the limitations and risks associated with the use of generative AI in supply chains. Generative AI tools, he notes, are only as good as their input data and rushed implementations can result in poor and indeed damaging results.

“The quality and availability of data from supply chain partners is also of critical importance and organisations must take all possible steps to ensure it meets the required standard. Rather than rush into something out of fear of being left behind by competitors, it is much more advisable to take a measured approach to the implementation of generative AI tools in the supply chain, ensuring they are based on rock-solid data from across the supply chain.”

Sheehan agrees that organisations need to look beyond the hype and ensure that AI initiatives are solidly grounded.

“The journey to developing these applications for AI in the supply chain is not trivial. Frequently this journey is hampered by disparate and disconnected systems, unclean data sets and, in many instances, an unproven return on the potential return on the effort required.”