What AI and ML Can bring to Supply Chains

Businesses of all sizes continually seek to enhance their supply chain management's efficiency, accuracy, and resilience. The advent of AI, particularly Gen AI and machine learning (ML), has opened new avenues for companies to elevate their optimisation strategies.

A recent CGI study, commissioned by Supply Chain Digital and Manufacturing Digital, highlights the strategic importance of AI investment. The research found that 69% of executives believe AI, advanced analytics, and digital twins will be crucial in supporting their supply chain initiatives over the next few years.

Furthermore, Epicor and Nucleus Research report that 63% of high-growth companies—those with revenue growth exceeding 20% in the past three years—have already incorporated Gen AI into their supply chain operations to address cost and operational challenges.

This data underscores the growing recognition of AI's potential to transform supply chain management, especially as businesses navigate ongoing economic uncertainties.

“Over the past few years, a spotlight has been put on the global supply chain as continued disruptions reveal an overreliance on out-of-date practices like spreadsheet tracking and a lack of reliable demand forecasting,” explains Fang Chang, Executive Vice President and Chief Product Officer at Coupa.

“AI and ML can ultimately increase the efficiency and resilience of the supply chain through improved forecasting. AI can quickly analyse vast amounts of data, including historical sales, market trends and external factors like weather and economic conditions, leading to more precise inventory management and ultimately benefiting a business' bottom line.”

Companies big and small are constantly looking for new ways to improve the efficiency, accuracy and all-round resilience when it comes to supply chain management. 

AI, alongside the game-changing evolutions that are generative AI (Gen AI) and machine learning (ML), have unlocked a new level of potential for firms to take optimisation to the next level. 

Proof of the extent to which organisations are treating AI investment as a strategic priority can be seen in the results of a recent study carried out by CGI, commissioned by Supply Chain Digital and sister title Manufacturing Digital, which revealed the technology is already a key area of focus for leaders as they look to successfully navigate an ongoing period of uncertainty. 

Asked which technologies will support their supply chain initiatives over the next couple of years, more than two-thirds (69%) of executives opted for AI, advanced analytics and digital twins. 

Meanwhile, according to Epicor and Nucleus Research, a significant proportion (63%) of businesses identifying as ‘high growth’ – those that have achieved revenue growth of 20% or more over the past three years – have already integrated Gen AI into their respective supply chain operations to manage cost and operational challenges. 

“Over the past few years, a spotlight has been put on the global supply chain as continued disruptions reveal an overreliance on out-of-date practices like spreadsheet tracking and a lack of reliable demand forecasting,” explains Fang Chang, Executive Vice President and Chief Product Officer at Coupa.

“AI and ML can ultimately increase the efficiency and resilience of the supply chain through improved forecasting. AI can quickly analyse vast amounts of data, including historical sales, market trends and external factors like weather and economic conditions, leading to more precise inventory management and ultimately benefiting a business' bottom line.”

The remarkable rise of Gen AI and ML

Many have been caught off guard by Gen AI and ML’s rise to mainstream prominence, which really began towards the tail end of 2022 – coinciding with the launch of OpenAI’s groundbreaking tool ChatGPT. 

Greg Hanson, Group Vice President and Head of EMEA North at the US tech giant, Informatica, was not among them.

“The ability to harness data and exploit AI’s potential is going to be the difference between the winners and losers in the market,” he says. 

“This is particularly the case in supply chains, where organisations increasingly need the agility to react in real-time to dynamic requests, quickly responding to supply chain shocks or black swan events.”

Greg highlights that adoption of AI and ML remains in its early stages, with most global supply chains still only harnessing these technologies to streamline and improve the “low-hanging-fruit challenges” they face, such as generating a single, 360-degree view of supplier profiles. 

More widespread and deeper adoption will not suddenly happen overnight, as demonstrated by EY and HFS which found just 7% of businesses have successfully implemented Gen AI into their operations. Meanwhile, 62% have had to reassess their projects altogether. 

The same report showed that, even among those already rolling out Gen AI, only 28% have achieved a “low-human-touch” supply chain, while half have achieved end-to-end visibility. 

Greg’s professional opinion is one supported by countless other technology experts – that AI and ML can only be as successful as the data feeding it.

He continues: “To realise the transformative powers that these two powerful technologies offer, organisations will first need to ensure they have the solid data foundations in place.”

How Coupa is leveraging AI

Coupa is no stranger to harnessing the power of AI to enhance its operations and provide a first-class service to customers, even branding itself as an AI-driven total spend management platform. 

Its community-generated AI empowers companies to make smarter, more profitable business decisions and improve operating margins, leveraging insights from US$6tn worth of direct and indirect spend data across a global network of more than 10 million buyers and suppliers.

Crucially, Coupa’s AI continuously learns to predict, prescribe and automate actions, integrating across the platform to enhance supply chain visibility, future-proof operations and mitigate risks. 

Shedding light on how Coupa’s AI-powered capabilities are enhancing global supply chains, Fang adds: “Our Supply Chain Prescriptions solution identifies and prioritises the highest cost drivers in the supply chain, offering actionable insights to reduce expenses and carbon emissions.

“By providing stakeholders with enhanced scenario management data, businesses can make informed decisions, optimise transportation routes and validate cost-saving strategies through digital twin technology. 

“This tool transforms data analysis into strategic work, ensuring efficient and sustainable operations.”

Coupa’s outstanding work in the AI space continues at pace, with a host of additional AI-driven automations announced throughout this year, including at the Coupa Inspire conferences in Las Vegas and Vienna.

These innovations are set to support businesses by increasing payment automation, optimising workflow processes and creating more profitable buying experiences. 

AI eases supply chain data management 

Advances in AI and ML are allowing Informatica to bring intelligence and automation into the data arena. 

For example, its Gen AI application, CLAIRE GPT, enables customers to use conversational prompts to effortlessly access, understand and use datasets.

It means companies can effectively manage supply chain data from disparate sources and create a trusted foundation of reliable, clean, well-governed data, thus improving wider operations. 

Greg references a public health organisation, responsible for ensuring the availability of safe and effective medicine throughout the entire drug lifecycle, as a high-profile beneficiary. 

“They’ve been empowered to create a 360-degree view of a drug’s lifecycle, from inception, research, manufacturing and regulatory changes through to marketing. 

“This includes visibility of the global drug supply chain, product ingredients, suppliers and facilities, bringing them into one trusted view. It empowers the organisation to gain visibility into all aspects and touchpoints and ensure their integrity, enabling them to respond to changes in supply and demand and, ultimately, save lives.”

The future of AI in supply chain

Greg’s take is that supply chains are on the cusp of major changes, with the firms behind them increasingly grappling with regular disruption and expected to decarbonise at pace to comply with global sustainability reporting requirements. 

Enter AI and ML, which, over the coming years, look set to aid forecasting and planning while increasing visibility, traceability and transparency. 

“In this new era, AI and ML knowledge is power,” Greg goes on. “For example, supply chain managers can forecast product demand based on historical sales and market trends or run ‘what if’ stimulations to prepare for unexpected events and better understand discrepancies in the supply chain. 

“Enhanced visibility allows for sustainable decisions on a day-by-day basis, so companies can proactively address issues and optimise their environmental operations responsibility rather than simply reporting historically on what has happened. 

“Businesses can also easily trace materials and inventory by supplier, to quickly see which suppliers are meeting sustainability goals and even view geographical regulatory requirements to improve their own business.”

Fang is largely on the same page. He says AI and ML will continue to play a central role in making supply chains more responsive and resilient, while there will be a focus on ensuring the switch to digitisation is more straightforward for businesses and suppliers. 

“We expect a continued focus on predictive analytics, allowing businesses to better forecast both demand and shortages brought on by geopolitical tension and the continuously-changing climate,” he adds.

“Additionally, there will be an emphasis on improved integration so that AI and ML can more seamlessly integrate with existing systems and take little to no technical expertise to deploy and use.”

Greg emphasises, however, that the data management foundation is critical to rolling out AI and ML capabilities, comparing it to the machinery used by manufacturers to make cars. 

He concludes: “Companies that can effectively manage supply chain data from disparate sources – and create a trusted foundation of reliable, clean, governed data – can take advantage of the capabilities in AI and ML data management.”

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