Adopting new capabilities in supply chain technology is a priority for organizations, but they are approaching the project cautiously.
A new survey finds that investment in advanced technologies such as generative AI for supply chains is strong, but companies are re-evaluating the value and risks of GenAI.
For example, a recent report from global professional services firm EY found that while companies are enthusiastic about using GenAI in supply chain applications, the majority of GenAI projects have been put on hold as other priorities take precedence.The report is based on a survey of 460 senior supply chain leaders around the world conducted by EY and global research consultancy HFS Research in February and March 2024.
According to the report, 73% of respondents plan to deploy GenAI technology in their supply chain, but only 7% have completed implementation, and 62% are reevaluating projects to determine the risks and expected ROI of more widespread use of generative AI.
According to Glenn Steinberg, EY global supply chain and operations leader and one of the report's authors, there are challenges and risks that are causing supply chain leaders to pause GenAI projects.
The five main challenges, according to survey respondents, are concerns about the quality and governance of data feeding into GenAI applications, uncertainty about evolving regulatory regimes, risks around privacy and cybersecurity, the complexities of hardware and software integration, and a skills shortage for GenAI practitioners.
If you are already furthest along in your journey to an autonomous supply chain, it's data that counts, and so you'll have the best chance of success with GenAI.
Glenn SteinbergEY Global Supply Chain and Operations Leader
According to Steinberg, supply chains are moving from a linear model with one-to-one relationships to a networked, multi-party ecosystem. At the same time, supply chains are evolving to become more autonomous, with many of the activities handled by systems rather than humans.
Organisations that are further along in the transition to autonomous supply chains are more likely to be successful with generative AI because they have more effective data strategies, he said.
“For the companies that are already furthest along in their journey to autonomous supply chains, it's all about the data,” Steinberg said, “which is why they're more likely to be successful with GenAI.”
While GenAI is relatively new, AI itself is not foreign to supply chain applications: EY research found that 90% of respondents have deployed some form of traditional AI in their supply chain.
But there's a big difference between using traditional and generative AI in the supply chain, according to Matthew Barton, EY EMEIA supply chain and operations leader and report co-author.
Traditionally, AI has focused primarily on predictive analytics, such as sensing demand, generating forecasts for upcoming sales or predicting when equipment will break down or need repairs, Barton said.
“GenAI and LLM [large language models] “This typically emerges as a subset of AI, but it opens up entirely new territory, because suddenly you have the opportunity to ask questions in a natural language context and interpret and process vast amounts of data,” he said.
Organizations are concerned about risks such as hallucinations, and know it's important to get the data foundation for GenAI right before relying on it to make decisions, Barton said. To this end, organizations are making more use of search augmentation generation (RAG), which combines LLM with external and internal data sets.
“If you come into a new company as a supply chain planner and there are hundreds of reports across the supply chain, it's going to take you months to learn,” he says. “But if you have access to those reports in RAG AI, you can just ask questions like, 'Who are our best-performing customers?' or 'Why were we short on service options last week? What products are involved?' And the AI ββis pulling all that data from its own core, so it's not fabricating data.”
Barton said concerns about GenAI's results, including hallucinations and possible bias in the dataset, are among the main reasons supply chain leaders are giving it pause on adoption.
“Boards are starting to get involved in this space, asking their executives if there are responsible AI frameworks that they can see,” he said. “This is making companies realize that they have work to do there. The key is having AI-ready data.”
Cost concerns drive further development of supply chain technology
Other research shows that organizations are increasingly adopting advanced technologies to mitigate supply chain challenges.
According to the 2024 Agility Index report by Nucleus Research and ERP vendor Epicor, rising costs in the supply chain are one of the top concerns for organizations, and companies are deploying technologies such as generative AI, machine learning, automation and advanced robotics to address this.
The Agility Index is a survey of 1,700 supply chain leaders across industries around the world.
According to the results, 58% of all organizations and 63% of fast-growing organizations say they are currently using GenAI in the supply chain, primarily through ERP systems or supply chain management applications. The most common use cases for GenAI in the supply chain are customer-facing chatbots (72% of respondents), product description generation (67%), in-application assistants (63%), narrative and management reporting (59%), and natural language queries (53%).
“The Agility Index shows that organizations are turning to these emerging technologies to combat rising costs and make their supply chains more resilient, especially with the uncertainty of the future,” said Sam Hamway, senior analyst at Nucleus Research. “This is exacerbated by the fact that the highest-growth organizations have the capital to invest in these research and development initiatives and are prepared to take risks to become more resilient.”
But GenAI adoption is difficult to measure, Hamway said. The technology is new, immature and still being integrated into corporate strategies and roadmaps. Plus, most use cases in the supply chain are coming from features that supply chain technology vendors are adding to existing applications, he said. For that reason, the Agility Index didn't look at the maturity of GenAI implementations.
“The thing to keep in mind is that the majority of GenAI initiatives aren't being undertaken by actual companies, but rather are embedded into existing software applications,” Hamway says. “In many cases, they're so deeply embedded into workflows that they go largely unnoticed or won't be recognized by many as a GenAI initiative within their organization.”
The use of GenAI chatbots added to existing applications is different from organizations building their own GenAI applications, which may be put on hold due to data privacy and other concerns, Hamway said.
“While many organizations may have paused building their own stuff, in terms of GenAI existing within organizations, it's there and it's growing,” he said.
Hamway said the growing use of generative AI is also a different issue than the use of traditional machine learning-based AI in the supply chain.
“Traditional machine learning is so embedded in the workflow of business applications that most people are unaware of its existence,” he says. “Any kind of forecasting is typically a machine learning model, and that's especially true in supply chain. For example, supply chain planning is typically an optimization problem solved by machine learning algorithms.”
Supply chain technology solves problems
Another recent survey of 150 North American supply chain leaders conducted by supply chain network and electronic data exchange provider TrueCommerce revealed that companies are facing a variety of supply chain challenges and are adopting new technologies to become more efficient, accurate and flexible.
The vast majority of respondents (95%) expect some kind of supply chain difficulty this year, with survey participants citing inflationary pressures, labor shortages and changes in order pricing due to cybersecurity threats as the biggest challenges.
Addressing order fulfillment challenges involves implementing technology that can increase organizational efficiency, improve customer experience and improve product on-time delivery, according to Ryan Tierney, senior vice president of product management at TrueCommerce.
“Over the years, businesses have had to adapt to how they sell and buy products in the marketplace,” Tierney said. “More and more businesses are moving from traditional B2B and brick-and-mortar to B2C and direct-to-consumer.”
He said the biggest supply chain challenge identified in the report was inventory accuracy and visibility, especially now with regulations around product tracking and traceability in industries such as food and beverage, companies are also facing issues knowing where their products are and when they will be shipped.
“If you say you have a product but you don't have it, it's going to be a bad experience for the customer and they're not going to return,” Tierney said.
Companies are adopting supply chain technology to help manage issues such as multi-channel fulfillment and supply chain visibility, he said.
According to the report, supply chain leaders are eager to upgrade their supply chain systems, with 70% of respondents saying they plan to spend more on supply chain software this year than they did last year. Additionally, 91% of respondents plan to invest in ERP systems to support their supply chain processes, with 70% planning to invest this year and 21% planning to invest by 2027.
According to Tierney, much of this investment is being driven by the migration from on-premise systems to cloud ERP systems.
“Cloud ERP adoption continues to grow,” he says. “Companies are constantly faced with changing market expectations and must make investments to keep up.”
Jim O'Donnell is a senior news writer for TechTarget Editorial covering ERP and other enterprise applications.
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