Procurement professionals — like most executives in the electronics supply chain – are excited by artificial intelligence. Half of supply chain leaders (CSCOs) surveyed by Gartner Inc. plan to implement generative AI (GenAI) in the next 12 months, with an additional 14 percent already in the implementation stage.
For all the promise of AI, though, some supply chain leaders view AI as a solution waiting for a problem. Noha Tohamy, distinguished VP analyst in Gartner’s Supply Chain Practice, disagrees.
“There are so many opportunities where GenAI can help humans get to insights more easily,” she told EPSNews. “That will result in significant productivity improvement – [businesses will] do more with the same or fewer staff. “
GenAI can have the additional benefit of getting more from existing technologies by making the interface more intuitive through natural language, she added. “This would improve adoption levels of those technology and generate higher return from previous technology investments.”
CSCOs expect GenAI will enhance productivity, improve business agility and reduce costs. But organizations are still so early on in their adoption that some of the expected benefits might be hyped or overestimated, and the risks might not be fully understood and therefore minimized, Tohamy said. “This is especially true in supply chain and procurement where many of the use cases require working and sharing data with external partners.”
Supply chain use cases
In procurement, GenAI will have the highest impact on sourcing and contract life cycle management and supplier information discovery and management, according to Gartner. The electronics supply chain is already using AI to quickly sift through and analyze reams of data; recommend product substitutions and identify alternative suppliers.
Source: Gartner, January 2024
Master electronics distributor Waldom Electronics is using a machine learning model to actively predict how inventory will sell and is exploring the use of AI to automate both the proactive and reactive sides of the buying process. Catalog distributor Digi-Key is using AI/ML for part classification and coding based on description and parametric data; customer vetting and the categorization of firmographic data and content keywords; recommending parts based on availability; and for data verification, autocorrection and notification based on invoices, contracts and other sources.
AI is also being used in the research and development of product designs. Minus “a few tweaks,” designs generated by AI are very accurate, industry participants say. Finding and vetting suppliers quickly are use cases cited by the Harvard Business Review that enhance supply chain agility.
One of the most impactful use cases, Tohamy said, is allowing supply chain staff to interact with the technology in natural language to ask questions and get in-context answers about KPIs and supply chain performance. Other uses include areas such as code augmentation, providing more insights into supply chain key performance indicators and staff assistance chatbots.
“Instead of a planner, for example, needing to navigate through multiple systems, reports, dashboards, excel spreadsheets, charts, through GenAI [users] can ask a question like: What’s my projected on time delivery metric for my customer, then proceed to better understand the drivers behind the metric and potential actions to take to resolve an issue or collaborate with internal teams to make better decisions,” Tohamy explained.
CSCOs are dedicating 5.8 percent of their function’s budget, on average, to GenAI, Gartner found. CSCOs see GenAI as supportive of their broader digital transformation objectives. But the supply chain lags other functions, such as marketing and sales, in adoption. Sixty-five percent of CSCO respondents said they will hire dedicated staff and experts to help deploy the technology in 2024.
The projected budget data shows that supply chain leaders are serious about making progress on GenAI solutions this year and that they also recognize the need for additional resources to successfully move beyond small-scale pilots, Tohamy said. CSCOs may also be factoring in impacts on employee roles required of their staff as they shift to higher value-add activities, while lower-level tasks are increasingly automated.
A solution to some problems
HBR offers this perspective on supply chain AI: AI tools are not a solution to all problems. The value of AI diminishes in stable supplier markets where alternative suppliers are already well known and where buyers face minimal uncertainty, according to the publication. Moreover, AI tools offer limited added benefits for robust relationships with partners who already share in-depth information.
Supply chain companies can capitalize on early learnings and technology investments from other functional partners, Tohamy said, and become “fast followers.” But it’s unlikely a single AI solution can build a corporation’s supply-chain capabilities, HBR concluded.
The post Which Supply Chain Problems Can AI Solve? appeared first on EPS News.
