Top performing supply chain organizations are investing in artificial intelligence and machine learning (AI/ML) to optimize their processes at more than twice the rate of low performing peers, according to a survey by Gartner, Inc. The survey also revealed that the best supply chain organizations are using productivity, rather than efficiency or cost savings, as their key focus to sustain business momentum over the next three years.
“Top performing supply chain organizations make investment decisions with a different lens than their lower performing peers,” says Ken Chadwick, VP analyst in Gartner’s Supply Chain Practice. “Enhancing productivity is the key factor that will drive future success and the key to unlocking that productivity lies in leveraging intangible assets. We see this divide especially in the digital domain where the best organizations are far ahead in optimizing their supply chain data with AI/ML applications to unlock value.”
Key takeaways:
- The divide between high and low performers in optimizing processes with AI/ML hints at a deeper rift in strategy among organizations. Top performers increasingly prioritize extracting value from their digital assets to drive productivity, rather than making digital investments to achieve efficiencies such as cost savings.
- Among the Top 5 digital investments expected to deliver value, high performers are farther along in implementation across the board. These include partner with IT to establish unbreachable data security mechanisms (74% vs. 61%); create ethical and binding data privacy frameworks for use of customer data (68% vs. 50%); write cybersecurity measures into supplier and staff contracts (66% vs. 57%); capture supply chain specific customer satisfaction data (58% vs. 40%); and analyze and leverage supply chain specific customer use and satisfaction data (57% vs. 35%).
- When managing resource investments, top performing organizations were far ahead in collaborating with suppliers to maintain supplier consistency and working exclusively with suppliers that have their own risk controls.
“Capturing, protecting and then leveraging an organization’s data through the use of AI/ML is an example of how organizations are increasingly turning towards intangible assets to extract new sources of value,” says Chadwick. “High performing organizations are moving beyond the initial implementation stage to full adoption, resulting in better decision making that unlocks new sources of value.”
