Improve the Supply Chain With Advanced Analytics and AI

Gartner survey shows supply chain leaders using advanced analytics and AI to augment and automate supply chain decision making.

The rapid emergence of artificial intelligence (AI) and advanced analytics has lead supply chain leaders to explore  potential use cases. Gartner surveyed 260 of these leaders in late 2017 to assess their plans for exploring such technologies.

96% of respondents use predictive analytics, 85% use prescriptive analytics and 64% use AI

“When considering these insights, the reader should keep in mind that they reflect a population of advanced analytics leaders,” says Noha Tohamy, research vice president and distinguished analyst at Gartner. “The findings may not necessarily be reflective of all supply chain organizations, but can help supply chain leaders gauge their current practices and future plans and guide them along their analytics maturity journey.”

Key findings

Within advanced supply chain companies, defined as those using two or more of the three advanced analytics techniques — predictive analytics, prescriptive analytics and artificial intelligence — 96% of respondents use predictive analytics, 85% use prescriptive analytics and 64% use AI.

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“These findings should motivate supply chain leaders to assess their current usage of the three techniques and examine closely any divergence from other organizations,” said Tohamy. “If your organization has yet to deploy any of these capabilities, there should be a good reason for it. The findings can also help with choosing which technique to adopt first.”

The most common outcome is to either augment or automate human decision making

Ultimately, there are many technical applications for these three techniques but the most common outcome is to either augment or automate human decision making and in doing so elevate the productivity of human employees.

Gartner defines decision-making augmentation as using technology that generates insights and recommends actions for business users but leaves it to the human to analyze those insights and approve and execute the recommended actions.

Decision-making automation, on the other hand, is using technology that generates insights and recommended decisions and executes those decisions without human intervention.

Augment or automate?

On average, 58% of respondents already use predictive and prescriptive analytics for augmenting some human supply chain decisions, while half of respondents use them to automate some decision making. Just 10% of respondents have no plans to use analytics for decision augmentation in the next two years, rising to 12% for decision automation.

AI adoption is not as widespread currently. Today, 31% of respondents use AI for decision automation, and 34% for decision augmentation

“Areas like order fulfillment, production planning and demand forecasting are strong candidates for increased automation with advanced analytics,” says Tohamy. “Collaborative processes like sales and operations planning or risk management will continue to be better fits for decision-making augmentation.”

AI adoption is not as widespread currently. Today, 31% of respondents use AI for decision automation, and 34% for decision augmentation. When asked if they plan to use AI for either use case within two years, the proportion saying yes rises to 76% and 78% respectively.

“Supply chain leaders should focus on decision augmentation as a first step toward AI adoptions,” says Tohamy. “To compete effectively in future, however, midterm plans must include process automation. We advise leaders start to work with business leaders now to understand the technologies and build a vision for supply chain process automation in the near future.”

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