The Supply Chain of the Future – Tomorrow's Vision or Today's Reality?
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AI adoption in supply chain is accelerating, but barriers to success abound, analyst says
Artificial intelligence (AI) will revolutionize how—and how well—we manage supply chains in the future. And that future is much closer than many supply chain professionals may think, said Gartner's Supply Chain Executive Conference in Phoenix, Arizona, in May.
Gartner defines artificial intelligence as technology that learns from data and experience, without human direction, and then comes up with (often unexpected) results. It incorporates a variety of data science technologies, such as natural language processing, machine learning, optimization, cameras, speech recognition, and many others, in different combinations. Tohamy broke AI into two categories: augmentation, where technologies assist humans by improving their decisions and eliminating some human bias; and automation, which makes decisions entirely on its own, better and/or faster than a human could.
AI can help supply chain organizations do many things more quickly and easily than humans can, Tohamy said. For example it can harmonize data from disparate systems across an enterprise, find missing data and identifying errors, very quickly process transactions, and refine processes based on experience. AI can also be applied in supply chain decision making. Some of the examples Tohamy cited included:
- Identifying and mitigating risk exposure. By pulling data from many different sources, the technology can not only identify patterns and highlight risk but also recommend mitigating actions by examining previous responses to similar situations and identifying which were most successful.
- Demand sensing and shaping. To show how AI could be used to sense and shape demand, Tohamy highlighted the example of Vivanda's FlavorPrint program. Vivanda, a food technology company, has analyzed hundreds of thousands of ingredients, products, and recipes and mapped that data to aroma chemicals that describe the way a particular food, beverage, or ingredient tastes. Based on consumer-provided data, the company can create a unique "digital taste identifier" for that person. It can then shape demand by matching the consumer's flavor preferences to recipes, ingredients, and food products and making recommendations. Vivanda shares this data with food manufacturers, ingredients suppliers, and other food and beverage industry customers, enabling them to sense demand.
- Supply chain planning. AI could be layered on top of or inserted in the middle of a company's complex hierarchy of supply chain planning analyses and decisions, or what Tohamy called the "planning stack." The technology could then be used to identify and resolve exceptions to the plan more quickly than a human could.
Improvements in AI's natural language comprehension and response could also make it easier for humans to use and interact with complex software. Gartner predicts that by 2020, 80 percent of supply chain enterprise software applications will include conversational AI, such as "chatbots" that converse with users verbally or by text. One manufacturer Tohamy knows of is piloting the use of conversational AI in a product-configuration system.
There are a number of challenges to implementing artificial intelligence in supply chains. For one thing, there is not yet enough good-quality data to successfully apply it across the extended supply chain, Tohamy said. For another, it requires a lot of effort to maintain the data, ensure that it's aligned with corporate priorities, and prevent human bias from creeping in. There may be too much hype and not enough understanding yet of how it works and where to apply it, she added.
Supply chain organizations are already struggling to find enough data scientists to analyze the data that they are collecting. Organizations that adopt AI will be challenged to make sure they have the right people to develop, implement, and maintain those applications. Because so much will change after artificial intelligence, they will also need to redefine future individual and team performance metrics, she counseled. "Ask yourself now, how are you going to measure the success of your team in the age of AI?"
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