How Supply Chain Managers Can Use
Artificial Intelligence to Their Advantage

Artificial intelligence (AI) now touches almost every industry and walk of life.

Driverless cars are chauffeuring themselves through Pittsburgh’s roads, robots dominate the activity within Amazon’s warehouses, and increasingly sophisticated algorithms for demand planning and design have gone digital. Personalization and machine learning continue to improve customers’ experiences and open countless doors for the tech industry—and the supply chain space lies right in the middle of many of these changes.

According to a study by Gartner, supply chain organizations expect the level of machine automation in their processes to double in the next five years. Today’s computing capacity and big data sets empower supply chain leaders to make smarter decisions. So, what could the future hold for supply chain managers?


As machine learning grows increasingly complex, tools may eventually make better decisions than even the best supply chain managers. Predictive analytic capabilities allow machines to automatically place orders based on a customer’s order pattern, minimizing delivery time—which means next-day delivery will become too slow for some customers. Key players in tech and supply chain, such as Amazon, Oracle, Salesforce, and GE, are already positioning themselves to take advantage of these benefits.

Amazon is a clear leader in supply chain sophistication, and is rumored to have 1,000 employees working in AI. On top of that, the e-commerce giant is also bringing AI to the masses through Amazon Machine Learning and Amazon Echo, changing the lines of communication between consumers and suppliers. The data it collects helps sense demand on a precise, individual shopper level.

Similarly, Oracle’s Adaptive Intelligent Applications leverage insights generated from its Data Cloud to learn from more than 5 billion business and consumer profiles. Its enterprise applications can automatically find the best options to distribute goods across specific geographic locations, bringing down costs for shippers.

At Dreamforce, Salesforce revealed details about its newest AI service, Einstein, which could push Salesforce into the demand tracking arena. The new service collects and processes data from customers’ email, social media accounts, calendar, and devices—providing Salesforce with a wealth of information to enhance their machine learning algorithms. Einstein has the potential to shake up supply chain management by boosting Salesforce’s power-player status into the predictive demand market.

GE Predix is also becoming a power player in the AI market as it brings industrial automation to the cloud. As of July 2016, GE had nearly 12,000 developers working on Predix. The company acquired ShipXpress in August 2016, and plans to combine its cloud-based collaboration and rail-shipment reporting software with Predix’s data analytics capabilities to effectively monitor industrial assets. Predix links supply chain, distribution, manufacturing, and engineering services into a single intelligent system. The new software will have significant impacts on rail, truck, and intermodal shipping.

Peering Into the Future

With more and more companies hopping on the AI bandwagon, how will it affect supply chain planning across industries? Retailers and their consumers will be the top beneficiaries from this new technology as this industry has the most to gain from demand data for supply chain management. Aside from retail, AI could affect virtually any manufacturing and distribution business. In the next decade, it is expected that AI will transform healthcare and finance markets, and there is no reason to think it will not eventually trickle into most industries over time.

Technology has long been both a disruptor and differentiator for supply chains. The AI race is on, and companies are competing to provide the quickest and most personalized service. Across industries, supply chain managers should consider AI’s impact on their demand algorithms, product design, transportation fleet, manufacturing floor, and warehouse operations. It is difficult to forecast exactly where AI will take supply chains in the distant future, but for now it is clear supply chain managers must consider how AI can improve their own operations—or risk losing ground to their competition.

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