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The Future Is Here: Artificial Intelligence in Supply Chain/Logistics Management

Edited for Logistics & Pallet Management by Paul W. Norman May 2023

In February 2019, U.S. executive orders outlined U.S. priorities in Artificial Intelligence (see DisCo’s post on the American AI Initiative here). In May 2019, the Organization for Economic Cooperation and Development (OECD) approved key principles to guide a healthy adoption of AI. Amidst this focus on regulating AI, private businesses are eager to incorporate these technologies to improve performance. This post examines an area where AI application has been pervasive and robust across industries: supply chain management (SCM), and more specifically, transportation, logistics, and shipping material supply management.

What We Mean by AI

AI refers to a category comprised of a whole host of technologies that mimic cognitive functions, traditionally ascribed to the human mind, including neutral networks, natural language processing, robotics, expert systems, intelligent systems and manual labor manufacturing. In the commercial context today, the term is most often used to refer to speech and vision recognition systems, machine learning, and deep learning.

Machine learning is a branch of AI where systems can “learn” from data, identify patterns, and make decisions with minimal human assistance. In all cases, learning occurs when a machine takes the output, observes the accuracy of the output, and updates its own model so that better outputs will occur. In wood pallet manufacturing and recycling for example, pallet making machines can use AI to calculate and record changes in customers’ pallet size and weight orders and learn from all of the data variances. In this way, pallet suppliers can minimize waste and maximize customization of order to increase profits and better manage customers’ expectations.

Consumers do not need to look far to see the vast capabilities offered by the applications of these technologies. Virtual assistants like Apple’s Siri and Amazon’s Alexa use machine learning and natural language processing to parse and classify voice commands. Interactive “chat bots” on many retail, commercial, industrial or banking websites use natural language generation to respond to customer’s questions. Cantarero Pallets, Inc., a leading manufacturer and supplier of wood pallets located in Wauconda, IL., uses AI enhanced chat bot technology to make response to customer inquiries for quotes, etc., almost instantaneous. “When a potential customer sends us a quote request form from our website, our system admin triggers an immediate order response flow sending that information to me via my cell phone, wherever I may be so I can contact them back immediately,” says Anwar Cantarero, CEO of Cantarero Pallets, Inc. “ The AI technology empowers us and enhances our immediate response to developing new customers, and quickly responding to current customers’ needs. The response from new customers has been overwhelming.”

From autonomous vehicles to wood pallet manufacturing to facial recognition devices and personalized medicine, the use cases of AI extend across industries.

Getting Goods to the End Consumer

Behind the scenes, companies have been applying AI in supply chain management in ways less obvious to the end consumer. From the automotive industry, transportation, logistics to the pharmaceutical and consumer electronics industries, businesses are dealing with increasingly complex and globalized supply chains. In modern multi-tier supply chains, hundreds or thousands of suppliers may contribute to a single product. The process of procuring raw materials, managing trading partners, and sequentially planning and executing tasks with huge volumes of data has become a much larger task, requiring heavy-duty data analytics. Unnecessary delays, caused by mismanagement, could result in supply-demand mismatch, shortages, overstocking, and poor customer experience.

Demand Forecasting. Machine learning has been used to forecast demand using historical shipping data since the early 2000s. Procter & Gamble Company (P&G), for instance, has used sophisticated modeling to reconcile demand signals from point-of-sale data, retailer warehouse and outlet inventory, and retailer forecasts for over a decade. In 2018, P&G announced that it will globally adopt the demand planning tool by E2Open, an AI software provider for supply chains. P&G, along with Amazon, UPS, Walgreens Boots Alliance, and other Fortune 500 companies, are using advanced machine learning algorithms to optimize demand plans for product launches, adjust stocking strategies, and/or find optimal delivery routes. Some retailers are now incorporating competitor pricing data, store traffic, and weather data to improve demand forecasts.

Process Engineering. Supply chain managers are also using robotic process automation (RPA)—software bots that mimic human actions—to automate repetitive, rule-based operational tasks. While RPA has traditionally involved less AI, this is changing. Businesses are coupling automation with predictive analytics to better manage operational processes. Some specialized supply chain-focused AI software providers, like AspenTech, help with plant production schedules and manage assets at chemical companies, wastewater treatment facilities, and metals and mining companies, where shutting down machinery could have environmental, health, and safety implications.

Risk Management. The AI-enabled rapid decision-making has also helped mitigate unexpected disruptions to a supply chain—whether it be a cyber-attack, bankruptcy, or failure by a supplier to meet regulatory standards. KPMG, for example, was commissioned to build a tool to review thousands of documents to facilitate an acquisition deal. The client had sold billions of dollars' worth of assets to the acquiring firm and needed to send to the acquirer the documents relevant to the sold assets, while withholding sensitive internal documents. KPMG used machine learning and cognitive automation technology to analyze 2 million documents and classify those that would go to the seller versus the acquirer. As this case exemplifies, machine-learning software can significantly reduce transaction costs for the seller and acquirer and lessen the impact of the merger or acquisition to supply chains.

In sum, businesses are applying AI to optimize supply chain management across industries. In addition to having more personalized products, the benefit to consumers also includes the more cost-effective means in which businesses are delivering those products.

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