Artificial intelligence (AI) is impacting every economic sector including supply chain operations. Tech writer Jack M. Germain observes, “AI has changed the supply chain process from reactive to proactive, which creates a larger change in how data-driven processes will operate in the future. The true role of AI in the supply chain is to enhance and augment human intelligence and decision making. That is much different than what some people view as making human intelligence obsolete.”[1] In Part 1 of this article, I provided a general overview of why experts insist AI solutions are necessary for improved supply chain operations. In Part 2, I want to discuss specific ways AI can improve supply chain operations. Because supply chains have become so complex and geographically dispersed, Mike Hulbert, vice president of consumer business at Noodle.ai, insists they are vulnerable to “operations entropy.”[2] He explains, “[Operations entropy is] the disruption of well-laid plans by forces that are generally thought to be unpredictable. With advances in computers, data storage and machine learning, operations entropy is finally being defeated.” Below are some specific ways AI solutions can be used help defeat operations entropy and improve supply chain processes.
Area Where AI Can Optimize Supply Chains
Artificial intelligence systems are not standalone solutions. By that I mean they rely on data generated by other systems. For example, in the modern industrial setting, AI systems are part of an Internet of Things (IoT) ecosystem. IoT ecosystems involve embedded sensors (i.e., the things being connected) that generate enormous amounts of data, connectivity (i.e., the IoT), and analytics (i.e., embedded in AI systems that analyze the data for insights and action). AI provides its greatest benefit as part of these ecosystems and can be leveraged in the following ways.
Asset Tracking. Andrew Meola (@AMeolaTheStreet), Director of Subscription Marketing for Business Insider Intelligence, explains, “One of the biggest trends poised to upend supply chain managers is asset tracking, which gives companies a way to totally overhaul their operational efficiency by giving them the tools to make better decisions and save time and money. And this transformation is already underway. A recent survey by GT Nexus and Capgemini found that 70% of retail and manufacturing companies have already started a digital transformation project in their supply chain operations. Asset tracking is not new by any means. … [However,] newer asset tracking solutions offer much more vital and usable data, especially when paired with other IoT technologies.”[3] AI is what makes the data vital and usable.
Improved Supervisory Control and Data Acquisition (SCADA) Monitoring. Jim Chappell (@JamesPChappell), Global Head of AI and Advanced Analytics at AVEVA, explains, “Real-time and historical data is typically used for trending, reporting, and HMI visualization. AI allows companies to get much more value and insight from this historical data through state-of-the-art technologies such as multi-variate machine learning and deep learning. By integrating software infused with AI into existing industrial IT infrastructures, businesses can greatly amplify the value and ROI by detecting and solving operational and maintenance issues before they become larger problems that often result in unplanned downtime. This alone can increase uptime by 10% annually, resulting in substantial avoided costs and efficiency gains.”[4]
Industrial Waste Management. Robert J. Bowman, managing editor of SupplyChainBrain, writes, “If we want a world without waste, we might need something other than the human brain to achieve it. The answer, as with so many other aspects of business today, lies in artificial intelligence — in this case, its ability to eliminate industrial waste in manufacturing.”[5] Stephen Pratt, chief executive officer of Noodle.ai, told Bowman, the World Bank estimates that global industrial waste today is 18 times larger than municipal solid waste — “the things we call trash.” According to Pratt AI offers “a fresh approach to waste control. The difference, says Pratt, is the use of complex algorithms to predict when excess parts, products and practices are threatening to clog up the works. Such alerts allow humans to take action to head off the problem before it affects the flow of product.”
Risk Management. Journalist Arthur Cole (@acole602) notes AI can be used to assess risk.[6] He explains, “Today’s management stacks tend to flood workers with alerts without assigning any priority. AI has the ability to quantify risk so organizations gain broad visibility into the most crucial detriments to efficient operations. Even if the problem requires time and expertise, that money is well spent, and solutions highlight the ways AI and human intelligence can work together to produce the most desirable outcomes.”
Inventory Management. Trade journalist Rumzz Bajwa (@rumzzbajwa) writes, “Because of AI’s ability to process huge amounts of data, identify trends, and take into account recent world events, companies are now using AI to study consumer habits and the ups and downs of seasonal demand. This allows companies to prevent stocking unwanted inventory, which is not only a waste of space but also means the customers are not getting what they want, which really translates into a loss of revenue. Inventory management is an overall complex process, with many aspects like order processing and packing involved. Companies strive for accurate inventory management because it prevents understocking, overstocking, or sudden stock-outs in unpredictable circumstances, all of which could translate into hefty costs. AI can automate various processes in inventory management, reducing the risk of error, and providing valuable predictive data on supply and demand.”[7]
Better Decision-Making. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), assert if you can improve a company’s decision making you can dramatically improve its bottom line. They explain, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”[8] Bajwa adds, “Given the complexity of modern supply chains, it’s no surprise that supply chain professionals are often faced with difficult decisions. Huge amounts of data to sift through and limited end-to-end visibility makes these decisions even more difficult and risky. Supply chain optimization software integrated with AI allows machines to analyze large amounts of data and detect patterns that are hard for humans to see. AI can then offer actionable insights to professionals, allowing them to make AI-backed decisions, and make them fast and at the right time. This can have a major impact on the overall efficiency of a supply chain.”
Concluding Thoughts
The above list is illustrative, not exhaustive. Ryan Abbott, professor of law and health sciences at the University of Surrey School of Law, insists, “AI has tremendous potential to impact the global supply chain. It can do this by taking over time-consuming and error-prone manual work. This can involve AI more efficiently predicting demand, improving delivery times, reducing costs, and taking over customer support roles. The complexity of global logistics networks involving hundreds of sourcing, production, and distribution systems makes the use of AI critical to ensuring smart and agile decisions.”[9] From planning to production to distribution, AI can make a difference.
Cole predicts, that as companies’ familiarity with AI matures, they will find new ways to leverage its capabilities. He explains, “Most of the thinking around AI in the supply chain tends to center on how it will enhance today’s processes. But as markets evolve into the new century, AI will also help create and manage entirely new forms of multi-layered, dynamic chains serving highly virtualized and cloud-based business models. Even today’s emerging omnichannel environments require precise coordination between customer-facing infrastructure, warehousing, transportation, fulfillment, and a range of other disparate functions. … Much of this will have to be automated to accommodate the speed of business, something that can only be done through advanced intelligent systems that talk to each other with perhaps intermittent human oversight.”
Footnotes
[1] Jack M. Germain, “AI’s Potential to Manage the Supply Chain,” Tech News World, 30 October 2020.
[2] Mike Hulbert, “Five Ways to Leverage AI in Supply Chain Management,” SupplyChainBrain, 16 May 2021.
[3] Andrew Meola, “How AI and IoT devices will revolutionize supply chain logistics and management in 2021,” Business Insider, 22 February 2021.
[4] Jim Chappell, “Unlocking the Value of Supply Chain with AI-driven Processes,” Supply Chain Digital, 15 May 2021.
[5] Robert J. Bowman, “How AI Is Tackling Waste in Factories and the Supply Chain,” SupplyChainBrain, 7 June 2021.
[6] Arthur Cole, “How AI can simplify, streamline, and enhance supply chain operations,” VentureBeat, 4 June 2021.
[7] Rumzz Bajwa, “How AI is Enhancing Supply Chain Performance,” Global Trade, 28 May 2021.
[8] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[9] Germain, op. cit.
