In the years leading up to the Covid-19 pandemic, most people paid little attention to supply chain operations. Following the outbreak, that all changed. Supply chain disruptions dominated the headlines. People suddenly understood what had always been true: supply chains shape our lives. A year into the crisis, Sriram Narayanan, the Kesseler Family Endowed Faculty Fellow of Supply Chain Management at Michigan State University, explained, “Supply chain management plays a central role in the quality of life we enjoy every day, whether it be products we shop in a retail store or meals that our kids are served in school. It makes products and services affordable, accessible and available to every human being in the planet.”[1] A few months later, journalist Amanda Mull wrote, “Everyday life in the United States is acutely dependent on the perpetual motion of the supply chain, in which food and medicine and furniture and clothing all compete for many of the same logistical resources.”[2]
As Mull pointed out, the term “supply chain” masks the enormous complexity of supply chain operations and is often misunderstood. She wrote, “As everyone has been forced to learn, when the works get gummed up — when a finite supply of packaging can’t keep up with demand, when there aren’t enough longshoremen or truck drivers or postal workers, when a container ship gets wedged sideways in one of the world’s busiest shipping lanes — the effects ripple outward for weeks or months, emptying shelves and raising prices in ways that can seem random. … All of this was supposed to be better by now. Not perfect — even a triumphant end to the pandemic wouldn’t stop climate change or political unrest from throwing their own wrenches into global logistics — but better.” One of the things that was supposed to make supply chains better was artificial intelligence (AI).
AI was Supposed to Make Things Better
Just prior to the global pandemic, anyone who paid attention to supply chain operations knows that there was great optimism about how artificial intelligence would enhance global value networks. For example, in 2018, marketing specialist and business consultant Lucy Benton wrote, “Artificial Intelligence … is already enhancing our lives as consumers, now it is picking up momentum in supply chain management and logistics. … With the volumes of data in supply chains and logistics growing every day, the need for more sophisticated processing solutions is becoming more urgent. That’s why many companies are adopting such AI computing techniques as machine learning, deep learning, and natural language processing.”[3]
Early adopters were providing evidence of the value of AI. In January 2018, the staff at Business Insider reported, “AI’s ability to streamline so many supply chain and logistics functions is already delivering a competitive advantage for early adopters by cutting shipping times and costs. A cross-industry study on AI adoption conducted in early 2017 by McKinsey found that early adopters with a proactive AI strategy in the transportation and logistics sector enjoyed profit margins greater than 5%. Meanwhile, respondents in the sector that had not adopted AI were in the red.”[4]
As it turned out, AI was not a silver bullet solution to supply chain operations — a truth that became apparent during the height of the pandemic. One challenge faced by organizations trying to implement AI solutions was finding the right data. Shannon Vaillancourt, President and Founder of RateLinx, explained, “We can leverage artificial intelligence to use this data to make the right decision to move forward faster, smarter and cheaper. Without the good data, however, AI may just as well be making the wrong decision.”[5] Despite data challenges, Vaillancourt remained optimistic. He wrote, “The most compelling aspect of AI for transforming supply chain management, I believe, is its ability to perform nonroutine tasks faster and more accurately than a person is able to. With the sheer volume of supply chain data we can generate now, it is simply impossible to sort and analyze this data without AI assistance. But leaders must also make sure they have the proper foundation in place to fully leverage the benefits of this technology.”
What happened?
The fact that AI didn’t solve all supply chain challenges during the pandemic had some critics declaring, “AI ain’t ready for prime time yet.” Retired supply chain expert Steven Knepp explains, “The saying ‘AI ain’t ready for prime time yet’ revolves around the lack of integration between carriers, port operators, airlines, and truckers. Of all the aspects of movement, fragmentation abounds between each portion of the process with very little digital access or coordination available in a single platform. If each portion of the process does not function properly it causes delays and can challenge the cost of the supply chain.”[6] When Knepp talks about integration, he is referring to the integration of data flows between portions of the supply chain. Just as Vaillancourt pointed out several years before the pandemic, data is the lifeblood that flows through the supply chain and feeds AI solutions.
According to Knepp, the biggest obstacle to improved AI solutions is figuring out how to share data. He explains, “The biggest barrier to having the new technology operate as a single platform is that AI is based on an open source of data collection. Why is this a barrier? Simply due to the competitive nature of each carrier within the carrier’s network of operations or routes. Why would a competing carrier allow its proprietary pricing and services to be shared with everyone? … Somehow data standards or guard rails must be developed to act as a street cop that regulates the data exchange.”
Of course, not all supply chain-focused AI solutions require shared data from competing enterprises. Ahmer Inam, Chief Data & Artificial Intelligence Officer at Relanto, believes companies should learn from supply chain failures during the pandemic. He explains, “Artificial intelligence, combined with other technologies and innovations, could bring about some long-lasting improvement across the entire supply chain, from the manufacturing floor to the retail shelf.”[7] He adds, “AI makes it possible to use data and analytics to identify and map out the inventory that is getting affected by the supply chain disruption. If a business lacks visibility of a ship transporting its materials, then it should use the crisis as an opportunity to justify prioritizing supply chain digital transformation with data, the internet of things and advanced analytics (e.g., machine learning and simulation). A business needs to know where its goods are at all times to successfully gauge what impact supply side constraints will have on its operations and ability to meet market demand expectations. This is especially true of complex supply chains that rely on many players operating globally.”
Inam’s vision bumps against Knepp’s concerns about data sharing; however, there are still numerous benefits AI can provide organizations in times of uncertainty. During the pandemic, Enterra Solutions® developed the Enterra Global Insights and Decision Superiority System™ (EGIDS™), which provided clients the ability to rapidly explore a multitude of options and scenarios. Supply chains are simply too complex to comprehend without AI assistance. Some of this complexity can be addressed by letting AI solutions make routine decisions so that supply chain professionals can concentrate on decisions requiring more nuanced consideration. For that reason, Enterra® is focused on advancing Autonomous Decision Science™ (ADS®) to help supply chain professionals deal with information overload.
Concluding Thoughts
There remain challenges in portions of the global supply chain that seem intractable today; however, allowing those bottlenecks to discourage advances in other areas of the supply chain would be a terrible mistake. Like other business areas, supply chain operations are defined by decision-making. Bain analysts, Michael C. Mankins and Lori Sherer, explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[8] They add, “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.” AI may not be ready for prime time in all supply chain areas, however, decision-making is not one of those areas.
Footnotes
[1] Caroline Brooks, Deon Foster and Meredith Mescher, “The supply chain shapes our lives,” MSU Today, 31 March 2021.
[2] Amanda Mull, “Americans Have No Idea What the Supply Chain Really Is,” The Atlantic, 21 September 2021.
[3] Lucy Benton, “6 Ways AI is Impact the Supply Chain,” The Network Effect, 27 September 2018.
[4] Staff, “AI in supply chain and logistics: How AI will reshape the logistics and transportation industry,” Business Insider, 22 January 2018.
[5] Shannon Vaillancourt, “AI In Supply Chain Management: It’s Only As Good As Your Data,” Forbes, 2 October 2018.
[6] Steven Knepp, “AI ain’t ready for prime time in the supply chain,” Supply Chain Management Review, 10 March 2023.
[7] Ahmer Inam, “How AI Could Solve the Supply Chain Crisis,” SupplyChainBrain, 22 November 2021.
[8] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
