Why hasn’t artificial intelligence fully transformed supply chains? That’s a question posed by analysts from the Boston Consulting Group (BCG).[1] Their answer: “The root cause of the problem lies not with technology but with how and where companies are applying it.” Even though supply chains haven’t been fully transformed by artificial intelligence, the staff at Logility insists the technology has made a significant impact. They explain, “It’s worth noting that 79% of companies with high-performing supply chains — those that operate in the most efficient and cost-effective way across all key supply chain processes to most effectively match demand to supply — show greater revenue growth than their average industry competitor. These high-performing companies weather supply chain disruptions better, make speedier decisions, and perform more accurate forecasting. What’s propelling these successes are automation, artificial intelligence (AI), and machine learning (ML).”[2]
AI and the Supply Chain
Over the past couple of years, there have been an increasing number of discussions about the need for supply chains to become more flexible, adaptable, and agile. For example, Rakesh Prasad, Senior Vice President for Digital Business at Innover, observes, “Businesses are looking to deploy adaptive Digital Supply Chains that are equipped to deal with uncertainties and can process and analyze vast quantities of fast-moving data from connected systems using AI/ML & analytics. … Artificial Intelligence and Machine Learning must be integrated with operations to draw actionable insights, predict events, and prescribe relevant actions.”[3] BCG analysts believe supply chain professionals aren’t thinking broadly enough about how to use AI. They write, “Most still focus on using AI for analytics and prediction — for example, to forecast demand and plan production. Companies have not pursued the more valuable application of using AI to make recurring decisions by recognizing patterns in big data that humans cannot see.”
As noted above, Logility analysts agree with the BCG analysts that “speedier decisions” are becoming more important as does Prasad who highlights the necessity to “analyze vast quantities of fast-moving data.” It’s a point I try to make whenever I discuss the reasons Enterra Solutions® decided to focus on advancing Autonomous Decision Science™ (ADS®). Using ADS, cognitive technology plays the role of the data scientist or subject matter expert to help businesses optimize their operations and run at the speed of the marketplace. As a result, organizations can make decisions that take advantage of market opportunities as quickly as possible. As BCG analysts explain, “To unlock the full potential [of AI], companies need to deploy an AI-powered learning system that is integrated across functions. This system makes decisions based on enterprise-wide and external data and continuously learns from the outcomes to improve performance. Analytical engines automate decision making instead of just providing insights to practitioners, who must retain the burden of making decisions.”
Of course, not all decisions can or should be automated. However, even when human decision-making is required, cognitive technologies can help. Marc-Roger Gagné, a Data Protection Officer at Interfima, explains, “Apart from decision-making that is fully automated, artificial intelligence systems have also leveraged different forms of cognitive computing to optimize the combined efforts of human and artificial intelligence.”[4] He adds, “Artificial intelligence is everywhere, and it is thought that its biggest impact would be felt in the supply chain.”
Supply chain professionals are still exploring all the ways that emerging technologies can improve supply chain operations. Craig Civil, Director of Data Science and Artificial Intelligence at BSI, explains, “Technology increasingly appears to offer promising answers to complex problems in business operations, and the supply chain is no different. Logistics professionals should explore integrating cutting-edge systems, particularly those centered around artificial intelligence, to fill gaps that the human workforce can’t effectively manage. By combining human oversight and experience with the AI tools, leaders can better protect supply chains against current and future global challenges.”[5]
One of the AI-driven technologies highlighted by Civil is the Digital Twin. He explains, “A digital twin is a virtual replica of the supply chain that can include assets, warehouses and materials. The advantage of a digital twin is it allows supply chain professionals to simulate the flow of materials, acting out a multitude of possible ‘what-if’ scenarios. For example, a digital twin could predict how a supply chain will be impacted if there is unrest in a location where warehouses are located, or if materials get lost due to extreme weather conditions. Creating potential scenarios and watching how each will impact the supply chain provides a unique vantage point to effectively judge risk and efficiency.” The ability to run hundreds of potential scenarios in a short period of time can be a real asset during volatile times — and most people agree we are living in a volatile time. During the pandemic, Enterra® developed the Enterra Global Insights and Decision Superiority System™ that can assist organizations with their “what if” decision-making challenges.
Concluding Thoughts
BCG analysts conclude, “Success requires fostering people’s trust in AI and introducing a new operating model, among other enablers. Companies that make the right investments will increase their resilience to market volatility and talent scarcity and achieve higher sustained performance. … The death of supply chain management is inevitable. To hasten its demise, companies must think differently about how and where they apply AI. Instead of using it to analyze data and enhance visibility, they need to trust AI’s ability to continuously learn and make decisions that optimize performance. The first companies to master the challenges will lead the way in capturing the full value of a self-regulating supply chain.” Civil reminds us, however, that talented people are just as important as cutting-edge technology. “AI can be extremely helpful in the supply chain,” he explains, “but companies shouldn’t rely on it alone. Human expertise is still essential, as technology comes with its own set of unique challenges. By harnessing the strengths of both elements, while strategically utilizing each to offset the other’s weaknesses, supply chain effectiveness can be dramatically enhanced.” As this article’s headline states, AI is a tool. In the coming years, companies that master using the AI tool to make their people and their processes more effective will prove to be the most competitive and successful organizations.
Footnotes
[1] Pepe Rodriguez, Stefan Gstettner, Ashish Pathak, Ram Krishnan, and Michael Spaeth, “Why AI-Managed Supply Chains Have Fallen Short and How to Fix Them,” Boston Consulting Group, 1 September 2022.
[2] Staff, “AI, Machine Learning and Automation – No Longer Nice-to-Haves for the Modern Supply Chain,” Logility Blog, 25 October 2022.
[3] Rakesh Prasad, “Harnessing AI & analytics to establish a smarter, adaptive Supply Chain,” PCQuest, 27 July 2022.
[4] Marc-Roger Gagné, “AI and the Supply Chain,” Irish Tech News, 23 October 2022.
[5] Craig Civil, “How Artificial Intelligence Bolsters Global Supply Chains,” SupplyChainBrain, 5 October 2022.
