The search for better supply chain management dates long back. During World War II when the U.S. Navy tackled the Polaris missile program, facing nearly 70,000 tasks and thousands of contractors, they adopted PERT (Program Evaluation and Review Technique), which enabled them to deliver the missile 18 months early. Around the same time, DuPont revolutionized plant shutdowns with the Critical Path Method (CPM), cutting shutdown time by 32 days and saving $1 million in the process.

But today’s supply chains are vastly more complex. Walmart handles 200 million transactions weekly [1], Amazon’s fulfillment centers could fit 28 football fields [2], and Toyota measures inventory in hours, not days. The recent blockage of the Ever Given in the Suez Canal, costing $400 million per day [3], is just one example of the challenges we face. While PERT and CPM laid the groundwork, traditional methods are no longer sufficient.

The complexity of current supply chains

Modern supply chain relies on massive ERP databases and CRM systems, with constantly updating information, to track inventory, monitor demand, and optimize logistics. These tools often require a lot of expertise and a deeper understanding of how they are implemented, making them inaccessible to many within the organization. This creates a skills gap that limits adoption and forces companies to invest heavily in specialized teams, requiring extensive training and onboarding which can take months, if not years, and incur significant operational costs. Even with these teams in place, the sheer volume of data often leads to analysis paralysis, delaying critical decisions and hindering a company’s ability to respond quickly to market shifts or disruptions. Take, for example, Nike’s infamous supply chain glitch in 2000. Their overreliance on complex demand-planning software resulted in a $100 million loss and a significant backlog of orders. This costly mistake highlights the danger of relying solely on outdated tools [4]. Overall, enterprises are too dependent on traditional, sluggish methods, reminding of Einstein’s observation, “Insanity is doing the same thing over and over and expecting different results”.

How can Generative AI help?

The complexity of modern supply chains makes it hard to get a clear picture of the current state and to watch out for future disruptions and opportunities. Here are the ways Generative AI can help to simplify and facilitate better decision making:

a. Beyond Spreadsheets and Dashboards, Using Natural Language

NLP models, trained on massive datasets of supply chain-specific information, can understand complex queries like “What’s the impact of the recent flood on my electronics components from Indonesia?”, deciphering the crucial relationships between locations, events, products, and potential disruptions to provide actionable intelligence. This approach also involves leveraging specialized data structures to interpret and respond to questions, disambiguating intent based on domain-specific vocabulary, and optimizing for interactive performance and low cost. Planners asking, “What’s the impact of recent port congestion on my furniture shipments from Vietnam?” need a solution that understands not just the words but the intricate relationships between shipping routes, product types, and real-time events. Generative AI can create specialized data structures and domain-specific compilers to interpret and respond to supply-chain questions accurately.

b. Anticipating Disruptions Before They Happen

Searching through the large dimensional decision space can help to anticipate disruptions, mitigate risks, and optimize resources proactively. In contrast, the reactive approaches tend to be fragile, as seen in the COVID-19 pandemic when global trade plummeted by 5.3% in 2020 [5], while a staggering 94% of Fortune 1000 companies suffered disruptions [6]. Businesses need to adapt to proactive approaches that predict in real-time and at-scale. Such a transformation requires algorithms that find patterns and similarities at a deeper level over vast datasets of historical supply chain events. Identifying semantic similarities and patterns with subtle trends and anomalies can predict potential disruptions before they occur. Integrating these AI models with real-time data feeds from sources like IoT sensors, news outlets, and social media, businesses can maintain a constant pulse on emerging risks and dynamically adjust their strategies to ensure uninterrupted flow of goods and services. Generative AI can help collate, combine, analyze, and synthesize insights to make supply chain truly resilient and agile.

c. Democratizing Insights

Generative AI doesn’t just benefit data scientists and supply chain experts. By making data accessible and understandable to everyone, it empowers all stakeholders, from procurement managers to warehouse operators, to make informed, data-driven decisions. No longer confined to complex spreadsheets or technical jargon, insights become accessible through intuitive interfaces powered by natural language processing and data visualization tools.

Generative AI is an exciting new way to take supply chain forward, empowering businesses to know better and act faster. Tursio and FirstShift have partnered on one such effort to bring domain-specific knowledge together with specialized data and language models. Read more about it in the whitepaper here.

References:

1. “The Most Powerful Man in Payments,” Jessica Leber, MIT Technology Review, March 29, 2012.
2. In-Person Amazon Fulfillment Center Tours, Amazon.
3. “The cost of the Suez Canal blockage,” Mary-Ann Russon, BBC News, March 29, 2021.
4. Case Study 16: Nike’s 100 Million Dollar Supply Chain ‘Speed Bump’, October 16, 2022.
5. World Trade Organization, Press Release No. PRESS/876, August 8, 2024.
6. “94% of the Fortune 1000 are seeing coronavirus supply chain disruptions: Report,” Erik Sherman, Fortune, February 21, 2020