A Problem Persists: Reliance on the Just-In-Case (JIC) Supply Chain Model

The Just-In-Time Supply Chain Model vs. Just-In-Case Supply Chain Model

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Several years ago, when the pandemic became a full-blown global crisis, most supply chains used the “just-in-time” model, which relies on resources and goods being available right when they’re needed. The benefits of this model include cost reductions in storage and inventory. Global supply chains, and the model they followed, weren’t prepared for the pandemic's widespread consumer panic. As a result, when consumers began buying up toilet paper, paper towels, pasta, and other staples across most of the U.S., retailers struggled to replace those products, leaving consumers empty-handed and frustrated. Furthermore, consumer demand remained at an all-time high and shelves remained either sparsely stocked or empty for weeks on end. (In fact, it wasn’t just basic goods that were lacking; everything was in short supply, even semiconductors were hard to come by). As a result, most suppliers shifted away from a “just-in-time” supply chain model to a “just-in-case” supply chain model, meaning they stocked up on necessary resources and produced an oversupply of products ready to ship, guaranteeing that shelves would remain stocked for consumers. (Incidentally, the dilemma of which model to follow isn’t a new one, and it’s something I’ve written about in the past.)[1]

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As has occurred in past cycles of change, moving entirely to the just-in-case model proved problematic again. But, Paul Noble, founder and CEO of Verusen, explains, “It’s really what everyone did,” adding, “supply chain managers understood that that they were tying up their company’s working capital and warehousing space to keep excess inventory. ... They did it because they couldn’t balance capital and risk decisions effectively. They didn’t have the full picture.”[2] As restrictions eased and many countries declared premature victory over the pandemic, new anxieties began to arise: the fear of an economic recession and runaway inflation. Thus, a balance between having both models became a necessity once again. But striking that balance isn’t each to achieve. As Jessica L. Cavanaugh, a business process design consultant at Uniorg Inc., explains, “It is difficult not to react by drastically reducing inventories, and it is hard to determine the right amount of inventory when significant uncertainty exists.”[3] Furthermore, the “just-in-case” supply chain model persists. For example, PS Subramaniam, a partner at Kearney, writes, “[A]n inventory glut of high-end electronic components is an expensive problem. Excess inventory is a $250+ billion problem in the U.S. alone.”[4]

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So, how do companies determine the right amount of inventory needed in such extraordinary times?

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Why AI Is the Answer

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AI systems, especially the one integrated into the Enterra Autonomous Decision Science? (ADS?) platform, helps answer these tough, uncertain, questions. After all, constantly determining these answers can lead to decision fatigue; but, with the Enterra ADS? platform companies can avoid that burnout. Using human-like reasoning, trustworthy generative AI, high-dimensional mathematics, and real-time optimization, all while learning from the results, Autonomous Decision Science? helps determine inventory needs while enhancing the value chain. The Enterra Intelligent Inventory Management System? is part of the Enterra Supply Chain Intelligence System?. These systems can help companies manage their inventory better and are ideal tools when facing uncertainty — something with which those in the supply chain industry have almost always had to contend.

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Concluding Thoughts

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Determining the right amount of inventory is not as daunting of a task when artificial intelligence solutions augment the expertise of supply chain professionals. AI solutions, when integrated with a company’s internal data system, relieve some of the pressure for professionals who must manage the balance between just-in-time and just-in-case scenarios. In addition, by leveraging AI solutions, companies don’t have to face losing large amounts revenue on in-stock items that become outdated.

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Footnotes

[1] Stephen DeAngelis, “If Inventory Optimization was Easy, People Would Stop Writing About It,” Enterra Insights, 1 August 2019.

[2] Verusen staff, "Stop Relying on Excess Inventory as an Insurance Policy," SupplyChainBrain, 5 October 2022.

[3] Jessica L. Cavanaugh, “How Do I Reduce Inventory, Yet Meet On-Time Delivery Goals?” Industry Week, 26 January 2023.

[4] PS Subramaniam, “The Next Supply-Chain Challenge Isn’t a Shortage – It’s an Inventory Glut,” Harvard Business Review, 29 September 2023.