Every supply chain professional knows the theoretical ideal: stock as little as possible, meet demand with precision timing, minimize carrying costs. Just-in-time inventory management represents mathematical perfection—maximum efficiency, minimum waste, optimal cash flow. Simply put, make money!
There's just one problem: nobody's actually that good. And the market hasn't allowed us even to contemplate pure just-in-time operation for years now. The model only functions when everything goes well and is 100% predictable, simultaneously and continuously.
In modern global supply chains, this doesn’t happen.
The Semiconductor Lesson
The automotive industry provided the clearest demonstration of just-in-time's fundamental fragility. When chip shortages cascaded through production networks in 2021-2022, the consequences weren't merely inconvenient—they were catastrophic.
Assembly lines stopped. Dealerships sat empty. Consumers waited months for vehicles. Manufacturers hemorrhaged billions in lost revenue. All because a system designed for maximum efficiency had zero tolerance for component unavailability. You can only move as fast as the slowest component in a dependent chain of assembly for finished goods.
The irony was brutal: decades of carefully optimized inventory savings evaporated the moment the system couldn't deliver actual products to actual customers. The cost of being "efficient" dwarfed the benefits it had generated as throughput came to a hault.
This wasn't a black swan event. It was the predictable outcome of a system architected with no margin for error.
The Permanent Shift
The lessons from recent years aren't temporary. Consider what we've witnessed: pandemic shutdowns, port congestion that delayed container ships for weeks, geopolitical tensions disrupting established trade routes, climate events that closed critical logistics corridors, and labor shortages across the supply chain.
Each disruption reinforced the same thesis: zero buffer equals zero resilience.
The data confirms this fundamental recalibration. Throughout 2025, the Logistics Managers' Index showed inventory costs consistently above 70.0—the threshold for significant expansion. This wasn't volatility; it was strategic repositioning. In August alone, inventory costs hit 79.2, the second-highest reading since October 2022.
Companies aren't holding more inventory by accident. They're deliberately building buffers because the cost of stockouts now exceeds the cost of carrying stock.
What Tariffs Revealed
The 2025 tariff situation provided a real-time case study in how risk changes inventory calculus.
Facing potential duties on imports, companies engaged in massive forward buying—pulling months of procurement decisions into compressed timeframes. The numbers were staggering when you looked at actual corporate expenditures. Tech manufacturers alone spent millions on duty charges for products like laptops and computing equipment.
But the direct duty payments were just the visible cost. The operational impact manifested in working capital tied up months early, warehouses filled with pre-positioned inventory, and transportation networks strained by artificially compressed import cycles.
By November, the Logistics Managers' Index captured an extraordinary pattern: downstream inventory levels hit 65.8 while upstream levels fell to 46.3—a 19.5-point differential representing the largest such spread in the Index's history. Inventory that had been stockpiled upstream for months was finally transferred to retailers for the holiday season.
This wasn't dysfunction. It was calculated risk management at scale. Companies chose carrying costs over tariff exposure because the mathematics favored early procurement.
The Traditional Math Still Works
Here's what's interesting: the fundamental supply chain optimization equations haven't changed.
You still forecast demand, stock fast-moving items adequately, keep slow-moving inventory lean, and position goods as close to end markets as economics allow. The goal remains to lower transportation costs and cycle times between the supplier and the consumer.
None of that math has changed.
What's changed is the risk weighting in those equations. You're no longer optimizing purely for cost efficiency. You're optimizing for efficiency plus resilience, speed plus reliability, lean operations plus buffer capacity.
The challenge isn't theoretical—it's operational. How do you continuously recalculate the optimal balance as conditions shift?
The Data Imperative
Consider a practical scenario. The November Logistics Managers' Index showed transportation pricing at 64.9—the fastest expansion rate since February. After observing three consecutive data points of increases, you can confidently say prices are rising. This isn't a blip; it's a trend.
That single insight changes everything. If transportation costs are increasing for the same lanes and volumes, you face immediate strategic choices: absorb the cost and accept margin compression, pursue spot-market alternatives hoping for better rates, or reposition inventory closer to end markets to reduce per-shipment transportation costs.
Each option has different cost implications, timing requirements, and risk profiles. Making the right choice requires real-time data and the analytical infrastructure to evaluate alternatives quickly.
The companies navigating this successfully aren't guessing. They're measuring continuously and adjusting rapidly.
The Digital Marketplace Complicates Everything
Traditional supply chain optimization assumed relatively stable demand patterns and a predictable geographic distribution of customers. Stock near major markets, replenish from regional distribution centers, optimize for known lanes.
The digital marketplace has shattered those assumptions.
Cyber Monday 2025 demonstrated that consumers have fully embraced buying from anywhere and expecting delivery anywhere. E-commerce doesn't respect traditional geographic boundaries or distribution logic.
This creates a fundamental tension: you want inventory positioned close to demand, but demand is now geographically dispersed and unpredictable. Near-shoring production to Mexico helps reduce transit times from Asia, but doesn't solve the last-mile distribution challenge when orders come from everywhere.
Manufacturing investments in the U.S. will take 2-3 years to materially impact supply chains, offering near-term advantages for future near-shoring. But today, companies must solve the optimization problem with the infrastructure they have.
Looking Forward
The current data suggests we're moving toward a more balanced equilibrium between inventory, warehousing, and transportation. The extreme forward-buying behavior driven by tariff concerns is subsiding. The massive upstream-to-downstream inventory transfer has largely completed. Transportation capacity and pricing are finding a more normal relationship.
There are no major disruptions visible on the immediate horizon. If the Federal Reserve delivers expected rate cuts and affordability concerns get addressed—either through wage growth or price stabilization—the fundamental conditions for 2026 look reasonably healthy.
But here's what I'm watching: can companies maintain the discipline of "balanced" optimization, or will the pressure for short-term efficiency drive a gradual slide back toward just-in-time vulnerability?
No Going Back
Nobody's returning to pure just-in-time. The religious devotion to absolute inventory minimization has ended. The lessons of recent years—from semiconductor shortages to pandemic disruptions to tariff-driven chaos—have permanently altered how companies think about supply chain risk.
But abandoning just-in-time doesn't mean abandoning optimization. It means optimizing for different variables.
The mathematics of supply chain management remain sound: stock fast-moving items adequately, keep slow-moving inventory lean, position goods near demand where economics justify it. These principles endure.
What's new is the requirement for continuous recalculation. In a market where transportation costs can shift in weeks, tariff policies can change procurement timelines overnight, and consumer buying patterns can pivot with a viral social media trend, the ability to quickly reassess your optimal inventory position becomes a competitive advantage.
The math hasn't changed. The velocity at which you must perform that math has accelerated dramatically. While some of the math is easy, some is not.
And that velocity requires something most companies still lack: real-time data infrastructure capable of answering complex optimization questions in hours, not weeks. Think about it - if you get the answer you are seeking too late or the work effort to get the answer limits the cadence of getting the answer, this limits your ability to adjust accordingly.
The world isn't going back to a place where just-in-time makes sense. The question for 2026 is whether your organization can calculate the new optimal balance fast enough to win.
Steve Beda is an Executive Vice President at Trax Technologies, where he analyzes freight market trends and advises Fortune 500 companies on transportation spend optimization.