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Cost Reduction through Inventory Optimization for OEMs

For Original Equipment Manufacturers, inventory management is a matter of great importance. It is their master lever to unlock streamlined operations, cost savings and demand in the national and international markets. But what are the relevant elements that help in actionable strategies? Here are a few mentioned with global examples on execution.

Predicting the Unpredictable: Smarter Demand Forecasting

Gone are the days of relying on gut feelings or outdated spreadsheets. Today’s OEMs use predictive analytics to anticipate market shifts with startling accuracy. A great example would be what Dell Technologies does. They do their analysis in real time on customer data and macroeconomic trends. By doing so, they managed to reduce excess component inventory by 30% while maintaining near-perfect delivery rates. What they used was ML models that adjust procurement plans hourly, not monthly.

Siemens takes this further with digital twins, virtual replicas of production lines that simulate demand scenarios. When the company noticed a surge in orders for industrial automation parts, these simulations allowed them to recalibrate inventory without overstocking. The result? A 20% drop in obsolete stock.

JIT Isn’t Dead—It’s Evolving

Toyota introduced the Just-In-Time or JIT system back in the 1970s and its modern version is still saving the company billions of dollars every year. However, the 2011 Thailand floods exposed its supply vulnerability when the company’s suppliers were underwater, halting production for weeks. Bosch follows a hybrid model where it keeps a safety stock in place, this ensured their European assembly lines stayed active even when their suppliers in Malaysia faced lockdowns due to the pandemic. 

Safety Stock: The two-ended sword of dilemma

Too much safety stock strangles cash flow; too little risks shutdowns. Ford’s response to the 2020 chip shortage shows how to strike a balance. Using AI tools that recalculate safety stock needs daily, the automaker reduced capital tied up in buffer inventory by 15%—even as rivals idled plants.

Contrast this with Samsung’s 2018 misstep. Underestimating demand for OLED screens left the company scrambling to fulfill iPhone orders, costing $300 million in lost sales. The takeaway? Static safety stock formulas fail. Dynamic, data-driven models win.

Suppliers are Partners, Not Vendors

Apple’s iPhone 5 production delays in 2012 led Apple to learn a lesson on over-reliance. With its collaboration now with Pegatron and Luxshare, Apple’s suppliers let them monitor Apple’s inventory in real time. This ensured that lead time was cut by 25% and Aplle’s suppliers became collaborators rather than vendors.

Walmart and P&G went a level beyond by doing something called Vendor-Managed Inventory (VMI).

By letting P&G track Walmart’s shelf-level stock data, the retailer reduced out-of-stock incidents by 10%—a win-win that boosted sales for both companies.

Lean Isn’t Just for Factories

Caterpillar’s lean journey began with a simple question: Why store months’ worth of hydraulic components when customers order weekly? By mapping every step from raw material to delivery, the company identified $2 billion in waste over a decade. Their factories now run with 40% less buffer stock, proving lean principles apply as much to warehouses as assembly lines.

John Deere’s story echoes this. By redesigning workflows at its Iowa plant, the company reduced raw material waste by 22%, a figure highlighted in its latest sustainability report.

Tech’s Double-Edged Sword

While AI integrations in large-scale businesses have definately made life easier, it is not without complications and errors. Amazon warehouses have over 500,000 robots that sort, fetch & pack items, ensuring seamless transfers of products to customers. However, a software glitch in 2023 led to a series of malfunctions and robots collided into shelves and each other. This led to the need to ensure smarter fail-safe and rollback options.

EOQ in the Real World

Procter & Gamble’s diaper division shows Economic Order Quantity (EOQ) done right. By integrating data on global demands, shipping costs & the possible need for backup stocks, P&G optimized P&G optimized batch sizes, saving $500 million annually.

But EOQ has pitfalls. Tesla’s 2018 battery component overorder—a $150 million mistake—reveals what happens when models ignore demand reality.

Location, Location, Location

Fashion major Zara has has cracked the code in supply chain management. With 85% of all production close to its Spanish HQ, it is able to get products from sketch to store in a matter of just 15 days. This ensures Zara has a 30% less inventory while still staying relevant in comparison to competition.

On the other hand, H&M used a decentralized network which left the company with $4.3 billion in unsold clothes in 2018.

Inventory Forecasting- How Sankey is helping in the Revolution

Effective supply chain management in commercial vehicle (CV) manufacturing plants hinges on accurate demand forecasting. However, disparate data sources and the lack of integrated forecasting models often lead to spare-part overstocking, shortages, and inventory imbalances.

To address this, Sankey Solutions has developed a data-driven demand forecasting system. The solution begins by integrating historical consumption and production data from APIs, SAP systems, and BOM records. This data undergoes cleaning, transformation, and exploratory analysis before being stored on AWS S3 for secure access.

Machine learning models are developed using MLflow, allowing rigorous training, evaluation, and reproducibility. Once trained, models are containerized via Docker and deployed as API-based services, enabling seamless integration with enterprise applications.

The system outputs granular, day-wise part demand forecasts. These predictions empower planners to make data-informed decisions, optimizing inventory levels and ensuring uninterrupted production flow across multiple plant locations.

The Path Forward

From Toyota’s JIT evolution to Amazon’s robot armies, successful OEMs treat inventory as a living system, not a static asset.

AI is now easily accessible and can use calable tools to cut costs, spur innovation, and streamline operations without having to make significant infrastructure investments. Sankey Solutions provides customised AI solutions that meet the demands of contemporary automakers, ranging from digital manufacturing cockpits to predictive maintenance. Our data-driven, real-time insights assist companies in making better decisions, maximising efficiency, and maintaining a competitive edge in a rapidly evolving mobility landscape.

We believe in embracing tools like predictive analytics but temper them with human judgment. We must work on building supplier ecosystems, not transactional relationships. Recognizing that today’s perfect formula becomes tomorrow’s liability in our era of constant disruption.’