Client:
Industrial manufacturing company operating within a capital-intensive sector.
Challenge:
The company’s distribution and warehousing network was expansive, and inventory served as both a critical cost driver and a core service-level determinant.
The client faced two concurrent pressures.
- Leadership was pushing for significant reductions in inventory to unlock working capital.
- The company was experiencing widespread stock-outs across their storage network, resulting in missed shipments and deteriorating customer satisfaction.
The supply chain team needed a tool that could help them understand the true inventory levels required to meet service expectations. They also needed visibility into excess inventory levels that could be safely reduced. Without clear information, the organization was caught in a reactive loop—chasing outages while over-investing in inventory that wasn’t moving.
Strategic Objectives:
The client’s strategic objective was twofold:
- Quantify and align inventory levels with actual service level requirements for the business, in a way that accounts for the volatility of demand and the variability in production and lead times.
- Identify actionable opportunities to reduce inventory across SKUs—without increasing the risk of stock-outs.
Ultimately, they needed a balanced, data-backed approach that would improve both working capital efficiency and service reliability, while being easy enough to maintain internally after the project concluded.
Approach:
Waypost collaborated with supply chain and operations leadership to define specific parameters influencing inventory behavior. These included forecasted demand, target fill rates, order and production lead times, and business-specific nuances around batching, supplier variability, and planning tolerances.
Using these parameters, an Excel-based tool was designed to simulate optimal inventory ranges at both the SKU level and across the broader portfolio. While the tool was intentionally simple, it provided a robust modeling engine and was capable of incorporating sophisticated supply chain variables and calculating dynamic inventory targets based on real-world constraints.
key steps:
- An analysis of each inventory driver, including variability of demand, order patterns, and lead time accuracy.
- Analyze inventory data by SKU, surfacing both understock risks and overstock opportunities.
- Implementation of an ABC analysis enabled the client to strategically adjust service level expectations. Notably, they reduced fill rate targets for ‘C’ items—materials with minimal customer impact—freeing up significant working capital without meaningful service trade-offs.
- Introduction of a structured process to flag and manage Slow-Moving and Obsolete (SLOB) inventory. This process helped the client reclaim warehouse space and avoid unnecessary third-party storage costs.
Assessment Areas:
- Forecasting accuracy and demand volatility
- Lead time variation across suppliers and production
- Order quantity minimums and batch processing practices
- SKU classification and customer impact of stock-outs
- Warehouse storage constraints and third-party logistics usage
Tools and Methodologies:
- Custom-built Excel-based inventory simulation tool
- ABC Inventory Classification Analysis
- Scenario modeling to test trade-offs between service level and working capital
- Working capital performance benchmarking
Deliverables:
- A dynamic inventory modeling tool tailored to the client’s parameters
- SKU-level inventory targets with supporting analysis
- Inventory strategy recommendations segmented by SKU importance
- A documented SLOB identification and reduction process
Conclusion:
This project produced meaningful results. The company achieved a 17% reduction in overall inventory levels, unlocking substantial working capital. At the same time, they improved their On-Time/In-Full (OTIF) shipment rate by 8%, proving that service performance and inventory efficiency are not mutually exclusive.
The Excel-based inventory tool proved to be both powerful and practical—it empowered the internal team to continue managing their inventory proactively, even as the business grew, and the distribution network evolved. By embedding structured processes around inventory segmentation and obsolescence, the company also built internal resilience against future stock imbalances.
“The Optimal Inventory tool was a game-changer for us in how we thought about the inventory network.” — Director of S&OP