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inventory Case Study

Inventory Optimization with ABC-XYZ Segmentation for Operational Clarity

In inventory-heavy businesses, stock visibility alone does not lead to better decisions. High stock levels can mask dead inventory, stock-out risk, and uneven movement across SKUs.

This case study shows how ABC-XYZ segmentation revealed where value was stuck, which items created operational risk, and how teams prioritized purchasing, replenishment, and working capital decisions.

Problem

A mid-sized manufacturing company struggled with:

  • Excess capital locked in slow-moving or low-value inventory
  • Limited visibility into which SKUs mattered most
  • Reactive replenishment due to unpredictable demand patterns
  • High number of SKUs with unclear financial contribution
  • No clear picture of stock health, item-level turnover, or demand risk

insights

  • Focused revenue on top 3.5% SKUs driving ~60% of revenue.
  • Unlocked $3M+ in dead stock tied to low-value items.
  • Reduced stock-out risk on high-velocity SKUs.
  • Flagged unpredictable, low-value SKUs for phase-out.
  • Replaced warehouse guesswork with turnover visibility.

solution

The goal: Unlock working capital and improve inventory turnover.
  • Surface which SKUs created value, which drove stock-outs, and which tied up cash.
  • Prioritize action using value vs variability to reduce cost and operational risk.

what we built

  • Developed a data-driven inventory intelligence dashboard using ABC-XYZ segmentation in Power BI, layered with stock health status, item-level visibility, and ITR (Inventory Turnover Ratio).
  • Tech Stack : MySQL, SQL, Power BI

outcome

  • By highlighting high-impact SKUs, exposing dead stock, and aligning stock levels with demand patterns, the business unlocked tied-up capital, improved planning, and increased inventory efficiency.
This ABC-XYZ overview shows how inventory value and demand variability are distributed across SKUs. A-class items contribute the majority of revenue despite representing a small portion of total items, while C-class, high-variability (CZ) items lock up capital with weak turnover. This view helps prioritize where to protect availability, where to plan buffers, and where to reduce or phase out stock.
This ABC-XYZ overview shows how inventory value and demand variability are distributed across SKUs. A-class items contribute the majority of revenue despite representing a small portion of total items, while C-class, high-variability (CZ) items lock up capital with weak turnover. This view helps prioritize where to protect availability, where to plan buffers, and where to reduce or phase out stock.
Dashboard with ABC-XYZ filters showing revenue by segment, category-to-segment mapping, and a detailed table of SKUs with demand, lead time, safety stock, reorder point, stock status, and value in warehouse.
This operational view connects strategic ABC-XYZ segmentation to day-to-day execution. Managers can filter by value and demand variability, see which categories and SKUs fall into high-risk or high-value zones, and take immediate actions such as replenishing AZ items, monitoring BY items, or rationalizing low-value CZ SKUs. This bridges planning with execution on the warehouse floor.
Dashboard showing stock status distribution (in stock, below reorder point, out of stock), count of items to reorder, and a table of SKUs with reorder flags, safety stock, and current stock status.
This view highlights immediate operational risk by surfacing which SKUs are out of stock or below reorder thresholds. Warehouse and procurement teams can prioritize replenishment for high-impact items, prevent stock-outs on fast movers, and avoid over-ordering low-value inventory. It turns inventory monitoring into a clear, action-oriented workflow.

See what this could look like for you, practically

If any of these stories felt familiar, we can start small.

You don’t need a big team or a heavy rebuild to get value. We’ll begin with 1–2 questions your business actually needs answered and build only what you’re ready to use.

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