Cutting Procurement Cost Across 12,000 SKUs Without Adding Inventory Risk

Cutting Procurement Cost Across 12,000 SKUs Without Adding Inventory Risk

AI-based demand forecasting narrowed a 12,000-SKU catalog to the ~960 items driving most procurement activity, moving the team to forecast-driven ordering.

3%–8%Forecast-driven ordering on priority SKUs cut procurement cost without raising stock-out risk

ITChamps built an AI-enabled inventory planning and demand forecasting solution for a CNC machine tool builder managing approximately 12,000 SKUs, prioritizing the ~960 SKUs that drove the majority of procurement activity.

The Challenge

12,000 SKUs made optimal inventory levels a guessing game

The large SKU base and varying demand patterns made it difficult to determine optimal inventory levels and procurement quantities, while the business still needed to protect internal service levels.

  • Approximately 12,000 SKUs were active across the catalog.
  • Demand patterns varied significantly by SKU, making manual planning unreliable.
  • Procurement teams lacked a systematic way to separate high-impact items from the long tail.
  • Excess procurement and excess inventory coexisted with the risk of shortages on critical parts.

The Solution

Prioritize the vital few SKUs, then forecast and plan around them

The solution identified the SKUs that constituted the majority of procurement activity, then applied AI-based demand forecasting to that priority set to drive replenishment and procurement decisions.

01SKU Impact Analysis

Analyzed approximately 12,000 SKUs and identified the ~960 SKUs driving the majority of procurement activity.

02Demand-Pattern Analysis

Analyzed historical demand patterns for the priority SKUs to surface trends and seasonality.

03AI-Based Forecasting

Generated AI-based demand forecasts for each priority SKU.

04Inventory & Replenishment Planning

Converted forecasts into optimal inventory levels, replenishment quantities, and procurement timing.

05Forecast-Driven Procurement Rollout

Moved the procurement team from buffer-based ordering to a forecast-driven model.

Results

Measured business impact

12,000SKUs analyzed

Full catalog scope covered by the initial impact analysis.

~960Priority SKUs

High-impact SKUs identified for detailed monitoring and forecasting.

3%–8%Cost reduction

Reduction in inventory/procurement-related cost from forecast-driven ordering.

Excess inventory

Reduced reliance on excess inventory buffers month-on-month.

Highlights

  • Reduced overall month-on-month procurement requirements.
  • Maintained optimal inventory levels while protecting internal service requirements.
  • Gave the procurement team a repeatable, forecast-driven ordering process.
  • Focused planning effort on the SKUs with the greatest cost impact.

Services and technology

Inventory PlanningDemand ForecastingProcurement AnalyticsAI Demand ForecastingSKU Impact AnalysisInventory Optimization

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