Forecast-Driven Procurement Across a 3,000-SKU Portfolio
Prioritizing the ~256 SKUs behind most procurement activity let the team forecast demand with AI and order against it, instead of leaning on inventory buffers.
ITChamps implemented an AI-enabled inventory planning and demand forecasting solution for a hydraulic gear pump and systems manufacturer managing approximately 3,000 SKUs, prioritizing the ~256 SKUs driving most procurement activity.
The Challenge
A 3,000-SKU base with varying demand made procurement planning difficult
The large SKU base and varying demand patterns made it challenging to determine optimal inventory levels and procurement quantities, while the objective remained protecting internal service requirements.
- Approximately 3,000 SKUs were active across the portfolio.
- Demand patterns varied by SKU, complicating manual procurement planning.
- The business needed to reduce unnecessary procurement and excess inventory.
- Internal service requirements had to be protected throughout.
The Solution
Prioritize high-impact SKUs, then forecast and plan replenishment
The solution identified the SKUs constituting the majority of procurement activity, analyzed their demand patterns, and generated AI-based forecasts to drive a forecast-driven procurement model.
Analyzed approximately 3,000 SKUs and identified around 256 SKUs driving the majority of procurement activity.
Analyzed historical demand patterns for the priority SKUs.
Generated AI-based forecasts to determine expected future demand for each priority SKU.
Converted forecasts into optimal inventory requirements, replenishment quantities, and procurement timing.
Enabled the procurement team to order according to anticipated demand rather than historical buffers.
Results
Measured business impact
Full catalog scope covered by the initial impact analysis.
High-impact SKUs prioritized for detailed monitoring and forecasting.
Reduction in procurement/inventory-related cost.
Reduced overall month-on-month procurement requirements.
Highlights
- Enabled ordering according to anticipated demand rather than historical buffers.
- Maintained optimal inventory levels while protecting internal service requirements.
- Reduced overall month-on-month procurement requirements.
- Established a repeatable forecast-driven procurement model.
Services and technology
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