A leading US specialty retailer engaged ProCogia to create a custom demand-planning engine to correct stock-out and over-stock problems caused by the inadequacies of its existing forecasting and replenishment systems. ProCogia implemented a customized forecasting engine and an ordering tool with a 3-tier architecture.
Results included an Improved forecast accuracy for 20-25% of SKUs, and an estimated annual savings between $30m to $50m.
One of the leading specialty retailers in the US was facing inventory stock-out and over-stocking problems because of exponential business growth and highly variable demand.
The existing forecasting and replenishment systems were primitive and not able to capture the supply and demand-side variability.
The company engaged ProCogia to implement a forecasting engine and ordering tool that would optimize the inventory levels while maintaining desired service levels.
ProCogia collaborated with the company’s leadership to implement:
- A cross-functional team with accountability for marketing, product, supply, market intelligence, sales, and finance
- A standardized statistical demand-forecast process across categories
- Timely incorporation of trade inputs from external sources
- A revamped Dates and Gates process for new team initiatives
ProCogia’s customized forecasting and ordering solutions:
- Improved forecast accuracy for 20-25% of SKUs
- Yielded estimated savings $230,776 for 170 POC SKU’s
- Yielded estimated annualized savings (considering all SKUs) in the range of $30m to $50m
- Non-productive inventory reduced in more than 50% of SKUs
- Lost sales savings = $103,800
- Over stock savings = $126,976
We provide data consultancy to organizations to optimize your investment in people, processes, and technology. This is typically through data strategy engagements, roadmaps, transformations, and independent technology advice.
We use open source technology to leverage the full potential of your data. Predictive and prescriptive results are actioned using AI and Machine Learning (ML).
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