Use case · Retail & E-commerce

Demand forecasting and replenishment

Machine learning forecasts at SKU and location level that drive replenishment, allocation and markdown decisions across channels.

The challenge

What organisations face

Spreadsheet-based planning cannot keep pace with promotions, weather, seasonality and shifting channel mix. Retailers carry excess stock in some locations while running out in others, tying up working capital, forcing late markdowns and disappointing customers. Planners lack trusted, granular forecasts to act on.

The solution

What VulcanTech engineers

VulcanTech would engineer a forecasting platform that combines sales history, promotions, pricing, calendar and external signals in a governed data pipeline, training hierarchical models at SKU-location level. Forecasts feed replenishment and allocation recommendations within the planning or ERP system. Planner overrides are captured and measured, and accuracy dashboards show where models need attention.

Expected outcomes

What changes for the business

  • Better stock positioning across stores and fulfilment centres
  • Fewer late markdowns and missed sales
  • Planners working from trusted, granular forecasts

Capabilities

Service lines involved

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