Portfolio Multi-branch Retail Chain (East Africa)
AI & Intelligent Apps Retail & Distribution East Africa

AI-Powered Demand Forecasting Reduces Stockouts by 40% for African Retailer

Multi-branch Retail Chain (East Africa)

40%
Reduction in stockouts
Across all 18 branches
25%
Inventory turnover improvement
6 months post go-live
18%
Overstock write-off reduction
Direct margin improvement
87%
Forecast accuracy
SKU-branch level

The Challenge

A fast-growing retail chain with 18 branches across East Africa was managing inventory through a combination of spreadsheets and institutional knowledge. Stockouts were a persistent problem — costing an estimated 12% of potential revenue annually. Overstock on slow-moving lines was tying up working capital.

The business had years of transaction data but no way to extract value from it. Their ERP system generated reports, but nobody had the time or skills to turn data into actionable insights.

Our Approach

Our discovery phase revealed that the data quality was better than the client expected — clean transaction records going back four years. The challenge wasn’t data collection; it was data activation.

We proposed a pragmatic phased approach:

Phase 1: Build a solid data foundation — consolidate data sources, establish a modern analytics platform, and deliver immediate visibility through Power BI dashboards.

Phase 2: Layer in predictive analytics — demand forecasting models trained on the existing transaction history, integrated back into their ERP replenishment workflows.

What We Built

  • Microsoft Fabric — unified analytics platform connecting ERP, POS, and supplier data
  • Azure Machine Learning — demand forecasting models with branch-level and SKU-level granularity
  • Power BI — real-time inventory dashboards accessible on mobile devices for branch managers
  • Automated replenishment alerts — ML-generated purchase recommendations pushed to procurement via Power Automate

The Outcomes

Six months after go-live:

  • 40% reduction in stockout incidents across all branches
  • 25% improvement in inventory turnover
  • 18% reduction in overstock write-offs
  • Branch managers now make replenishment decisions in minutes, not days

The forecasting model — trained on 4 years of data — achieves 87% accuracy at the SKU-branch level, accounting for seasonality, local events, and promotional lift.

The Human Element

We ran a structured training programme for the procurement team and branch managers alongside the technical implementation. Adoption was the variable that would determine success — and it succeeded because we invested in it.