Purpose

Enable proactive, risk-aware inventory and procurement decisions by transforming stock, vendor, and movement data into a unified supply-chain decision intelligence framework—balancing availability, working capital, and expiry risk.

Business Context & Challenge

The organization operates a multi-location retail and distribution network with complex inventory flows across warehouses, stores, and vendors. Core challenges included:
  • Limited visibility into vendor fulfillment reliability
  • Reactive reordering driven by static thresholds
  • Difficulty identifying overstock, stockout, and expiry risks early
  • Fragmented views of inter-location inventory movement
  • High dependency on manual judgment for redistribution decisions

Inventory decisions directly impacted working capital, service levels, and write-off risk, yet analytics was not structured to support proactive, risk-based decision-making.

Analytical Strategy

This program reframed inventory analytics from stock reporting to supply-chain decision intelligence, guided by five principles:

  1. Inventory treated as capital with time sensitivity
  2. Replenishment driven by scenario-based logic, not fixed rules
  3. Vendor performance evaluated through patterns and consistency
  4. Risks (stockout, overstock, expiry) identified before operational impact
  5. Stock movement optimized at a network level, not in isolation

The objective was to shift inventory management from reactive firefighting to anticipatory control.

Key Capabilities Delivered

This program consists of multiple integrated analytical capabilities, working together as a single decision system:

1. Vendor Fulfillment & Procurement Intelligence

  • Tracked fulfilled vs unfulfilled quantities across vendors and items
  • Identified recurring fulfillment gaps and severity patterns
  • Enabled evidence-based vendor performance discussions

This moved procurement conversations from anecdotal feedback to data-backed accountability.

2. Intelligent Reorder Point & Demand Scenarios

  • Designed a flexible reordering framework supporting:
    • Min–Max replenishment
    • Average consumption-based logic
    • Hybrid scenarios combining both approaches
  • Integrated historical demand, trends, and order history

This replaced rigid replenishment rules with context-aware decision support.

3. Overstock, Stockout & Capital Risk Visibility

  • Created analytical views to:
    • Identify overstocked items tying up capital
    • Flag stockout-risk items before service disruption
  • Prioritized risks based on severity and business impact
  • Inventory planning shifted from reaction to early intervention.

4. Inventory Movement & Network Intelligence

  • Enabled visibility into inter-location stock movement patterns
  • Used geospatial and tabular views to analyze:
    • Transfer volumes
    • Imbalances across locations
    • Redistribution opportunities

This allowed inventory teams to manage stock as a connected network, not siloed locations.

5. Expiry-Aware Stock Allocation & Risk Mitigation

  • Designed a rule-driven allocation model to address short-expiry inventory risk
  • Prioritized redistribution of items expiring within defined risk windows (e.g., next 180 days)
  • Allocation logic considered:
    • Expiry proximity
    • Location-level demand deficits
    • Available on-hand quantities
    • Operational feasibility (preventing fractional or impractical transfers)

This capability shifted stock redistribution from manual, intuition-based decisions to transparent, model-driven risk mitigation.

    Solution Architecture (High Level)

    Architecture aligned with finance and operational realities to ensure trust and adoption

    • Source Systems: ERP inventory, purchase, and transfer data
    • Data Platform: Centralized analytics layer built on Microsoft Fabric
    • Modeling: Item-, vendor-, and location-level aggregation models
    • Analytics Layer: Decision-focused analytical views with drill-down capability
    • Consumption: Procurement, warehouse, inventory, and operations teams

      My Role

      I led this program end-to-end, including

      • Translating supply-chain and inventory risks into analytical models
      • Designing vendor performance, replenishment, and allocation logic
      • Embedding business rules and feasibility constraints into analytics
      • Aligning analytics outputs with operational workflows
      • Driving adoption across procurement, warehouse, and operations teams

        Outcomes & Impact

        • Improved visibility into vendor reliability and fulfillment risk
        • More disciplined and explainable reordering decisions
        • Reduced risk of stockouts, overstock, and expiry-related losses
        • Better utilization of short-dated inventory
        • Stronger linkage between inventory analytics and working capital control

        Representative Solutions Within This Program

        • Item Fulfillment Analysis
        • Reorder Point Analysis (multi-scenario framework)
        • Inventory Transfer & Geospatial Analysis
        • Expiry-Aware Stock Allocation Model

        Why This Program Matters

        This program transformed inventory analytics from static stock monitoring into a risk-aware, decision-support system, enabling the business to manage availability, capital, and expiry proactively at scale.