Program Overview
In an era of volatile demand and rising carrying costs, the ability to interpret and act on data is a supply chain superpower. This course empowers professionals to apply data analytics across the inventory lifecycle—from classification and stocking decisions to reorder point calculation, buffer optimization, and safety stock calibration. Participants will use historical consumption, seasonality, lead time variability, and supplier reliability data to drive smarter inventory segmentation (ABC/XYZ), multi-echelon planning, and inventory-rightsizing strategies. The program also explores how demand shaping (via promotions, pricing, and channel prioritization) can be integrated into inventory planning to balance cost, service, and agility. Dashboards, simulation models, and real datasets from FMCG, pharma, and retail use cases bring concepts to life.
Features
- Apply analytical models to optimize inventory parameters (ROP, EOQ, safety stock)
- Use segmentation techniques (ABC, XYZ, FSN) to tailor inventory strategies
- Interpret demand signals to align inventory with sales levers and market shifts
- Build dashboards and KPIs to monitor inventory health, ageing, and turns
Target audiences
- Inventory and Demand Planners
- Supply Chain Analysts
- Replenishment Managers
- Data & Ops Strategy Teams
Curriculum
- 4 Sections
- 16 Lessons
- 1 Day
- Inventory Analytics – Segmentation & Planning Fundamentals4
- 1.1Concepts: Multi-dimensional classification, target stock levels, segmentation for decision-making
- 1.2Keywords: ABC-XYZ, FSN, Velocity Bands, Cost-Service Trade-off, Inventory Stratification
- 1.3Use Case: Classify SKUs using sales variability and contribution to define planning policies
- 1.4Toolkit: Inventory segmentation model (ABC × XYZ matrix + planning action grid)
- Metrics & Visualization for Inventory Health4
- 2.1Key KPIs: Inventory Turnover Ratio, Days of Cover, Fill Rate, Excess/Obsolete %
- 2.2Keywords: Stock Cover Dashboard, Inventory Ageing Heatmap, Shelf-Life at Risk
- 2.3Real-life Insight: Pharma company used shelf-life based age tracking to reduce write-offs by ₹1.2 Cr annually
- 2.4Exercise: Build an interactive inventory health dashboard with actionable metrics
- Analytics for Demand Shaping & Market Responsiveness4
- 3.1Concepts: Using data to align demand with supply via pricing, promotion, and availability
- 3.2Keywords: Lift Modeling, Cannibalization Effect, Elasticity, Cross-SKU Demand Transfer
- 3.3Case: FMCG firm used analytics to shift demand toward surplus SKUs using regional offers
- 3.4Toolkit: Demand shaping lever matrix (pricing, channel, promo, pack-size)
- Advanced Data Tools & Simulations4
- 4.1Tools: Power BI, Tableau, Python/Pandas, Excel Solver, SQL for Inventory Cubes
- 4.2Keywords: What-if Analysis, Safety Stock Simulation, Scenario Planning
- 4.3Exercise: Simulate a demand surge scenario – plan response with replenishment logic, inventory buffer, and promo control
- 4.4Template: Inventory analytics simulator (inputs: demand volatility, lead time, holding cost)
Instructor
