A leading Indian grocery and general merchandise retail chain, operating 50+ stores across 20+ cities in 15+ states, was experiencing severe inventory misalignment. Despite a wide assortment of 33 product families, ranging from food essentials to automotive goods, the company incurred 4M+ in losses over three years due to unsold and expired inventory. Additionally, the chain suffered a 23% declines in its sales to inventory ratio, signaling inefficiencies in demand forecasting, stock planning, and store level product relevance.
The client engaged AEM Consultancy (Analyze . Eliminate . Maximize) to uncover the underlying drivers, rationalize the product portfolio, and deploy a predictive inventory system that responds to real consumer behavior across diverse regions.
Our preliminary assessment revealed several operational, strategic, and analytical gaps:
Without strategic intervention, the chain risked accelerating losses, poor cash flow, and reduced profitability across its network.
We conducted a comprehensive diagnostic exercise combining 4.5 years of sales history, external variable analysis, store performance clustering, and demand forecasting.
Key insights from the investigation:
We uncovered strong seasonal trends influenced by:
Historic patterns were not being used in planning, leading to missed opportunities during predictable demand windows.
Consumption behavior correlated with: Holidays, Fuel price index, Region specific festivals, Economic cycles - none of these were part of the client's planning process.
13 product families performed so poorly that they effectively generated zero contribution for 4.5 years, representing dead stock and recurring losses.
Stores were categorized using clustering analysis into: High volume stores, Medium volume stores, Underperforming stores. Each required a different inventory strategy, but all were receiving identical replenishment guidelines.
The existing systems used simple manual forecasting. We found ML based models would significantly improve forecasting accuracy, especially for seasonal and location specific demand.
Models identified as ideal for this case:
We invented a holistic inventory transformation program combining analytics, machine learning, and operational restructuring.
We recommended a phased removal of the 13 non performing product families using a structured exit plan. This freed shelf space, eliminated dead stock losses, and improved capital utilization.
We built a predictive demand engine using: SARIMA for seasonality, Prophet for holiday effects, Random forest & XGBoost for multi-variable forecasting across regions.
The models integrated: Historical sales, Holidays, Local festivals, Fuel prices, City-level demographics, store category (high/medium/low volume). This enabled precise forecasting for each store.
We created a seasonality based stock up model to increase inventory for high demand products during: December peak, mid-year consumption spikes, major holidays and festivals. This reduced stockouts and captured previously missed revenue.
Instead of uniform stocking, we implemented:
This transformed the client's approach from 'one-size-fits-all' to 'data-driven localization.'
High volume stores received high accuracy machine learning models and tighter replenishment planning. Underperforming stores implemented targeted improvements to drive conversions and reduce stagnant inventory.
AEM Consultancy (Analyze . Eliminate . Maximize) transformed the client's inventory management into a science driven, customer centric system powered by analytics and machine learning. By eliminating dead inventory, forecasting real demand, tailoring assortments at the store level, and adjusting for seasonal peaks, the retail chain achieved significant financial recovery and operational efficiency.
The company now operates with higher turnover, fewer losses, better availability, and data backed decisions, securing a sustainable and profitable future.
For expert assistance in leveraging data to optimize your retail operations, streamline inventory management, and boost sales performance, please contact AEM Consultancy.