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Overview
Overview
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.
The Challenge
The Challenge
Our preliminary assessment revealed several operational, strategic, and analytical gaps:
Several misalignment between inventory and demand: Stock level had no correlation with what customers actually purchased.
Lack of data driven inventory planning: All stores followed a centralized, uniform stocking strategy, ignoring geographic, demographic, and cultural variations (e.g., festival driven demand, local preferences).
Low performing product families: 13 out of 33 product families recorded near zero sales over 4.5 years, consuming shelf space and capital.
Poor peak season management: Seasonal demand spike (e.g., December, summer months, festivals) caused: stock outs of high demand products, excess build up of slow moving goods.
Store level underperformance hidden by aggregate data: Specific branches were overstocked despite consistently low footfall and low conversion.
Without strategic intervention, the chain risked accelerating losses, poor cash flow, and reduced profitability across its network.
AEM Investigation & Root Cause Analysis
AEM Investigation & Root Cause Analysis
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:
Time Series Trend Analysis
We uncovered strong seasonal trends influenced by:
December holiday purchasing surges
Mid-year food and household consumption peaks
Festival driven demand variation
Fuel price fluctuation affecting logistics and consumer spending
Historic patterns were not being used in planning, leading to missed opportunities during predictable demand windows.
External Factor Modeling
Consumption behavior correlated with: Holidays, Fuel price index, Region specific festivals, Economic cycles - none of these were part of the client's planning process.
Product Portfolio Gap
13 product families performed so poorly that they effectively generated zero contribution for 4.5 years, representing dead stock and recurring losses.
Store Level Behavior Segmentation
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.
Forecasting Capability Gaps
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:
ARIMA/SARIMA - for strong seasonal patterns
Facebook Prophet - highly effective with holiday effects
Random Forest Regressor - for multi factor forecasting (prices, holidays, oil prices, demographics)
XGBoost Regressor - for high accuracy demand prediction across diverse stores
The AEM Solution
The AEM Solution
We invented a holistic inventory transformation program combining analytics, machine learning, and operational restructuring.
Product Portfolio Rationalization
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.
Machine Learning-Driven Demand Forecasting
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.
Dynamic Inventory Planning for Peak Seasons
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.
Localized Inventory Strategy (Store - Specific Plans)
Instead of uniform stocking, we implemented:
Store-specific assortments
Region-based inventory levels
Demand patterns tailored by city and customer behavior
Reduced quantities for underperforming stores
Expanded assortment for high-volume stores
This transformed the client's approach from 'one-size-fits-all' to 'data-driven localization.'
Performance Based Store Prioritization
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.
Results & Impact
Results & Impact
Within 12 months, the retail chain achieved substantial improvements:
14M+ inventory loss eliminated by discontinuing non performing product lines
18% improvement in sales to inventory ratio, reversing the earlier 23% decline
Optimized stock levels aligned to real customer demand
Higher inventory turnover, with reduced dead stock and improved cash flow
Zero overstocking during peak seasons, thanks to seasonal forecasting
Shift from reactive to proactive management, powered by ML driven insights
Enhanced store level performance, with localized strategies improving customer satisfaction and availability.
The organization gained a modern, data driven retail planning system that enables long term profitability.
Outcome Summary
Outcome Summary
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.
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