Client is a prominent retail grocery chain in India, operates 54 stores across 22 cities in 16 states. The company offers a diverse range of 33 unique product families, including daily groceries, beverages, cleaning supplies, and even automotive parts. Over the past three years, client has faced a critical challenge: significant inventory waste and mounting costs due to unsold stock. This issue has resulted in a substantial financial loss of ₹14 million on inventory costs and a 23% decrease in the sales-to-inventory ratio, indicating that inventory is piling up without corresponding sales. The client sought a comprehensive analysis to understand customer trends at each store and optimize inventory ordering to reverse these negative trends.

Client's primary challenge was a lack of clear insight into specific store performance and customer purchasing patterns, leading to substantial financial losses from unsold inventory. Despite a broad product assortment, a significant portion of inventory remained stagnant, masking the true sales potential and contributing to rising operational costs. The company needed to identify which stores were underperforming in specific inventory categories to implement targeted stock management strategies and improve overall profitability. The absence of a data-driven approach to inventory management was leading to a disconnect between stock levels and actual customer demand.

AEM Consulting undertook a thorough data collection and analysis initiative, leveraging 4.5 years of sales data from January 1, 2020, to August 8, 2024. The analysis incorporated key external factors such as holidays and oil prices, which were identified as significant influencers of sales performance in India's oil-dependent economy.

Key Insights

1. Product Assortment Inconsistency with Demand: Despite a consistent offering of 33 product family types across all stores for 4.5 years, a critical insight emerged: 13 of these 33 product families recorded on average of zero sales over the entire period. This highlighted a severe misalignment between the stocked inventory and actual customer demand, suggesting that client had established its brand identity for a limited set of essential goods, and customers were not frequently purchasing other categories from them.

2. Strong Sales Seasonality & Holiday Impact: A clear and consistent sales pattern was observed, with significant peaks in December (holiday shopping) and mid-year (summer months), and declines in January. This confirmed the strong influence of holidays and events on customer purchases.

3. Disparate Store-Level Performance: While Jaipur City stores (especially Type A) consistently exhibited strong sales, two specific stores (located in different cities and of different types) were identified as severe underperformers, deviating from observed trends and warranting deeper investigation.

4. Customer Preference and Brand Niche: The data strongly implied that client's customers primarily associated the brand with a specific set of core grocery items. Customers would visit company for these preferred goods but were not inclined to purchase the broader range of inventory, particularly the non-performing 13 product families. This highlighted a need to align inventory with the company's established brand niche and customer expectations.

Root Causes Identified:

• Failure to Understand Customer Preferences: The primary root cause was clients's inability to adapt its inventory to evolving customer preferences. The continued stocking of 13 non-performing product families despite minimal sales was a clear indicator of this disconnect.

• Lack of Dynamic Inventory Management for Seasonal Peaks: While sales patterns were clear for holidays, the company failed to adjust inventory strategically, leading to either stockouts of popular items or continued accumulation of slow-moving goods during peak sales periods.

• Centralized, Undifferentiated Inventory Strategy: The consistent product assortment across all 54 stores, without accounting for regional or store-specific demand, compounded the problem of unsold inventory.

Implemented Actions (Based on Recommendations):

Based on these insights, client, in collaboration with AEM, implemented a multi-faceted strategy:

1. Product Portfolio Rationalization: Implemented a decisive strategy to discontinue stocking the 13 non-performing product families across all stores. This involved a phased removal of existing stock and prevention of new orders for these categories.

2. Dynamic Inventory Adjustment for Holidays: Developed and implemented a data-driven inventory forecasting model to significantly increase stock levels for high-demand product families during identified peak sales periods (December, mid-year, and specific holidays). This ensured availability of popular items when customer traffic was highest.

3. Localized Inventory Strategy: Began piloting a more localized inventory strategy, particularly focusing on optimizing stock for the underperforming stores identified. This involved a deeper dive into their specific local demographics and purchasing habits to tailor product offerings.

4. Performance-Based Store Focus: Prioritized forecasting accuracy for high-volume stores (e.g., Type A stores in Jaipur) to maximize their potential, while simultaneously developing tailored improvement strategies for underperforming branches.

The implementation of these strategic changes yielded significant and immediate positive impacts for client within one year:

• Elimination of Inventory Loss: The direct consequence of discontinuing non-performing product lines was the complete elimination of the ₹14 million annual inventory loss. This represented a substantial improvement in cost efficiency.

• Significant Sales-to-Inventory Ratio Improvement: Sales saw an 18% increase, reversing the previous 23% decrease in the sales-to-inventory ratio. This indicates that the company is now selling a higher proportion of its stocked inventory, leading to reduced holding costs and improved cash flow.

• Optimized Stock Levels: The stores are now stocking more relevant inventory, aligning with actual customer preferences and leading to higher product turnover.

• Data-Driven Decision Making: The project successfully transitioned client from reactive, broad-stroke inventory management to a proactive, data-driven approach that leverages customer insights for strategic stocking and sales maximization.