Inventory Optimization of Retail Grocery Stores using ABC & XYZ Analysis

This research investigates the challenges of inventory optimization in retail grocery stores and proposes a practical solution through a hybrid ABC-XYZ classification framework. Retail environments often suffer from inventory imbalances due to high SKU diversity, varying product demand, and perishability. This study addresses these inefficiencies by analyzing six months of sales data from a mid-sized grocery retailer and segmenting 36 key product categories using dual criteria 1. ABC Analysis for identifying high-value SKUs and 2. XYZ Analysis for assessing demand variability. The integrated ABC-XYZ matrix reveals that 1. Class A items (e.g., Milk, Biscuits) contribute over 72% of total sales, highlighting their critical role in revenue. Class Z items (e.g., Fruits, Personal Hygiene) show >60% demand variability, requiring dynamic inventory strategies. Based on this segmentation, the study recommends differentiated inventory policies - such as Just-inTime (JIT) for AX items, safety stock buffers for AZ and BZ items, and rationalization for CZ items. The findings demonstrate that the ABC-XYZ framework offers a cost-effective, data-driven inventory planning tool for grocery retailers. This study contributes to the academic field by bridging classical inventory theory with practical application in the fast-moving consumer goods (FMCG) sector, particularly within the Pakistani retail context. It also provides a foundation for future research involving automation, perishability modeling, and real-time demand forecasting.