Retail Inventory and Optimization Tool Retailers often collect large amounts of sales and invento...Retail Inventory and Optimization Tool Retailers often collect large amounts of sales and invento...
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Retail Inventory and Optimization Tool
Retailers often collect large amounts of sales and inventory data but lack a system to turn it into clear, actionable decisions, making it hard to know which products to reorder, which are slow-moving, and which risk stockouts or overstocking.
I built an end-to-end analytics pipeline to solve this. Using Python and Pandas, I cleaned a raw retail dataset, handling missing values based on what each one actually meant in context (e.g. a missing discount treated as "none," while a missing inventory value was filled with the average rather than falsely assumed to be zero). The cleaned data was loaded into MySQL for exploratory analysis, then connected to an interactive Power BI dashboard summarizing revenue, stock levels, demand fulfillment, and sales trends by category, store, and season.
Key findings included a 95.71% demand fulfillment rate (revealing real, recoverable lost sales), evenly balanced sales across all products categories and seasons, and one store carrying noticeably higher inventory than others despite similar sales flagging a possible overstocking risk.
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