Explore products currently in inventory.
Identify factors influencing inventory reorganization or reduction.
Provide insights and recommendations supported by data.
Data extraction and exploration
Using SQL queries, I explored MintClassics inventory, sales, and warehouse data to identify key trends and isolate products with low turnover or redundant storage.
Insight: Enabled discovery of underperforming items and warehouses with overlapping inventory.
Sales performance prediction
This scatterplot regression shows how sales volume correlates with inventory levels.
Insight: Helped identify which products have matching sales trends, supporting stock level justification and streamlining.
Identifying high- and low-stock products
This visualization highlights quantity in stock across all product lines.
Insight: Guided the decision on which lines could be trimmed or redistributed across fewer warehouses.
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Understanding drivers of sales
This Key Influencer visual reveals what factors most affect order volume.
Insight: Helped isolate product lines and models worth keeping or retiring based on customer behavior.
Breaking down total stock by category, line, and product
This interactive visual shows how inventory is distributed across categories.
Insight: Identified potential consolidation opportunities and outliers that may be removed or relocated.