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SKU-level analytics and inventory performance

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A SKU is a single product variant. Same shirt, different sizes and colors. You have hundreds of SKUs. Some sell ten units per week. Some sit for months. You do not know which to reorder and which to kill. SKU-level analytics reveals inventory truth. Which variants sell. Which waste shelf space. Which drive profit. Which drain cash. This article explains how to track SKU performance and optimize inventory allocation.

Understanding SKU metrics and inventory data

What a SKU is and why it matters

SKU means stock keeping unit. One product with many variants. Blue shirt size small is one SKU. Blue shirt size large is another SKU. Red shirt size small is another SKU. Same product. Different SKUs. Each SKU has different demand. Each needs individual tracking.

SKU revenue vs product revenue

Product revenue combines all SKUs. Blue shirt total revenue. SKU revenue breaks it down. Blue shirt size small revenue. Blue shirt size large revenue. Red shirt size small revenue. SKU view shows which variants sell.

Tracking SKU-level sales and revenue

Setting up SKU tracking in your system

Each SKU needs a unique identifier. Your inventory system tracks it. Your analytics system tracks it. Connect them. Revenue flows by SKU. Inventory flows by SKU. Set this up correctly. Do not guess SKU performance.

Matching inventory to sales data

Inventory system shows you have one hundred blue shirts size small. Sales data shows you sold two last month. Mismatch. Either inventory is wrong or sales data is wrong. Reconcile them. Accurate SKU data requires accurate inventory and sales tracking.

Identifying best-selling SKUs

Volume leaders vs revenue leaders

Volume leader: one hundred units sold, low price, low revenue. Revenue leader: five units sold, high price, high revenue. Which is better. Depends. High-volume SKUs are cashflow. High-revenue SKUs are profit. Track both.

Fast movers vs slow movers

Fast movers sell out quickly. Need reordering. Small inventory risk. Fast movement. Slow movers sit for months. Tie up cash. Risk of obsolescence. Fast movers are good. Slow movers are problems.

Identifying dead-stock SKUs

SKUs that never sell

Some SKUs never sell. Zero revenue. Dead inventory. Why stock them. Why tie up cash. Identify them. Liquidate them. Free up cash for fast movers.

SKUs that need liquidation

Some SKUs sold well last year but do not sell now. Obsolete. Fashion items past season. Technology items outdated. Liquidate them. Clear inventory. Free up shelf space. Free up cash.

Optimizing SKU mix and variants

Which variants are worth stocking

Blue shirt sells in all sizes. Stock all sizes. Red shirt sells only in size small and medium. Stock only those. Analyze demand by variant. Stock what sells. Do not stock what does not.

When to discontinue a SKU

SKU zero sales for three months. Discontinue it. SKU negative profit after all costs. Discontinue it. SKU decline trajectory. Forecast its decline. Discontinue before it becomes a problem.

Using SKU data to improve inventory turnover

Inventory allocation by SKU performance

Fast-moving SKUs get large inventory. Slow-moving SKUs get small inventory. Fast movers need reordering. Slow movers sit. Allocate inventory proportional to velocity. Turn cash faster. Reduce inventory cost.

Reorder timing based on sales velocity

SKU selling one hundred units per month needs reorder every month. SKU selling five units per month needs reorder every six months. Time reorders to sales velocity. Do not overstock. Do not understock.

Forecasting SKU demand

Growth forecast by SKU

SKU growing ten percent per quarter needs increasing inventory. SKU flat needs stable inventory. SKU declining needs decreasing inventory. Forecast demand by SKU. Adjust inventory accordingly.

Seasonal demand patterns

Summer clothing SKUs peak in summer. Holiday decoration SKUs peak in November. Snow boots peak in winter. Know seasonal SKUs. Forecast high and low demand. Allocate inventory seasonally.