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How to Analyze Inventory Data: Turnover, Stockouts, and Carrying Cost

How to analyze inventory data starts with calculating four core metrics — inventory turnover, days on hand, stockout rate, and carrying cost — because these four numbers together tell you whether your inventory is optimized or quietly eroding your margins.

Learn the practical methods for analyzing inventory data in spreadsheets, including how to structure your data, which metrics to calculate, and how to identify slow-moving stock and stockout risks before they hurt revenue.

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How do I calculate inventory turnover in Excel?What metrics should I track for inventory analysis?How do I identify dead stock in my inventory spreadsheet?How do I calculate the right reorder point for each SKU?What is a good inventory turnover ratio by industry?

What this export contains

SKU
Product Name
Units On Hand
Units Sold (30d)
Reorder Point
Lead Time (Days)
Unit Cost
Carrying Cost %
Stockout Flag
Category

What usually goes wrong with it

  • Inventory data in ERP and warehouse systems exports to spreadsheets that are hard to analyze

    Operations and supply chain teams regularly export inventory data from NetSuite, SAP, or warehouse management systems to Excel or CSV, but identifying which SKUs are turning too slowly or approaching stockout requires manual filtering and formula work.

    DataMimi Calculate inventory turnover by dividing cost of goods sold for the period by average inventory value — track it by SKU and category to identify fast and slow movers in each product line.

  • Dead stock is hard to identify without a consistent turnover calculation

    Products that have been sitting in inventory for 90+ days without selling often go unnoticed until they're written off — a regular inventory turnover analysis by SKU and category catches them before they become a larger write-down.

    DataMimi Flag dead stock automatically by adding a formula column: if Days On Hand (Units On Hand / Average Daily Sales) is greater than 90, the SKU is at risk and should trigger a markdown or return-to-vendor

  • Reorder point calculations are often done manually and inconsistently across SKUs

    Many teams calculate reorder points ad hoc or use outdated numbers — a systematic analysis of actual lead times and sales velocity is needed to keep reorder points accurate as demand patterns change.

    DataMimi Upload your inventory export to Datamimi and ask 'which SKUs have the worst turnover this quarter' or 'which categories are approaching stockout based on current sales velocity' without building manua

Common questions

How to analyze inventory data to reduce carrying costs and stockouts?

How to analyze inventory data for cost reduction starts with calculating inventory turnover (COGS / Average Inventory) by SKU and category to find slow movers, then calculating Days On Hand to flag items approaching stockout. High turnover with low days on hand means healthy flow; low turnover with high days on hand means excess stock to address.

What columns should I include in an inventory analysis spreadsheet?

Include SKU, Product Name, Units On Hand, Units Sold (by period), Reorder Point, Lead Time in days, Unit Cost, and Category. Add calculated columns for Days On Hand (Units On Hand / Average Daily Sales) and Inventory Value (Units On Hand × Unit Cost) to enable turnover and carrying cost analysis.

How do I calculate the reorder point for a SKU?

Reorder Point = (Average Daily Sales × Lead Time in Days) + Safety Stock. Calculate average daily sales from your last 30–90 days of sales data, use your supplier's actual lead time, and add a safety stock buffer of 20–30% of the lead time demand. Recalculate quarterly as demand patterns change.

What does Datamimi cost for inventory analytics?

Datamimi's Free plan is $0/month with 40 credits and no credit card required. Paid plans: Lite at $9/month, Starter at $24/month (1,500 credits, 3 files), Pro at $59/month (5,000 rollover credits), Team at $199/month (20,000 credits, 5 users).

Try it with your own file

DataMimi reads the file you actually have — merged cells, headers below row one, totals pasted at the bottom — and shows which rows and columns every number came from.

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