Retail
Stock turnover, stockouts, mix and store performance. In retail a mistake costs on both sides: too little sells less, too much becomes idle capital.
Talk to a specialistStockouts and dead stock are the same mistake, in opposite directions.
In retail the purchasing decision is made many times a week, and almost always with incomplete information: yesterday’s sales on one screen, stock on another, and the store manager’s hunch as the tiebreaker.
The cost of that improvisation shows up split, which helps hide it. A stockout becomes a sale that never happened and so appears in no report. Dead stock becomes idle capital that only bothers anyone at inventory time. Neither is traced back to the decision that caused it.
The questions we answer
- Which product went out of stock, in which store, and how much sales did it cost?
- Which item has been sitting how long, and how much capital does it tie up?
- What is the ideal mix per store profile, rather than the chain average?
- Which store performs above the potential of its catchment and which is below?
- Does the demand forecast beat the buyer’s instinct?
The indicators of this industry
The ones that show up in most projects in this industry. The final set is always agreed with you an indicator nobody uses is dead weight on a dashboard.
- Stock. Turnover by product and by store, days of cover, stockout rate, estimated lost sales, non-moving items, tied-up capital, inventory accuracy.
- Sales. Sales per square metre, average ticket, items per transaction, store conversion rate, sales per assistant, share by category.
- Mix and price. ABC curve of products, margin by category, price elasticity, private label share, promotion performance.
- Store. Like-for-like comparison between stores, performance against catchment potential, operating cost per store, productivity per hour worked.
The solutions we use most here
- Business Intelligence. POS, stock and purchasing in a single flow, broken down by store and by product.
- Data Analytics. Demand forecasting and product curve by store profile.
- Power BI. A mobile dashboard so the store manager decides with the indicator in hand.
How we run it
We start by integrating POS, stock and purchasing and the first difficulty is usually inconsistent product records between them, which is governance work before it is dashboard work. With a clean base, the turnover and stockout indicators by store come in. Demand forecasting comes later, and is always compared with the current method before replacing it.
How we measure results
The most frequent indicators are reduction in stockouts, stock turnover, capital tied up in slow-moving items and forecast accuracy compared with the previous process.
We now have a far more robust, fast and visual view of each management area’s results.
Sales director national fuel distributor
About this industry
We have many stores on different systems. Is it possible?
It is, and it is common in a chain that grew by acquisition. The integration handles each source and resolves the record differences along the way. That standardisation work is usually the bulk of the project and it is what finally makes the chain-level number add up.
How do you measure a stockout if the sale never happened?
By inference: comparing the item’s expected sales in that context with actual sales during the out-of-stock period. It is not exact, and we do not promise it is, but it is enough to prioritise which stockout to fix first.
Will the store manager actually use this?
Only if it is on their phone and answers the question they have at eight in the morning. A store dashboard is designed for that reality few indicators, clear decision. When adoption is the bottleneck, Data Literacy usually solves more than rebuilding the dashboard.
Does demand forecasting work for seasonal products?
It does, and that is where it helps most provided there is history from previous seasons. Without history the model has no way to learn the pattern, and we say so before you invest.
How much do stockouts cost across your chain?
Thirty minutes of conversation is usually enough to size it.
We understand processes before recommending technology. Automation, data, artificial intelligence, and custom software for companies seeking efficiency, control, and scale.