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Stock, purchasing and orders
Wholesale
Stock that adds up, purchasing that works from a forecast and seeing per customer what margin is left. With the data already sitting in the ERP and the order flow.
- 67.7%Of order emails become a correct draft order
- 5,017Order emails in that measurement
- 86%Forecast accuracy, up from 40%
Three places where the sector gets stuck
- 01
Stock and availability
Too much of what moves slowly and just too little of what the customer needs tomorrow. A forecast per item shows where stock can come down and where a lost sale is looming.
- 02
Purchasing on gut feel
Ordering often runs on experience and last month's Excel list. With lead times, seasons and open orders in one calculation, there is an order proposal the buyer can check and adjust.
- 03
Margin per customer
Discounts, volume tiers and exceptions pile up until nobody knows which customer still pays off. An overview per customer and product group shows where the margin leaks away.
What we build, on the company's own data and connected to what already runs
- 01
Demand forecast and order proposal
A forecast per item, translated into an order proposal. The buyer keeps the final say and sees what the proposal is based on.
- Forecast per item and branch
- Season, promotions and lead time included
- Reorder point and safety stock
- Alert when a lost sale is looming
- 02
Margin and price per customer
Seeing what each customer and product group delivers, after discounts and agreements. Then a price conversation becomes a conversation with figures.
- Margin per customer and product group
- Discounts and volume tiers in view
- Early signal when a customer is drifting away
- Cross-selling based on order history
- 03
Orders from email and portal
Orders that come in by email, PDF or portal are ready in the ERP as draft orders. The order desk checks and confirms.
- Draft order from email and PDF
- Item and customer recognised from the company's own master data
- What is missing is listed at the top
- Connection with ERP and web shop
Masparts
Sales knows in advance which quote stands a chance
A dealer in exclusive car parts left the data from its web shop unused. Now three models run in the existing system: for parts that sell together, for the chance per quote and for customers who deviate from their ordering pattern.
Read the case