Inzicht en schaalbaar groeien door een datawarehouse op eigen server
Zuiver
Data & AI for wholesale
Wholesale margins depend on having the right stock at the right time.
Orders, stock, purchasing and customer data come from the ERP system, the webshop and suppliers’ systems. When those sources remain separate, purchasing and sales teams only see after the fact where they ran out of stock or bought too much. Data and AI bring those sources together and show demand by product and customer.
Challenges,
Thousands of products, seasonal patterns and promotions make it difficult to decide how much of each product to stock. A stockout can cost you a customer; excess stock costs money. At the same time, buyers expect faster delivery and greater visibility. That calls for a reliable source of truth and forecasts rather than estimates.
How we support you,
We build the data foundation that gives sales, purchasing and logistics the same figures, then add models that predict demand by product and location. For Zuiver, we built a data warehouse on its own server, and for Vivara, a data platform serving as a single source of truth. These are just some examples of what is possible. The right solution always depends on the challenge and the organisation.
DSL in numbers,
40
Data experts
10+
Years of experience
200+
Projects completed
8,7
Client satisfaction
Get in touch with our experts,
Want to know what we can do for your organisation?
Younes Seghrouchni
Managing Consultant GenAI & Data StrategyE-mail info@datasciencelab.nl
Carmen Wolvius
Managing Consultant Data Science & AIE-mail info@datasciencelab.nl
Stef Stoelwinder
Business DevelopmentE-mail info@datasciencelab.nl
Where we do that,
Latest blog posts,
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The organisations that do this,
Frequently asked questions,
AI can combine historical orders with seasonal patterns, promotions and current stock levels to reveal demand by product sooner. This gives you better insight into what to order, which customers are leaving and where margins are being lost.
Start with one product group or one warehouse where stockouts or write-offs are common. You can use that group’s order history to test quickly whether a forecast outperforms the current estimate before tackling the entire catalogue.
Order history by product and customer, stock levels, purchase orders and supplier lead times, supplemented by promotions, seasonal patterns and sometimes the weather. Most of this data is already in the ERP system and webshop; the work lies in bringing it together and cleaning it.
When purchasing and sales teams currently rely on experience to decide what to order, and there is enough historical data to learn a pattern. A model gives the buyer a recommendation for each product; the buyer decides, and the model learns from any deviations.
By showing recommendations in the system buyers already use to place orders, and agreeing who manages the model when the product range or suppliers change. That way, it remains a useful tool that evolves with the business.