With warehouses throughout Europe and thousands of customers and suppliers, logistics is always a hot topic for spare-parts supplier Kramp. But how could the organization supply the right product to the right company at the right time? Eraneos and Kramp worked together to solve this very question, with the goal to optimize this process through the use of data.
After initial data analysis and several workshops, Eraneos defined three primary areas of interest that were causing disruptions in Kramp’s operations. The goal was to reduce the risk of stockouts as much as possible by optimizing each of these areas.
We approached Kramp’s challenges through the lens of supply and demand. On the demand side, we found Kramp was using inventory management software to place incoming orders, which also had a forecasting algorithm on top that served as a planning tool. It became clear there were some limitations both in the company’s inventory management and in the forecasting methodology. That quickly became the main area of focus for us as we knew we had to improve this by revising Kramp’s buffer stock sizes and deep diving into its seasonal sales patterns.
On the supply side, we identified two main obstacles that, once solved, would greatly improve Kramp’s response times and stock levels. Firstly, container shipments with long lead times were causing preventable stockouts. And secondly, manual purchase order handling wasn’t always prioritized and thus wasn’t as efficient as it should be. By designating alternative short-term suppliers and creating daily overviews for the business, we ensured these critical points would be kept in check.
Kramp is a solid and reliable spare parts supplier for many businesses throughout Europe. With more than half a million items in its assortment, it supplies companies in the agricultural industry with the necessary parts for the smooth operation of its clients’ products
Insight into the company’s data allowed us to consult Kramp on the best ways to reduce stock shortages. In the end, the company managed to improve its response time, reduce unnecessary stock, and successfully activate its data.