Modulai built a predictive model for one of the largest rental equipment providers in the Nordics, forecasting when lent-out equipment would be returned. By enabling smarter logistics planning, the system helps minimize unnecessary transport between local shops and main warehouses.
Challenge
As one of the largest rental equipment providers in the Nordics and a supplier to thousands of construction sites, the client needed a system capable of predicting when lent-out equipment would be returned. The goal was to optimize their vast logistics chain by minimizing unnecessary transport between local shops and main warehouses.
Solution
In close collaboration with the company's data scientists, Modulai processed and analyzed their large database of customers, construction projects, and past rentals. The model takes detailed characteristics of the equipment, customers, geography, and season into account to estimate when equipment will be returned. Several regression and classification approaches were developed and assessed.
Tools
The system was delivered as a Python service designed to run on the client's internal infrastructure and integrate with their ERP system. Multiple regression and classification approaches were evaluated during development.
Value created
The model gives the client a way to anticipate when rented equipment will return, rather than reacting once it does. With that forecast, they can plan transport between local shops and central warehouses ahead of time, cutting unnecessary moves across a logistics chain that spans thousands of construction sites and keeping equipment available where it is most likely to be needed next.
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