The company was using two separate optimization tools: one to optimize the planning of interventions, setting appointments to highest priority customers, and a market solution to optimize the routing of the maintenance teams. Challenging the system in place required:
- To integrate the two decision levels by building a planning tool that selects the priority customers with a consideration of the resulting maintenance team routing cost;
- To deal with the resulting very large scale problem (selecting 1200 customers out of 2500 pending visits, and routing over these 1200 points) in just a few minutes to allow testing different scenarios in terms of customer priority;
- To evaluate the benefit of the integrated approach via scientific comparative simulations.
The decision support tool provides a complete maintenance intervention planning prescribing the routes and the associated selection of customers in a few minutes.
The comparison with the current practice has revealed a major productivity gain (+30%) as measured by the profit function, resulting in improved maintenance team productivity and customer satisfaction.
Simulations pointed out the need to increase the fleet size to reach even better profit margin (by a further +7%).