ESCF Operations Practices: Insights from Science

We regularly publish a new issue in our ESCF Operations Practices series. In this high-level series, we highlight important findings and results obtained with our companies. The series emboddies our long-term and intimate relationships with our members.

The two issues below are freely available. All issues in the ESCF Operations Practices series can be found here (members only).

Transportation planning at Jan de Rijk Logistics

Nowadays, the outsourcing of logistics to service providers has become an integral part of the supply chain. Trucking companies are responsible for offering reliable, cost efficient, innovative and sustainable logistics. In order to provide excellent service to the customers and to control costs at the same time, logistic service providers need to organize the planning process of all freight requests efficiently. On daily operations, planners have to consider multiple parameters and variables of freight requests to design efficient transportation plans. This describes the need for a decision support algorithm as designed for Jan de Rijk Logistics.

Two models were developed that provide fast and high-quality decision suggestions to the planners of the organization during the planning process. Planners are able to compare and assess various scenarios and then plan the optimal combination of asset, driver, and route. As a consequence, reduction on planning time and decreasing emission levels are realized. Altogether, Jan de Rijk Logistics feels that their planning is more efficient in terms of cost, service level, and in the end customer satisfaction thanks to the implemented decision support algorithms.

Predicting throughput time at Fokker

Availability of machines is very important in achieving operational excellence. In Aerospace, this need is especially high, to make sure that airplanes can keep up with flight plans and passengers, as well as cargo, can get to their destination in time. However, machines have to be maintained from time to time. Then, it helps to have a good estimate of when the maintenance activity will be ready. This enables the maintenance department to take appropriate measures, such as keeping the optimal number of spare parts in stock and optimally planning for down time of the machine.

This operations practice describes how Fokker Services implemented a technique for predicting the throughput time of their maintenance activities on airplane engines. It shows that they managed to improve their throughput time prediction, which potentially means higher customer satisfaction can be achieved. We expect that – with the advent of Internet of Things – such ‘data-driven condition-based maintenance’, will not just be important for Fokker Services, but for all companies that maintain expensive machinery.