Chair of Logistics and Quantitative Methods

New collaboration: Data-based capacity management at Lufthansa Technik Logistik Services


In a joint project with Lufthansa Technik Logistik Services GmbH, the Chair of Logistics and Quantitative Methods is developing innovative methods for predicting and planning capacity requirements.

Digitalisation opens up new possibilities

In the global network of Lufthansa Technik Logistik Services GmbH (LTLS), thousands of spare aircraft parts – from standard screws to airplane engines – are moved from place to place each day. However, fluctuations in quantities and demands for ever shorter throughput times make planning and managing these material flows a difficult task, which is nevertheless very important. In order to better manage these tasks, the intention of this scheme is to create a digital image of every material movement, which makes all of the information centrally accessible.


Pilot project in goods receiving to demonstrate potential for improvement

To-date, the volumes which are supplied or delivered on a daily basis to central goods receiving are subject to a great deal of uncertainty. Unforeseeable fluctuations mean that where the volume of shipments is higher than anticipated, it becomes necessary to request staff to perform overtime hours or higher levels of costs may ensue when critical shipments (such as urgently required spare aircraft parts) cannot be delivered to the correct destinations in time. On the other hand, volumes of shipments which are less than anticipated give rise to costs due to under-utilisation of the capacities which are maintained at the ready.


The aim of a 100-day project by LTLS and researchers from Professor Richard Pibernik's team, at the Chair for Logistics of the University of Würzburg, is to improve manpower planning in central goods receiving departments by using the data on material movements. In connection with this, machine learning processes are deployed in order to use the data provided to create forecasts and direct proposals for decision making.


Results permit improved planning, including in downstream processes      

If companies succeed in using data on material movement to improve forecasting, then this will reduce the uncertainty regarding the anticipated volume of shipments. By making appropriate dynamic capacity adjustments, costs from over-utilisation (overtime hours, delays) and under-utilisation (unused capacities) can be reduced. The experience derived from analysing the database may also be used to improve decision making in resolving issues requiring decisions downstream from the process.                                                      



Prof. Dr. Richard Pibernik, Chair for Logistics and Quantitative Methods,

Von Fabian Taigel