TY  - CONF
A1  - Eschenbächer, Jens
A1  - Wiethölter, Jost
A1  - Kühl, Linus
T1  - Data-driven supply chain analysis: Development and potential analysis of a model-based damage prediction approach and its integration into SCM
T2  - Proceedings of the 27th International Symposium on Logistics
N2  - Against the setting of an increasing need for innovation and low margins, companies in the logistics
sector are facing highly competitive pressure. One field with high potential for optimization lies within
damage quotas. The use of big data analytics or data mining represents a promising approach to face
this challenge. However, within supply chain management, data mining is hardly being researched on
regarding damage quotas and thus not being utilized to its full possible extend. At the current time it
seems to predominantly be used for route and utilization optimization while the analysis of delivery
damages is hardly considered.
The aim of this research is therefore to showcase an initial approach for data mining in logistics to predict
delivery damage probabilities and to validate this by means of a multiple case study research. To create
a sound basis for evaluation, the groundwork is laid out based on CRISP-DM by the analysis of reference
data (German road-cargo market).
As a central result it is noted that data mining can systematically be used to help reducing the damages
by forecasting the probabilities of damages occurring during transport in dependence of different factors.
The approach can be utilized across different markets as long as sufficient data tracking delivery
damages is being collected within a company. Challenges arise in the field of air- and sea-freight.
Y1  - 2023
UR  - https://www.hb.fh-muenster.de/opus4/frontdoor/index/index/docId/17884
UR  - https://www.islconf.org/wp-content/uploads/2023/07/ISL_2023_Final_Proceedings.pdf
SN  - 13 978-0-85358-352-3
SP  - 136
EP  - 144
ER  -