TY - GEN
T1 - Key Figure Systems
T2 - 2020 International Conference on Big Data in Management, ICBDM 2020
AU - Wuddi, Philipp Martin
AU - Fottner, Johannes
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/5/15
Y1 - 2020/5/15
N2 - Supply logistics in production and trading are subject to different types of deviations and problems. In order to control these, the implementation of a knowledge management system that suggests solutions automatically is goal of a research project at Technical University of Munich (TUM). This paper shows, which steps are necessary to build such a system. An example for supply logistics in general are tugger train systems. The design of the described steps aims to allow a transfer from tugger train systems to other logistical systems. First, this paper describes the idea of a knowledge management system and the why a key figure system is needed. The explained method starts with the display of necessary data analysis as one of two major chapters. Since the deviations in tugger train systems are divergent, real live data of tugger train systems are examined for deviations and problems. Holidays and their impact on human factors in supply logistics represent a short excursus in addition to the data analysis. Using the results from the data analysis, it is possible to develop a key figure system, starting with the definition of a key figure for every type of problem. After that, the key figures and especially their correlations are subject to validation, correction and documentation. This methodical process is the second major part. A short outline of future steps ends the paper.
AB - Supply logistics in production and trading are subject to different types of deviations and problems. In order to control these, the implementation of a knowledge management system that suggests solutions automatically is goal of a research project at Technical University of Munich (TUM). This paper shows, which steps are necessary to build such a system. An example for supply logistics in general are tugger train systems. The design of the described steps aims to allow a transfer from tugger train systems to other logistical systems. First, this paper describes the idea of a knowledge management system and the why a key figure system is needed. The explained method starts with the display of necessary data analysis as one of two major chapters. Since the deviations in tugger train systems are divergent, real live data of tugger train systems are examined for deviations and problems. Holidays and their impact on human factors in supply logistics represent a short excursus in addition to the data analysis. Using the results from the data analysis, it is possible to develop a key figure system, starting with the definition of a key figure for every type of problem. After that, the key figures and especially their correlations are subject to validation, correction and documentation. This methodical process is the second major part. A short outline of future steps ends the paper.
KW - AI in logistics
KW - intelligent system
KW - key figure system
KW - tugger train system
UR - http://www.scopus.com/inward/record.url?scp=85099016752&partnerID=8YFLogxK
U2 - 10.1145/3437075.3437090
DO - 10.1145/3437075.3437090
M3 - Conference contribution
AN - SCOPUS:85099016752
T3 - ACM International Conference Proceeding Series
SP - 125
EP - 129
BT - Proceedings of the 2020 International Conference on Big Data in Management, ICBDM 2020
PB - Association for Computing Machinery
Y2 - 15 May 2020 through 17 May 2020
ER -