Auditory Logistic Analysis : an exploratory approach

  • The accuracy of logistic performance becomes more and more a crucial factor for the competiveness of enterprises. Even small advantages decide on economic success or failure. Therefore, companies collect data at an increasing level of detail in order to gain more knowledge about in-depth manufacturing processes and their dynamics, serving as a basis for the adjustment of Production Planning and Control. In traditional logistics, the application of conventional statistical methods for the analysis of manufacturing data has led to formidable improvements of the achievements of logistic objectives. In order to reduce complexity, these traditional approaches rely on greatly simplified models, mostly based on averaging. In order to evaluate data on a higher level of detail for further logistic improvements, actual analyses of manufacturing data more and more include advanced statistical methods, such as Knowledge Discovery in Databases (KDD) and Explorative Data Analysis (EDA). The scientific discipline of Auditory Display has been successfully established in EDA as counterpart to graphical statistics. More specifically known as data sonification, an auditory display is particularly suitable to analyze chronological events, such as the workflow of a production. In this fundamental research, Auditory Display has been applied to the analysis of manufacturing processes. A methodical transfer from logistic to sonic parameters has been developed in order to investigate the potential of sonifications for the identification of bottlenecks in a production workflow, also in regard to the impact of cross-system dynamics. The developed method enables trained analysts to identify systematic order- and process-related bottlenecks, their temporal dynamic evolutions, and their overall impact on schedule adherence of a production workflow.

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Publishing Institution:IRC-Library, Information Resource Center der Jacobs University Bremen
Granting Institution:Jacobs Univ.
Author:Michael Iber
Referee:Katja Windt, Julian Klein, Michael Hülsmann
Advisor:Katja Windt
Persistent Identifier (URN):urn:nbn:de:gbv:579-opus-1003899
Document Type:PhD Thesis
Date of Successful Oral Defense:2014/02/27
Date of First Publication:2014/03/07
PhD Degree:International Logistics
School:SHSS School of Humanities and Social Sciences
Library of Congress Classification:H Social Sciences / HD Industries. Land use. Labor [incl. Management] / HD28-70 Management. Industrial management / HD30.19-30.29 Theory. Method. Relation to other subjects / HD30.2 Electronic data processing. Information technology Including artificial intelligence and knowledge management
Call No:Thesis 2014/03

$Rev: 13581 $