Datamining in mittelständischen Produktionsbetrieben: Chancen, Herausforderungen und Implementierungsstrategien

Abstract

This thesis explores the potentials and challenges associated with the use of data mining technologies in medium-sized manufacturing enterprises, with a particular focus on optimizing production processes through effective data usage. The paper begins with a presentation of the current data situation, characterized by the rapid increase of data-generating technologies such as the Internet of Things (IoT) and Big Data. These technologies produce a vast amount of data which, if effectively utilized, can significantly enhance decision-making and process efficiency. Despite the apparent advantages, challenges such as high costs and the complexity of technology application in medium-sized businesses are highlighted. This work provides an in-depth look into the specific challenges these companies face, including technological, organizational, and financial barriers. The key to overcoming these obstacles is a carefully planned implementation strategy that includes robust change management and the adaptation of technologies to meet the specific needs of the business. Through the analysis of various data mining methods such as classification, clustering, and regression analysis, this thesis demonstrates how these techniques can help gain valuable insights from data and enhance production efficiency. The importance of knowledge management and continuous assessment of the technological landscape is emphasized to improve the adaptive and predictive capabilities of the companies. This work concludes with a summary of the key findings and recommendations for medium-sized manufacturing enterprises. It underscores the need to view digital transformation as an ongoing process, where continuous learning and adaptation are crucial for maintaining long-term competitiveness. Thus, the thesis provides a valuable resource for businesses interested in harnessing the potentials of data mining in the manufacturing industry.

Date of Award	2024
Original language	German (Austria)
Supervisor	Sonja Straßer (Supervisor)