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Impact of process mining on competitive advantage

  • The purpose of this thesis is to examine how process mining might enhance and benefit processes to increase the competitive advantage, while also examining the difficulties businesses are facing when implementing process mining and the purpose for which they have implemented it. Presentations of cases from various industries are backed up by in-person interviews with representatives of various businesses. The findings show that process mining is a tool that users highly recommend since it produces results that have never been seen before, regardless of the field in which it is used and regardless of their initial purpose for choosing such a concept. It also emphasizes the importance of the staff and people in putting a new idea into practice, as well as their obstacles embracing anything new. Businesses that are competing for an advantage knock on many doors. The last ten years have seen many businesses of all kinds open their doors to process mining. A notion that identifies their shortcomings, provides them with room to grow and gives them transparency. One would assume that firms' focus is on keeping costs low in today's environment, where expenses climb enormously daily, therefore that's why they introduce innovative concepts. That may be true at first, but once the concept's genuine usefulness is realized, their focus is simple to change. Companies today recognize the need of process optimization if they wish to operate with a competitive edge and have a sound business plan. Until businesses decide to test the idea themselves, the network effect is important in such situations. Since the concept hasn't been on the market for very long and not many companies have had experience with it thus far, it was observed that the results of the literature review with regard to the content of the interviews were practically comparable. Finally, this paper provides recommendations for a transition from the conventional business models that firms are still using to more modern technical, data-based approaches. Only a broad analysis and conclusion are possible with the sample size of eleven companies and ten specialists.

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Author:Katerina Stojanovska
Advisor:Michael Hepp
Document Type:Bachelor Thesis
Year of Completion:2022
Granting Institution:Hochschule Furtwangen
Date of final exam:2022/08/29
Release Date:2022/08/31
Tag:Process mining
Page Number:49
Degree Program:IBM - International Business Management
Functional area:Business Strategy
Open-Access-Status: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt