Artificial Intelligence Suitability. Assessment of Manufacturing-Execution-System Functions
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Master's Thesis from the year 2020 in the subject Computer Sciences - Artificial Intelligence, grade: 2,0, Jönköping International Business School (School of Engineering), course: Final Project Work in Production Systems, language: English, abstract: The thesis work introduces an assessment framework consisting of decisive criteria and related indicators which describe qualitatively the suitability of AI for MES functions based on three criteria with related indicators. In addition, the researcher displays furthermore how the developed assessment framework can be used in order to assess the ...
Master's Thesis from the year 2020 in the subject Computer Sciences - Artificial Intelligence, grade: 2,0, Jönköping International Business School (School of Engineering), course: Final Project Work in Production Systems, language: English, abstract: The thesis work introduces an assessment framework consisting of decisive criteria and related indicators which describe qualitatively the suitability of AI for MES functions based on three criteria with related indicators. In addition, the researcher displays furthermore how the developed assessment framework can be used in order to assess the MES functions regarding their AI "readiness".In order to cope the findings through the thesis work an inductive research approach has been applied. Existing literature in the fields of intelligent manufacturing, Manufacturing-Execution-Systems, machine learning, deep learning, intelligent manufacturing, digital twin, and assessment methodologies have been extensively studied in order to base the theoretical developed framework on grounded theory.A case study was carried out in order to test the validity and reliability of the developed assessment framework for industry. The outcome of this thesis work was an assessment framework consisting of decisive criteria and related indicators when evaluating a MES function in respect to its AI suitability. Furthermore, an assessment checklist has been provided for the industry in order to be able to assess a MES function regards AI support in a quick and pragmatic manner. To generate a more generalizable assessment framework criteria and indicators have to be adapted, likewise testing the outcome of analogue and digital assessment methodologies will provide material for future studies.Artificial Intelligence arises in the manufacturing field very rapidly. Implementing Artificial Intelligence (AI) solutions and algorithms in the manufacturing environment is a well-known research field in academia. On the other hand, Manufacturing-Execution-System (MES) providers do not have a theoretical and pragmatic framework regarding the evaluation of MES functions in respect to their suitability for Artificial Intelligence.In order to be able to pre-evaluate whether a MES function shall be AI supported an intense literature research has been conducted. Academia shows few investigations regarding this field of research. Recent studies have been concerning about possible applications for MES functions in combination with AI. However, there is a lack of research in terms of pre-evaluating a MES function before embedding the function with AI support, since the development of AI solutions for MES functions without pre-evaluating those bears a waste of valuable resources.
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