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.
Inhaltsverzeichnis (Table of Contents)
- Introduction
- BACKGROUND AND PROBLEM DESCRIPTION
- PURPOSE AND RESEARCH QUESTIONS
- DELIMITATIONS
- OUTLINE
- Theoretical Background
- MANUFACTURING-EXECUTION-SYSTEM
- INTELLIGENT MANUFACTURING
- Digital twin in Manufacturing
- New-generation intelligent manufacturing
- Co-operation of MES and Machine Learning
- OVERALL-EQUIPMENT-EFFECTIVENESS (OEE)
- ARTIFICIAL INTELLIGENCE
- Machine Learning
- Artificial Neural Networks
- Usability of AI Methodologies in Manufacturing
- Method and Implementation
- RESEARCH APPROACH AND PROCESS
- Literature Overview
- Criteria and Indicator Selection
- Assessment Methodologies
- CASE STUDY – MES FUNCTION ASSESSMENT
- VALIDITY AND RELIABILITY
- RESEARCH APPROACH AND PROCESS
- Findings and Analysis
- ASSESSMENT FRAMEWORK
- Criteria and Indicator Development
- Criteria Priority
- Criteria Data
- Criteria AI Insight
- Analysis of Assessment Framework
- MES FUNCTION ASSESSMENT
- Analogue Assessments
- Digital Assessments
- Analysis of MES Function Assessment
- FINDINGS CASE STUDY
- ASSESSMENT FRAMEWORK
- Discussion and Conclusions
- DISCUSSION OF METHOD
- DISCUSSION OF FINDINGS AND ANALYSIS
- Analysis of Assessment Framework (RQ1)
- Analysis of Assessment Methodologies (RQ2)
- CONTRIBUTION TO ACADEMIA AND INDUSTRY
- CONCLUSIONS
- FUTURE RESEARCH
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This thesis work investigates the suitability of Manufacturing-Execution-System (MES) functions for Artificial Intelligence (AI) support. It aims to develop an assessment framework consisting of criteria and indicators to evaluate the AI readiness of MES functions. The work also includes a case study to assess the validity and reliability of the developed framework.
- Evaluating the suitability of MES functions for AI support.
- Developing an assessment framework to evaluate the AI readiness of MES functions.
- Analyzing the criteria and indicators for AI suitability in MES functions.
- Testing the validity and reliability of the assessment framework through a case study.
- Exploring the potential of AI to improve manufacturing efficiency and profitability.
Zusammenfassung der Kapitel (Chapter Summaries)
The introduction presents the background and problem description, outlining the increasing complexity of manufacturing environments and the need for data-driven decision-making. It also states the purpose and research questions of the thesis, which focus on developing an assessment framework for AI-supported MES functions. Chapter 2 provides a theoretical background, discussing relevant concepts such as MES, intelligent manufacturing, overall equipment effectiveness (OEE), and AI methodologies like machine learning. Chapter 3 focuses on the research approach and process, including literature overview, criteria and indicator selection, and assessment methodologies. It also outlines the case study used to evaluate the assessment framework. Chapter 4 presents the findings and analysis, detailing the assessment framework, criteria and indicators, and the results of the case study. Finally, chapter 5 discusses the findings and conclusions, addressing the implications for academia and industry, and proposing directions for future research.
Schlüsselwörter (Keywords)
The thesis work focuses on the intersection of Manufacturing-Execution-System (MES), Artificial Intelligence (AI), and intelligent manufacturing. Key concepts include assessment frameworks, AI suitability, assessment methodologies, and the application of AI in the context of MES functions for optimizing manufacturing processes and improving overall equipment effectiveness (OEE).
- Arbeit zitieren
- Yasin Sengöz (Autor:in), 2020, Artificial Intelligence Suitability. Assessment of Manufacturing-Execution-System Functions, München, GRIN Verlag, https://www.grin.com/document/903511
