Artificial Intelligence Suitability. Assessment of Manufacturing-Execution-System Functions

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
• 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
• 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).