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

Table of Contents

1 Introduction

1.1 BACKGROUND AND PROBLEM DESCRIPTION

1.2 PURPOSE AND RESEARCH QUESTIONS

1.3 DELIMITATIONS

1.4 OUTLINE

2 Theoretical Background

2.1 MANUFACTURING-EXECUTION-SYSTEM

2.2 INTELLIGENT MANUFACTURING

2.2.1 New-generation intelligent manufacturing

2.2.2 Digital twin in Manufacturing

2.2.3 Co-operation of MES and Machine Learning

2.3 OVERALL-EQUIPMENT-EFFECTIVENESS (OEE)

2.4 ARTIFICIAL INTELLIGENCE

2.4.1 Machine Learning

2.4.2 Artificial Neural Networks

2.4.3 Usability of AI Methodologies in Manufacturing

3 Method and Implementation

3.1 RESEARCH APPROACH AND PROCESS

3.1.1 Literature Overview

3.1.2 Criteria and Indicator Selection

3.1.3 Assessment Methodologies

3.2 CASE STUDY – MES FUNCTION ASSESSMENT

3.3 VALIDITY AND RELIABILITY

4 Findings and Analysis

4.1 ASSESSMENT FRAMEWORK

4.1.1 Criteria and Indicator Development

4.1.2 Criteria Priority

4.1.3 Criteria Data

4.1.4 Criteria AI Insight

4.1.5 Analysis of Assessment Framework

4.2 MES FUNCTION ASSESSMENT

4.2.1 Analogue Assessments

4.2.2 Digital Assessments

4.2.3 Analysis of MES Function Assessment

4.3 FINDINGS CASE STUDY

5 Discussion and Conclusions

5.1 DISCUSSION OF METHOD

5.2 DISCUSSION OF FINDINGS AND ANALYSIS

5.2.1 Analysis of Assessment Framework (RQ1)

5.2.2 Analysis of Assessment Methodologies (RQ2)

5.3 CONTRIBUTION TO ACADEMIA AND INDUSTRY

5.4 CONCLUSIONS

5.5 FUTURE RESEARCH

Objectives and Research Themes

The primary objective of this thesis is to develop an assessment framework that enables manufacturing companies to evaluate the suitability of their Manufacturing-Execution-System (MES) functions for Artificial Intelligence (AI) support. It addresses the research gap regarding how to pre-evaluate whether embedding AI algorithms into specific MES functions is beneficial, thereby helping to optimize resource allocation and improve operational efficiency.

• Development of a comprehensive assessment framework with decisive criteria and indicators.
• Evaluation of AI methodologies, including machine learning and neural networks, within a manufacturing context.
• Analysis of the integration of digital twins and MES data for AI suitability.
• Implementation of a case study to test the validity and reliability of the developed framework.
• Provision of practical assessment methodologies (analogue and digital) for industry practitioners.

Excerpt from the Book

Summary of Chapters

1 Introduction: Provides the background and motivation for the research, defining the problem of missing frameworks for AI-supported MES functions and outlining the purpose and research questions.

2 Theoretical Background: Explores the foundations of MES, intelligent manufacturing, digital twins, and artificial intelligence, establishing the theoretical base for the assessment framework.

3 Method and Implementation: Describes the qualitative inductive research approach, the literature review process, and the development of the assessment framework and checklist used in the case study.

4 Findings and Analysis: Details the three criteria of the assessment framework (Priority, Data, AI Insight) and demonstrates their application through analogue and digital assessment methodologies and a concrete case study.

5 Discussion and Conclusions: Discusses the validity and reliability of the study, summarizes the contributions to academia and industry, and offers final conclusions and suggestions for future research.

Keywords

Manufacturing-Execution-System, Artificial Intelligence, Assessment Framework, AI suitability, Intelligent Manufacturing, Assessment Methodologies, Machine Learning, Digital Twin, OEE, Production Management, Data Analysis, Decision-making, Industrial Automation, Case Study, Research Methodology.

Frequently Asked Questions

What is the core focus of this research?

The research focuses on developing an assessment framework to evaluate the suitability of Manufacturing-Execution-System (MES) functions for integration with Artificial Intelligence (AI) solutions.

What are the central thematic fields?

The key themes include Manufacturing-Execution-Systems, Intelligent Manufacturing, Artificial Intelligence (specifically machine learning and neural networks), and assessment methodologies within a production environment.

What is the primary research goal?

The goal is to answer which criteria are decisive for deciding if a MES function should be AI-supported and how a MES provider can use a systematic framework to perform this assessment.

Which research methodology is employed?

The study utilizes a qualitative, inductive research approach, primarily based on an extensive literature review and a practical case study conducted within a manufacturing company.

What does the main body of the work cover?

The main body covers the definition of the assessment framework, the three central criteria (Priority, Data, and AI Insight), and the practical implementation of these criteria through checklists, interviews, and workshops.

Which keywords best characterize this work?

The work is best characterized by terms such as Manufacturing-Execution-System, Artificial Intelligence, Assessment Framework, Intelligent Manufacturing, and AI Suitability.

How does the digital twin contribute to the evaluation?

The digital twin serves as a crucial data source; the research highlights that a function must provide data available for a digital twin to be considered suitable for AI integration.

Why is the "Priority" criteria significant?

Priority ensures that AI investment is focused on functions with the highest impact on a company's Overall-Equipment-Effectiveness (OEE), preventing the waste of resources on non-critical features.

What was the outcome of the case study?

The case study involving the "Material requirements list" function demonstrated that 11 out of 13 indicators were fulfilled, achieving an 85% suitability rate for AI support.