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.
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
1.1 Background and Problem Description
The manufacturing area evolves into an environment with a growing degree of automation, computational assistance and system complexity (Kusiak, 2018). To keep on track with different data in the manufacturing environment a Manufacturing-Execution-System (MES) is being used. A MES receives all production information in real-time by obtaining the latest data from robots, machine monitors and operators (Kletti, 2019). It collects all the necessary data for each manufacturing order which ever takes places in a manufacturing company (Kletti, 2015). Manufacturing-Execution-Systems are divided into function groups, modules and functions. A MES has a diversity set of data.
For instance, data about the manufacturing area, machines, operators, assets, production control station/status, material management, personnel time recording, personnel resource planning, production inspection, complaint management, quality management and checking incoming and outgoing goods. Data can be visualized individually for every unique MES user. Nevertheless, this approach to collect data and to have it visualized for the decision-making process does not provide manufacturing companies automatically a higher Overall-Equipment-Effectiveness (OEE) (Focke and Steinbeck, 2018; Gentsch, 2018a). Providers of Manufacturing-Execution-System try to counter this weakness of a MES, to be a data visualization tool, with embedded Artificial Intelligence (AI) algorithms to back up various functions in a MES in order to aim for higher profitability. This can be achieved by using automated analysing tools which grant a better understanding of data through AI insights. (Focke and Steinbeck, 2018; Kletti, 2019; Turner, 2019).
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.
- 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
