This thesis provides an especially designed overview for the needs of decision makers in the production industry on the field of machine learning. By concerning economic and technological factors, as well as the individual challenges for companies, the goal of this thesis is to serve as a guideline for the usage of machine learning in production systems.
After the revolutionary change caused by the introduction of the steam engine, the production line, electronics and IT, into the manufacturing industry, a new disrupting change is expected. Nowadays the rapidly increasing digitalization of the economy leads to the fourth industrial revolution. This global phenomenon is called ‘Industrie 4.0’ (GER) or ‘Smart Factory’ (US), and it combines production technology with information and communication technology. Especially, data based optimization in production is one of the predominant goals of Industrie 4.0. For the automatized analysis of large amounts of data, machine learning is an effective instrument and therefore a central element in Industrie 4.0.
Recent progress in machine learning has been driven by the development of new learning algorithms and by the increasing availability of data and low-cost computation power. For many applications - from computer vision to adaptive robots – it was very difficult to devise deterministic rules. However, for these applications, it is possible to collect data, and now the idea is to use algorithms that learn from data, instead of being manually programmed. Thus, machine learning has the potential to transform data into valuable knowledge for decision making, while making improvements possible to the production system, with approaches such as predictive maintenance. The transfer of machine learning from the lab to the ‘real world’ leads to an increased interest in learning techniques, demanding further effort in explaining, on how machine learning works, and what it can be used for in other disciplines.
However, the entry barrier to the diverse field of machine learning is high. With many different algorithms, theories and methods, it is hard to oversee, and therefore its influence remains limited. In addition, a recent study states that about 47% of jobs in the US are at high risk of computerization within the next decades. Therefore, employees feel insecure, and demand answers on what effect machine learning will have on their future role in the factory.
Table of Contents
1 Introduction
1.1 Objective
1.2 Structure of Thesis
1.3 Research Method and Study Design
2 Impact of Machine Learning in Industrie 4.0
2.1 Market Pull is Changing the World of Manufacturing
2.2 Key Challenges for Production Systems in an Evolving Business World
2.3 Industrie 4.0
2.4 Ubiquitous Computing and Visualization
2.5 Impact on Human Employment
2.5.1 Computerisation in Non-Routine Manual Tasks
2.5.2 Computerisation in Non-Routine Cognitive Tasks
2.5.3 Implications for Employment
3 Paradigm Shift from Abstract Models to Real World Data
3.1 What Machine Learning is and Why it is a Promising Approach
3.2 Machine Learning Techniques
3.2.1 Regression
3.2.2 Classification and Clustering
3.2.3 Dimensionality reduction
3.2.4 Association rule mining
3.3 Learning Types
3.3.1 Supervised Learning
3.3.2 Unsupervised Learning
3.3.3 Reinforcement Learning
3.4 Algorithm Selection: Implicit vs. Explicit Knowledge Representation
4 Applications of Machine Learning in Production
4.1 Descriptive Analytics
4.2 Diagnostic Analytics
4.3 Predictive Analytics
4.4 Prescriptive Analytics
5 Guidelines for the Usage of Machine Learning in Production
5.1 Domain Maturity: Machine Learning
5.2 Domain Maturity: Production
5.3 Infrastructure: Connection Task
5.4 Data: Capturing Task
5.5 Security: Cyber Security and Accountability Task
5.6 People: Knowledge and Acceptance Task
5.7 Strategy: Cooperate Design Task
Objectives & Core Themes
This thesis aims to provide a comprehensive guideline for decision-makers in the manufacturing industry regarding the integration of machine learning into production systems. By analyzing the current economic and technological landscape, the work addresses how machine learning can transform data into actionable insights, helping companies overcome barriers to entry and successfully implement automated, data-driven processes.
- Technological and economic challenges of Industry 4.0.
- Machine learning techniques and their applicability in manufacturing.
- Methods for data-driven decision-making, including Descriptive, Diagnostic, Predictive, and Prescriptive Analytics.
- Guidelines for infrastructure, data management, and security in production environments.
- Strategies for managing human resources and organizational change in the age of automation.
Excerpt from the Book
1.1 Objective
Regarding this thesis, we understand production systems to be comprised of both the technological elements (e.g. machines and tools) and organizational elements (e.g. labor and information) [9]. While the amount of data captured in production systems increases [2], among other reasons, due to a growing number of sensors fitted to machinery, the value of data has yet to be attained [10]. Machine learning seems to be a promising technology for data handling [5]. However, its implementation into production systems can be regarded as a highly interdisciplinary project [11].
The collaboration of different disciplines, especially data science and production is required in implementation projects [2]. Whereas machine learning expert knowledge is needed to decide upon the appropriate techniques for the data analysis, manufacturing domain expert knowledge is required for the interpretation of the results of the data analytics methods [12]. Although the interaction between the two disciplines is vital for the implementation of machine learning into production systems, the cooperation could be challenging, due to the distinct diversity of these disciplines, which we discuss in the following paragraphs.
Summary of Chapters
1 Introduction: This chapter outlines the motivation for the thesis, focusing on the accessibility of machine learning for production systems and the research methodology employed.
2 Impact of Machine Learning in Industrie 4.0: The chapter explores market trends and key production challenges, discussing the role of Industry 4.0 and the disruptive impact of machine learning on employment.
3 Paradigm Shift from Abstract Models to Real World Data: This section details the transition from deterministic expert systems to data-driven machine learning, explaining various techniques and learning types suitable for production tasks.
4 Applications of Machine Learning in Production: The chapter categorizes machine learning usage using the Business Analytics framework, illustrating how different analytics levels can resolve specific industrial challenges.
5 Guidelines for the Usage of Machine Learning in Production: This final chapter provides a practical roadmap for businesses, addressing domain maturity, infrastructure requirements, and strategies for organizational change.
Keywords
Industry 4.0, Machine Learning, Manufacturing, Production Systems, Data Analytics, Artificial Intelligence, Predictive Maintenance, Automation, Skill Development, Digitalization, Smart Factory, Organizational Culture, Infrastructure, Cyber Security, Decision Support
Frequently Asked Questions
What is the core focus of this thesis?
The thesis focuses on providing a practical guideline for the implementation of machine learning within manufacturing and production systems to improve efficiency and competitiveness.
What are the central themes covered in the book?
Key themes include the shift from deterministic to data-driven manufacturing, the classification of machine learning techniques, and practical guidelines for organizational and technical implementation.
What is the primary objective of this research?
The objective is to establish a basis for mutual understanding between machine learning and production domains and to provide actionable strategies for decision-makers.
Which scientific methods were utilized for this study?
The author utilized extensive literature research combined with 16 expert interviews with professionals from both the production and machine learning fields.
What topics are discussed in the main body of the work?
The main body covers the impact of Industry 4.0, specific machine learning techniques (e.g., Regression, Clustering), and the application of Descriptive, Diagnostic, Predictive, and Prescriptive analytics.
Which keywords best characterize this research?
Essential keywords include Industry 4.0, Machine Learning, Production Systems, Automation, Data Analytics, and Organizational Change.
Why does the author advocate for a shift in data capturing standards?
The author argues that inconsistent data capturing hinders the deployment of machine learning; therefore, standardizing data practices is essential to allow machines to learn effectively from the entire production system.
How does the thesis address the fear of job loss due to automation?
The thesis posits that machine learning acts primarily as a support tool rather than a total replacement for human labor, emphasizing the need for continuous training and change management to help employees adapt to new tasks.
- Arbeit zitieren
- Alexander Volz (Autor:in), 2017, Machine Learning. A Guideline for its Usability in Production Systems, München, GRIN Verlag, https://www.grin.com/document/489418
