scholarly journals Ontology-Based Analysis of Manufacturing Processes: Lessons Learned from the Case Study of Wire Harness Production

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
László Nagy ◽  
Tamás Ruppert ◽  
János Abonyi
Keyword(s):  
Industry 4.0 ◽  
Lessons Learned ◽  
Building Industry ◽  
Manufacturing Processes ◽  
Multilayer Network ◽  
Industrial Case Study ◽  
Wire Harness ◽  
Manufacturing Management ◽  
Harness Assembly

Effective information management is critical for the development of manufacturing processes. This paper aims to provide an overview of ontologies that can be utilized in building Industry 4.0 applications. The main contributions of the work are that it highlights ontologies that are suitable for manufacturing management and recommends the multilayer-network-based interpretation and analysis of ontology-based databases. This article not only serves as a reference for engineers and researchers on ontologies but also presents a reproducible industrial case study that describes the ontology-based model of a wire harness assembly manufacturing process.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 641-646
Author(s):  
Peter Burggräf ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro R. Pérez M. ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Lessons Learned

scholarly journals Managerial and Industry 4.0 solutions for fashion supply chains

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcello Braglia ◽  
Leonardo Marrazzini ◽  
Luca Padellini ◽  
Rinaldo Rinaldi
Keyword(s):  
Supply Chains ◽  
Lean Manufacturing ◽  
Industry 4.0 ◽  
Design Methodology ◽  
Industrial Case Study ◽  
Content Type ◽  
Enabling Technologies ◽  
Production Orders ◽  
Practical Implications

PurposeThe purpose of this paper is to present a structured framework whose objectives are to identify, analyse and eliminate fashion-luxury supply chains inefficiencies.Design/methodology/approachA Lean Manufacturing tool, the 5-Whys Analysis, has been used to find out the root causes associated with the problem identified from a data analysis of production orders of a fashion-luxury company. A case study, which explains the methodology and illustrates the capability of the tool, is provided.FindingsThis tool can be considered a suitable instrument to identify the causal factors of inefficiencies within luxury supply chains, suggesting potential countermeasures able to eliminate the problems previously highlighted. In addition, enabling technologies that deal with Industry 4.0 are associated with the root causes to enable further improvement of the supply chain.Practical implicationsThe effectiveness and practicality of the tool are illustrated using an industrial case study concerning an international Italian signature in the world of fashion-luxury footwear sector.Originality/valueThis framework provides practitioners with an operative tool useful to highlight where the major inefficiencies of fashion-luxury supply chains take place and, at the same time, individuates both the root causes of inefficiencies and the corresponding corrective actions, even considering Industry 4.0 enabling technologies.

Smart factories for single-family wooden houses – a practitioner’s perspective

2020 ◽  
Vol 21 (1) ◽  
pp. 64-84
Author(s):  
Alexander Vestin ◽  
Kristina Säfsten ◽  
Malin Löfving
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Building Industry ◽  
Single Family ◽  
Content Type ◽  
Wooden House ◽  
Multiple Case ◽  
House Building

Purpose The meaning of Industry 4.0 has started to be outlined for the construction industry, but there is still limited knowledge on the implications for the single-family wooden house building industry. The purpose of this paper is to expand the understanding of what the fourth industrial revolution implies for the single-family wooden house industry. The paper contributes with practitioners’ view of the content and meaning of a smart single-family wooden house factory. Design/methodology/approach An exploratory multiple case study was carried out at two Swedish single-family wooden house builders, combined with a traditional literature review. Findings As a result of a multiple case studies, the content and meaning of a smart single-family wooden house factory was elaborated on. In total, 15 components of a smart single-family wooden house factory were identified, of which 8 corresponded to the components of Industry 4.0 as described in other sectors. Research limitations/implications The study can be expanded to also include multi-family wooden house builders and other branches of the offsite wooden building industry. Practical implications Managers in the house-building industry who want to improve and strive for a smart single-family wooden house factory can learn from this study, get an insight of what other companies consider as important and how it relates to Industry 4.0. Originality/value To the best of the authors’ knowledge, this study is a first attempt to understand what Industry 4.0 mean and how it can be accomplished for the single-family wooden house offsite manufacturing industry.

scholarly journals A Framework for Designing Work Systems in Industry 4.0

2019 ◽  
Vol 1 (1) ◽  
pp. 2031-2040
Author(s):  
Bzhwen A Kadir ◽  
Ole Broberg ◽  
Souza da Conceição Carolina ◽  
Nik Grewy Jensen
Keyword(s):  
Industry 4.0 ◽  
Industrial Case Study ◽  
Lean Thinking ◽  
Work Systems ◽  
System Productivity ◽  
Physical Systems ◽  
Human Wellbeing ◽  
Challenges And Opportunities ◽  
Principles Of Design

AbstractThe introduction of new digital technologies in industrial work systems and increasing implementation of Cyber Physical Systems are evoking new and unknown challenges and opportunities related to aspects of human work and organisation. To ensure human wellbeing and overall system productivity, there is a need for interdisciplinary methods and approaches for dealing with the challenges and taking advantage of the opportunities. In this paper, we present a conceptual framework for designing Industry 4.0 enabled work systems, which serves to accommodate this need. The framework combines elements and principles of Design- and Lean thinking methodologies and Human Factors and Ergonomics, thus making it a practical, systematic, and iterative, human centred approach. We use examples from a retrospective industrial case study to illustrate elements of the framework and provide several implications for practitioners.

Virtual Commissioning for Advanced Manufacturing Using Digital Tools

2020 ◽  
Author(s):  
David Guerra-Zubiaga ◽  
Kevin Kamperman ◽  
Mohamed Aw
Keyword(s):  
Industry 4.0 ◽  
Product Lifecycle Management ◽  
Virtual Manufacturing ◽  
Manufacturing Processes ◽  
Global Trends ◽  
Industrial Setting ◽  
Virtual Commissioning ◽  
Industrial Internet ◽  
Physical Prototype

Abstract Following the Industry 4.0 paradigm with the rise of smart factories, there is a growing need in exploring digital manufacturing tools compatible with Industrial Internet of Things (IIoT) functionalities. This paper discusses the concept of Virtual Commissioning (VC) including applications in present and near-future advanced automation and production. Specifically, global trends towards Industry 4.0 and virtual manufacturing processes are explored in addition to how and why these emerging technologies could be applied. Furthermore, the advantages of VC processes are contrasted to Traditional Physical Commissioning (TPC) to highlight the evolution of Product Lifecycle Management (PLM) software and the optimization of manufacturing processes since the turn of the century. This research aims to use state-of-the-art PLM software to replicate a physical prototype in near-perfect functionality to demonstrate the effectiveness of VC in an industrial setting. Developing a methodology for this research, the analysis is followed by a case study involving a Mini Festo Pick-and-Place (P&P) unit simulated in Siemens Tecnomatix Process Simulate and controlled via ladder logic executed in Totally Integrated Automation (TIA) Portal. As expected, the results of this case study validate the potential for optimized contemporary manufacturing solutions in which higher-quality goods are reliably produced with minimal delays at all-time low principal investments through the use of VC tools.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

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