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Sustainable Design, Manufacturing, Logistics, and Maintenance Using Big Data Analytics...
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Sustainable Design, Manufacturing, Logistics, and Maintenance Using Big Data Analytics and Artificial Intelligence

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 25066

Special Issue Editors

Prof. Dr. Hosang Jung

E-Mail Website
Guest Editor
Graduate School of Logistics, Inha University, Incheon 22212, Korea
Interests: logistics; supply chain management; sustainable operations management; data analytics; machine learning
Prof. Dr. Chi-Guhn Lee

E-Mail Website
Guest Editor
Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
Interests: Markov decision processes; reinforcement learning; machine learning; operations management; supply chain management; financial engineering; maintenance optimization; reliability engineering

Special Issue Information

Dear Colleagues,

We have witnessed a considerable shift in various industrial sectors in the perception of sustainable operations and growth. As such, research communities are paying increasing attention to ways to achieve sustainability by taking advantage of emerging technologies such as big data analytics and artificial intelligence. Successful application of such technologies will allow firms to operate with fewer resources to achieve the business goals, resulting in higher sustainability.

Though the importance and impact of sustainable operations management have been well discussed among practitioners and researchers, there has been limited research on incorporating big data analytics and/or artificial intelligence into it. Furthermore, most of the existing literature puts the effort to improve sustainability in only a few areas, such as manufacturing and logistics. Therefore, this Special Issue will attempt to widen the scope of sustainable applications, such as product design, physical asset management, and many more. For example, sustainable design is to integrate design requirements from the end users into the manufacturing process so as to improve the sustainability of products throughout their life cycle. Another example can be found in the area of physical asset management, where maintenance of physical assets should be done to prolong the life of assets without sacrificing the productivity of the overall system.

We encourage researchers and practitioners equally to submit their works to this Special Issue to share their ideas and efforts in improving sustainability of various operations. In particular, we would like to see the use of big data analytics, artificial intelligence, and alike in empirical studies and the development of new solutions over a wide range of areas, including design, manufacturing, logistics, and physical asset management. Papers that take interdisciplinary perspectives will also be considered favorably. While we will accept quantitative models, qualitative works, case studies, and empirical research, they need to show the practicability of their propositions if possible. Review papers are also welcomed if they provide new insights to the related practice.

Prof. Dr. Hosang Jung
Prof. Dr. Chi-Guhn Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big Data analytics for sustainable operations
  • Artificial Intelligence for sustainable operations
  • Sustainable design and manufacturing
  • Sustainable logistics and supply chain management
  • Maintenance optimization
  • Physical asset management
  • Sustainable operations management

Published Papers (5 papers)

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Research

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14 pages, 8472 KiB  
Article
Identifying Research Topics and Trends in Asset Management for Sustainable Use: A Topic Modeling Approach
by Hosang Jung and Boram Kim
Sustainability 2021, 13(9), 4792; https://doi.org/10.3390/su13094792 - 24 Apr 2021
Cited by 1 | Viewed by 2681
Abstract
Asset management is not new, and research has been conducted in private and public sectors on how to systematically maintain infrastructure or facilities for sustainable use and achieve the level of service desired by users or customers at the lowest life cycle costs. [...] Read more.
Asset management is not new, and research has been conducted in private and public sectors on how to systematically maintain infrastructure or facilities for sustainable use and achieve the level of service desired by users or customers at the lowest life cycle costs. This research identifies the research topics and trends in asset management over the past 30 years. To this end, latent Dirichlet allocation, a topic modeling approach, was applied to articles published in engineering journals and investigated the following three research questions: (1) what have the key topics been for the past three decades? (2) what are the main activities and target sectors of asset management? (3) how have the research topics and keywords changed over the past three decades? The analysis shows that the target field of asset management has broadened while the main activities of asset management have been limited to several popular activities such as life cycle cost analysis and reliability analysis. Some implications and future research directions are also discussed. Full article
(This article belongs to the Special Issue Sustainable Design, Manufacturing, Logistics, and Maintenance Using Big Data Analytics and Artificial Intelligence)
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20 pages, 1115 KiB  
Article
Achieving Sustainable New Product Development by Implementing Big Data-Embedded New Product Development Process
by Yufan Wang and Haili Zhang
Sustainability 2020, 12(11), 4681; https://doi.org/10.3390/su12114681 - 8 Jun 2020
Cited by 7 | Viewed by 3570
Abstract
Literature suggests that new product development (NPD) has an impact on sustainable organizational performance. Yet, previous studies in NPD have mainly been based on “experience-driven”, not data-driven, decision-making in the NPD process. We develop a research model to examine how the big data-embedded [...] Read more.
Literature suggests that new product development (NPD) has an impact on sustainable organizational performance. Yet, previous studies in NPD have mainly been based on “experience-driven”, not data-driven, decision-making in the NPD process. We develop a research model to examine how the big data-embedded NPD process affects the sustainable innovation performance of NPD projects. We test the proposed model and conduct the cross-national comparison using data collected on 1858 NPD projects in the United States of America (USA), the United Kingdom (UK), and Australia. The research findings suggest that big data-embedded business analysis, product design, and product testing increase sustainable innovation performance in all three countries. The study findings also reveal several surprising results: (1) in the USA, big data-embedded product testing has the highest effect on sales growth and gross margin, (2) in Australia, big data-embedded commercialization has the highest effect on sales growth and gross margin, and (3) in the UK, big data-embedded commercialization has the highest effect on second-year sales growth, first-year, and third-year gross margin; in addition, big data-embedded product testing has the highest effect on third-year sales growth and second-year gross margin. Full article
(This article belongs to the Special Issue Sustainable Design, Manufacturing, Logistics, and Maintenance Using Big Data Analytics and Artificial Intelligence)
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15 pages, 2193 KiB  
Article
Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance
by Shengbin Hao, Haili Zhang and Michael Song
Sustainability 2019, 11(24), 7145; https://doi.org/10.3390/su11247145 - 13 Dec 2019
Cited by 54 | Viewed by 9340
Abstract
Literature suggests that big data is a new competitive advantage and that it enhance organizational performance. Yet, previous empirical research has provided conflicting results. Building on the resource-based view and the organizational inertia theory, we develop a model to investigate how big data [...] Read more.
Literature suggests that big data is a new competitive advantage and that it enhance organizational performance. Yet, previous empirical research has provided conflicting results. Building on the resource-based view and the organizational inertia theory, we develop a model to investigate how big data and big data analytics capability affect innovation success. We show that there is a trade-off between big data and big data analytics capability and that optimal balance of big data depends upon levels of big data analytics capability. We conduct a four-year empirical research project to secure empirical data on 1109 data-driven innovation projects from the United States and China. This research is the first time reporting the empirical results. The study findings reveal several surprising results that challenge traditional views of the importance of big data in innovation. For U.S. innovation projects, big data has an inverted U-shaped relationship with sales growth. Big data analytics capability exerts a positive moderating effect, that is, the stronger this capability is, the greater the impact of big data on sales growth and gross margin. For Chinese innovation projects, when big data resource is low, promoting big data analytics capability increases sales growth and gross margin up to a certain point; developing big data analytics capability beyond that point may actually inhibit innovation performance. Our findings provide guidance to firms on making strategic decisions regarding resource allocations for big data and big data analytics capability. Full article
(This article belongs to the Special Issue Sustainable Design, Manufacturing, Logistics, and Maintenance Using Big Data Analytics and Artificial Intelligence)
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Review

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29 pages, 2966 KiB  
Review
A Systematic Review on Social Sustainability of Artificial Intelligence in Product Design
by Keeheon Lee
Sustainability 2021, 13(5), 2668; https://doi.org/10.3390/su13052668 - 2 Mar 2021
Cited by 10 | Viewed by 5006
Abstract
Emerging technologies such as artificial intelligence help operations management achieve sustainability. However, in sustainable operations management studies, scholars pay less attention to product design, which can be highly affected by artificial intelligence. In addition, sustainability is perceived as maintaining economic development while limiting [...] Read more.
Emerging technologies such as artificial intelligence help operations management achieve sustainability. However, in sustainable operations management studies, scholars pay less attention to product design, which can be highly affected by artificial intelligence. In addition, sustainability is perceived as maintaining economic development while limiting environmental harm caused by human activity. Therefore, social sustainability is treated as peripheral compared to economic and environmental sustainability. However, social sustainability now has gained more attention because it is the basis on which meaningful economic and environmental sustainability can be valid. Thus, I systematically reviewed present studies on product design considering artificial intelligence to understand what types of social sustainability are achieved when applying artificial intelligence to product design. This study discovered artificial intelligence can improve social sustainability in product design, but social sustainability diversity is necessary. These findings can contribute to the inclusion of different types of social sustainability in product design when using artificial intelligence. Full article
(This article belongs to the Special Issue Sustainable Design, Manufacturing, Logistics, and Maintenance Using Big Data Analytics and Artificial Intelligence)
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Other

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15 pages, 10372 KiB  
Technical Note
Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities
by Ju Sik Kim, Kyu Nam Choi and Sung Woo Kang
Sustainability 2021, 13(2), 557; https://doi.org/10.3390/su13020557 - 8 Jan 2021
Cited by 22 | Viewed by 3644
Abstract
Faults in electrical facilities may cause severe damages, such as the electrocution of maintenance personnel, which could be fatal, or a power outage. To detect electrical faults safely, electricians disconnect the power or use heavy equipment during the procedure, thereby interrupting the power [...] Read more.
Faults in electrical facilities may cause severe damages, such as the electrocution of maintenance personnel, which could be fatal, or a power outage. To detect electrical faults safely, electricians disconnect the power or use heavy equipment during the procedure, thereby interrupting the power supply and wasting time and money. Therefore, detecting faults with remote approaches has become important in the sustainable maintenance of electrical facilities. With technological advances, methodologies for machine diagnostics have evolved from manual procedures to vibration-based signal analysis. Although vibration-based prognostics have shown fine results, various limitations remain, such as the necessity of direct contact, inability to detect heat deterioration, contamination with noise signals, and high computation costs. For sustainable and reliable operation, an infrared thermal (IRT) image detection method is proposed in this work. The IRT image technique is used in various engineering fields for diagnosis because of its non-contact, safe, and highly reliable heat detection technology. To explore the possibility of using the IRT image-based fault detection approach, object detection algorithms (Faster R-CNN; Faster Region-based Convolutional Neural Network, YOLOv3; You Only Look Once version 3) are trained using 16,843 IRT images from power distribution facilities. A thermal camera expert from Korea Hydro & Nuclear Power Corporation (KHNP) takes pictures of the facilities regarding various conditions, such as the background of the image, surface status of the objects, and weather conditions. The detected objects are diagnosed through a thermal intensity area analysis (TIAA). The faster R-CNN approach shows better accuracy, with a 63.9% mean average precision (mAP) compared with a 49.4% mAP for YOLOv3. Hence, in this study, the Faster R-CNN model is selected for remote fault detection in electrical facilities. Full article
(This article belongs to the Special Issue Sustainable Design, Manufacturing, Logistics, and Maintenance Using Big Data Analytics and Artificial Intelligence)
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