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Data-Driven Intelligent Manufacturing for Circular Economy and Sustainability

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

Deadline for manuscript submissions: closed (30 December 2022) | Viewed by 21322

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Special Issue Editors

Dr. Miying Yang

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Guest Editor

Group of Sustainability, School of Management, Cranfield University, Cranfield MK43 0AL, UK
Interests: digital sustainability; sustainable supply chains; Industry 4.0; sustainable manufacturing

Prof. Dr. Steve Evans

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Guest Editor

Centre for Industrial Sustainability, Institute for Manufacturing, University of Cambridge, Cambridge CB3 0FS, UK
Interests: industrial sustainability; life cycle engineering; circular economy; industrial symbiosis; sustainable design; sustainable manufacturing

Prof. Dr. Saeema Ahmed-Kristensen

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Guest Editor

Initiative for Digital Economy INDEX, Business School, University of Exeter, Exeter EX4 4PU, UK
Interests: data-driven product development; design engineering; design innovation; knowledge management; creativity and cognition

Special Issue Information

Dear Colleagues,

In the current Industry 4.0 era, many firms have been exploring the adoption of the emerging digital technologies for sustainable intelligent manufacturing, e.g., Internet of Things (IoT), digital twin, artificial intelligence (AI), big data analytics, 3D printing, robotics, virtual reality, and cloud computing. The IoT has been extensively applied in factories and supply chains to monitor the production process and track and trace the logistics and warehouse operations. Big data analytics are used to analyse the large volume of data generated from IoT devices and other sources. AI is used to provide predictive and preventive functions of data analysis through learning algorithms. These digital technologies are recognised widely as a means to improve labour productivity, but with much less recognition that they are also a promising means to improve the sustainability performance of manufacturing and supply chains.

Despite their promising functions, recent research and practice have shown that manufacturing firms often fail in adopting these emerging digital technologies towards sustainability goals. Technology innovation alone is not sufficient to achieve sustainable intelligent manufacturing. It also requires the design of new, data-driven business models (e.g., digital product-service systems), and the reconfiguration of digital supply chains and business ecosystems to facilitate this transition. The current academic understanding of this topic and the applications in companies are still limited.

This Special Issue calls for academic papers on data-driven intelligent manufacturing for circular economy and sustainability from both technical aspects that show the potential of digital technologies to deliver better sustainability performance, as well as management aspects that relate to why and how these technologies could be implemented effectively. Relevant topics include but are not limited to:

Digitalisation and sustainability;
Digitalisation and circular economy;
Data-driven business model innovation for sustainability or circular economy;
The impact of digitalisation on supply chain sustainability;
The adoption of digital technologies for sustainability or circular economy;
The design, modelling, and simulation of business ecosystem towards sustainability;
Enablers and barriers of transition towards data-driven sustainable manufacturing;
Methods and tools for intelligent sustainable design and manufacturing;
Digital product-service system for sustainability or circular economy;
Supply chain implications of different sustainable business models;
Life cycle data for sustainability.

Contributions of any types are welcome, including literature reviews, conceptual studies, empirical studies, case studies, modelling, and simulation.

Dr. Miying Yang
Prof. Dr. Steve Evans
Prof. Dr. Saeema Ahmed-Kristensen
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

intelligent manufacturing
circular economy
data-driven design
data-driven manufacturing
data-driven business model innovation
data-driven ecosystem
data-driven supply chains
Industry 4.0 for sustainability
digital supply chain
digital product-service systems
digital twin
Industry 4.0 technologies (e.g., IoT, big data, AI, additive manufacturing, robotics)
sustainable value innovation
Life cycle data for sustainability

Published Papers (4 papers)

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Research

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26 pages, 865 KiB

Open AccessArticle

Unpacking Additive Manufacturing Challenges and Opportunities in Moving towards Sustainability: An Exploratory Study

by Wen Liu, Xielin Liu, Ying Liu, Jie Wang, Steve Evans and Miying Yang

Sustainability 2023, 15(4), 3827; https://0-doi-org.brum.beds.ac.uk/10.3390/su15043827 - 20 Feb 2023

Cited by 3 | Viewed by 2492

Abstract

The global market for Additive Manufacturing (AM) is expected to grow, which may increase the prominence of sustainability aspects in the manufacturing process. A growing number of AM academics and practitioners have started to pay attention to the environmental and societal impacts of AM instead of only focusing on its economic aspect. Yet, AM is still not widely adopted, and the research on AM sustainability is still at the nascent stage. This paper aims to better understand AM’s sustainable adoption and seeks to address three questions: what the sustainability implications of AM are; what challenges may prevent the broad adoption of AM; and what opportunities can enable AM sustainability. The research adopts a multiple case study method to investigate six AM companies that play different roles in the AM ecosystem, including AM design, AM machine, AM material, AM service, AM education, and AM consulting. The results from these studies reveal that AM has the potential to reduce environmental and social impacts; however, it might also cause negative consequences and lead to some rebound effects. We identified 43 categories (synthesized from 199 examples) of key challenges for AM adoption and proposed 55 key solutions in moving AM towards sustainability. It is evident that AM acts as a promising digital technology for manufacturing and has the potential to pave the way for a new era of sustainable manufacturing. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Manufacturing for Circular Economy and Sustainability)

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22 pages, 2009 KiB

Open AccessArticle

Exploring the Dynamic of a Circular Ecosystem: A Case Study about Drivers and Barriers

by Sophia Barquete, Ana Hiromi Shimozono, Adriana Hofmann Trevisan, Camila Gonçalves Castro, Leonardo Augusto de Vasconcelos Gomes and Janaina Mascarenhas

Sustainability 2022, 14(13), 7875; https://0-doi-org.brum.beds.ac.uk/10.3390/su14137875 - 28 Jun 2022

Cited by 5 | Viewed by 3198

Abstract

The circular economy (CE) aims to minimize the environmental impact caused throughout the entire production chain, which can be achieved by implementing circular strategies in collaboration with different actors within a business ecosystem. Although the close relationship between CE and business ecosystem concepts, which originated the term “circular ecosystem”, research about this subject is necessary, given the scarcity of empirical studies addressing the phenomenon. Therefore, this study aims to contribute by investigating a Brazilian circular ecosystem specialized in the manufacture of ecological tiles through recycled carton packages. The exploratory case study method was selected to characterize the ecosystem and identify 27 drivers and 17 barriers that enhance and hinder the ecosystem’s existence and functioning. Our findings, summarized by a framework, demonstrate the need for integration among the ecosystem’s actors so that its value proposition can be delivered. This issue is crucial for collecting post-consumer packaging for recycling and manufacturing ecological tiles. However, actors within the circular ecosystem face some obstacles to collecting the amount of packaging post-consumer material, such as the COVID-19 pandemic. Finally, this work generates discussions and future studies on circular ecosystems, especially in the Brazilian context, where there is little evidence in this research field. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Manufacturing for Circular Economy and Sustainability)

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21 pages, 4156 KiB

Open AccessEditor’s ChoiceArticle

Can Digital Technologies Increase Consumer Acceptance of Circular Business Models? The Case of Second Hand Fashion

by Fiona Charnley, Fabienne Knecht, Helge Muenkel, Diana Pletosu, Victoria Rickard, Chiara Sambonet, Martina Schneider and Chunli Zhang

Sustainability 2022, 14(8), 4589; https://0-doi-org.brum.beds.ac.uk/10.3390/su14084589 - 12 Apr 2022

Cited by 16 | Viewed by 8941

Abstract

Experimentation with, and the implementation of, circular business models (CBMs) has gained rapid traction within the textiles and fashion industry over the last five years. Substitution of virgin materials with bioderived alternatives, extending the lifecycle of garments through resale, and rental services and the recycling or upcycling of garments are some of the strategies being used to reduce the 1.2 billion tonnes of greenhouse gas emissions and 92 million tonnes of waste associated with the sector in 2017. However, whilst CBMs demonstrate environmental and economic benefits, low consumer acceptance is considered by business professionals and policymakers to be one of the main barriers to the transition towards a circular economy. Digitisation is widely acknowledged as a catalyst for innovation in many sectors and digital technologies are driving new ways to exchange and share goods and services, enabling companies to match the supply, and demand for, otherwise underused assets and products. Online platforms, in particular, have played a crucial role in driving the growth of used goods and resale in other consumer goods markets, such as consumer technology. A mixed methods approach, including a review of 40 organisations operating second hand fashion models, a consumer survey of over 1200 respondents and in-depth interviews with 10 organisations operating second hand fashion models, is adopted to reveal (a) the barriers to consumer acceptance of reuse models in the fashion industry, and (b) how digital technologies can overcome these barriers. Findings highlight the significant progress that organisations have made in using digitalisation, including data analytics, algorithms, digital platforms, advanced product imagery and data informed customer communications, to address barriers associated with convenience, hygiene, trust and security. Furthermore, the study identifies opportunities for the development of more sophisticated digital technologies to support increased transparency and address concerns associated with the quality, authenticity and sourcing of materials. Positioned at the interface of digitisation and consumer acceptance of circular business models, this study makes an important contribution to understanding consumer barriers and how to address them and concludes with a set of recommendations for practitioners. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Manufacturing for Circular Economy and Sustainability)

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Review

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40 pages, 4807 KiB

Open AccessReview

Automation of Life Cycle Assessment—A Critical Review of Developments in the Field of Life Cycle Inventory Analysis

by Bianca Köck, Anton Friedl, Sebastián Serna Loaiza, Walter Wukovits and Bettina Mihalyi-Schneider

Sustainability 2023, 15(6), 5531; https://0-doi-org.brum.beds.ac.uk/10.3390/su15065531 - 21 Mar 2023

Cited by 3 | Viewed by 3591

Abstract

The collection of reliable data is an important and time-consuming part of the life cycle inventory (LCI) phase. Automation of individual steps can help to obtain a higher volume of or more realistic data. The aim of this paper is to survey the current state of automation potential in the scientific literature published between 2008 and 2021, with a focus on LCI in the area of process engineering. The results show that automation was most frequently found in the context of process simulation (via interfaces between software), for LCI database usage (e.g., via using ontologies for linking data) and molecular structure models (via machine learning processes such as artificial neural networks), which were also the categories where the highest level of maturity of the models was reached. No further usage could be observed in the areas of automation techniques for exploiting plant data, scientific literature, process calculation, stoichiometry and proxy data. The open science practice of sharing programming codes, software or other newly created resources was only followed in 20% of cases, uncertainty evaluation was only included in 10 out of 30 papers and only 30% of the developed methods were used in further publication, always including at least one of the first authors. For these reasons, we recommend encouraging exchange in the LCA community and in interdisciplinary settings to foster long-term sustainable development of new automation methodologies supporting data generation. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Manufacturing for Circular Economy and Sustainability)

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