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Industrial Automation: Realising the Circular Economy through Autonomous Production
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Industrial Automation: Realising the Circular Economy through Autonomous Production

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 26240

Special Issue Editors

Dr. Chris Turner

E-Mail Website
Guest Editor
Research Fellow and Project Manager within the Manufacturing and Materials Department at Cranfield University
Interests: Circular Economy, Business Analytics; Business Process Management; Mixed Reality Visualisation; Industry 4.0; Data Mining; Cloud Manufacturing; Distributed and Sustainable Manufacturing
Prof. Lampros Stergioulas

E-Mail Website
Guest Editor
Surrey Business School, University of Surrey, UK
Interests: Circular Economy, Business Analytics, Strategic Planning, Data-driven Management, Digital Entrepreneurship, Information Systems, System Modeling and Simulation
Prof. Dr. Konstantinos Salonitis

E-Mail Website
Guest Editor
Sustainable Manufacturing Systems Centre, School of Aerospace Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
Interests: sustainable manufacturing systems; green manufacturing; environmental impact assessment; simulation; modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Emmanouilidis

E-Mail Website
Guest Editor
Through-life Engineering Services Institute, Manufacturing Department, School Of Aerospace, Transport and Manufacturing Building 50, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK
Interests: industrial sustainability; simulation and modeling; sensor technologies; systems engineering, throughlife engineering services; instrumentation and sensors; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in automation over recent years have given rise to the promise and potential of Autonomous Production. While the vision of ‘thinking’ production lines has still to be realised, Industry 4.0 enabling technologies, such as such as IoT (Internet of Things), Machine Learning and Cyber Physical Systems (CPS) in industrial settings present concrete opportunities towards more responsive, smarter and more efficient production. The route to autonomous production requires the integration of complex systems and the collection and analysis of multiple data streams. At the same time, increasing pressure on the environment from human activity necessitates a shift in attitudes to the way industry currently operates. The rise of the circular economy is one specific response to this need, promoting a holistic view of both production and consumption of goods and services. Automation and autonomous manufacturing provides a new way of looking at production where data evidence may be analysed and acted upon in real time leading to the potential for reductions in waste, longer reliable usage patterns for products, predictive monitoring of industrial processes and whole life consideration of products with particular regard to the recycling of defunct products and their remanufacture.

The use of machine learning enables insights from the production line to be systematically captured and employed by human experts for decision making. This is a crucial step in the development of fully autonomous production lines and promotes methods to identify more efficient and environmentally acceptable processes derived from sensed data collected from both inside and outside the organisation along with data mined from existing data stores. With intelligent systems use comes the need to explain the reasoning behind the results they produce to humans for the purposes of decision support, ensuring provenance and maintaining quality control. This need for explainable AI (Artificial Intelligence) in manufacturing systems supports the concept of ‘human in the loop’ to enable a new level of informed decision making to take place.

This Special Issue aims to bring together works relating to automation with particular regard to industrial sustainability. We invite you to contribute to this issue by submitting both case studies and research articles.

Dr. Chris Turner

Prof. Lampros Stergioulas

Dr. Kostas Salonitis

Dr. Christos Emmanouilidis

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

  • Manufacturing automation
  • Internet of Things (IoT)
  • Big data in manufacturing
  • Circular economy and manufacturing
  • Industrial sustainability
  • Artificial Intelligence
  • Human in the loop systems
  • Industry 4.0

Published Papers (3 papers)

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Research

37 pages, 3067 KiB  
Article
Circular Strategies Enabled by the Internet of Things—A Framework and Analysis of Current Practice
by Emilia Ingemarsdotter, Ella Jamsin, Gerd Kortuem and Ruud Balkenende
Sustainability 2019, 11(20), 5689; https://doi.org/10.3390/su11205689 - 15 Oct 2019
Cited by 88 | Viewed by 8229
Abstract
This paper focuses on how the Internet of Things (IoT) could contribute to the transition to a circular economy (CE), through supporting circular business model and design strategies. While literature has highlighted the opportunities for IoT to support circular strategies in business, little [...] Read more.
This paper focuses on how the Internet of Things (IoT) could contribute to the transition to a circular economy (CE), through supporting circular business model and design strategies. While literature has highlighted the opportunities for IoT to support circular strategies in business, little has been published about actual implementations in practice. The aim of this study was therefore to understand how companies to date have implemented IoT for circular strategies, and how these implementations compare to the range of opportunities described in literature. To that end, a two-step approach was followed. Firstly, building on academic literature, a framework was developed which categorizes different IoT-enabled circular strategies. The framework recognizes tracking, monitoring, control, optimization, and design evolution as IoT capabilities. Efficiency in use, increased utilization, and product lifetime extension are distinguished as circular in-use strategies, while reuse, remanufacturing, and recycling are distinguished as circular looping strategies. The framework complements previously published work, as it adds additional detail to the categorization, and allows for easy mapping of diverse cases. Secondly, 40 cases from practice were analyzed and mapped to the framework. This way, practice-based insights were derived about the current distribution of IoT-enabled circular strategies implemented in practice. The results show that current implementation of IoT-enabled circular strategies mainly supports two strategies in the use phase: efficiency in use and product lifetime extension. Only a small number of the reviewed cases display IoT-enabled looping (reuse, remanufacturing, and recycling). Similarly, few cases describe ‘design evolution’ for CE, i.e., the feedback of data from products in use to support circular design. Based on these results, this study identifies the need for future research to further investigate why IoT-enabled looping strategies and design evolution for circular strategies have not been implemented to scale. Full article
(This article belongs to the Special Issue Industrial Automation: Realising the Circular Economy through Autonomous Production)
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19 pages, 4694 KiB  
Article
Sustainable Production in a Circular Economy: A Business Model for Re-Distributed Manufacturing
by Chris Turner, Mariale Moreno, Luigi Mondini, Konstantinos Salonitis, Fiona Charnley, Ashutosh Tiwari and Windo Hutabarat
Sustainability 2019, 11(16), 4291; https://doi.org/10.3390/su11164291 - 08 Aug 2019
Cited by 64 | Viewed by 9159
Abstract
The emergence of new technologies such as the Internet of Things, big data, and advanced robotics, together with risks such as climate change, rising labour costs, and a fluctuating economy, are challenging the current UK manufacturing model. In this paper, business models for [...] Read more.
The emergence of new technologies such as the Internet of Things, big data, and advanced robotics, together with risks such as climate change, rising labour costs, and a fluctuating economy, are challenging the current UK manufacturing model. In this paper, business models for re-distributed manufacture (RdM) are developed using anIDEF (Icam DEFinition for Function Modelling) description to serve as a guide for the implementation of the RdM concept in the consumer goods industry. This paper explores the viability of a re-distributed business model for manufacturers employing new manufacturing technologies such as additive manufacturing or three-dimensional (3D) printing, as part of a sustainable and circular production and consumption system. An As-Is value chain model is presented alongside the proposed new business model for a sustainable re-distributed manufacturing system. Both are illustrated via a case study drawn from the shoe manufacturing industry. The case study shows that there is a need for robust facilities in close proximity to the customer. These facilities are store fronts which can also manufacture, remanufacture, and provide services. The reduction in transportation and increase in customer involvement throughout the process are the main benefits that would accrue if a re-distributed model is implemented in the given industry. Full article
(This article belongs to the Special Issue Industrial Automation: Realising the Circular Economy through Autonomous Production)
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16 pages, 3189 KiB  
Article
Simulation to Enable a Data-Driven Circular Economy
by Fiona Charnley, Divya Tiwari, Windo Hutabarat, Mariale Moreno, Okechukwu Okorie and Ashutosh Tiwari
Sustainability 2019, 11(12), 3379; https://doi.org/10.3390/su11123379 - 19 Jun 2019
Cited by 62 | Viewed by 7486
Abstract
This paper presents an investigation on how simulation informed by the latest advances in digital technologies such as the 4th Industrial Revolution (I4.0) and the Internet of Things (IoT) can provide digital intelligence to accelerate the implementation of more circular approaches in UK [...] Read more.
This paper presents an investigation on how simulation informed by the latest advances in digital technologies such as the 4th Industrial Revolution (I4.0) and the Internet of Things (IoT) can provide digital intelligence to accelerate the implementation of more circular approaches in UK manufacturing. Through this research, a remanufacturing process was mapped and simulated using discrete event simulation (DES) to depict the decision-making process at the shop-floor level of a remanufacturing facility. To understand the challenge of using data in remanufacturing, a series of interviews were conducted finding that there was a significant variability in the condition of the returned product. To address this gap, the concept of certainty of product quality (CPQ) was developed and tested through a system dynamics (SD) and DES model to better understand the effects of CPQ on products awaiting remanufacture, including inspection, cleaning and disassembly times. The wider application of CPQ could be used to forecast remanufacturing and production processes, resulting in reduced costs by using an automatised process for inspection, thus allowing more detailed distinction between “go” or “no go” for remanufacture. Within the context of a circular economy, CPQ could be replicated to assess interventions in the product lifecycle, and therefore the identification of the optimal CE strategy and the time of intervention for the current life of a product—that is, when to upgrade, refurbish, remanufacture or recycle. The novelty of this research lies in investigating the application of simulation through the lens of a restorative circular economic model focusing on product life extension and its suitability at a particular point in a product’s life cycle. Full article
(This article belongs to the Special Issue Industrial Automation: Realising the Circular Economy through Autonomous Production)
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