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Sustainability
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Data-Driven Development for Sustainable Smart Product-Service Systems
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Data-Driven Development for Sustainable Smart Product-Service Systems

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 8751

Special Issue Editors

Dr. Xinyu Li

E-Mail Website
Guest Editor
Delta-NTU Corporate Laboratory for Cyber-Physical Systems, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: smart product–service systems; knowledge management
Special Issues, Collections and Topics in MDPI journals
Dr. Pai Zheng

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: human–robot collaboration; smart product-service systems; engineering informatics; smart manufacturing systems
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Peng

E-Mail Website
Guest Editor
State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
Interests: smart technologies for manufacturing and services; big data-driven production management; cognitive intelligence-enabled design; manufacturing and supply chains
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Responding to a call for “doing more with less material” in circular economy, two concepts are proposed for promoting sustainability in the industrial system. One is a circular system aiming to achieve use of “less material”, transforming the linear lifespan into a circular one via several reversible strategies (e.g., re-design, remanufacturing, redistribution, reuse, and recycle). The other one is a product–service system for ‘doing more’, which largely extends the product lifespan with the innovation of multiple add-on services.

Enabled by emerging information and communication technology (ICT), such as advanced ubiquitous sensing, cloud computing, deep learning, and knowledge graphs, the intersection of two prevailing concepts, so-called sustainable smart PSS (SSPSS), is nurtured. Analyzing big lifecycle data and massive empirical knowledge collected from sensing devices and generated by stakeholders, SSPSS can better perform its sustainable use/reuse, maintenance, reconfigure, and recycle processes throughout the whole lifecycle and enhance user experience with highly customized on-demand services and reasonable consumptions of cyber-physical resources. Therefore, it provides a promising manner to realize sustainable development in the industrial system.

This Special Issue, entitled “Data-driven Development for Sustainable Smart Product–Service Systems”, concentrates on (big) data-driven ideations and methodologies for co-developing/re-developing sustainable products, services, and integrated systems. The scope of this Special Issue includes but is not limited to the following topics:

  • New-generation ICT-enabled SSPSS architecture;
  • Systematic framework and development process for SSPSS;
  • Sustainable products and services innovation with context awareness;
  • Multisource heterogeneous sustainability data analytics;
  • Information/knowledge management systems design for SSPSS;
  • Intelligent decision making in sustainable production and operation management;
  • Quantified evaluation of sustainability in exploiting cyber-physical resources;
  • Informatics-based sustainable evolvement in product and service iterations;
  • Case studies on SSPSS development and implementation;
  • Other methodologies, tools, and systems toward SSPSS.

As the overlapping scope of the two emerging fields of smart product–service systems and smart circular economy suggests, this Special Issue is intended to renovate the production–consumption pattern in co-developing/re-developing both physical components and digital services, through state-of-the-art informatics-based approaches, tools, and systems.

Dr. Xinyu Li
Prof. Dr. Pai Zheng
Prof. Dr. Tao Peng
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

  • smart product–service systems
  • circular system
  • sustainability
  • reversible design and development
  • data-driven manners
  • knowledge-based systems

Published Papers (3 papers)

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Research

17 pages, 4757 KiB  
Article
A Data-Driven Packaging Efficiency Optimization Method for a Low Carbon System in Agri-Products Cold Chain
by Jingjie Wang, Xiaoshuan Zhang, Xiang Wang, Hongxing Huang, Jinyou Hu and Weijun Lin
Sustainability 2022, 14(2), 858; https://doi.org/10.3390/su14020858 - 12 Jan 2022
Cited by 5 | Viewed by 2126
Abstract
The of monitoring the Internet of Things (IoT) in the cold chain allows process data, including packaging data, to be more easily accessible. Proper optimization modelling is the core driving force towards the green and low-carbon operation of cold chain logistics, laying the [...] Read more.
The of monitoring the Internet of Things (IoT) in the cold chain allows process data, including packaging data, to be more easily accessible. Proper optimization modelling is the core driving force towards the green and low-carbon operation of cold chain logistics, laying the necessary foundation for the development of a data-driven modelling system. Since efficient packaging is necessary for loss control in the cold chain, its final efficiency during circulation is important for realizing continuous loss prevention and efficient supply. Thus, it is urgent to determine how to utilize these continuously acquired data and how to formulate a more accurate packaging efficiency control methodology in the agri-products cold chain. Through continuous monitoring, we examined the feasibility of this topic by focusing on the concept of data-driven evaluation modelling and the dynamic formation mechanism of comprehensive packaging efficiency in cold chain logistics. The packaging efficiency in the table grape cold chain was used as an example to evaluate the comprehensive efficiency evaluation index system and data-driven evaluation framework proposed in this paper. Our results indicate that the established methodology can adapt to the continuity of comprehensive packaging efficiency, also reflecting the comprehensive efficiency evaluation of the packaging for different times and distances. Through the evaluation of our results, the differences and the dynamic processes between different final packaging efficiencies at different moments are effectively displayed. Thus, the continuous improvement of a low-carbon system in cold chain logistics could be realized. Full article
(This article belongs to the Special Issue Data-Driven Development for Sustainable Smart Product-Service Systems)
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19 pages, 1255 KiB  
Article
Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven
by Xiangang Cao, Pengfei Li and Song Ming
Sustainability 2021, 13(15), 8548; https://doi.org/10.3390/su13158548 - 31 Jul 2021
Cited by 9 | Viewed by 2890
Abstract
Currently, the Remaining Useful Life (RUL) prediction accuracy of stochastic deterioration equipment is low. Existing researches did not consider the impact of imperfect maintenance on equipment degradation and maintenance decisions. Therefore, this paper proposed a remaining useful life prediction-based maintenance decision model under [...] Read more.
Currently, the Remaining Useful Life (RUL) prediction accuracy of stochastic deterioration equipment is low. Existing researches did not consider the impact of imperfect maintenance on equipment degradation and maintenance decisions. Therefore, this paper proposed a remaining useful life prediction-based maintenance decision model under data-driven to extend equipment life, promoting sustainable development. The stochastic degradation model was established based on the nonlinear Wiener process. A combination of real-time update and offline estimation estimated the degradation model’s parameters and deduced the equipment’s RUL distribution. Based on the RUL prediction results, we established a maintenance decision model with the lowest long-term cost rate as the goal. Case analysis shows that the model proposed in this paper can improve the accuracy of RUL prediction and realize equipment sustainability. Full article
(This article belongs to the Special Issue Data-Driven Development for Sustainable Smart Product-Service Systems)
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18 pages, 3246 KiB  
Article
Towards the Human–Machine Interaction: Strategies, Design, and Human Reliability Assessment of Crews’ Response to Daily Cargo Ship Navigation Tasks
by Su Han, Tengfei Wang, Jiaqi Chen, Ying Wang, Bo Zhu and Yiqi Zhou
Sustainability 2021, 13(15), 8173; https://doi.org/10.3390/su13158173 - 21 Jul 2021
Cited by 4 | Viewed by 2351
Abstract
Human error is a crucial factor leading to maritime traffic accidents. The effect of human–computer interaction (HCI) also plays a leading role in human error. The objective of this study is to propose a method of interaction strategies based on a cognitive-processing model [...] Read more.
Human error is a crucial factor leading to maritime traffic accidents. The effect of human–computer interaction (HCI) also plays a leading role in human error. The objective of this study is to propose a method of interaction strategies based on a cognitive-processing model in crews’ daily navigation tasks. A knowledge-based ship HCI framework architecture is established. It provides an extensible framework for the HCI process in the maritime domain. By focusing on the cognitive process of a crew in the context of accident and risk handling during ship navigation, based on the information, decision, and action in crew context (IDAC) model, in combination with the maritime accident dynamics simulation (MADS) system, the MADS-IDAC system was developed and enhanced by the HCI structure and function design of the dynamic risk analysis platform for maritime management. The results indicate that MADS enhanced by HCI can effectively generate a strategy set of various outcomes in preset scenarios. Moreover, it provides a new method and thought for avoiding human error in crew interaction and to lower the risk of ship collision as well as effectively improving the reliability of HCI. Full article
(This article belongs to the Special Issue Data-Driven Development for Sustainable Smart Product-Service Systems)
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