Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

AI and Machine Learning towards Circular and Sustainable Industry

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 26392

Share This Special Issue

Special Issue Editors

Prof. Dr. Vinit Prida

E-Mail Website
Guest Editor

Department of Business Administration, Technology and Social Sciences, Lulea university of technology, 97187 Luleå, Sweden
Interests: servitization; business model innovation; industrial ecosystems; digitalization; ciricualr economy
Special Issues, Collections and Topics in MDPI journals

Dr. Wiebke Reim

E-Mail Website
Guest Editor

Entrepreneurship and Innovation, Luleå University of Technology, 97187 Luleå, Sweden
Interests: business models; digitalization; product-service systems; servitization; circular economy

Special Issue Information

Dear Colleagues,

Industries are heavily investing in new digital technologies such as AI, machine learning, and big data analysis, aiming for higher efficiency and new business opportunities. AI is expected to radically change the way companies operate and generate value. For example, in the manufacturing industry, AI creates a lot of opportunities for companies, such as preventive and predictive maintenance, identification of defects in the manufacturing process, demand and forecasting tools or inventory planning. These smart and connected digitalized machines and solutions are expected to result in a higher efficiency of resources and equipment as well as enable cost reductions by decreasing energy consumption, lowering scrap rates, and diminishing production downtime. Despite the positive economic effect, these technologies have a huge potential to contribute to sustainability. However, sustainability benefits are seldom the focus of AI initiatives, and they are very hard to measure on a holistic perspective. Together with the concerns that AI will steal a lot of jobs, this raises questions regarding the triple-bottom-line effects of AI and machine learning.

This Special Issue calls for a more critical discussion and outlook for circular economy and sustainability benefits from AI and machine learning practices within traditional industries. Such a perspective is needed because prior studies have acknowledged that many companies develop AI-based solutions without a clear analysis of the economic, environmental, and social effects of their efforts. IT is important to increase our understanding of how companies can utilize AI for sustainable benefits, through sharing knowledge about successful cases, common challenges, and useful models. In addition, the implementation of AI-based business models usually requires a radical transformation of the companies’ ecosystems to a state in which value is co-created among providers, ecosystem partners, and customers by optimizing resource usage and operation and leveraging digital technologies.

Against this backdrop, this Special Issue explores contributions focusing on increasing knowledge on how firms can leverage AI, machine learning, and bid data analysis to achieve triple-bottom-line benefits. In particular, knowledge is sought concerning sustainable business models based on AI and machine learning, which have big potential for an economic, environmental, and social impact by incorporating the logic of a circular economy. Existing research to build and debate contributions to this Special Issue is manifold and includes but is not limited to the following:

Opportunities and benefits with AI and machine learning for sustainability
AI driving circular economy implementation in B2B and B2C industrial contexts
Rebound effect of AI and machine learning
Industrial successful cases of sustainability gains through AI, machine learning, Big data analysis
Measuring and communicating triple-bottom-line effects of AI and machine learning
AI and machine learning from a multiactor ecosystems perspective for system level change
Role of SMEs in driving change towards AI implementation for circularity and sustainability
Implementation of AI and machine-learning enabled business models innovation
Customer and supplier perspectives on AI and machines learning
Organizational transformation issues related to AI and machine learning implementation and exploitation

In addition to original academic researcher papers, the issue will invite commentaries on these topics by business practitioners, as well as academic papers from scholars from multiple disciplines including sustainability, strategic management, engineering, marketing, information technology, and operation management.

Prof. Vinit Parida
Dr. Wiebke Reim
Guest Editors

References

Chui, K. T., Lytras, M. D., & Visvizi, A. (2018). Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11(11), 2869.
Frishammar, J., & Parida, V. (2019). Circular business model transformation: A roadmap for incumbent firms. California Management Review, 61(2), 5-29.
Ilie, C., Ploae, C., Melnic, L. V., Cotrumba, M. R., Gurau, A. M., & Alexandra, C. (2019). Sustainability through the Use of Modern Simulation Methods—Applied Artificial Intelligence. Sustainability, 11(8), 2384.
Lahti, T., Wincent, J., & Parida, V. (2018). A definition and theoretical review of the circular economy, value creation, and sustainable business models: where are we now and where should research move in the future?. Sustainability, 10(8), 2799.
Parida, V., Sjödin, D., & Reim, W. (2019). Reviewing literature on digitalization, business model innovation, and sustainable industry: Past achievements and future promises. Sustainability, 11, 391.
Parida, V., Burström, T., Visnjic, I., & Wincent, J. (2019). Orchestrating industrial ecosystem in circular economy: A two-stage transformation model for large manufacturing companies. Journal of Business Research, 101, 715-725.
Parida, V., & Wincent, J. (2019). Why and how to compete through sustainability: a review and outline of trends influencing firm and network-level transformation. International Entrepreneurship and Management Journal, 15(1), 1-19.
Pham, A. D., Ngo, N. T., Truong, T. T. H., Huynh, N. T., & Truong, N. S. (2020). Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability. Journal of Cleaner Production, 121082.
Rajput, S., & Singh, S. P. (2019). Connecting circular economy and industry 4.0. International Journal of Information Management, 49, 98-113.
Zhang, H., Song, M., & He, H. (2020). Achieving the Success of Sustainability Development Projects through Big Data Analytics and Artificial Intelligence Capability. Sustainability, 12(3), 949.

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

Artificial Intelligence (AI)
Machine Learning
industry 4.0
Circular Economy
Sustainability
Circular Business Models
Industrial Ecosystems
Big Data
Digitalization
Servitization

Published Papers (2 papers)

Download All Papers

Research

Jump to: Review

20 pages, 2697 KiB

Open AccessArticle

An Authoritative Study on the Near Future Effect of Artificial Intelligence on Project Management Knowledge Areas

by Thordur Vikingur Fridgeirsson, Helgi Thor Ingason, Haukur Ingi Jonasson and Hildur Jonsdottir

Sustainability 2021, 13(4), 2345; https://doi.org/10.3390/su13042345 - 22 Feb 2021

Cited by 27 | Viewed by 14538

Abstract

The purpose of this study is to explore how Artificial Intelligence (AI) might augment the project management profession in each of the 10 categories of project management knowledge areas, as defined in the Project Management Body of Knowledge (PMBOK) of the Project Management Institute (PMI). In a survey, a group of project management experts were asked to state their insights into AI’s likely effect on project management in the next 10 years. Results clearly illustrated that AI will be an integrated part of future project management practice and will affect project management knowledge areas in the near future. According to these findings, the management of cost, schedule, and risk, in particular, will be highly affected by AI. The research indicates that AI is very useful for processes where historical data is available and can be used for estimation and planning. In addition, it is clear that AI can monitor schedules, adjust forecasts, and maintain baselines. According to the findings, AI will have less impact in knowledge areas and processes that require human leadership skills, such as developing and managing teams and the management of stakeholders. The results indicate proprietarily the project management knowledge areas as defined by PMI that AI is likely to augment and sustain. Full article

(This article belongs to the Special Issue AI and Machine Learning towards Circular and Sustainable Industry)

► Show Figures

Figure 1

Review

Jump to: Research

26 pages, 4796 KiB

Open AccessReview

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

by Lara Waltersmann, Steffen Kiemel, Julian Stuhlsatz, Alexander Sauer and Robert Miehe

Sustainability 2021, 13(12), 6689; https://doi.org/10.3390/su13126689 - 12 Jun 2021

Cited by 44 | Viewed by 9685

Abstract

Sustainability improvements in industrial production are essential for tackling climate change and the resulting ecological crisis. In this context, resource efficiency can directly lead to significant advancements in the ecological performance of manufacturing companies. The application of Artificial Intelligence (AI) also plays an increasingly important role. However, the potential influence of AI applications on resource efficiency has not been investigated. Against this background, this article provides an overview of the current AI applications and how they affect resource efficiency. In line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper identifies, categorizes, and analyzes seventy papers with a focus on AI tasks, AI methods, business units, and their influence on resource efficiency. Only a minority of papers was found to address resource efficiency as an explicit objective. Subsequently, typical use cases of the identified AI applications are described with a focus on predictive maintenance, production planning, fault detection and predictive quality, as well as the increase in energy efficiency. In general, more research is needed that explicitly considers sustainability in the development and use phase of AI solutions, including Green AI. This paper contributes to research in this field by systematically examining papers and revealing research deficits. Additionally, practitioners are offered the first indications of AI applications increasing resource efficiency. Full article

(This article belongs to the Special Issue AI and Machine Learning towards Circular and Sustainable Industry)

► Show Figures