Competitive Sustainable Manufacturing: Making Sustainability Make Business Sense
A special issue of Sustainability (ISSN 2071-1050).
Deadline for manuscript submissions: closed (15 August 2020) | Viewed by 10206
Special Issue Editor
Interests: development of food provision models for the reduction of consumer food waste; eco-intelligent manufacturing; optical detection of food fouling on surfaces; improved models for industrial food waste management; modelling of energy consumption in industry
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Special Issue Information
Dear Colleagues,
The need for more sustainable solutions for the manufacturing industry has never been clearer. With our accumulated and verified knowledge of the human impact on the climate, biodiversity, and the resultant vulnerability of the human race, efforts are required to identify and implement approaches that reduce our environmental impacts, whilst maintaining commercial incentive and supporting social wellbeing and development.
Milton Friedman said the ‘business of business is business’, which is now often rebuked but holds some truth—businesses must be allowed to operate for traditional drivers of business: to grow and to make profit. If they do not, then any environmental benefit they may provide will only ever be on a small scale. Therefore, it is important to make sustainable options economically and socially viable: to underpin the three pillars of sustainability.
There is a plethora of published research that identifies more sustainable practices for industry, but there is often a disconnect between true industrial needs and these new proposed practices. The reality is that very few outputs from academia actually make it into industrial practice. The time has come to be more pragmatic in our approaches to industrially-focused research. The community must provide economically viable solutions that plausibly fit within the capabilities of the target industry. Solutions must make business sense.
In this Special Issue, we seek to break down some of these barriers and overcome the disparity between what is good for the environment and what is good for industry. With a focus primarily on the manufacturing industry and its supply chain, we invite manuscripts that provide industrially relevant solutions for:
- Resource efficiency improvements with short payback times;
- Latent capabilities within manufacturing (not at the expense of the environment);
- New business models that seek to de-materialise society (e.g., product service systems)
- Competitive circular economy approaches;
- Multifactor eco-intelligent decision making for businesses;
- Incorporating consumers into the supply chain (e.g., for CE or product takeback);
- Supply and demand balancing to reduce overproduction/waste;
- Any other relevant topic in this area.
Contributors from a wide range of fields are invited to submit their articles for this Special Issue which will be specifically interested in those technological, operational or business model improvements that make short- and long-term business sense.
Dr. Elliot Woolley
Guest Editor
Manuscript Submission Information
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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
- sustainable manufacturing
- water, energy and material efficiency
- resource recovery
- waste reduction
- sustainable consumption
- impact assessment
- sensors and monitoring
- Artificial intelligence
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