Holistic Framework to Data-Driven Sustainability Assessment

In recent years, the Twin-Transition reference model has gained notoriety as one of the key options for decarbonizing the economy while adopting more sustainable models leveraged by the Industry 4.0 paradigm. In this regard, one of the most relevant challenges is the integration of data-driven appro...

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Bibliographic Details
Published inSustainability Vol. 15; no. 4; p. 3562
Main Authors Peças, Paulo, John, Lenin, Ribeiro, Inês, Baptista, António J, Pinto, Sara M, Dias, Rui, Henriques, Juan, Estrela, Marco, Pilastri, André, Cunha, Fernando
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2023
Subjects
Continuous improvement
Decarbonization
Decision making
Decision theory
Elkington, John
Fuzzy logic
Industrial research
Manufacturing industry
Portugal
Standardization
Sustainability
Sustainable development
Portugal
Germany
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Summary:In recent years, the Twin-Transition reference model has gained notoriety as one of the key options for decarbonizing the economy while adopting more sustainable models leveraged by the Industry 4.0 paradigm. In this regard, one of the most relevant challenges is the integration of data-driven approaches with sustainability assessment approaches, since overcoming this challenge will foster more agile sustainable development. Without disregarding the effort of academics and practitioners in the development of sustainability assessment approaches, the authors consider the need for holistic frameworks that also encourage continuous improvement in sustainable development. The main objective of this research is to propose a holistic framework that supports companies to assess sustainability performance effectively and more easily, supported by digital capabilities and data-driven concepts, while integrating improvement procedures and methodologies. To achieve this objective, the research is based on the analysis of published approaches, with special emphasis on the data-driven concepts supporting sustainability assessment and Lean Thinking methods. From these results, we identified and extracted the metrics, scopes, boundaries, and kinds of output for decision-making. A new holistic framework is described, and we have included a guide with the steps necessary for its adoption in a given company, thus helping to enhance sustainability while using data availability and data-analytics tools.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15043562