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Exploring Data-Driven decision-making for enhanced sustainability

 
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<title>Exploring Data-Driven decision-making for enhanced sustainability</title>
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<namePart>Chavez, Zuhara</namePart>
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<namePart>KTH Royal Institute of Technology</namePart>
<namePart>Department of Sustainable Production Development</namePart>
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<abstract displayLabel="Summary">The industry transition towards digital transformation opens the possibilities to utilize data for enhancing sustainability in industrial operations and build capabilities towards resilient and circular operations, i.e., shift towards industry 5.0. This paper explores how data-driven decision-making (DDDM) can enable sustainable and resilient supply chain operations within the manufacturing industry. A series of in-depth interviews were conducted with experts, researchers, and company representatives across the manufacturing industry and universities in Sweden. The findings show a consensus among companies, researchers, and literature about the potential of data utilization for sustainability purposes; however, in most cases, the complete transformation towards data-driven has not happened yet. Companies have uncertainty about what data is needed rather than its lack. Reliability & validity of data become essential to exploit the potential of the data organizations already possess. Based on the literature and interview data, a conceptual model is proposed, including three identified parameters connected to DDDM, 1) data and IT infrastructure, 2) current operations, and 3) an improved triple bottom line performance. The model captures the interconnections between
such parameters, depicting the benefits and challenges of DDDM and its relation to more sustainable and resilient supply chain operations within the manufacturing industry. In a data-driven approach, real-time analysis of complex & extensive amounts of data gives unlimited possibilities to improve manufacturing operations through decision-making</abstract>
<note type="statement of responsibility">Zuhara Chavez... [et al.]</note>
<note>This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).</note>
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<topic>Industria manufacturera</topic>
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<topic>Cadena del suministro</topic>
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<topic>Análisis de datos</topic>
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<topic>Data driven</topic>
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<topic>Toma de decisiones</topic>
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<topic>Modelos paramétricos</topic>
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<title>KTH Royal Institute of Technology, Department of Sustainable Production Development, Södertälje, Sweden, 2022</title>
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<text>12 p.</text>
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