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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03917naa a2200553 4500 | |
| 001 | oai:DiVA.org:kth-324946 | |
| 003 | SwePub | |
| 008 | 230327s2022 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3249462 URI |
| 024 | 7 | a https://doi.org/10.3233/ATDE2201582 DOI |
| 040 | a (SwePub)kth | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a kon2 swepub-publicationtype |
| 100 | 1 | a Chavez, Zuharau KTH,Hållbar produktionsutveckling (ML)4 aut0 (Swepub:kth)u1ceceu9 |
| 245 | 1 0 | a Exploring Data-Driven Decision-Making for Enhanced Sustainability |
| 264 | 1 | b IOS Press,c 2022 |
| 338 | a print2 rdacarrier | |
| 500 | a QC 20230327 | |
| 520 | a 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. | |
| 650 | 7 | a TEKNIK OCH TEKNOLOGIERx Maskinteknikx Produktionsteknik, arbetsvetenskap och ergonomi0 (SwePub)203072 hsv//swe |
| 650 | 7 | a ENGINEERING AND TECHNOLOGYx Mechanical Engineeringx Production Engineering, Human Work Science and Ergonomics0 (SwePub)203072 hsv//eng |
| 653 | a Data-driven | |
| 653 | a decision-making | |
| 653 | a digitalization | |
| 653 | a sustainability | |
| 653 | a sustainable manufacturing | |
| 653 | a Manufacture | |
| 653 | a Metadata | |
| 653 | a Supply chains | |
| 653 | a Sustainable development | |
| 653 | a Data driven | |
| 653 | a Data driven decision | |
| 653 | a Decisions makings | |
| 653 | a Digital transformation | |
| 653 | a In-depth interviews | |
| 653 | a Industrial operations | |
| 653 | a Manufacturing industries | |
| 653 | a Supply chain operation | |
| 653 | a Decision making | |
| 700 | 1 | a Gopalakrishnan, Maheshwaranu KTH,Hållbar produktionsutveckling (ML)4 aut0 (Swepub:kth)u15ohrbp |
| 700 | 1 | a Nilsson, Viktoru KTH,Industriell ekonomi och organisation (Inst.)4 aut0 (Swepub:kth)PI000000 |
| 700 | 1 | a Westbroek, Arvidu KTH,Industriell ekonomi och organisation (Inst.)4 aut0 (Swepub:kth)u19faoto |
| 710 | 2 | a KTHb Hållbar produktionsutveckling (ML)4 org |
| 773 | 0 | t Advances in Transdisciplinary Engineeringd : IOS Pressg , s. 392-403q <392-403 |
| 856 | 4 | u https://doi.org/10.3233/ATDE220158y Fulltext |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-324946 |
| 856 | 4 8 | u https://doi.org/10.3233/ATDE220158 |
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