{"title":"Data-driven digital transformation in operations and supply chain management","authors":"Konstantina Spanaki , Denis Dennehy , Thanos Papadopoulos , Rameshwar Dubey","doi":"10.1016/j.ijpe.2025.109599","DOIUrl":null,"url":null,"abstract":"<div><div>Data-driven digital transformation is a dynamic capability that enables organisations to derive actionable insights and achieve a competitive edge. Data-driven technologies have played a pivotal role in evolving operations and supply chains, making them more responsive and efficient. Data-driven technologies now support advanced functions such as supply chain analytics, blockchain for security and transparency, and AI for innovation and efficiency. Research has long stressed the benefits of improved visibility and collaboration in the operations and supply chain management (O&SCM). Despite rigorous research, there remains a disconnect between theoretical frameworks and their real-world application. This gap suggests further research to better align academic insights with practical implementations in OSCM and a more comprehensive and integrated approach to understanding and applying data-driven digital transformation strategies in O&SCM. This special issue (SI) aims to deepen the theoretical understanding of data-driven digital transformation within O&SCM. We believe the 20 accepted papers out of 97 submissions contribute meaningful theoretical insights to O&SCM research and practice. These contributions not only enrich the theoretical discourse in data-driven digital transformation and O&SCM but also provide practical pathways for future research and application in diverse industry settings.</div></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":"284 ","pages":"Article 109599"},"PeriodicalIF":9.8000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Production Economics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925527325000842","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
Data-driven digital transformation is a dynamic capability that enables organisations to derive actionable insights and achieve a competitive edge. Data-driven technologies have played a pivotal role in evolving operations and supply chains, making them more responsive and efficient. Data-driven technologies now support advanced functions such as supply chain analytics, blockchain for security and transparency, and AI for innovation and efficiency. Research has long stressed the benefits of improved visibility and collaboration in the operations and supply chain management (O&SCM). Despite rigorous research, there remains a disconnect between theoretical frameworks and their real-world application. This gap suggests further research to better align academic insights with practical implementations in OSCM and a more comprehensive and integrated approach to understanding and applying data-driven digital transformation strategies in O&SCM. This special issue (SI) aims to deepen the theoretical understanding of data-driven digital transformation within O&SCM. We believe the 20 accepted papers out of 97 submissions contribute meaningful theoretical insights to O&SCM research and practice. These contributions not only enrich the theoretical discourse in data-driven digital transformation and O&SCM but also provide practical pathways for future research and application in diverse industry settings.
期刊介绍:
The International Journal of Production Economics focuses on the interface between engineering and management. It covers all aspects of manufacturing and process industries, as well as production in general. The journal is interdisciplinary, considering activities throughout the product life cycle and material flow cycle. It aims to disseminate knowledge for improving industrial practice and strengthening the theoretical base for decision making. The journal serves as a forum for exchanging ideas and presenting new developments in theory and application, combining academic standards with practical value for industrial applications.