Abstract
PurposeProduction systems occupy geographically dispersed organizations with limited visibility and transparency. Such limitations create operational inefficiencies across the Supply Chain (SC). Recently, researchers have started exploring applications of Digital Twins Technology (DTT) to improve SC operations. In this context, there is a need to provide comprehensive theoretical knowledge and frameworks to help stakeholders understand the adoption of DTT. This study aims to fulfill the research gap by empirically investigating DTT readiness to enable transparency in SC.Design/methodology/approachA comprehensive literature survey was conducted to develop a theoretical model related to Supply Chain Transparency (SCT) and DTT readiness. Then, a questionnaire was developed based on the proposed theoretical model, and data was collected from Indian manufacturers. The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) to confirm the proposed relationships.FindingsThe findings from the study confirmed a positive relationship between DTT implementation and SCT. This study reported that data readiness, perceived values and benefits of DTT, and organizational readiness and leadership support influence DTT readiness and further lead to SCT.Originality/valueThis study contributes to the literature and knowledge by uniquely mapping and validating various interactions between DTT readiness and sustainable SC performance.
Reference138 articles.
1. Steering supply chains from a complex systems perspective;European Journal of Management Studies,2022
2. Analysis of barriers in implementation of digital transformation of supply chain using interpretive structural modelling approach;Journal of Modelling in Management,2020
3. Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm's sustainable performance;Operations Management Research, Operations Management Research,2020
4. Complex adaptive system mechanisms, adaptive management practices, and firm product innovativeness;R&D Management,2014
5. Evolution, emergence, and learning in complex systems;Emergence,2003