Data driven predictive maintenance for large-scale asset-heavy process industries in Singapore

Abstract

PurposeThis paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries.Design/methodology/approachThe TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model.FindingsThis research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention.Practical implicationsThis paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers.Originality/valueThis research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders.