The objective of this study is to investigate the perceptions of practitioners/experts about the prioritisation of healthcare performance measures and their relationship with lean supply chain management (LSCM) practices. The study will also prioritise the drivers and resources required to implement LSCM in a healthcare operations context. The prioritisation is based on the relative weights of various initiatives on a range of performance measures. Twenty-four LSCM initiatives were identified using a comprehensive literature review. Q-sort method was used to divide those initiatives into four categories. Fuzzy AHP was then used to prioritise the four categories based on relative weight of importance of each category on three different performance dimensions. The result shows that continuous improvement is a dominating LSCM initiative in increasing operational and financial performance, while enterprise alignment/integration is a dominating initiative in enhancing organisational image and operational performance. However, lack of homogeneity among LSCM initiatives suggests that there is a need for careful consideration when implementing them in healthcare organisations. Furthermore, customer needs and the influence of competitor’s actions are the most important drivers to encourage hospitals to adopt an LSCM strategy. This is one of the first studies to examine the prioritisation and ranking of LSCM constructs on performance within the context of the healthcare industry.

Purpose: This paper proposes the adoption of Industry 4.0 (I4) technologies and lean techniques for improving operational performance in the healthcare sector.Design/methodology/approach: The research adopted a systematic literature review and feedback of healthcare professionals to identify the inefficiencies in the current healthcare system. A questionnaire was used to get feedback from the patients and the hospital staff about the current practices and issues, and the expected impact of technology on existing practices. Data were analysed using descriptive statistics, correlation analysis and multiple regression analysis.Findings: The results indicate that I4 technologies lead to the improvement of the operational performance, and the perceptions about I4 technologies are made through the pre-medical diagnosis. However, a weak correlation between lean practices and healthcare operational performance compared to that of I4 technologies and operational performance indicate that lean practices are not fully implemented in the Sri Lankan healthcare sector to their full potential.Research limitations/implications: This study is limited to two government hospitals, with insights from only the doctors and nurses in Sri Lanka. Furthermore, the study is limited to only selected aspects of I4 technologies (big data, cloud computing and IoT) and lean concepts (value stream mapping and 5S). Therefore, recommendations on the adoption of I4 technologies in the healthcare sector need to be made within the scope of the study investigation.Practical implications: The implementation of I4 technologies needs careful consideration of process improvement as part of the overall plan for achieving the maximum benefits of technology adoption.Originality/value: The findings of the research can be used as a benchmark/guide for other hospitals to explore the adoption of I4 technologies, and how process improvement from lean concepts could influence the overall operational performance.

Organizations in both developed and developing economies are paying great attention to the Industry 4.0 revolution and associated uses of technologies due to its potential benefits to the manufacturing industry. However, there are a limited number of empirical studies due to its early stage of adoption around the world, especially regarding the key technological factors that are necessary. This article addresses this research gap by identifying the factors that enable successful Industry 4.0 technologies adoption in an emerging economy country, grouping them, and ranking the groups based on priorities for adoption. The study adopts a mixed-method research methodology. Q-sort technique and analytic hierarchy process, respectively, were used to group enabling factors and prioritize the groups for Industry 4.0 technologies adoption. Thereafter, semistructured interviews of key stakeholders in the manufacturing sector in Thailand were carried out to validate and support findings from the quantitative analysis. Five industry experts from automotive and electronic parts/components manufacturers were interviewed. The results show that human capital is the most important readiness dimension for Industry 4.0 technologies implementation. Interoperability and data handling were found to be the next in importance. On the contrary, hardware and technology systems, such as data security and technological infrastructure, were identified as the least important of the technology readiness dimensions. These findings provide a different perspective to extant studies that posited that technology-based factors as the most important for Industry 4.0 success.

Purpose: This study investigates the enablers and challenges of digital supply chains (DSCs) adoption and develops a digital supply chain maturity (DSCM) model as a basis for developing guidelines for DSC adoption in the digital transformation journey.Design/methodology/approach: The research involves a systematic literature review (SLR) of Industry 4.0 (I4) adoption in supply chain (SC) practices to identify key enablers and associated maturity levels. The literature search of published articles during the 1997–2020 period and subsequent screening resulted in 64 articles. A DSCM model was developed using the categorization of important enablers and associated levels transitioning from the traditional SC to the DSC ecosystem.Findings: Four broader categories of DSC enablers and challenges were identified from the content analysis of SLR. Digital strategy alongside I4 technologies and human capital were prominent in DSC adoption as I4 technologies and human capital depend on other enablers such as dynamic capabilities (DCs). Lack of infrastructure and financial constraints to implementing I4 were significant challenges in the DSC adoption.Research limitations/implications: The proposed DSCM model provides a holistic view of enablers and maturity levels from traditional SC to DSC adoption. However, the DSCM model needs to be empirically validated and streamlined further using inputs from practitioners.Practical implications: The proposed DSCM model can be used as a framework to guide practitioners in assessing maturity and developing implementation plans for successful DSC adoption.Originality/value: This research introduces a novel DSC maturity model through a holistic view of enablers and maturity levels from traditional SC to DSC adoption.

Purpose: The purpose of this paper is to examine how certain limitations of the current approaches to planning and scheduling of aircraft heavy maintenance can be addressed using a single integrated framework supported by unified data structures.Design/methodology/approach: The “unitary structuring technique”, originally developed within the context of manufacturing planning and control, is further enhanced for aircraft heavy maintenance applications, taking into account the uncertainty associated with condition‐based maintenance. The proposed framework delivers the advanced functionalities required for simultaneous and dynamic forward planning of maintenance operations, as well as finite loading of resources, towards optimising the overall maintenance performance.Findings: Execution of maintenance operations under uncertainty involves materials changes, rectification and re‐assembly. It is shown that re‐scheduling of materials (spare‐parts), resources and operations can be taken care of by simultaneous and dynamic forward planning of materials and operations with finite loading of resources, using the integrated framework.Research limitations/implications: As part of adopting the proposed framework in practice, it needs to be guided by an overall methodology appropriate for application‐specific contexts.Practical implications: The potential direct benefits of adopting the proposed framework include on‐time project completion, reduced inventory levels of spare‐parts and reduced overtime costs.Originality/value: Existing approaches to aircraft maintenance planning and scheduling are limited in their capacity to deal with contingencies arising out of inspections carried out during the execution phase of large maintenance projects. The proposed integrated approach is, capable of handling uncertainty associated with condition‐based maintenance, due to the added functionalities referred to above.