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This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management.

The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method.

The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies.

This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications.

Increasing complexity in construction projects evokes interest in application of innovative digital technologies in construction. Digital twins (DT), which bring these innovative technologies together, have strong interactions with lean construction (LC). To highlight the collaborative nature of DT and LC, the paper explores the interactions between LC and DT and assesses benefits, costs, opportunities and risks (BOCR) of DT in LC to analyze significant obstacles and enablers in DT adoption in LC.

BOCR approach comprehensively considers both the positive and the negative attributes of a problem. At the first step, BOCR criteria for DT are identified through literature review and expert opinions, at the second step dependencies among BOCR criteria for DT in LC are determined by neutrosophic analytic hierarchy process (AHP), through a questionnaire survey. Integrating BOCR into neutrosophic AHP enables achieving more meaningful preference scores.

Cost of skilled workforce is the most important factor and opportunity to reduce waste is the second most important factor in adoption of DT in LC. The results were analyzed to rank the BOCR of adoption of DT in LC.

This study, in a novel way, performs BOCR analysis through neutrosophic AHP to reflect experts' judgments more effectively by neutrosophic AHP's better handling of vagueness and uncertainty. The paper provides a model to better understand the significant factors that influence adoption of DT in LC.