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Exploration and Prioritization of Just in Time Enablers for Sustainable Healthcare: An Integrated GRA-Fuzzy TOPSIS ApplicationSingh Kaswan, Mahender; Rathi, Rajeev; Singh, Mahipal; Garza-Reyes, Jose Arturo; Antony, Jiju; Lovely Professional University, Phagwara, India; University of Derby; Heriot-Watt University, Edinburgh (Emerald, 2021-06-21)The increased healthcare costs, improved service quality, and sustainability-oriented customer demand have forced the healthcare sector to relook their current process. The present work deals with the identification, analysis, and prioritization of Just in Time (JIT) enablers in the healthcare sector. JIT leads to waste reduction, improves productivity, and provides high quality patient care. The practical implementation of JIT depends on vital factors known as enablers. The enablers have been found through the comprehensive literature review and prioritized using responses from different healthcare facilities of national capital region of India. Grey Relational Analysis (GRA) has been used in the present study to rank enablers and ranks were further validated using fuzzy TOPSIS and sensitivity analysis. It has been found that top management support, teamwork, and real-time information sharing are the most significant enablers of JIT in healthcare with grey relational grades 0.956, 0.832, and 0.718, respectively. The corresponding closeness coefficients of the fuzzy TOPSIS for the enablers were found as 0.875, 0.802, and 0.688, respectively. The findings of the present research work will facilitate the healthcare organizations to implement a comprehensive JIT approach that further leads to improved patient care at low cost. The present study is unique in terms of the exploration of the readiness measures or enablers of JIT using GRA and fuzzy TOPSIS. The findings of the present research work will facilitate the healthcare organizations to optimize their resources for better patient care.
Lean Six Sigma project selection in a manufacturing environment using hybrid methodology based on intuitionistic fuzzy MADM approachSingh, Mahipal; Rathi, Rajeev; Antony, Jiju; Garza-Reyes, Jose Arturo; Lovely Professional University, Phagwara, India; Heriot-Watt University, Edinburgh; University of Derby (IEEE, 2021-02-08)Project selection has a critical role in the successful execution of the lean six sigma (LSS) program in any industry. The poor selection of LSS projects leads to limited results and diminishes the credibility of LSS initiatives. For this reason, in this article, we propose a method for the assessment and effective selection of LSS projects. Intuitionistic fuzzy sets based on the weighted average were adopted for aggregating individual suggestions of decision makers. The weights of selection criteria were computed using entropy measures and the available projects are prioritized using the multi-attribute decision making approach, i.e., modified TOPSIS and VIKOR. The proposed methodology is validated through a case example of the LSS project selection in a manufacturing organization. The results of the case study reveal that out of eight LSS projects, the assembly section (A8) is the best LSS project. A8 is the ideal LSS project for swift gains and manufacturing sustainability. The robustness and reliability of the obtained results are checked through a sensitivity analysis. The proposed methodology will help manufacturing organizations in the selection of the best opportunities among complex situations, results in sustainable development. The engineering managers and LSS consultants can also adopt the proposed methodology for LSS project selections.