Evaluation of key indicators affecting the performance of healthcare supply chain agility

ABSTRACT

In this study, essential factors of healthcare supply chain have been investigated. Factors were selected through an integrated approach, in which experts played a pivotal and decisive role in each phase. A novel hybrid methodology comprising Best-Worst-Method (BWM) and Interpretive structural modelling (ISM) is employed. Best-Worst-Method is utilised to determine the different weights of healthcare supply chain agility factors, and ISM and MICMAC analysis are utilising to examine interrelations among final selected factors. A case study in local pharmacies examined the effectiveness of the proposed hybrid model in the real world. The application of the hybrid BWM-ISM method demonstrates that ‘Proper IT infrastructure’ and ‘Strategic planning’ are the most significant factors, respectively. They will facilitate local pharmacies to accomplish agility practices in the healthcare supply chain thus, increasing effectiveness and adaptability to a variety of situations. This research helps public healthcare decision-makers by changing the organisation’s response to critical situations and unexpected events by implementing corrective measures within local pharmacies.

KEYWORDS:

• Agility
• healthcare supply chain
• Best-Worst method
• interpretive structural modelling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mohammad Reza Rouhani-Tazangi

Mohammad Reza Rouhani-Tazangi received his MSc in Industrial Engineering at the Department of Industrial Engineering, University of Tehran, Iran. His research interests include logistics, sustainability, healthcare system evaluation, and data analysis.

Mohammad Amin Khoei

Mohammad Amin Khoei has graduated as an MSc student in project management from the university of Tehran. Also, in the year 2019, he finished his bachelor in industrial engineering at the university of Tehran. His main research interests are focused on Operations Management, Supply Chain Management, Business Analytics, And data-driven Decision-making.

Dragan Pamucar

Dragan Pamucar is an Associate Professor at University of Belgrade, Faculty of Organizational Sciences. Dr. Pamucar received a PhD in Applied Mathematics with specialisation of Multi-criteria modelling and soft computing techniques, from University of Defence in Belgrade, Serbia in 2013 and an MSc degree from the Faculty of Transport and Traffic Engineering in Belgrade, 2009. His research interest are in the field of Computational Intelligence, Multi-criteria decision making problems, Neuro-fuzzy systems, fuzzy, rough and intuitionistic fuzzy set theory, neutrosophic theory. Application areas include wide range of logistics and engineering problems. According to Scopus and Stanford University, he is among the World top 2% of scientists as of 2020. According to WoS and Clarivate, he is among top 1% of highly cited researchers.

Benyamin Feghhi

Benyamin Feghhi achieved his MSc in Industrial Engineering from the Department of Industrial Engineering, University of Tehran, Iran. He graduated from the University of Tehran with a BSc in 2019. His research interests include transportation and logistics, sustainability, location-routing, cross-docking, and multi-objective optimisation.