The role of Industry 4.0 Technologies on Performance Measurement Systems of Supply chains during Global Pandemics: An Interval-Valued-Intuitionistic-Hesitant-Fuzzy Approach

This paper investigates Supply Chain (SC) Performance Measurement Systems (PMSs)(SCPMSs) that are suitable and applicable to evaluate SC performance during unexpected events such as global pandemics. Furthermore, the contribution of Industry 4.0 Disruptive Technologies (IDTs) to implement SCPMSs during such Black Swan events is investigated in this study. The research methodology is based upon a novel qualitative and quantitative mixed method. A Systematic Literature Review (SLR) was initially employed to identify two complete lists of SCPMSs and IDTs. Then, a novel Interval-Valued-Intuitionistic-Hesitant-Fuzzy (IVIHF)- Delphi method was firstly developed in this paper to screen the extracted SCPMSs. Afterward, the PEARL indicator of the Hanlon method was innovatively applied to prioritise the identified IDTs for each finalised SCPMS. Two high-score SCPMSs including the SC operations reference model (SCOR) and sustainable SCPMS were recommended to improve measuring the performance of the pharmaceutical SC of emerging economies such as Iran; in which the societal, biological, and economic issues were undeniable, particularly during unexpected events. Employing nine IDTs such as Simulation, Big Data Analytics, Cloud Technologies, etc., would facilitate implementing sustainable SCPMS from distinct perspectives. This is one of the first papers to provide in-depth insights into determining the priority of contribution of IDTs in applying different SCPMSs during global pandemics. Proposing a novel multi-layer mixed-methodology involving SLR, IVIHF-Delphi, and the PEARL indicator of the Hanlon method is another originality offered by this paper