# Evaluating and Ranking SCPMS Enablers Using ISM and SWARA
## 1 Introduction  
A performance measure (PM) is a summing up of a process or activity in measurable and comparable terms of efficiency or effectiveness. It also leads to an increase in SC competitiveness and sustainability improvement [1]. Generally, performance measures are enabled through customized measurable metrics that can assess the process or activity for its performance measurement. In sustainable supply chain management, performance assessment is viewed as a crucial component of competitive strategy to boost organizational efficiency and profitability (SCM) [2]. Recently, organizational performance metrics and performance measurement-based research domains have attracted researchers and practitioners [3]. The supply chain is changing because of the Internet of things (IoT), cyber-physical systems (CPSs), cloud computing, big data analytics, radiofrequency identification (RFID), and other Wi-Fi-enabled technologies. 4.0 [4]. The various data-capturing and analysis processes in various domains of logistics 4.0 and warehouse 4.0 in real time help visibility, transparency, and leagility. The efficient component of product tracking and tracing helps in improving information and communication effectiveness during production, storage, sales, transportation, etc. [5]. Considering the constant revolutionary developments in the supply chain, intelligent supply chain performance measurement is the need of I4.0 [6]. The Fourth Industrial Revolution, often known as the digitization of industries, includes CPSs and IoT [7]. Although there are numerous advantages to the digital industrial revolution, many firms find it challenging to implement [8]. Cloud-based supply chain management (C-SCM) helps with supply chain responsiveness (SCR) by responding to market requirements in a timely and effective manner [9]. SCPM is crucial for determining success, satisfying client needs, and having a clear grasp of the process. It may help further with waste detection, process improvement, and process bottleneck investigation, accomplishing progress, progress monitoring, shared communication, and prompt decision making [10]. Performance evaluation is important for coming up with strategies, getting the word out about them, and making diagnostic control systems by measuring the effect [11]. A study investigating relationships among 14 key variables of SCPMS using ISM and MICMAC was carried out [12]. Similar to this, research was conducted utilizing ISM and MICMAC to examine the interactions among the 14 primary barriers of SC [13]. A study identifying and ranking eight performance measures of automotive SC was undertaken [14]. An investigation of the dependent and independent obstacles of SC in the Indian automobile industry was conducted using ISM and fuzzy MICMAC, wherein they found a 'lack of awareness about PMS' to be a significant barrier [15]. Past studies were focused on the key enablers and barriers before supply chain 4.0, which necessitated the present study considering Wi-Fi-enabled technologies and digital industrial transformation. The primary research questions of the current study are based on the aforementioned: (i) What are the key enablers that make intelligent SCPMS possible? (ii) What kind of relationship exists among such enablers? (iii) How can these enablers be classified into various categories consisting of dependent, independent, autonomous, and linkage? When implementing intelligent SCPMS, there are important enablers that influence it and important barriers that present resistance [16]. ISM- and MICMAC-based studies are more commonly used for relationship modeling of enablers, barriers, and critical success factors (CSFs) [17; 18; 19]. On the basis of the foregoing assumptions, the current study will aid in the development of an intelligent SCPMS that can manage difficulties linked to supply chain 4.0 in its measurement and control. The enablers are ranked in order of importance using stepwise weight assessment ratio analysis (SWARA).