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Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

The purpose of the paper is to examine the role of institutional distance as a determinant of outward foreign direct investment (OFDI) from India. The study combines a nuanced view of institutional distance, with traditional location factors to analyze Indian OFDI flows to developed and emerging economies (EEs) during the period 2009 to 2017.

The paper employs fixed effects panel regression model on an unbalanced panel data set.

The findings suggest that India's OFDI is undeterred by the isomorphic pressures caused by regulatory and normative institutional distance, but cognitive institutional distance acts as a deterrent in developed economies. Indian MNEs engage in institutional arbitrage as they simultaneously engage in strategies of institutional escapism and institutional exploitation. The study also finds that emerging economies have emerged as an important destination for strategic asset seeking FDI, in addition to developed economies.

The findings of the study present important implications for policymakers and corporate managers. For policymakers, the study points toward the need for improving the general business environment at home to prevent escapist OFDI and trade enhancement as a tool to overcome cognitive barriers and behavioristic stereotypes. For corporate managers, the study's findings underline the importance of adopting different strategies for dealing with different isomorphic pressures in developed and emerging economies.

The study adds value to the sparse literature using the IBV in the emerging markets context, to supplement and enrich existing theoretical frameworks. It is a pioneering study in its use of institutional distance as an explanatory factor for Indian OFDI and provides evidence of institutional arbitrage.

This study aims to determine the minimum force required to pull out a surface mount component in printed circuit boards (PCBs) during the wave soldering process through both experimental and numerical procedures.

An efficient experimental technique was proposed to determine the minimum force required to pull out a surface mount component in PCBs during the wave soldering process.

The results showed that the pullout force is approximately 0.4 N. Comparing this value with the simulated force exerted by the solder wave on the component (≅ 0.001158 N), it can be concluded that the solder wave does not exert sufficient force to remove a component.

This study provides a deep understanding of the wave soldering process regarding the component pullout, a critical issue that usually occurs in the microelectronics industry during this soldering process. By applying both accurate experimental and numerical approaches, this study showed that more tests are needed to evaluate the main cause of this problem, as well as new insights were provided into the depositing process of glue dots on PCBs.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.