Risk is inherent in all parts of life and brings consequences, but when it specifically emerges in supply chains, it is susceptible. Therefore, this study aims at identifying and assessing supply chain risks and developing criteria for managing these risks. Supply chain (SC) risks consist of complex, uncertain, and vague information, but risk assessment techniques in the literature have been unable to handle complexity, uncertainty, and vagueness. Therefore, this study presents a holistic approach to supply chain risk management. In this paper, neutrosophic (N) theory is merged with the analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) to deal with complexity, uncertainty, and vagueness. Then the proposed methodology is practically implemented through a case study on the automotive industry. SC resilience, SC agility, and SC robustness were selected as criteria for managing supply chain risks and analyzed using N-AHP. Furthermore, seventeen risks were identified and assessed by using N-TOPSIS. Results suggest supply chain resilience is the most important criterion for managing supply chain risks. Moreover, supplier delivery delays, supplier quality problems, supplier communication failures, and forecasting errors are the most vulnerable risks that occur in supply chains of the automotive industry in Pakistan.

This research proposes a generic smart cloud-based system in order to accommodate multiple scenarios where agriculture farms using Internet of Things (IoTs) need to be monitored remotely. The real-time and stored data are analyzed by specialists and farmers. The cloud acts as a central digital data store where information is collected from diverse sources in huge volumes and variety, such as audio, video, image, text, and digital maps. Artificial Intelligence (AI) based machine learning models such as Support Vector Machine (SVM), which is one of many classification types, are used to accurately classify the data. The classified data are assigned to the virtual machines where these data are processed and finally available to the end-users via underlying datacenters. This processed form of digital information is then used by the farmers to improve their farming skills and to update them as pre-disaster recovery for smart agri-food. Furthermore, it will provide general and specific information about international markets relating to their crops. This proposed system discovers the feasibility of the developed digital agri-farm using IoT-based cloud and provides solutions to problems. Overall, the approach works well and achieved performance efficiency in terms of execution time by 14%, throughput time by 5%, overhead time by 9%, and energy efficiency by 13.2% in the presence of competing smart farming baselines.

In today’s emerging environment sustainable supply chain risks play a vital role in firms’ performance more than ever, because risks tend to disrupt sustainable operations, which ultimately reduces a firm’s performance, but these risks can be managed through supply chain integration practices, which leads to higher firms’ performance. Therefore, this paper examines the relationship between sustainable supply chain risks, supply chain integration, and firm’s financial performance. This study employs 296 survey observations along with financial data of published annual statements to estimate the quantitative causal-effects of three dimensions of sustainable supply chain risks on supply chain integration and financial performance. The findings of the study suggest that sustainable internal business process risks, sustainable supply risks, and sustainable demand risks have a negative relationship with supply chain integration. Furthermore, results of the study explored that all the three supply chain integration practices have a positive impact on firms’ financial performance, which suggests that implementing supply chain integration practices reduces the effect of supply chain risks and increases the firm’s performance.

Purpose: This paper aims to examine the factors that drive the exporter’s influence toward halal meat supply chain adoption by encompassing the diffusion of innovation (DOI) theory in the context of Pakistan. Design/methodology/approach: Using DOI as an underpinning theory, this study evaluates halal supply chain adoption by collecting data from the members of associations from Pakistan. A total of 258 useable responses were received, and PLS-SEM was adopted using SmartPLS. Findings: The exporter’s adoption of the halal supply chain is determined not only by perceived relative advantage and perceived compatibility but also by perceived complexity, religious beliefs and awareness. Research limitations/implications: Though this study has practical and managerial implications, it has few limitations. Further studies need to be conducted in other contexts as well with a larger population. Originality/value: There are limited studies that have tested DOI theory in the context of the halal meat supply chain in Pakistan. Therefore, the author extends the diffusion theory of innovation in the current work. Further, this paper will be a helpful reference guide for academicians, practitioners and researchers.