Systems thinking (ST) is an interdisciplinary domain that offers different ways to better understand the behavior and structure of a complex system. Over the past decades, several publications can be identified in academic literature, focusing on different aspects of systems thinking. However, two critical questions are not properly addressed in the extant body of ST literature: (i) How to conduct the content analysis exclusively to derive the prominent statistics (i.e., influential journals, authors, affiliated organizations and countries) pertaining to the domain of ST? (ii) How to get better insights regarding the current and emerging trends that may evolve over time based on the existing body of ST literature? To address these gaps, the aim of this research study is to provide a comprehensive insight into the domain of systems thinking through bibliometric and network analysis. Beginning with over 6000 accumulated publications, the analysis narrowed down to 626 prominent articles with proven influence published over the past three decades. Leveraging rigorous bibliometric tools analysis, this research unveils the influential authors, leading journals and top contributing organizations and countries germane to the domain of systems thinking. In addition, citation, co-citation and page rank analysis used to rank top influential articles in the area of systems thinking. Finally, with the aid of the network analysis, key clusters in the existing literature are identified based on the research areas of systems thinking. The findings of this research will serve as a bluebook for practitioners and scholars to conduct future research within systems thinking context.

The purpose of this research paper is to implement a machine learning model with the integration of the supply chain occupational reference (SCOR) model to develop an artificial intelligence-based system for resilient and sustainable supplier selection for a pharmaceutical company. Initially, the SCOR 4.0 model with the integration of Best Worst Method (BWM) has been used to develop the framework of customer satisfaction and to identify the critical elements of the suppliers. Later, the gradient boosting machine learning model has been implemented to classify the supplier as well as rank the suppliers from best to worst based on the acceptability score. The result shows that the gradient boosting algorithm performs well as a classifier, where the supplier with the most acceptability score represents the best supplier and the supplier with the least acceptability score represents the worst supplier. This study contributes to our understanding of how and when integrated SCOR and machine learning models can help improve supplier selection.

Purpose: In recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.Design/methodology/approach: The authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.Findings: The findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.Research limitations/implications: The methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.Practical implications: This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.Social implications: The proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.Originality/value: This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

The dairy industry plays a significant role in the global food system, providing essential nutrients for human consumption and creating rural employment. A small-scale dairy can assist a family in maintaining their livelihoods in Bangladesh. However, it is also associated with various environmental and social impacts, making it crucial for achieving sustainability. The triple bottom line of sustainability intends to achieve sustainability through improving productivity, implementing sustainable practices, and incorporating waste management. The dairy industry can continue to provide nutritious diets, ensuring sustainability practices. This research is a follow-up paper of Nasir et al. to find better sustainable results. It considers the triple bottom line of sustainability theory to improve the farm environment by reducing waste, managing resources efficiently, and promoting environmentally friendly practices. This paper is a case study on a dairy farm of 400 cattle in Bangladesh. The system dynamics method and simulation modeling were employed to draw dairy supply chain networks and examine the existing dataset to find better utilization of the dairy waste produced on the farm. Consequently, the simulation model incorporates waste management and value addition concepts to find better resource exploitation for gaining sustainable outcomes. Finally, this paper summarizes the simulation outcomes and articulates possible extensions for achieving further economic, social, and environmental benefits for the industry and surrounding community.