Green Supply Chain Management practices and impact on firm performance: The moderating effect of collaborative capability

https://doi.org/10.1016/j.cie.2022.108389

A bi-objective optimization model for the medical supplies' simultaneous pickup and delivery with drones

Sharing economic responsibility: Assessing end user's willingness to support E-waste reverse logistics for circular economy

Inclusive purchasing and supply chain resilience capabilities: Lessons for social sustainability

Applications of drone technology are gradually becoming widespread all over the world. Remote medical support, commodities transportation within due time, live-action movies, cinematography, distant communication support, and many other provisions are provided by drones right now. However, the selection of drones for a particular job is quite sensitive and not all drones are feasible for any job. In this case, we need to scientifically assess and confirm drone performance in logistics and transportation. In this study, we present a Bayesian Network (BN) approach to predict the overall performance of drone technology through four prime criteria (factors), namely: physical specification, technical responses, functional performance, and economic cost. To that end, we have developed a Bayesian network approach to portray the causal relationships between the various factors that affect drone selection based on their performance, and subsequently, predicted the posterior probability of drone performance conditioned upon the aforementioned salient criteria. To evaluate the model further, a number of analyses, such as Bayesian inference algorithm (belief propagation) and sensitivity analyses have been carried out. The results show that when the physical specification criteria are met, the economic factor is most imperious to the overall performance of the drone, followed by functional performance and technical responsiveness. This research would invoke richer dialogue for the researchers and practitioners to select and use suitable drones and develop subsequent policies for different transportation scenarios under uncertainty. 

Assessing the performance of unmanned aerial vehicle for logistics and transportation leveraging the Bayesian network approach

Aerial Bots in the Supply Chain: A New Ally to Combat COVID-19

Purpose: The disruption caused by COVID-19 exhorts to reiterate the role of operations and supply chain management (OSCM) in achieving social sustainability. Therefore, the present study aims to develop a conceptual understanding of the OSCM ecosystem's role in enabling the world to accelerate towards social sustainability. Design/methodology/approach: The study uses the integrative review method to achieve the stated objectives. The study first identifies the societal disruptions caused by COVID-19. Then based on dynamic capabilities (DC) theory, stakeholder theory and real-life examples, the study puts forward the stakeholder dynamic capabilities (SDC) view as an approach to overcome these social challenges. Findings: Taking the SDC view, the study identified ten social challenges aggravated by the COVID-19. Response actions for OSCM have been proposed to mitigate these challenges. Research limitations/implications: The pandemic has brought new challenges to the OSCM to achieve social sustainability. Therefore, the study's proposed response actions aim to assist OSCM managers in leveraging their expertise to do good for society and create a better world. Moreover, the study also provides avenues for future research on the topic. Originality/value: Based on the SDC view, the study attempts to conceptualise social sustainability for OSCM during a pandemic. The SDC view helps capture internal and external social challenges emerging due to COVID-19 and utilise firms' capabilities to overcome these challenges. © 2021, Emerald Publishing Limited.

The role of operations and supply chains in mitigating social disruptions caused by COVID-19: a stakeholder dynamic capabilities view

Previous pandemics in 1918, 1957, 1968, and the ongoing COVID-19 pandemic have provided sufficient evidence of health concerns caused by influenza pandemics. The existing health care system is overwhelmed by the surging demand of susceptible and infected individuals due to the COVID-19 outbreak. It is crucial to identify and isolate infected individuals to prevent pandemic spread. Thus, a mixed-integer linear programming model is proposed in this study for the location-allocation of health care facility networks (i.e., temporary testing laboratories). The objective of this study is to ensure that test samples from various geographical locations reach testing laboratories as soon as possible and at minimum cost to ensure timely testing. Hence, the proposed model has two objectives: (i) minimization of the total cost and (ii) minimization of the maximum travel time from a patient node to a testing facility. Furthermore, to prevent capacity underutilization, the capacity of temporary testing laboratories is tailored in the model. A case study in Maharashtra, India, is used to demonstrate the real-life applicability of the proposed model. The study results has interesting implications for decision- and policy-makers.

/pdf/a-location-allocation-model-for-influenza-pandemic-outbreaks-3vmvd36fm7.pdf

A location-allocation model for influenza pandemic outbreaks: A case study in India

/pdf/social-media-in-operations-and-supply-chain-management-a-2nr4yn1yen.pdf

Social Media in Operations and Supply Chain Management: A Systematic Literature Review to Explore the Future

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Papers

Aerial Bots in the Supply Chain: A New Ally to Combat COVID-19

The role of operations and supply chains in mitigating social disruptions caused by COVID-19: a stakeholder dynamic capabilities view

A location-allocation model for influenza pandemic outbreaks: A case study in India

Social Media in Operations and Supply Chain Management: A Systematic Literature Review to Explore the Future