Prescriptive analytics applications in sustainable operations research: conceptual framework and future research challenges.

In the broad sphere of Analytics, prescriptive analytics is one of the emerging areas of interest for both academicians and practitioners. As prescriptive analytics has transitioned from its inception to an emerging topic, there is a need to review existing literature in order to ascertain development in this area. There are a very few reviews in the related field but not specifically on the applications of prescriptive analytics in sustainable operations research using content analysis. To address this gap, we performed a review of 147 articles published in peer-reviewed academic journals from 2010 to August 2021. Using content analysis, we have identified the five emerging research themes. Through this study, we aim to contribute to the literature on prescriptive analytics by identifying and proposing emerging research themes and future research directions. Based on our literature review, we propose a conceptual framework for studying the impacts of the adoption of prescriptive analytics and its impact on sustainable supply chain resilience, sustainable supply chain performance and competitive advantage. Finally, the paper acknowledges the managerial implications, theoretical contribution and the limitations of this study.

Selection of suitable biomass conservation process techniques: a versatile approach to normal wiggly interval-valued hesitant fuzzy set using multi-criteria decision making.

A country that relies on developing industrialization and GDP requires a lot of energy. Biomass is emerging as one of the possible renewable energy resources that may be used to generate energy. Through the proper channels, such as chemical, biochemical, and thermochemical processes, it can be turned into electricity. In the context of India, the potential sources of biomass can be broken down into agricultural waste, tanning waste, sewage, vegetable waste, food, meat waste, and liquor waste. Each form of biomass energy so extracted has advantages and downsides, so determining which one is best is crucial to reaping the most benefits. The selection of biomass conversion methods is especially significant since it requires a careful study of multiple factors, which can be aided by fuzzy multi-criteria decision-making (MCDM) models. This paper proposes the normal wiggly interval-valued hesitant fuzzy-based decision-making trial and evaluation laboratory model (DEMATEL) and the Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE) for assessing the problem of determining a workable biomass production technique. The proposed framework is used to assess the production processes under consideration based on parameters such as fuel cost, technical cost, environmental safety, and CO2 emission levels. Bioethanol has been developed as a viable industrial option due to its low carbon footprint and environmental viability. Furthermore, the superiority of the suggested model is demonstrated by comparing the results to other current methodologies. According to comparative study, the suggested framework might be developed to handle complex scenarios with many variables.

Using network data envelopment analysis to assess the sustainability and resilience of healthcare supply chains in response to the COVID-19 pandemic.

The widespread outbreak of a new Coronavirus (COVID-19) strain has reminded the world of the destructive effects of pandemic and epidemic diseases. Pandemic outbreaks such as COVID-19 are considered a type of risk to supply chains (SCs) affecting SC performance. Healthcare SC performance can be assessed using advanced Management Science (MS) and Operations Research (OR) approaches to improve the efficiency of existing healthcare systems when confronted by pandemic outbreaks such as COVID-19 and Influenza. This paper intends to develop a novel network range directional measure (RDM) approach for evaluating the sustainability and resilience of healthcare SCs in response to the COVID-19 pandemic outbreak. First, we propose a non-radial network RDM method in the presence of negative data. Then, the model is extended to deal with the different types of data such as ratio, integer, undesirable, and zero in efficiency measurement of sustainable and resilient healthcare SCs. To mitigate conditions of uncertainty in performance evaluation results, we use chance-constrained programming (CCP) for the developed model. The proposed approach suggests how to improve the efficiency of healthcare SCs. We present a case study, along with managerial implications, demonstrating the applicability and usefulness of the proposed model. The results show how well our proposed model can assess the sustainability and resilience of healthcare supply chains in the presence of dissimilar types of data and how, under different conditions, the efficiency of decision-making units (DMUs) changes.

Examining the barriers to operationalization of humanitarian supply chains: lessons learned from COVID-19 crisis.

Humanitarian supply chains (HSC) have vital significance in mitigating different disruptive supply chain risks caused due to natural or man-made activities such as tsunami, earthquakes, flooding, warfare, or the recent COVID-19 pandemic. Each kind of disaster poses a unique set of challenges to the operationalization of HSC. This study attempts to determine the critical barriers to the operationalization of HSC in India during the COVID-19 pandemic. Initially, we determined and validated 10 critical barriers to HSC operationalization through a Delphi method. Further, we analyzed the barriers by computing the driving and dependence power of each barrier to determine the most critical ones. To do so, we coined a distinct form of interpretive structural modeling (ISM) by amalgamating it with the neutrosophic approach, i.e. Neutrosophic ISM. The findings indicate, "lack of Government subsidies and support, lack of skilled and experienced rescuers, and lack of technology usage" are the most critical barriers that influence the streamline operations of HSC during the COVID-19 outbreak, unlike other disruptions. This is the first-of-its-kind research work that has identified and analyzed the critical barriers to HSC operationalization during COVID-19 in the Indian context. The results and recommendations of the study can aid policymakers and HSC professionals in formulating suitable strategies for successful HSC operations. Supplementary Information: The online version contains supplementary material available at 10.1007/s10479-022-04752-x.

Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries.

There has been an increased interest among scholars to investigate supply chain resilience (SCRes) in manufacturing and service operations during emerging situations. Grounded in the SCRes theory, this study provides insights into the impact of the COVID-19 outbreak on the automobile and airline supply chain. Both the short and long-term response strategies adopted by the two supply chains are assessed, using a combination of qualitative and quantitative techniques in three distinct phases. In phase one, we use a sequential mixed-method for resilience evaluation, integrating Time-to-Recovery (TTR) and Financial Impact (FI) analysis. In phase two, we conduct an empirical survey involving 145 firms to evaluate the short-term SCRes response strategies. In the third phase, we conduct semi-structured interviews with supply chain executives both from the automobile and airline industries to understand the long-term SCRes response strategies. Our findings indicate that: (i) the automobile industry perceived that the best strategies to mitigate risks related to COVID-19, were to develop localized supply sources and use advanced industry 4.0 (I4.0) technologies. (ii) The airline industry on the other hand, perceived that the immediate need was to get ready for business continuity challenges posed by COVID-19, by defining their operations both at the airports and within the flights. (iii) Importantly, both the sectors perceived Big Data Analytics (BDA) to play a significant role by providing real-time information on various supply chain activities to overcome the challenges posed by COVID-19. (iv) Cooperation among supply chain stakeholders is perceived, as needed to overcome the challenges of the pandemic, and to accelerate the use of digital technologies.