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• Antecedents to big data analytics proficiency are investigated. • Implications for quality and cost performance are analysed. • The contingency of environmental competitiveness is considered. While big data analytics (BDA) carries great potential for supply chain management (SCM), actual advanced applications of these approaches are still limited in practice. Within this context, we seek to identify what can foster an SCM professional's proficient use of BDA, and define BDA proficiency as an individual's ability to effectively use BDA. Grounding our research in the theoretical framework of technology acceptance, we consider perceived ease of use, perceived usefulness, attitude toward, and behavioral intention to use BDA as potential influential antecedents. Using primary survey data from 212 SCM professionals, we find that all but one of these hypothesized relationships are confirmed among the sample. This offers valuable insight on how to drive the intention of SCM professionals to use BDA, which serves as a foundation to become proficient in it. In addition, we hypothesize about the impact of BDA proficiency on generating business value in terms of both quality and cost performance, two important outcome variables in SCM research. While the impact on cost performance is confirmed, the impact on quality performance is non-significant, bringing greater clarity to the differentiated performance impacts of BDA proficiency. When further scrutinizing these links to performance under the moderating effect of environmental competitiveness, we not only find the impact of BDA proficiency on cost performance to be greater under higher levels of environmental competitiveness, but also that under such greater levels the link from BDA proficiency to quality performance becomes significant. Overall, our research provides insight not only into what can foster an SCM professional's BDA proficiency, but also into its impact on performance under various levels of environmental competitiveness. [ABSTRACT FROM AUTHOR]

• Best strategies for a large-scale COVID-19 vaccine distribution are developed. • Storage and transportation requirements, as well as costs are considered. • An equitable distribution is considered due to limited vaccine availability. • Best location and allocation decisions for cold storage facilities are provided. • The model is evaluated in a real-world case study. While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities. [ABSTRACT FROM AUTHOR]

• We investigate a location-allocation problem for convalescent plasma banks. • Optimal locations are determined, then collection facilities are allocated. • A MILP model considering transportation time and cost is solved via CPLEX. • Results are validated by a comparison study using NSGA-II and NSGA-III. • The model is implemented within the context of India and results are presented. With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers. [ABSTRACT FROM AUTHOR]

• The role of operations and SCM in managing epidemics and pandemics is reviewed. • Five research areas are identified, providing an integrative view of the field. • Ten future research directions are developed. • A starting point for researchers to investigate this domain is provided. Epidemics have been posing significant challenges to health, existence, and continuity. From the emergence of an outbreak to its elimination, managing an epidemic/pandemic entails many operations and supply chain management decisions that can contribute to a lessening of its impact. With these decisions, epidemic-/pandemic-imposed challenges related to forecasting, planning, supply, manufacturing, storage, and transportation can be addressed in an effort to curtail and end the epidemic/pandemic. We have witnessed these disruptions first-hand during the COVID-19 pandemic, which has had a destructive effect on many well-established supply chains, threatening the existence of firms. The role of operations and supply chain management is thus pivotal for navigating epidemics/pandemics. Against this background, we present a systematic literature review on the role of operations and supply chain management during epidemics and pandemics, illustrating its potential and calling for future research. Leveraging bibliographic coupling analysis, we identify major research areas and contributions that serve as a foundation to propel these domains forward. We further critically review these research areas, identifying multiple themes of which many have been perennially relevant, while others have come to the fore only recently due to the COVID-19 pandemic. Our review provides an integrative view of the field, concurrently advancing theory, and offering ten distinct future research directions. Overall, this paper is meant to serve as a starting point for researchers in operations and supply chain management aiming to investigate this increasingly important domain. [ABSTRACT FROM AUTHOR]

• We compare a seller's disaster payment period policies to boost demand. • We consider Advance, Cash, Credit or Disaster Grace Period (ACCD) payment options. • The trade-off between default/disaster risk and demand is investigated. • We identify the optimum replenishment cycle and payments period for a seller's profit. Sellers can offer various payment schemes for buyers to boost demand, with the advance, cash, and credit payment policies being popular approaches. To provide further insight into the dynamics of these policies, we consider them in addition to a Disaster Grace Period payment option, which has become increasingly prevalent. The seller's motivation for offering Advance, Cash, Credit or Disaster Grace Period (ACCD) payment options include the desire for a stronger relationship to the buyer, a smoother cash flow throughout the supply chain, and a potential increase in demand. In addition, the disaster grace period payment scheme can help in better navigating worldwide emergencies, such as pandemics, but also natural emergencies like severe storms, floods, tornadoes, and landslides, which have been happening with an increasing frequency over the last decades. However, the increase in the payment period increases the default/disaster risk, rendering a trade-off between risk and demand, making these crucial parameters in influencing the seller's profit. Within this context, we aim to find the optimum replenishment cycle and payments period for a seller to increase their total profit. To do so, we perform numerical analysis to examine the impact of the demand coefficient, interest rate, default risk, disaster risk, and the disaster demand coefficient on profit. The results offer managerial insights on which payment option to choose in a particular setting to maximize profit. We for example find that sellers should offer a disaster grace period when the disaster demand increases and the risk of a disaster decreases. [ABSTRACT FROM AUTHOR]