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As companies look to differentiate themselves with the help of their suppliers, the need for robust and sophisticated purchasing and supply management (PSM) processes that allow for informed decision-making becomes increasingly important. These processes often rely on sophisticated data analytics to inform the design of a company's category management strategy, such as its supply network design, its supplier relationship management, and its supplier performance management. Therefore, data analytics skills are crucial for PSM professionals. To foster these skills, we developed an innovative approach for teaching students how to use data analytics tools and techniques in PSM through the use of a teaching case called "Savingtools." This case, developed in collaboration with a company undergoing a PSM transformation, illustrates the value of data-driven category management in PSM. The case further demonstrates the principles and tools related to PSM data management, spend analysis, and classification, and allows for in-depth data analysis, including visualizations. Our approach has been shown to effectively enhance student learning and comprehension, and we believe that it prepares future supply chain leaders while advancing PSM pedagogy. [ABSTRACT FROM AUTHOR]

This article takes a public policy perspective and captures the implications of sectoral logistical capabilities for export competitiveness to obtain directives for developmental interventions in the logistics sector. We specifically develop a logistical policy intervention framework that links trade logistics performance and exports at the country-level with the objective to identify strategies for a nation's logistics policy formulation. Theoretically, the framework relies on the “macro-institutional view of the logistics systems environment,” which is an emerging global value chain perspective to model global trade scenarios. This perspective accounts for the influence of diverse operational capabilities at a sectoral scale, multiple institutions, and socio-economic environments in governing the performance of a nation's logistics systems in global trade. Methodologically, a rigorous nonlinear regression modeling approach highlights nonlinear discontinuities associated with the impact of logistical policy interventions fostering sectoral logistical capabilities (for example, developing logistics infrastructure or improving the state of logistics service quality in the nation) to boost the export viability of a country in the global economy. The results also highlight critical environmental bottlenecks that govern the efficacy of such interventions. This article pioneers to mainstream the public policy underpinning of logistics systems, and via this contextualization, it contributes to the agenda of mastering policy challenges in engineering and technology management. [ABSTRACT FROM AUTHOR]

This article investigates the relationship between intellectual capital, supply chain learning, and supply chain adaptability. Specifically, relying on organizational learning theory, a research model is proposed linking intellectual capital (consisting of the trichotomy of human, organizational, and social capital), supply chain learning (consisting of supplier learning and customer learning) and supply chain adaptability. We scrutinize these relationships in two culturally-diverse contexts, China and USA, and develop hypotheses concerning the differences in the relative strengths of these relationships. Data from 300 Chinese and 229 USA manufacturers in high-tech industries are used to test these hypotheses. While the relationships are generally supported, especially insightful are the results where they differ between the two countries. While human capital is more important in influencing organizational capital in USA, social capital is more important in China. Social capital also plays a more significant role in influencing customer learning in China, while no statistical differences are detected in social capital's influence on supplier learning across the two countries. The divergence observed can be explained by the cultural differences between the two countries. Implications are provided for managers in both countries to improve supply chain adaptability. [ABSTRACT FROM AUTHOR]

• A production planning model for product transition is developed. • Leaning effects in production rate for two consecutive products are considered. • An optimization model for developing the product transition plan is proposed. • Numerical examples are developed and sensitivity analyses are conducted to derive managerial insights. To generate continuing profits as well as to retain market share, companies need to regularly introduce new products into the market, replacing outdated products and enticing buyers to purchase new products. Thus, planning the product transition from the phase-out of a product to the phase-in of a product is a challenging task for both production planners and marketing managers. One significant concern in particular is how to account for the learning effect associated with the production of two successive products that share a single type of production resource. To address this problem of designing production plans for two successive products, we develop an MINLP (Mixed-Integer Non-Linear Programming) model that minimizes the total operating cost for managing the product transition. In doing so, we consider the number of production resources, the lengths of the product production periods, and the number of setups for both products during the ramp-down and ramp-up periods. We study the behaviors of the proposed model with the help of numerical experiments by varying the values of key parameters, including the learning effect and its impact on performance under different operational scenarios. Leveraging the results from the sensitivity analyses, the operational characteristics for managing the product transition are discussed. [ABSTRACT FROM AUTHOR]

This article scrutinizes the link between supplier leveraging and product innovation performance with the contingency of intellectual capital. Supplier leveraging refers to the utilization of knowledge obtained from suppliers for the benefit of the firm and product innovation performance to the firm's ability to introduce new and radically different products. Intellectual capital is represented by the trichotomy of structural, human, and social capital. Applying the knowledge-based view, a positive moderation effect emanating from structural and human capital is hypothesized, with however a negative moderation effect emanating from social capital. The expectation for the positive moderation is grounded in the enabling knowledge processing capabilities of structural and human capital, enhancing the effectiveness of supplier leveraging for the generation of product innovation performance. The negative moderation of social capital is suggested due to it likely limiting the transferability and aggregation capacity for knowledge, in addition to also potentially questioning the legitimacy of the suppliers' information, and thus making knowledge leveraged from suppliers less effective in informing product innovation performance. Data collected from 576 manufacturing firms confirm these hypotheses. Overall, by considering the interplay between supplier leveraging and intellectual capital, and its effect on product innovation performance, this study offers important new insight on the dynamics governing product innovations within supply chains. [ABSTRACT FROM AUTHOR]

This study examines the past, present, and future of Supply Chain Resilience (SCR) research in the context of COVID-19. Specifically, a total of 1717 papers in the SCR field are classified into 11 thematic clusters, which are subsequently verified by a supervised machine learning approach. Each cluster is then analyzed within the context of COVID-19, leading to the identification of three associated capabilities (i.e., interconnectedness, transformability, and sharing) on which firms should focus to build a more resilient supply chain in the post-COVID world. The derived insights offer invaluable guidance not only for practicing managers, but also for scholars as they design their future research projects related to SCR for greatest impact. [ABSTRACT FROM AUTHOR]

• 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]

While the adoption, use, and impact of mobile devices and applications (MDA) in downstream (B2C) settings have received considerable attention, studies on these issues in the upstream (B2B) context have been largely absent. The present work fills this gap and investigates the use of MDA for supply chain management (SCM). In doing so, we address several shortcomings noted in extant technology acceptance research and merge this framework with the resource-based view. First, arguments are developed for the central role played by behavioral intention in the relationship from perceived ease of use and perceived usefulness to usage behavior, positioning behavioral intention as an important process element. Second, competitive pressure is theorized to represent an influential contingency, moderating the impact of behavioral intention on usage behavior. Third, the impact of usage behavior on five important strategic performance measures necessary for competitive success (delivery, flexibility, quality, cost, and innovation performance) is assessed, pinpointing the value of MDA for SCM across these crucial measures of performance. And fourth, information uncertainty is positioned as a contextual force that can enhance the value of using MDA for competitive performance. Survey data from 217 adopters of MDA for SCM are used to test our expectations. [ABSTRACT FROM AUTHOR]

While online reverse auctions (ORAs) have become a popular tool to conduct business-to-business procurement, academic research and practical guidance are still limited in delineating salient differences to offline procurement, specifically as these pertain to the corporate buyer's goal orientation and preparation process preceding the negotiations. This article focuses on these fundamental dimensions characterizing the strategic sourcing approach, and contrasts their pursuit in ORAs and offline procurement. We consider a multi-item request for quotation (RFQ) and investigate the dimensions of price competitiveness and risk reduction as part of a buyer's goal orientation, and the aspects of information search and extent of analysis as part of the buyer's preparation process. Drawing on tenets from transaction cost economics, complemented with the literature in industrial buyer behavior, we theorize that the goal of price competitiveness is pursued to a greater degree in ORAs, whereas the goal of risk reduction is pursued to a greater degree in offline procurement. Furthermore, we suggest that both information search and extent of analysis are greater in ORAs than in offline procurement. These contentions are tested with data collected from 825 procurement professionals. Various robustness checks and exploratory extensions taking into account contingency factors are conducted. Our primary contributions lie in the provision of a theoretically grounded framework contrasting ORAs to offline procurement along the dimensions of a buyer's goal orientation and preparation process, and the empirical test with a large-scale survey focusing on the unique context of multi-item RFQs. Insights for academic theory and managerial practice are discussed. [ABSTRACT FROM AUTHOR]

The year 2020 can be earmarked as the year of global supply chain disruption owing to the outbreak of the coronavirus (COVID-19). It is however not only because of the pandemic that supply chain risk assessment (SCRA) has become more critical today than it has ever been. With the number of supply chain risks having increased significantly over the last decade, particularly during the last 5 years, there has been a flurry of literature on supply chain risk management (SCRM), illustrating the need for further classification so as to guide researchers to the most promising avenues and opportunities. We therefore conduct a bibliometric and network analysis of SCRA publications to identify research areas and underlying themes, leading to the identification of three major research clusters for which we provide interpretation and guidance for future work. In doing so we focus in particular on the variety of parameters, analytical approaches, and characteristics of multi-criteria decision-making techniques for assessing supply chain risks. This offers an invaluable synthesis of the SCRA literature, providing recommendations for future research opportunities. As such, this paper is a formidable starting point for operations researchers delving into this domain, which is expected to increase significantly also due to the current pandemic. [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 importance of environmental sustainability initiatives (ESI) for engineering and supply chain management practice and research is undeniable. However, despite the widespread attention to this domain, investigations in the effects of ESI on performance have been ambiguous and led to contradicting results, calling for the examination of contingencies influencing the relationship. This study works to bring insight and clarity into this debate by a comparative analysis of ESI among efficient and inefficient plants in Europe and the U.S. Specifically, using a sample of 402 plants located in Europe and the U.S., we study the impact of an explicit set of ESI (recycling, waste reduction, pollution prevention, ISO 14000 certification) on plant efficiency, an important performance measure which is, however, rarely considered. Basing our rationale on the resource-based view of the firm, we suggest that plants more heavily engaged in ESI are also characterized by a greater level of efficiency. We test this contention in both European and U.S. plants. Extending this cross-national comparison, we further study whether the emphasis on ESI differs in the two country groups. Relying on institutional theory, we suggest that the pursuit of ESI is enhanced among European plants, compared to their U.S. counterparts. Our findings and conclusions draw attention to the importance of contingencies to be considered and bring light to the seemingly contradictory findings in prior research. Important implications for engineering and supply chain management practice and research are highlighted. [ABSTRACT FROM PUBLISHER]

Manufacturers are faced with growing obstacles to quality coordination and assurance, as illustrated by the recent quality failures for toy cars and pet food. The objective of this paper is to contribute insight into these obstacles based on the theoretical domain of competitive capability progression (CCP), as well as the related concept of performance frontiers. Four hypotheses are developed and tested with data collected in a worldwide survey of manufacturing plants. In order to provide a relevant context for both CCP and the theory of performance frontiers, we focus on two sets of countries: more recently industrialized economies in Asia (China, Taiwan, South Korea), and long-standing industrialized economies in Western Europe (Germany, Finland, Italy, Sweden). We analyze the effectiveness of quality management in these two groups in terms of their impact on perceived and actual quality performance, and offer explanations for the difficulties that exist in global quality management. Our findings stress the importance of having a common understanding of what defines good quality across plants globally. Contributions to both theory and practice are highlighted. [ABSTRACT FROM PUBLISHER]

• 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]