Showing papers in "Annals of Operations Research in 2020"
TL;DR: The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic and can be of value for decision-makers to design SCs that can react adaptively to both positive changes and negative changes.
Abstract: Viability is the ability of a supply chain (SC) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. In this paper, we theorize a new notion-the viable supply chain (VSC). In our approach, viability is considered as an underlying SC property spanning three perspectives, i.e., agility, resilience, and sustainability. The principal ideas of the VSC model are adaptable structural SC designs for supply-demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs. Further, we demonstrate how the VSC components can be categorized across organizational, informational, process-functional, technological, and financial structures. Moreover, our study offers a VSC framework within an SC ecosystem. We discuss the relations between resilience and viability. Through the lens and guidance of dynamic systems theory, we illustrate the VSC model at the technical level. The VSC model can be of value for decision-makers to design SCs that can react adaptively to both positive changes (i.e., the agility angle) and be able to absorb negative disturbances, recover and survive during short-term disruptions and long-term, global shocks with societal and economical transformations (i.e., the resilience and sustainability angles). The VSC model can help firms in guiding their decisions on recovery and re-building of their SCs after global, long-term crises such as the COVID-19 pandemic. We emphasize that resilience is the central perspective in the VSC guaranteeing viability of the SCs of the future. Emerging directions in VSC research are discussed.
545 citations
TL;DR: A systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications suggests that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic.
Abstract: The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe Humanitarian logistics literature has extensively studied epidemic impacts; however, there exists a research gap in understanding of pandemic impacts in commercial SCs To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications The literature review findings suggest that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic The streamlining of the literature helps us to reveal several new research tensions and novel categorizations/classifications Most centrally, we propose a framework for operations and supply chain management at the times of COVID-19 pandemic spanning six perspectives, ie, adaptation, digitalization, preparedness, recovery, ripple effect, and sustainability Utilizing the outcomes of our analysis, we tease out a series of open research questions that would not be observed otherwise Our study also emphasizes the need and offers directions to advance the literature on the impacts of the epidemic outbreaks on SCs framing a research agenda for scholars and practitioners working on this emerging research stream
450 citations
TL;DR: This study develops and test an analytical model for a throughput analysis and uses it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines.
Abstract: Manufacturing flexibility improves a firm’s ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.
191 citations
TL;DR: This study explores digital business transformation through the lens of four emerging technology fields: artificial intelligence, blockchain, cloud and data analytics (i.e., ABCD), finding wide-reaching and diverse applications among a variety of vertical sectors.
Abstract: This study explores digital business transformation through the lens of four emerging technology fields: artificial intelligence, blockchain, cloud and data analytics (i.e., ABCD). Specifically, the study investigates the operations and value propositions of these distinct but increasingly converging technologies. Due to the dynamic nature of innovation, the potential of this ABCD hybridization, integration, recombination and convergence has yet to be considered. Using a multidisciplinary approach, the findings of the study show wide-reaching and diverse applications among a variety of vertical sectors, presenting exploratory research avenues for future investigation. The study also highlights the practical implications of these new technologies.
147 citations
TL;DR: An integration of Unified theory of user acceptance of technology and trust theory is proposed for exploring the adoption of AIMDSS and a high predictive power of this proposed model in explaining AIM DSS adoption is demonstrated.
Abstract: Compared to the booming industry of AIMDSS, the usage of AIMDSS among healthcare professionals is relatively low in the hospital. Thus, a research on the acceptance and adoption intention of AIMDSS by health professionals is imperative. In this study, an integration of Unified theory of user acceptance of technology and trust theory is proposed for exploring the adoption of AIMDSS. Besides, two groups of additional factors, related to AIMDSS (task complexity, technology characteristics, and perceived substitution crisis) and health professionals’ characteristics (propensity to trust and personal innovativeness in IT) are considered in the integrated model. The data set of proposed research model is collected through paper survey and Internet survey in China. The empirical examination demonstrates a high predictive power of this proposed model in explaining AIMDSS adoption. Finally, the theoretical contribution and practical implications of this research are discussed.
133 citations
TL;DR: The theoretical framework grounded in institutional theory and resource-based view and drawn thirteen hypotheses suggests that coercive pressures under the mediation effect of top management belief and participation have significant influence on resource selection and influence on environmental performance.
Abstract: The long-term viability of an organization hinges on social, environmental, and economic measures. However, based on extensive review of the literature, we have observed that measuring and improving the sustainable performance of supply chains is complex. We have grounded our theoretical framework in institutional theory and resource-based view and drawn thirteen hypotheses. We developed our instrument scientifically to validate our model and test our research hypotheses. The data was collected from the Indian auto components industry following Dillman’s total design test method. We gathered 205 usable responses. Following Peng and Lai’s (J Oper Manag 30(6):467–480, 2012) arguments, we have tested our model using variance-based structural equation modeling (PLS-SEM). We found that the constructs used for building our theoretical model possess construct validity and further satisfy the specified criteria for goodness of fit. The hypotheses test further suggests that coercive pressures under the mediation effect of top management belief and participation have significant influence on resource selection (i.e. supply chain connectivity and supply chain information sharing). The supply chain connectivity and supply chain information sharing have significant influence on environmental performance. Contrary to our belief, the normative and mimetic pressures have no significant influence on top management participation. The managerial implications of the findings are also discussed.
127 citations
TL;DR: This study explores the feasibility of AI utilization within an organization on six factors such as job-fit, complexity, long-term consequences, affect towards use, social factors and facilitating conditions for different elements of OM by mining the collective intelligence of experts on Twitter and through academic literature.
Abstract: In this digital era, data is new oil and artificial intelligence (AI) is new electricity, which is needed in different elements of operations management (OM) such as manufacturing, product development, services and supply chain. This study explores the feasibility of AI utilization within an organization on six factors such as job-fit, complexity, long-term consequences, affect towards use, social factors and facilitating conditions for different elements of OM by mining the collective intelligence of experts on Twitter and through academic literature. The study provides guidelines for managers for AI applications in different components of OM and concludes by presenting the limitations of the study along with future research directions.
125 citations
TL;DR: In this paper, the authors study supply chain finance problems in supply chains selling fashionable products and compare the optimal systems performances between the two supply chains, and prove that the blockchain-supported supply chain incurs a lower level of operational risk than the traditional supply chain.
Abstract: Today, supply chain finance is a very important topic. Traditional supply chains rely on banks to support the related financing activities and services. With the emergence of blockchain technology, more and more companies in different industries have considered using it to support supply chain finance. In this paper, we study supply chain financing problems in supply chains selling fashionable products. Modeling under the standard newsvendor problem setting with a single manufacturer and a single retailer employing a revenue sharing contract, we develop analytical models for both the traditional and blockchain-supported supply chains. We derive the optimal contracting and quantity decisions in each supply chain with Nash bargaining between the manufacturer and retailer. We analytically show how the revenue sharing contract can coordinate both types of supply chains. We then compare the optimal systems performances between the two supply chains. We prove that the blockchain-supported supply chain incurs a lower level of operational risk than the traditional supply chain. We have shown that if the service fees by banks are sufficiently high, adopting blockchain technology is a mean-risk dominating policy which brings a higher expected profit and a lower risk for the supply chain and its members. For robustness checking, we examine other commonly seen supply chain contracts and alternative risk measures, and analytically reveal that the results remain valid.
116 citations
TL;DR: For a three-stage supply chain consisting of a supplier, a manufacturer and a retailer, the optimal pricing strategies of the supply chain considering the traceability awareness of consumers in two scenarios are studied.
Abstract: Blockchain technology is an emerging technology developed in recent years. It has powerful information traceability function. The blockchain technology plays an important role in monitoring product quality and responding to product safety problems. Under considering the traceability awareness of consumers and the cost of using the blockchain technology, should the supply chain adopt the blockchain technology? The research on this issue deserves great attentions. In this paper, for a three-stage supply chain consisting of a supplier, a manufacturer and a retailer, we study the optimal pricing strategies of the supply chain considering the traceability awareness of consumers in two scenarios. These two scenarios are: scenario N (i.e., the supply chain does not adopt the blockchain technology) and scenario B (i.e., the supply chain adopts the blockchain technology). On this basis, we discuss the conditions that the supply chain adopts the blockchain technology by comparing the optimal profits of the supply chain and its members in two scenarios. Further, we discuss the problem of supply chain coordination when adopting the blockchain technology. The results show that it is conditional for the supply chain to adopt the blockchain technology, and the condition is related to the traceability awareness of consumers, the production costs of the supplier and manufacturer, and the cost of using the blockchain technology. We also find that under a certain condition, the revenue sharing contract can realize a Pareto improvement for the supply chain that adopts the blockchain technology.
93 citations
TL;DR: An up-to-date literature review of different SMAA methods and their applications in various areas is provided and some guidelines to assist decision-makers in the choice of a SMAA method on a specific decision-making context are provided.
Abstract: Stochastic multicriteria acceptability analysis (SMAA) is a family of multiple criteria decision making (MCDM) methods dealing with incomplete, imprecise, and uncertain information on the evaluations and preference model parameters. As it provides a general framework that has extensions to deal with various specificities in MCDM problems, the development of SMAA methods and their applications in real-life decision-making problems have been increased over the recent years. This paper provides an up-to-date literature review of different SMAA methods and their applications in various areas. First, we selected, from different on-line data base, 118 articles published between 1998 and 2017. We categorized the selected papers into theoretical and applied. While the theoretical papers were analyzed based on the method’s aggregation procedure, type of problem, type of method’s outputs and inputs, the applied papers were separated and analyzed by application areas. Then, we provide some descriptive statistics, analyzing the papers regarding to publication year and journals of publication. Finally, we provide some guidelines to assist decision-makers in the choice of a SMAA method on a specific decision-making context and some future research directions.
91 citations
TL;DR: Evidence is provided that a coordinated production–ordering contingency policy in the supply chain within and after the disruption period has been developed and tested to reduce the negative impacts of the ‘postponed redundancy’.
Abstract: Performance impacts of ordering and production control policies in the presence of capacity disruptions are studied on the real-life example of a retail supply chain with product perishability considerations. Constraints on product perishability typically result in reductions in safety stock and increases in transportation frequency. Consideration of the production capacity disruption risks may lead to safety stock increases. This trade-off is approached with the help of a simulation model that is used to compare supply chain performance impacts with regard to coordinated and non-coordinated ordering and production control policies. Real data of a fast moving consumer goods company is used to perform simulations and to derive novel managerial insights and practical recommendations on inventory, on-time delivery and service level control. In particular, for the first time, the effect of ‘postponed redundancy’ has been observed. Moreover, a coordinated production–ordering contingency policy in the supply chain within and after the disruption period has been developed and tested to reduce the negative impacts of the ‘postponed redundancy’. The lessons learned from experiments provide evidence that a coordinated policy is advantageous for inventory dynamics stabilization, improvement in on-time delivery, and variation reduction in customer service level.
TL;DR: This paper presents a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together based on long-short-term-memory and VGG-16 networks that show significant improvement in the performance, as evident from the validation result.
Abstract: People start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents.
TL;DR: The findings indicate that supply chain visibility has significant influence on social and environmental performance under the moderation effect of product complexity.
Abstract: Understanding supply chain sustainability performance is increasingly important for supply chain researchers and managers. Literature has considered supply chain sustainability and the antecedents of performance from a triple bottom line (economic, social, and environmental) perspective. However, the role of supply chain visibility and product complexity contingency in achieving sustainable supply chain performance has not been explored in depth. To address this gap, this study utilizes a contingent resource-based view theory perspective to understand the role of product complexity in shaping the relationship between upstream supply chain visibility (resources and capabilities) and the social, environmental, and economic performance dimensions. We develop and test a theoretical model using survey data gathered from 312 Indian manufacturing organizations. Our findings indicate that supply chain visibility has significant influence on social and environmental performance under the moderation effect of product complexity. Finally, we have outlined our research limitations and further research opportunities.
TL;DR: Research shows that the risk management of urban rainstorm and waterlogging disasters, together with social media data, is a feasible way to obtain on-site data of disasters and carry out risk assessment of disasters.
Abstract: Due to the climate change and the rapid progress of urbanization, extreme weather disasters such as urban rainstorm and waterlogging are frequent. Therefore, how to find the waterlogging points in the presence of disasters and how to optimize the distribution of urban emergency logistics and reduce the negative impact of disasters have become a hot and difficult issue for government departments and scholars. First of all, the idea and method of using the big data of microblogging to obtain urban rainstorm and waterlogging disasters and public sentiment are put forward. In addition,this thesis constructed the location-routing problem model of urban emergency logistics in the situation of rainstorm and waterlogging disaster, and found out the dynamic emergency distribution path of Nanjing in the situation of waterlogging disaster by using NSGA-III algorithm. Research shows that the risk management of urban rainstorm and waterlogging disasters, together with social media data, is a feasible way to obtain on-site data of disasters and carry out risk assessment of disasters. At the same time, the emergency logistics location-positioning model and algorithm can provide a reference for similar disaster emergency logistics distribution network and the conclusion can provide empirical reference for cities to cope with rainstorm and waterlogging disasters.
TL;DR: Investigation of corporate governance factors that could shape the decision for sustainability reporting revealed that the Age of the Youngest Director has a negative effect, while “Independent Directors” and the “presence of Lead Independent Director” variables seem to strengthen the decision to develop environmental disclosures.
Abstract: This study investigated corporate governance factors that could shape the decision for sustainability reporting. Specifically, this paper sets out to investigate the environmentally sensitive properties of distinct corporate governance characteristics vis-a-vis sustainable concerns strengthening the decision to develop environmental disclosures. Regarding explanatory variables, the study focused on corporate governance because it sets the rules and processes by which a firm is managed. Five plausible individual characteristics were employed in our proposed model, namely, “Independent Directors”, “presence of Lead Independent Director”, “frequency of Audit Committee Meetings”, “presence of Sustainable Committee”, and the “Age of the Youngest Director”. We utilized the Environmental facet of Environmental, Social and Governance score calculated by Bloomberg as a proxy driving the decision to disseminate environmental information. Hypotheses were tested using a Logit model for a sample of a total of 278 firms from the United States listed S&P 500. Results revealed that the “Age of the Youngest Director” has a negative effect, while “Independent Directors” and the “presence of Lead Independent Director” variables seem to strengthen the decision to develop environmental disclosures. Implications are valuable to different stakeholders and policy makers interested in improving corporate governance initiatives to reducing agency costs and enhancing corporate sustainable transparency.
TL;DR: This paper proposes aggregation of the nearest consistent matrices (ANCM) with the acceptable consensus in AHP-GDM, simultaneously considering the consensus and consistency of the individual PCMs.
Abstract: Analytic hierarchy process (AHP) is widely used in group decision making (GDM). There are two traditional aggregation methods for the collective preference in AHP-GDM: aggregation of the individual judgments (AIJ) and aggregation of the individual priorities (AIP). However, AHP-GDM is sometimes less reliable only under the condition of AIJ and AIP because of the consensus and consistency of the individual pair-wise comparison matrices (PCMs) and prioritization methods. In this paper, we propose aggregation of the nearest consistent matrices (ANCM) with the acceptable consensus in AHP-GDM, simultaneously considering the consensus and consistency of the individual PCMs. ANCM is independent of prioritization methods while complying with the Pareto principal of social choice theory. Moreover, ANCM is easy to program and implement in resolving highly complex group decision making problems. Finally, two numerical examples illustrate the applications and advantages of the proposed ANCM.
TL;DR: An efficient blood supply chain that can fulfill hospitals blood demand quickly with the lowest cost is designed using simulation and optimization processes to avoid the worst consequences of a disaster using a neural-learning process.
Abstract: In recent years, attention to blood supply chain in disaster circumstances has significantly increased. Disasters, especially earthquakes, have adverse consequences such as destruction, loss of human lives, and undermining the effectiveness of health services. This research considers a six-echelon blood supply chain which consists of donors, blood collection centers (permanent and temporary), regional blood centers, local blood centers, regional hospitals, and local hospitals. For the first time, we considered that helicopters could carry blood from regional hospitals to local hospitals and return injured people that cannot be treated in local hospitals to regional hospitals due to the limited capacity. In addition to the above, different transportations with limited capacities regarded, where the optimal number of required transportations equipment determined after the solution process. This research aims to avoid the worst consequences of a disaster using a neural-learning process to gain from past experiences to meet new challenges. For this aim, this article considers three objective functions that are minimizing total transportation time and cost while minimizing unfulfilled demand. The model implemented based on a real-world case study from the most recent earthquake in the Iran–Iraq border which named the deadliest earthquake of 2017. Based on our results, we learned how to design an efficient blood supply chain that can fulfill hospitals blood demand quickly with the lowest cost using simulation and optimization processes. Moreover, we performed in-depth analyses and provided essential managerial insights at last.
TL;DR: This paper proposed a multi-objective mixed-integer programming for energy-efficient hybrid flow shop scheduling with lot streaming in order to minimize both the production makespan and electric power consumption.
Abstract: Hybrid flow shop scheduling problems are encountered in many real-world manufacturing operations such as computer assembly, TFT-LCD module assembly, and solar cell manufacturing. Most research considers the scheduling problem in regard to time requirements and the steps needed to improve production efficiency. However, the increasing amount of carbon emissions worldwide is contributing to the worsening global warming problem. Many countries and international organizations have started to pay attention to this problem, even creating mechanisms to reduce carbon emissions. Furthermore, manufacturing enterprises are showing growing interest in realizing energy savings. Thus, the present research study focuses on reducing energy costs and completion time at the manufacturing-system level. This paper proposed a multi-objective mixed-integer programming for energy-efficient hybrid flow shop scheduling with lot streaming in order to minimize both the production makespan and electric power consumption. Due to a trade-off between these objectives and the computational complexity of the proposed multi-objective mixed-integer program, this study adopts the genetic algorithm (GA) to obtain approximate Pareto solutions more efficiently. In addition, a multi-objective energy efficiency scheduling algorithm is also developed to calculate the fitness values of each chromosome in GA.
TL;DR: It is shown that while returns on the aggregate market portfolio cannot explain Bitcoin returns, other asset pricing risk factors, such as interest rates and implied stock market and foreign exchange market volatilities, are important determinants of Bitcoin returns.
Abstract: Bitcoin is emerging as a distinct asset class among investors given its seemingly detached price behavior relative to market and economic fundamentals. Its incomparably high returns in recent years has further fuelled intense interest and investment into Bitcoin and cryptocurrencies at large. This paper cautions that Bitcoin prices, despite their seemingly attractive independent behavior relative to economic variables, may still be exposed to the same types of market risks which afflict the performance of conventional financial assets. Using a Markov regime-switching model to distinguish between regimes of high and low Bitcoin price volatility, this paper shows that while returns on the aggregate market portfolio cannot explain Bitcoin returns, other asset pricing risk factors, such as interest rates and implied stock market and foreign exchange market volatilities, are important determinants of Bitcoin returns. Distinguishing between periods of high and low Bitcoin price volatility reveals heterogeneity in the explanatory power of market risk factors; in particular, Bitcoin returns are more difficult to explain during periods of high volatility relative to periods with low volatility. This finding can partially explain why extant studies, which neglect to distinguish between exchange rate regimes in Bitcoin, have difficulty linking Bitcoin prices to economic fundamentals.
TL;DR: To retain sustained earnings and development, green manufacturing should be the bottom line of involved firms and the importance of corporate stakeholders should be promoted in consideration of enterprises’ practice performance and future development.
Abstract: This study explores the relationship among stakeholders, green manufacturing, and practice performance in the fashion business in China and focuses on assisting companies to enhance environmental awareness and green manufacturing practices. We collect research data by developing questionnaires for various Chinese enterprises. A five-point Likert scale is adopted to enable respondents to indicate the extent to which they agree with the items. Through tests and analyses, the questionnaire is validated as reliable, the structural equation model has a good fitting degree, and hypotheses are proved true. Specifically, corporate stakeholders have a significant positive impact on green manufacturing and practice performance, and green manufacturing has a significant positive impact on practice performance in the context of Chinese fashion businesses. Moreover, corporate stakeholders can have a positive impact on practice performance through green manufacturing. We also propose some policy implications, including implementing compulsive policies and regulations and encouraging and establishing preferential policies, such as tax concessions. Moreover, enterprises should actively strive to improve green manufacturing technology and management level to ensure the smooth implementation of green manufacturing practices. To retain sustained earnings and development, green manufacturing should be the bottom line of involved firms. We also emphasize that the importance of corporate stakeholders should be promoted in consideration of enterprises’ practice performance and future development.
TL;DR: This article proposes a plan that would reliably contain the pandemic, mitigate its economic consequences, and boost societal confidence and requires the implementation of four strategies over 90 days.
Abstract: A logical strategy to contain the Covid-19 pandemic is to completely isolate everyone for 2 weeks (the incubation period of the virus). However, such a strategy can have prohibitive economic and social costs and, therefore, will be difficult to implement. At the same time, the current situation is leading to an expanding humanitarian, health and economic crisis. Based on principles of the Theory of Constraints, we propose in this article the "Shutting-down Transmission Of Pandemic" (STOP Covid-19) plan that would reliably contain the pandemic, mitigate its economic consequences, and boost societal confidence. This plan requires the implementation of four strategies over 90 days: (a) stop all international, domestic passenger air and intercity bus/train travel; (b) create administrative zones of about 1 million people; (c) stop all non-emergency cross-zonal travel except for transportation of goods, and (d) deploy an information-driven service value chain to control the spread of the pandemic within a zone.
TL;DR: This study attempts to understand how swift-trust, commitment and collaboration among the humanitarian actors engaged in disaster relief operations are overcome by seeking answers to questions related to the topics of swift- trust, collaboration and agility in humanitarian supply chains.
Abstract: Humanitarian organizations are increasingly facing challenges in terms of improving the efficiency and the effectiveness of their disaster relief efforts. These challenges often arise due to a lack of trust, poor collaboration and an inability to respond to disaster affected areas in a timely manner. Our study attempts to understand how these challenges are overcome by seeking answers to questions related to the topics of swift-trust, collaboration and agility in humanitarian supply chains. For instance, in our study we have attempted to examine how information sharing and supply chain visibility in humanitarian supply chains improve the swift-trust among the humanitarian actors engaged in disaster relief operations. Further, we attempt to understand how-swift trust, commitment and collaboration among the humanitarian actors improve the agility in humanitarian supply chains. In our study we provide both theoretical and data-driven answers to our stated research gaps. Our theoretical model is firmly grounded in organizational information process theory and relational view. We tested our research hypotheses using variance based structural equation modelling with survey data collected using a web based pre-tested instrument from 147 NGOs respondents drawn from the National Disaster Management Authority database. Our results help to advance the theoretical debates surrounding “swift-trust”, “collaboration” and “agility” in humanitarian settings. We further provide direction to managers engaged in disaster relief operations. The humanitarian actors engaged in disaster relief often fail to understand how to build swift-trust. Moreover, how swift-trust further affects commitment and collaboration which in turn further affect agility in humanitarian supply chains. Thus humanitarian organizations must understand how information sharing and supply chain visibility is key to swift-trust among humanitarian actors and agility in humanitarian supply chains. Finally, we outline the limitations of our study and offer some future research directions for investigation.
TL;DR: RO based mathematical modeling to address risks and its applicability for SCND for close loop supply chain is proposed, demonstrated and applied in practical cases and shows that the topology obtained from integrated treatment of risk and uncertainty called as RORU model, outperform other supply chain networks on various network performance indicators.
Abstract: Closed loop supply chain network design (CL-SCND) is a critical economic and environmental activity. The closing of the loop to handle return, uncertainty in business environment, various supply chain risks, impact network design processes and performance of the firm in the long term. Thus, it is important to design robust and reliable supply chain structures and obtain network configurations which can always outperform the other configurations under the worst cases of risks and uncertainty. A generic closed-loop supply chain network based on mixed integer programming formulation is proposed with direct shipping to the customer from manufacturing plants as well as shipping through distribution centers under supply risks, transportation risk and uncertain demand using a robust optimization (RO) approach. A large number of numerical tests are carried out to test the performance of the model by considering a total of four levels of uncertainty for four different network structures types. The results of the tests confirm that the risk and uncertainty based integrated supply chain network models are more efficient (cost effective) than the other set of network configurations which treats the supply chain risks and uncertainty post-ante. To demonstrate the applicability of the proposed model, the case of an Indian e-commerce firm which wants to redesign its supply chain structure is presented. The results of case study show that the topology obtained from integrated treatment of risk and uncertainty called as RORU model, outperform other supply chain networks on various network performance indicators such as supply chain costs, the number of facilities open or close and the amount of products flowing through supply chain echelon. Thus, RO based mathematical modeling to address risks and its applicability for SCND for close loop supply chain is proposed, demonstrated and applied in practical cases.
TL;DR: A novel hybrid approach, based on fuzzy theory, chance constrained programming, and goal programming approach, is developed for solving the proposed bi-objective mixed-integer linear programming model for designing a perishable pharmaceutical supply chain network under demand uncertainty.
Abstract: In this paper, a bi-objective mixed-integer linear programming model is formulated for designing a perishable pharmaceutical supply chain network under demand uncertainty The objectives of the proposed model are to simultaneously minimize the total cost of the network and lost demand amount The proposed model is multi-product and multi-period and includes simultaneous facilities location, vehicle routing, and inventory management; hence, it is considered an operational-strategic model Procurement discounts, the lifetime of products, storing products for future periods, lost demand, and soft and hard time windows are the main assumptions of the proposed model A novel hybrid approach, based on fuzzy theory, chance constrained programming, and goal programming approach, is developed for solving the proposed bi-objective model The validity of the proposed model and developed solution approach is evaluated using data from Avonex, a prefilled syringe distribution chain serving 11 health centers in Tehran The proposed model indicates that some lost sales exist, and to overcome the lost sales, the case company needs to invest a little more in addition to the initial investment of around 2 billion tomans The results obtained from implementing the model and the sensitivity analysis, using real-world data, confirm the efficiency and validity of the proposed mathematical model and solution approach
TL;DR: This paper analyzes the procedure used by FIFA up until 2018 to rank national football teams and define by random draw the groups for the initial phase of the World Cup finals and proposes modifications to that procedure guided by a qualitative and statistical analysis of the FIFA ranking.
Abstract: This paper analyzes the procedure used by FIFA up until 2018 to rank national football teams and define by random draw the groups for the initial phase of the World Cup finals. A predictive model is calibrated to form a reference ranking to evaluate the performance of a series of simple changes to that procedure. These proposed modifications are guided by a qualitative and statistical analysis of the FIFA ranking. We then analyze the use of this ranking to determine the groups for the World Cup finals. After enumerating a series of deficiencies in the group assignments for the 2014 World Cup, a mixed integer linear programming model is developed and used to balance the difficulty levels of the groups.
TL;DR: In this article, a bibliometric analysis was conducted to assess the current level of research on the humanitarian supply chain (HSC) and its essential role in curbing the increase in human-made and natural disasters.
Abstract: Humanitarian supply chain (HSC) has attracted enormous interest from both practitioners and academics lately, mainly because of its essential role in curbing the increase in human-made and natural disasters. Knowing what has been done in the field so far as to design a robust research agenda to tackle future HSC challenges is an important research objective. In this study, a bibliometric analysis was conducted to assess the current level of research on the HSC. A search of documents dealing with HSC was realized in the Web of Science database, a world-leading publisher-independent global citation database. The search identified 1152 documents, and the data collected was analyzed by means of a bibliometric tool called Bibliometrix. Key findings are presented and discussed, followed by some potential future research avenues.
TL;DR: This study helps industrial managers to evaluate their suppliers based on the resultant influential criterion and, further, it strengthens the global supply chains with agility and robustness.
Abstract: Industries across the globe are under growing pressure to rethink and redesign their supply chain operations to maintain their competitive advantage. The supplier selection process in supply chain management holds a pivotal position in its exploration of new strategies to stay competitive in global markets. This study considers supplier selection with two different strategic perspectives, including lean and agile. Selecting suppliers based on their leagile practices helps the focal industries to make their supply chain operations healthier, especially if the focal industry is a major supplier of multinational companies. China is considered as a case context in this study with the application of textile sectors since this country occupies the top position with regard to exports. The common criteria involved in leagile supplier selection were collected from existing literature resources and were fine-tuned with insights from field experts. Further, the case industrial managers also assisted with the evaluation of the influential criteria for the leagile supplier selection process. Based on the replies and the assistance of a decision-making trial and evaluation laboratory tool, the most influential criterion and interdependencies among other criteria were identified. This study helps industrial managers to evaluate their suppliers based on the resultant influential criterion and, further, it strengthens the global supply chains with agility and robustness.
TL;DR: This paper targets food loss on the supply side, with a focus on the industrial food processing environment, and maps food loss in each processing stage, that is sustainable operations.
Abstract: There are numerous studies on food loss on the demand side examining consumer behavior towards food choice and food waste generation at the household level. In this paper, we target food loss on the supply side, with a focus on the industrial food processing environment. More specifically, we map food loss in each processing stage, that is sustainable operations. Primary data were conducted through a survey (complemented with observations and documentary analysis) in 47 food processing companies in Belgium to identify hotspots and quantify food loss. The findings show that processing is by far the most important food loss hotspot. While transportation, changeover, interrupted production, human errors and product effects at this stage often lead to substantial or excessive losses, causes of food loss during packaging and before or after production have a smaller impact. At subsector level, however, there are substantial differences with respect to the most important causes. The originality of this research can be evaluated in three ways: one, identifying hotspots of food loss in the industrial processing environment; two, measuring the magnitude of losses across different product categories and causes and three, how sustainable operations plays a significant role in food loss prevention.
TL;DR: This study highlights the benefits of an interpretive approach where IS factor interrelationships can be modelled to demonstrate potential influence on other connected factors thereby, increasing the chances of project success.
Abstract: This study extends the debate surrounding the components of IS project success by reviewing success factors from the perspective of their interdependency and influence on each other. This research utilises interpretive structural modelling as the methodology and framework to develop the relationships between the selected factors. This approach is presented as a mechanism that can provide greater insight to the underlying causal interrelationships associated with IS project success and the successful transition to operations. The findings identify a number of key outcomes that have significant driving influence on other interconnected factors in the final model. This study highlights the benefits of an interpretive approach where IS factor interrelationships can be modelled to demonstrate potential influence on other connected factors thereby, increasing the chances of project success.
TL;DR: A data-driven MCDM framework is proposed in the context of the evidential reasoning approach and three challenges in the framework are met, including the transformation of observations into assessments, the learning of parameters and their constraints from historical data, and the generation of a data- driven solution.
Abstract: In the era of the Internet and big data, data permeate the entire process of multiple criteria decision making (MCDM). Therefore, generation of rational solutions from current observations and historical data has become an important and interesting issue. To address this issue, this paper proposes a data-driven MCDM framework in the context of the evidential reasoning approach. Three challenges in the framework are met, including the transformation of observations into assessments, the learning of parameters and their constraints from historical data, and the generation of a data-driven solution. The proposed framework is then used to model the diagnosis of thyroid cancer and generate data-driven diagnostic results. The three challenges in the application are met to aid radiologists in improving the diagnostic accuracy of thyroid cancers. To examine whether the application of the proposed data-driven MCDM framework to the diagnosis of thyroid cancer can help improve diagnostic accuracy, we conduct a case study by using the examination reports of three radiologists from July 2015 to October 2017 in the ultrasonic department of a tertiary hospital located in Hefei, Anhui Province, China.