Robust Sourcing Under Multilevel Supply Risks: Analysis of Random Yield and Capacity

Uncertainty and supply chain risk: The moderating role of supply chain flexibility in risk mitigation

Problem definition: Quick response is a classic operations strategy that allows a retailer to place a rapid replenishment order during the selling season using information learned from early sales. The benefits of quick response are widely studied in the literature under the condition that the manufacturer’s wholesale prices are exogenously given. Motivated by the practice of emerging small and medium-sized enterprise (SME) fashion brands, this paper revisits the value of quick response for a retailer when a manufacturer can strategically set its wholesale prices. Methodology/results: We develop a game-theoretic model consisting of one manufacturer and one retailer. In contrast to the traditional quick response setting, the manufacturer can dynamically adjust wholesale prices for both regular and replenishment orders. First, we investigate whether and when quick response still benefits the retailer. We find that, under low or significantly high demand uncertainties, the firms share a common preferred ordering strategy, and quick response benefits the retailer as well as the supply chain. But, under moderately high demand uncertainty, the retailer’s favored ordering strategy conflicts with the manufacturer’s interest; as a result, the manufacturer would set wholesale prices to counter the retailer’s ordering strategy, which makes quick response detrimental to the retailer. Second, we search for mechanisms that can resolve this conflict and restore the beneficial effect of quick response. We show that letting the manufacturer commit to wholesale prices up front is ineffective in fixing the problem. However, if the retailer can propose a take-it-or-leave-it wholesale price for the replenishment order (possibly with the replenishment quantity) once the regular wholesale price is set, then quick response leads to a win–win outcome for both firms. Managerial implications: The findings caution retailers with weak power (e.g., SMEs) when adopting quick response, especially when facing moderately high demand uncertainties. The retailer, although weak, should be aware of the retailer’s natural ability to propose replenishment terms because, otherwise, the retailer can always forgo quick response; this opens up an opportunity to design more favorable arrangements. Funding: The work of J. Chen was supported by the National Natural Science Foundation of China [Grants 72171202, 72232007]; the work of Q. Hu was supported by the National Natural Science Foundation of China [Grants 72271057, 72091211]; the work of D. Shi was supported by the National Natural Science Foundation of China [Grant 72102205] and Shenzhen Stable Support Program for Higher Education Institutions; the work of F. Zhang was supported by the National Natural Science Foundation of China [Grants 71929201, 72131004]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0561 .

Mobile technologies and AI enable health data collection from devices, allowing effective monitoring. Traditional methods often compromise privacy, but federated learning (FL) offers a potential solution. However, current FL approaches face two issues: they don’t identify key health features for clinical intervention, and their aggregation overlooks patient multidimensional heterogeneity. This study seeks to develop a new FL method to tackle these challenges and enhance privacy in mobile health monitoring. This study proposes a novel FL method combining (1) a spatial and temporal attention-based prediction model (STA-Pred) that uses attention to identify key spatial and temporal features, and (2) a multidimensional heterogeneity-based aggregation protocol (MDH-Aggr), which aggregates components based on their heterogeneity to handle multidimensional differences. Experiments on three data sets show that our method outperforms existing methods in several patient-monitoring contexts. This study enhances understanding of how to leverage mobile technologies and AI to enable privacy-preserving health monitoring that promotes the social good. Additionally, it advances FL research through two innovative designs (STA-Pred and MDH-Aggr). History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Funding: Y. Chai, H. Liu, and Y. Liu are supported by the National Natural Science Foundation of China [Grants 72342011, 72322019, 72188101, and 72402001]. Dr. L. Wang’s work was in part supported by the National Natural Science Foundation of China [Grant 72271027], Hainan Provincial Natural Science Foundation of China [Grant 726MS0458], and Beijing Institute of Technology Research Fund Program for Young Scholars [Grant XSQD-202216004]. X. Liu is not supported by any funds or associated with any of the abovementioned funds. Supplemental Materials: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0521 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0521 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.

We propose a preference-learning algorithm for uncovering Decision Makers’ (DMs’) contingent evaluation strategies in the context of multiple criteria sorting. We assume the preference information in the form of holistic assignment examples derived from the analysis of alternatives’ performance vectors and textual descriptions. We characterize the decision policies using a mixture of threshold-based, value-driven preference models and associated latent topics. The latter serve as the stimuli underlying the contingency in decision behavior. Such a probabilistic model is constructed by using a flexible and nonparametric Bayesian framework. The proposed method adopts a hierarchical Dirichlet process as the prior so that a group of DMs can share a countably infinite number of contingent models and topics. For all DMs, it automatically identifies the components representing their evaluation strategies adequately. The posterior is summarized by using the Hamiltonian Monte Carlo sampling method. We demonstrate the method’s practical usefulness in a real-world recruitment problem considered by a Chinese IT company. We also compare the approach with counterparts that use a single preference model, implement the parametric framework, or consider each DM’s preferences individually. The results indicate that our approach performs favorably in both interpreting DMs’ contingent decision behavior and recommending decisions on new alternatives. Furthermore, the approach’s performance and robustness are investigated through a computational experiment involving real-world data sets. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: J. Liu received financial support from the National Natural Science Foundation of China [Grants 72071155 and 71701160]. M. Kadziński received financial support from the Polish National Science Center under the SONATA BIS project [Grant DEC-2019/34/E/HS4/00045]. X. Liao received financial support from the National Natural Science Foundation of China [Grant 71872144]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1292 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0328 ) at ( http://dx.doi.org/10.5281/zenodo.7608750 ).

With the increased globalisation and disruptions faced by businesses in this digital era and the occurrence of natural disasters such as floods and disease outbreaks in the world, supply chain risks and management of those risks are major challenges for businesses, especially for SMEs of clothing retailers in Australia. This study, hence, is carried out using an exploratory case study research method, and the data have been collected through semi-structured face-to-face interviews with key informants from managerial levels of 20 Australian SMEs of clothing retailing businesses to identify various supply chain risks and their management processes. This study finds five supply chain risks, namely supply risk, demand risk, financial risk, environmental risk, and operational risk, that the SMEs of clothing retailers mostly face in the supply chain. This study also finds that most of the investigated retailers lack a formal risk identification approach, though they informally use the reactive and proactive methods of risk identification. Furthermore, the assessment methods are not well established in most of the participating firms, and supplier monitoring receives more attention compared to their own performance to deal with their supply chain risks. This study contributes to the body of knowledge by being one of the first empirical studies to explore the SMEs of clothing retailers’ supply chain risks and their management processes in the Australian business context, which can add value in guiding supply chain design decisions for SMEs in other sectors.

Along with increasing supply chain risks due to economic and environmental changes, it is imperative to answer the question of how to reduce supply chain risks. This study examines supply chain collaboration as a risk mitigation strategy. The study examines three types of risks, namely supply risk, demand risk and process risk in relation to three types of collaboration, namely supplier collaboration, customer collaboration and internal collaboration, as a mechanism to mitigate those risks. The proposed relationship model is tested with data collected from 203 manufacturing companies in Australia. The results show that each area of collaboration effectively reduces its respective supply chain risk, but only the mitigation of process risk and demand risk has a direct effect on supply chain performance. In addition, both supply risk and demand risk increase process risk. We offer theoretical and practical implications of the findings.

The beef and red meat sector in Australia is undergoing rapid change because of globalization, a highly competitive beef and red meat market (local and export). Risk management in the Australian beef and red meat supply chain has become increasingly important as companies both large and small seek to extend their global reach. It is important to manage these supply chain risks. The purpose of the conceptual paper is to present a framework for managing supply chain risk in the Australian beef and red meat industry. The supply chain risk management strategy is critical and significant to strategically support the supply chain and business success in the long run. However, little research has been conducted in the industry so far. Based on the resource-based view (RBV) approach, the firms’ capability is considered as effective tools for mitigating the supply chain risk in the real operational environment. The paper identified major types of supply chain risks in the red meat industry. The conceptual framework focuses on the relationships among the firms’ capabilities and supply chain risks. This provides insights into the supply chain risk management. Although, the study focuses on the Australian red meat industry, the proposed framework can be validated with an empirical work in different sectors. Moreover, this paper contributes to the supply chain risk management literature.

In recent years, with the rapid development of computing technology, developing parallel procedures to solve large-scale ranking and selection (R&S) problems has attracted a lot of research attention. In this paper, we take fixed-budget R&S procedure as an example to investigate potential issues of developing parallel procedures. We argue that to measure the performance of a fixed-budget R&S procedure in solving large-scale problems, it is important to quantify the minimal growth rate of the total sampling budget such that as the number of alternatives increases, the probability of correct selection (PCS) would not decrease to zero. We call such a growth rate of the total sampling budget the rate for maintaining correct selection (RMCS). We show that a tight lower bound for the RMCS of a broad class of existing fixed-budget procedures is in the order of [Formula: see text], where k is the number of alternatives. Then, we propose a new type of fixed-budget procedure, namely the fixed-budget knockout-tournament ([Formula: see text]) procedure. We prove that, in terms of the RMCS, our procedure outperforms existing fixed-budget procedures and achieves the optimal order, that is, the order of k. Moreover, we demonstrate that our procedure can be easily implemented in parallel computing environments with almost no nonparallelizable calculations. Last, a comprehensive numerical study shows that our procedure is indeed suitable for solving large-scale problems in parallel computing environments. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: Y. Zhong was supported by the National Natural Science Foundation of China [Grant 72101047]. L. J. Hong was supported by the National Natural Science Foundation of China [Grants 72091211 and 72161160340]. G. Jiang was supported by the National Natural Science Foundation of China [Grants 72121001 and 72171060]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1221 .

Problem definition: New product introductions frequently face retail imitation risks. When start-ups use crowdfunding to finance new products, they expose themselves to an even more severe imitation risk specific to crowdfunding platforms. To mitigate this risk, start-ups can employ a progressive launch strategy by initially offering a preliminary version with fewer features or functionalities. Alternatively, they may opt for traditional bank financing, which only carries retail imitation risk. This paper examines how start-ups can effectively compete with crowdfunding copycats through the aforementioned product launch and financing strategies. Methodology/results: We develop a game-theoretical model to analyze the strategic interactions between a start-up and a copycat. Our findings reveal that start-ups may strategically withhold product quality provision to deter or weaken crowdfunding copycats. The optimal quality provision in crowdfunding exhibits nonmonotonic relationships with both crowdfunding imitation efficiency and crowdfunding market share. We also evaluate the social impact of different financing strategies and reveal that the socially optimal funding choice depends on the implementation of the progressive launch strategy. Notably, contrary to conventional wisdom, increasing crowdfunding copycat efficiency does not consistently improve consumer surplus or social welfare. Managerial implications: Although crowdfunding offers numerous advantages, start-ups considering this approach must recognize and address the potential threats posed by copycat imitation. Our research indicates that start-ups seeking external financing can effectively use progressive launch strategies to counter crowdfunding copycats—an approach that generally requires less time and resources than traditional intellectual property protection methods. Funding: G. Xiao acknowledges support from the National Natural Science Foundation of China [Grant 72422016] and the Research Grants Council of Hong Kong [GRF Grant PolyU 15500923]. X. Guo acknowledges support from the Research Grants Council of Hong Kong [GRF Grant PolyU 15507318] and the National Natural Science Foundation of China (72293564/72293560). Z. Chen acknowledges support from the National Natural Science Foundation of China [72542003]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0912 .

Purpose: The purpose of this case study is to develop and framework supply chain characteristics and risk mitigation strategies in the context of biodiesel downstream supply chain.Design/methodology/approach: This study employs an expert interview-based approach as a qualitative approach with a multi-perspectives view.Findings: There are vary strategies among perspectives, such as perspectives of organization and business types, stakeholder types, times and methods. These also shows that business strategy of collaborative, coordinative, and cooperative arise as alternative strategies for each perspective and each level of stakeholder. Those business strategies may apply in a vary operation strategies which linking through an energy security framework element as company’s competitive priorities.Research limitations/implications: The research scope includes only a certain area of the country’s territory and the target company’s supply chain areas of activity. The research method includes only internal stakeholders and experts as respondents and data sources. The level of analysis was only at corporate level in the corporate case study context. The research also targets only a downstream activities of biodiesel supply chain context. The interview-based approach as a qualitative approach faces some subjectivity challenges among respondents.Practical implications: The research result provides some positive implications for business practice, includes how to minimize the impact of supply chain risk on company’s business activities and performance, how supply chain experts and practitioners used risk mitigation practices, how to formulate strategic plans to minimize the impact of supply chain risk and enhance the effectivity and sustainability of the supply chain activities.Social implications: The implication for business practice was that company’s leaders implemented supply chain risk mitigation strategies that provide positive impacts on the more valuable relationship among supply chain actors and stakeholders.Originality/value: The first, is an activities areas and operation schemes-based of biodiesel supply chain point of view. The second, is a multi-perspectives-based biodiesel supply chain characteristics framework. The third, is an energy security framework-based biodiesel risk mitigation strategies framework.

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0112 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0112 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Infodemic is a significant concern for society, and numerous studies have explored artificial intelligence (AI)-based solutions to manage it. However, existing methods fail to make sense of the complex situation of infodemic and fail to manage infodemic in a proactive manner. To tackle this challenge, we formalize infodemic topic prediction (ITP) as a distinct research problem. ITP aims to identify emerging topics to provide a global view of the situation, as well as to predict the probability of a topic becoming an infodemic to enable proactive management. Although some relevant methods exist, they are insufficient in modeling the intertopic relationships where each topic is closely connected to all others in a group of topics (called multilateral intertopic relationships). This study proposes a new hypergraph-based ITP framework that models the multilateral intertopic relationship with a novel hypergraph. Specifically, we introduce a novel temporal topic hypergraph (TTH) where topics are treated as nodes, and the multilateral intertopic relationships in both semantic and temporal perspectives are modeled by hyperedges. The main novelties are twofold. First, our TTH is a novel hypergraph that can cope with newly emerged topics through the proposed directed and undirected hyperedges. Second, we propose a novel similarity-based transformation method (STM) that reduces the complexity of hypergraph transformation from [Formula: see text] to [Formula: see text], making it scalable for social media data. Evaluations on infodemics during the COVID-19 and the Mpox pandemic demonstrate the effectiveness of our framework. This study contributes to responsible infodemic management by formalizing the task of ITP and introducing a novel framework for it, which enables public health organizations, social media platforms, and policymakers to proactively make sense of and effectively respond to infodemic. History: This paper has been accepted by Kaushik Dutta for the Special Issue on the Responsible AI and Data Science for Social Good. Funding: L. Zhao, S. Ding, Y. Chai and S. Yang are supported by the National Natural Science Foundation of China [Grants 72293581, 72188101, 72342011, and 72322019]. J. Xie and X. Fang are not supported by any funds and are not associated with any of the above mentioned funds. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0660 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0660 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses, and there is uncertainty on which scoring function is used to model the responses. In this paper, we address the scoring function uncertainty of continuous response ranking problems by proposing a ranking model averaging (RMA) method. With a set of candidate models varied by scoring functions, RMA assigns weights for each model determined by a K-fold crossvalidation criterion based on pairwise loss. We provide two main theoretical properties for RMA. First, we prove that the averaging ranking predictions of RMA are asymptotically optimal in achieving the lowest possible ranking risk. Second, we provide a bound on the difference between the empirical RMA weights and theoretical optimal ones, and we show that RMA weights are consistent. Simulation results validate RMA superiority over competing methods in reducing ranking risk. Moreover, when applied to empirical examples—order priority, wine quality, and piston slap noise—RMA shows its effectiveness in building accurate ranking systems. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This research was supported by the Beijing Municipal Natural Science Foundation [Grant 1222002], the National Natural Science Foundation of China [Grants 12071457, 12201018, 12301364, 71925007, 72091212, and 72273120], National Quality Infrastructure [Grant 2022YFF0609903], the Chinese Academy of Sciences Project for Young Scientists in Basic Research [Grant YSBR-008], and the Natural Science Foundation of Anhui Province [Grant 2308085QA09]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0257 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0257 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The redundancy allocation problem (RAP) aims to find an allocation of redundant components that maximizes system reliability under resource constraints. In this paper, we consider the RAP for complex systems with heterogeneous components, which may have bridges or interconnecting subsystems and are generally more complicated in structure than commonly studied series-parallel systems. A major challenge of the RAP is that the reliability function is nonlinear. To tackle this, we develop six exact mixed integer linear programming (MILP) formulations, which, to our knowledge, are the first of such formulations for the problem. The key idea is to use binary variables and big-M constraints to successively linearize each product term in the reliability function. In addition, we propose a novel tree representation of the reliability function to establish the interconnections between different product terms, which is then used to reduce the number of decision variables and constraints needed in the linearization. We also propose an alternative form of the reliability function that only involves the failure probabilities of subsystems, and leverage this to derive better variable bounds, smaller big-M constants, and simpler formulations. We compare the six MILP formulations by solving them directly using an MILP solver based on three sets of test instances of varying complexity. Results indicate that all formulations achieve optimal solutions quickly for the first set and within reasonable computation times for the second set. For the third set, two formulations can always be solved within a given time limit. Overall, the MILP formulation derived based on the alternative form of the reliability function and a specific type of tree representation scheme performs the best. We also demonstrate that our approach is more robust and scalable than a benchmark branch-and-bound method. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by National Natural Science Foundation of China [Grants 72310107003, 72471189, 71901176]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0842 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0842 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

In this paper, we present an efficient scenario reduction method for optimization problems whose objective function is the higher moment coherent risk (HMCR) measures. Compared with existing approaches, our method places greater emphasis on the characteristics of optimization problems. Because the value of HMCR measures only depends on a small subset of scenarios that correspond to high cost or loss, our approach is based mostly on the concept of ineffective scenarios previously proposed in the literature, which entails identifying the scenarios whose removal from the problem results in no change of the optimal value. We test our method on a simple portfolio optimization problem with only nine risky assets and a realistic one with 50 risky assets and cardinality constraints. Results show that our scenario reduction method can yield a more accurate optimal solution and optimal value, along with a smaller reduced-scenario set. Even at the same reduction level, our method continues to outperform the existing scenario reduction methods. Interestingly, the portfolio produced by our method is also more diversified than others. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71720107002 and U1901223]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0375 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0375 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .