The Experts below are selected from a list of 8532 Experts worldwide ranked by ideXlab platform
Alexandre Dolgui - One of the best experts on this subject based on the ideXlab platform.
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ripple effect modelling of Supplier disruption integrated markov chain and dynamic bayesian network approach
International Journal of Production Research, 2020Co-Authors: Seyedmohsen Hosseini, Dmitry Ivanov, Alexandre DolguiAbstract:The ripple effect can occur when a Supplier Base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing o...
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Ripple effect modelling of Supplier disruption: integrated Markov chain and dynamic Bayesian network approach
International Journal of Production Research, 2020Co-Authors: Seyedmohsen Hosseini, Dmitry Ivanov, Alexandre DolguiAbstract:The ripple effect could occur when a disruption in Supplier Base cannot be localised and its consequences propagate downstream the supply chain (SC) adversely affecting the performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in single-echelon-single-event setting is desirable and indeed critical for some firms, modeling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, ripple effect assessment in multi-stage SCs is particularly challenged by consideration of both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model Based on integrating Discrete-Time Markov Chain (DTMC) and Dynamic Bayesian Network (DBN) model to quantify the ripple effect. We use DTMC to model the recovery and vulnerability of Suppliers. The proposed DTMC model is then equalized with a DBN model in order to simulate the propagation behavior of Supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of Supplier disruption on manufacturer in terms of total expected utility and service level. The ripple effect measure constructed is examined and tested using two case-studies. The findings suggest that our model can be of value in revealing latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritizing the contingency and recovery policies.
Dmitry Ivanov - One of the best experts on this subject based on the ideXlab platform.
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ripple effect modelling of Supplier disruption integrated markov chain and dynamic bayesian network approach
International Journal of Production Research, 2020Co-Authors: Seyedmohsen Hosseini, Dmitry Ivanov, Alexandre DolguiAbstract:The ripple effect can occur when a Supplier Base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing o...
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Ripple effect modelling of Supplier disruption: integrated Markov chain and dynamic Bayesian network approach
International Journal of Production Research, 2020Co-Authors: Seyedmohsen Hosseini, Dmitry Ivanov, Alexandre DolguiAbstract:The ripple effect could occur when a disruption in Supplier Base cannot be localised and its consequences propagate downstream the supply chain (SC) adversely affecting the performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in single-echelon-single-event setting is desirable and indeed critical for some firms, modeling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, ripple effect assessment in multi-stage SCs is particularly challenged by consideration of both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model Based on integrating Discrete-Time Markov Chain (DTMC) and Dynamic Bayesian Network (DBN) model to quantify the ripple effect. We use DTMC to model the recovery and vulnerability of Suppliers. The proposed DTMC model is then equalized with a DBN model in order to simulate the propagation behavior of Supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of Supplier disruption on manufacturer in terms of total expected utility and service level. The ripple effect measure constructed is examined and tested using two case-studies. The findings suggest that our model can be of value in revealing latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritizing the contingency and recovery policies.
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a supervised machine learning approach to data driven simulation of resilient Supplier selection in digital manufacturing
International Journal of Information Management, 2019Co-Authors: Ian M Cavalcante, Enzo Morosini Frazzon, Fernando Antonio Forcellini, Dmitry IvanovAbstract:Abstract There has been an increased interest in resilient Supplier selection in recent years, much of it focusing on forecasting the disruption probabilities. We conceptualize an entirely different approach to analyzing the risk profiles of Supplier performance under uncertainty by utilizing the data analytics capabilities in digital manufacturing. Digital manufacturing peculiarly challenge the Supplier selection by the dynamic order allocations, and opens new opportunities to exploit the digital data to improve sourcing decisions. We develop a hybrid technique, combining simulation and machine learning and examine its applications to data-driven decision-making support in resilient Supplier selection. We consider on-time delivery as an indicator for Supplier reliability, and explore the conditions surrounding the formation of resilient supply performance profiles. We theorize the notions of risk profile of Supplier performance and resilient supply chain performance. We show that the associations of the deviations from the resilient supply chain performance profile with the risk profiles of Supplier performance can be efficiently deciphered by our approach. The results suggest that a combination of supervised machine learning and simulation, if utilized properly, improves the delivery reliability. Our approach can also be of value when analyzing the Supplier Base and uncovering the critical Suppliers, or combinations of Suppliers the disruption of which result in the adverse performance decreases. The results of this study advance our understanding about how and when machine learning and simulation can be combined to create digital supply chain twins, and through these twins improve resilience. The proposed data-driven decision-making model for resilient Supplier selection can be further exploited for design of risk mitigation strategies in supply chain disruption management models, re-designing the Supplier Base or investing in most important and risky Suppliers.
Seyedmohsen Hosseini - One of the best experts on this subject based on the ideXlab platform.
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ripple effect modelling of Supplier disruption integrated markov chain and dynamic bayesian network approach
International Journal of Production Research, 2020Co-Authors: Seyedmohsen Hosseini, Dmitry Ivanov, Alexandre DolguiAbstract:The ripple effect can occur when a Supplier Base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing o...
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Ripple effect modelling of Supplier disruption: integrated Markov chain and dynamic Bayesian network approach
International Journal of Production Research, 2020Co-Authors: Seyedmohsen Hosseini, Dmitry Ivanov, Alexandre DolguiAbstract:The ripple effect could occur when a disruption in Supplier Base cannot be localised and its consequences propagate downstream the supply chain (SC) adversely affecting the performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in single-echelon-single-event setting is desirable and indeed critical for some firms, modeling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, ripple effect assessment in multi-stage SCs is particularly challenged by consideration of both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model Based on integrating Discrete-Time Markov Chain (DTMC) and Dynamic Bayesian Network (DBN) model to quantify the ripple effect. We use DTMC to model the recovery and vulnerability of Suppliers. The proposed DTMC model is then equalized with a DBN model in order to simulate the propagation behavior of Supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of Supplier disruption on manufacturer in terms of total expected utility and service level. The ripple effect measure constructed is examined and tested using two case-studies. The findings suggest that our model can be of value in revealing latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritizing the contingency and recovery policies.
Daniel Yung - One of the best experts on this subject based on the ideXlab platform.
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combinatorial auctions for procurement an empirical study of the chilean school meals auction
Management Science, 2012Co-Authors: Marcelo Olivares, Gabriel Y Weintraub, Rafael Epstein, Daniel YungAbstract:In this paper we conduct an empirical investigation of a large-scale combinatorial auction (CA)---the Chilean auction for school meals in which the government procures half a billion dollars worth of meal services every year. Our empirical study is motivated by two fundamental aspects in the design of CAs: (1) which packages should bidders be allowed to bid on and (2) diversifying the Supplier Base to promote competition. We use bidding data to uncover important aspects of the firms' cost structure and their strategic behavior, both of which are not directly observed by the auctioneer; these estimates inform the auction design. Our results indicate that package bidding that allows firms to express their cost synergies due to economies of scale and density seems appropriate. However, we also found evidence that firms can take advantage of this flexibility by discounting package bids for strategic reasons and not driven by cost synergies. Because this behavior can lead to inefficiencies, it may be worth evaluating whether to prohibit certain specific combinations in the bidding process. Our results also suggest that market share restrictions and running sequential auctions seem to promote competition in the long run, without significantly increasing the short-run cost for the government due to unrealized cost synergies. Our results highlight that the simultaneous consideration of the firms' operational cost structure and their strategic behavior is key to the successful design of a CA. More broadly, our paper is the first to provide an econometric study of a large-scale CA, providing novel and substantive insights regarding bidding behavior in this type of auction. This paper was accepted by Martin Lariviere, operations management.
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combinatorial auctions for procurement an empirical study of the chilean school meals auction
2011Co-Authors: Marcelo Olivares, Gabriel Y Weintraub, Rafael Epstein, Daniel YungAbstract:In this paper we conduct an empirical investigation of a large-scale combinatorial auction (CA); the Chilean auction for school meals in which the government procures half a billion dollars worth of meal services every year. Our empirical study is motivated by two fundamental aspects in the design of CAs: (1) which packages should bidders be allowed to bid on; and (2) diversifying the Supplier Base to promote competition. We use bidding data to uncover important aspects of the firms' cost structure and their strategic behavior, both of which are not directly observed by the auctioneer; these estimates inform the auction design. Our results indicate that package bidding that allows firms to express their cost synergies due to economies of scale and density seems appropriate. However, we also found evidence that firms can take advantage of this flexibility by discounting package bids for strategic reasons and not driven by cost synergies. Because this behavior can lead to inefficiencies, it may be worth evaluating whether to prohibit certain specific combinations in the bidding process. Our results also suggest that market share restrictions and running sequential auctions seem to promote competition in the long-run, without significantly increasing the short-run cost for the government due to unrealized cost synergies. Our results highlight that the simultaneous consideration of the firms' operational cost structure and their strategic behavior is key to the successful design of a CA. More broadly, our paper is the first to provide an econometric study of a large-scale CA, providing novel and substantive insights regarding bidding behavior in this type of auctions.
Jingjing Xu - One of the best experts on this subject based on the ideXlab platform.
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integrated inventory management and Supplier Base reduction in a supply chain with multiple uncertainties
European Journal of Operational Research, 2014Co-Authors: Dongping Song, Jingxin Dong, Jingjing XuAbstract:This paper considers a manufacturing supply chain with multiple Suppliers in the presence of multiple uncertainties such as uncertain material supplies, stochastic production times, and random customer demands. The system is subject to supply and production capacity constraints. We formulate the integrated inventory management policy for raw material procurement and production control using the stochastic dynamic programming approach. We then investigate the Supplier Base reduction strategies and the Supplier differentiation issue under the integrated inventory management policy. The qualitative relationships between the Supplier Base size, the Supplier capabilities and the total expected cost are established. Insights into differentiating the procurement decisions to different Suppliers are provided. The model further enables us to quantitatively achieve the trade-off between the Supplier Base reduction and the Supplier capability improvement, and quantify the Supplier differentiation in terms of procurement decisions. Numerical examples are given to illustrate the results.
