Pub Date : 2015-11-01DOI: 10.1109/IESM.2015.7380255
Abroon Qazi, J. Quigley, Alex Dickson, B. Gaudenzi, Şule Önsel Ekici
Risk identification and risk estimation are important stages of any risk management process. Existing research in Supply chain risk management has mainly focused on these two stages whereas risk evaluation has not been fully explored which is an equally significant stage involving evaluation of different risk mitigation strategies. The main purpose of this paper is to propose a method of evaluating different mitigation strategies through cost and benefit analysis. The proposed method introduces a unique concept of integrating cost and relative impact of different combinations of mitigation strategies within a network setting of interconnected risk triggers, risk factors and risk mitigation strategies. We have applied our method on a case study that was conducted in an aerospace supply chain. Our approach is useful in identifying an optimal combination of mitigation strategies against a given budget constraint. Furthermore, the model can also be used for determining such strategies in relation to a given level of risk exposure. We have incorporated NoisyOR function within the Bayesian Network model in order to reduce the complexity involved in eliciting a huge number of conditional probability values.
{"title":"Cost and benefit analysis of supplier risk mitigation in an aerospace Supply chain","authors":"Abroon Qazi, J. Quigley, Alex Dickson, B. Gaudenzi, Şule Önsel Ekici","doi":"10.1109/IESM.2015.7380255","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380255","url":null,"abstract":"Risk identification and risk estimation are important stages of any risk management process. Existing research in Supply chain risk management has mainly focused on these two stages whereas risk evaluation has not been fully explored which is an equally significant stage involving evaluation of different risk mitigation strategies. The main purpose of this paper is to propose a method of evaluating different mitigation strategies through cost and benefit analysis. The proposed method introduces a unique concept of integrating cost and relative impact of different combinations of mitigation strategies within a network setting of interconnected risk triggers, risk factors and risk mitigation strategies. We have applied our method on a case study that was conducted in an aerospace supply chain. Our approach is useful in identifying an optimal combination of mitigation strategies against a given budget constraint. Furthermore, the model can also be used for determining such strategies in relation to a given level of risk exposure. We have incorporated NoisyOR function within the Bayesian Network model in order to reduce the complexity involved in eliciting a huge number of conditional probability values.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130307039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-23DOI: 10.1109/IESM.2015.7380267
Abroon Qazi, J. Quigley, Alex Dickson, K. Kirytopoulos
Project complexity has been extensively explored in the literature because of its major contribution towards the failure of major projects in terms of cost and time overruns. Researchers have identified important factors that contribute to the project complexity and validated their findings through case studies. Few studies have even focused on developing tools for evaluating the project complexity. However, existing research has not explored an important aspect of linking project complexity to different types of project and supply chain risks. We propose a framework for establishing risk paths across project complexity elements, project and supply chain risks, and resulting consequences. Project complexity elements are the knowns at the commencement stage of a project whereas project and supply chain risks are the uncertainties that might realize within the life cycle of the project. We demonstrate application of our proposed framework through a simple simulation example using Bayesian Belief Network. The method can be an important contribution to the literature and beneficial to the practitioners in terms of introducing a new perspective of investigating causal paths of interacting project complexity elements and risks.
{"title":"Modelling project complexity driven risk paths in new product development","authors":"Abroon Qazi, J. Quigley, Alex Dickson, K. Kirytopoulos","doi":"10.1109/IESM.2015.7380267","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380267","url":null,"abstract":"Project complexity has been extensively explored in the literature because of its major contribution towards the failure of major projects in terms of cost and time overruns. Researchers have identified important factors that contribute to the project complexity and validated their findings through case studies. Few studies have even focused on developing tools for evaluating the project complexity. However, existing research has not explored an important aspect of linking project complexity to different types of project and supply chain risks. We propose a framework for establishing risk paths across project complexity elements, project and supply chain risks, and resulting consequences. Project complexity elements are the knowns at the commencement stage of a project whereas project and supply chain risks are the uncertainties that might realize within the life cycle of the project. We demonstrate application of our proposed framework through a simple simulation example using Bayesian Belief Network. The method can be an important contribution to the literature and beneficial to the practitioners in terms of introducing a new perspective of investigating causal paths of interacting project complexity elements and risks.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116348805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-23DOI: 10.1109/IESM.2015.7380298
Abroon Qazi, J. Quigley, Alex Dickson, B. Gaudenzi, Şule Önsel Ekici
Supply chains have become complex and vulnerable and therefore, researchers are developing effective techniques in order to capture the complex structure of the supply network and interdependency between supply chain risks. Researchers have recently started using Bayesian Belief Networks for modelling supply chain risks. However, these models are still focused on limited domains of supply chain risk management like supplier selection, supplier performance evaluation and ranking. We have developed a comprehensive risk management process using Bayesian networks that captures all three stages of risk management including risk identification, risk assessment and risk evaluation. Our proposed new risk measures and evaluation scheme of different combinations of control strategies are considered as an important contribution to the literature. We have modelled supply network as a Bayesian Belief Network incorporating the supply network configuration, probabilistic interdependency between risks, resulting losses, risk mitigation control strategies and associated costs. An illustrative example is presented and three different models are solved corresponding to different risk attitudes of the decision maker. Based on our results, it is not always viable to implement control strategy at the most important risk factor because of the consideration of mitigation cost, relative loss and probabilistic interdependency between connected risk factors.
{"title":"Evaluation of control strategies for managing supply chain risks using Bayesian Belief Networks","authors":"Abroon Qazi, J. Quigley, Alex Dickson, B. Gaudenzi, Şule Önsel Ekici","doi":"10.1109/IESM.2015.7380298","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380298","url":null,"abstract":"Supply chains have become complex and vulnerable and therefore, researchers are developing effective techniques in order to capture the complex structure of the supply network and interdependency between supply chain risks. Researchers have recently started using Bayesian Belief Networks for modelling supply chain risks. However, these models are still focused on limited domains of supply chain risk management like supplier selection, supplier performance evaluation and ranking. We have developed a comprehensive risk management process using Bayesian networks that captures all three stages of risk management including risk identification, risk assessment and risk evaluation. Our proposed new risk measures and evaluation scheme of different combinations of control strategies are considered as an important contribution to the literature. We have modelled supply network as a Bayesian Belief Network incorporating the supply network configuration, probabilistic interdependency between risks, resulting losses, risk mitigation control strategies and associated costs. An illustrative example is presented and three different models are solved corresponding to different risk attitudes of the decision maker. Based on our results, it is not always viable to implement control strategy at the most important risk factor because of the consideration of mitigation cost, relative loss and probabilistic interdependency between connected risk factors.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"37 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133290027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-21DOI: 10.1109/IESM.2015.7380289
Idris Lalami, Y. Frein, J. Gayon
Based on a case study in the automotive industry, this paper presents a mathematical model dedicated to support the master production scheduling process in powertrain plants. Powertrain plants produce engines, gearboxes, and chassis parts that supply the car assembly plants and spare parts centers. The proposed model helps to decide, every week, for each production line, on the quantities to be produced of each product over a planning horizon of several weeks. The model is a mixed integer linear program optimizing four objectives: satisfying the forecast customer demand, reaching safety stock levels, balancing stock levels between products, and leveling the production percentage of each product over the time. The model also considers the specific constraints of each production line. The contribution of this paper is to provide an overview of a production planning problem coming from a real case study, to describe the model designed according to the needs of the plant planning team, and to present the results obtained in terms of solution relevance and calculation time.
{"title":"A model for master production scheduling in automotive powertrain plants: A case study","authors":"Idris Lalami, Y. Frein, J. Gayon","doi":"10.1109/IESM.2015.7380289","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380289","url":null,"abstract":"Based on a case study in the automotive industry, this paper presents a mathematical model dedicated to support the master production scheduling process in powertrain plants. Powertrain plants produce engines, gearboxes, and chassis parts that supply the car assembly plants and spare parts centers. The proposed model helps to decide, every week, for each production line, on the quantities to be produced of each product over a planning horizon of several weeks. The model is a mixed integer linear program optimizing four objectives: satisfying the forecast customer demand, reaching safety stock levels, balancing stock levels between products, and leveling the production percentage of each product over the time. The model also considers the specific constraints of each production line. The contribution of this paper is to provide an overview of a production planning problem coming from a real case study, to describe the model designed according to the needs of the plant planning team, and to present the results obtained in terms of solution relevance and calculation time.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125143870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-21DOI: 10.1109/IESM.2015.7380330
Kunal Kumar, Christian Clavijo López, Oscar Augusto Tellez Sanchez, A. Gupta, Olivier Péton, T. Yeung, Adrien Vanuxem
Many models have been recently developed for the optimization of biomass related supply chains. However, models for biopower supply chains powered by animal waste have not received much attention yet. In this paper, we propose a mixed integer linear programming model for supplier selection and procurement planning for a biopower plant. The model integrates time window constraints for the collection of animal waste as well as inventory constraints. We show that the model is intractable with a state-of-the art commercial solver and propose a heuristic approach based on the Adaptive Large Neighbourhood Search (ALNS) framework. We show the efficiency of this approach on a case study in central France.
{"title":"Integrated strategic and tactical optimization of animal-waste sourced biopower supply chains","authors":"Kunal Kumar, Christian Clavijo López, Oscar Augusto Tellez Sanchez, A. Gupta, Olivier Péton, T. Yeung, Adrien Vanuxem","doi":"10.1109/IESM.2015.7380330","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380330","url":null,"abstract":"Many models have been recently developed for the optimization of biomass related supply chains. However, models for biopower supply chains powered by animal waste have not received much attention yet. In this paper, we propose a mixed integer linear programming model for supplier selection and procurement planning for a biopower plant. The model integrates time window constraints for the collection of animal waste as well as inventory constraints. We show that the model is intractable with a state-of-the art commercial solver and propose a heuristic approach based on the Adaptive Large Neighbourhood Search (ALNS) framework. We show the efficiency of this approach on a case study in central France.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129729061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-21DOI: 10.1109/IESM.2015.7380232
Anne-Laure Ladier, G. Alpan
In a cross-docking platform, goods are unloaded, transferred and reloaded into trucks with little or no storage in between. The crossdock truck scheduling problem addresses the hard problem of coordinating the truck operations. However, crossdock operations are mostly done manually: it is therefore important to take staffing issues into account while building the truck schedule. This article shows how a truck scheduling model and an employee timetabling and rostering model can be combined to address both problems in an integrated manner. Three approaches are compared. The sequential approach consists in sequentially solving the two problems: from the truck schedule calculated first, a workload is deduced and used as input for the employee timetabling and rostering process. The iterative approach consists in solving both problems one after another in an iterative manner until a stable point is reached. Two iterative procedures are proposed, employees-first and trucks-first.
{"title":"Integrating truck scheduling and employee rostering in a cross-docking platform - an iterative approach","authors":"Anne-Laure Ladier, G. Alpan","doi":"10.1109/IESM.2015.7380232","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380232","url":null,"abstract":"In a cross-docking platform, goods are unloaded, transferred and reloaded into trucks with little or no storage in between. The crossdock truck scheduling problem addresses the hard problem of coordinating the truck operations. However, crossdock operations are mostly done manually: it is therefore important to take staffing issues into account while building the truck schedule. This article shows how a truck scheduling model and an employee timetabling and rostering model can be combined to address both problems in an integrated manner. Three approaches are compared. The sequential approach consists in sequentially solving the two problems: from the truck schedule calculated first, a workload is deduced and used as input for the employee timetabling and rostering process. The iterative approach consists in solving both problems one after another in an iterative manner until a stable point is reached. Two iterative procedures are proposed, employees-first and trucks-first.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122604219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-21DOI: 10.1109/IESM.2015.7380222
Z. Azimi, M. Salari, J. Renaud, Angel B. Ruiz
Management of biomedical samples plays a central role in an efficient healthcare system and requires important resources. This paper addresses an assignment problem where several types of samples are collected at clinics and other collection centers, and then transported to medical laboratories to be analysed. For practical reasons, samples of the same type collected at the same collection center are consolidated and sent to the same laboratory. However, a collection center may send different types of samples to different laboratories. Moreover, demand for each type of sample at each collection center is uncertain but modelled by a known probability distribution. In this context, collection centers need to be allocated to laboratories in order to balance the workload between them while minimizing the total collecting distance. To tackle this problem, we first formulate it as linear integer model assuming deterministic demand. Then, a stochastic counterpart is presented, along with a solving method based on the sample average approximation technique (SAA). Numerical experiments inspired by a real-life case are conducted to illustrate how the proposed approach may be contribute to help decision makers to better manage the laboratories' capacity, increasing the system efficiency.
{"title":"Operating a biomedical samples laboratories network under stochastic demand","authors":"Z. Azimi, M. Salari, J. Renaud, Angel B. Ruiz","doi":"10.1109/IESM.2015.7380222","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380222","url":null,"abstract":"Management of biomedical samples plays a central role in an efficient healthcare system and requires important resources. This paper addresses an assignment problem where several types of samples are collected at clinics and other collection centers, and then transported to medical laboratories to be analysed. For practical reasons, samples of the same type collected at the same collection center are consolidated and sent to the same laboratory. However, a collection center may send different types of samples to different laboratories. Moreover, demand for each type of sample at each collection center is uncertain but modelled by a known probability distribution. In this context, collection centers need to be allocated to laboratories in order to balance the workload between them while minimizing the total collecting distance. To tackle this problem, we first formulate it as linear integer model assuming deterministic demand. Then, a stochastic counterpart is presented, along with a solving method based on the sample average approximation technique (SAA). Numerical experiments inspired by a real-life case are conducted to illustrate how the proposed approach may be contribute to help decision makers to better manage the laboratories' capacity, increasing the system efficiency.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129713763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-21DOI: 10.1109/IESM.2015.7380220
Etienne Joubert, M. Espinasse, M. Nakhla
Emergency Departments (ED) face chronic issues such as longer length of stay and decreased quality of care. These organizations have developed the use of Lean manufacturing techniques to improve patients flow. Adaptations of Lean in healthcare almost systematically return positive results and negative potential effects are underestimated. We use the still debating issue on whether a physician should take part of the triage to explore the pros and cons of a Lean technique in healthcare. We show that triage physicians impact “low-severity” patients length of care and do not impact inpatients one. We also show that physician triage is associated with a higher length of triage that can create a waiting lines before the triage operation. We discuss these results by showing that the decision to implement Lean techniques should be taken considering a risk / benefits trade-off, which is hardly the case actually.
{"title":"Patients flow optimization in ED: An operational research on the impacts of physician triage","authors":"Etienne Joubert, M. Espinasse, M. Nakhla","doi":"10.1109/IESM.2015.7380220","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380220","url":null,"abstract":"Emergency Departments (ED) face chronic issues such as longer length of stay and decreased quality of care. These organizations have developed the use of Lean manufacturing techniques to improve patients flow. Adaptations of Lean in healthcare almost systematically return positive results and negative potential effects are underestimated. We use the still debating issue on whether a physician should take part of the triage to explore the pros and cons of a Lean technique in healthcare. We show that triage physicians impact “low-severity” patients length of care and do not impact inpatients one. We also show that physician triage is associated with a higher length of triage that can create a waiting lines before the triage operation. We discuss these results by showing that the decision to implement Lean techniques should be taken considering a risk / benefits trade-off, which is hardly the case actually.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130840263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-21DOI: 10.1109/IESM.2015.7380131
P. Sun, P. Bon, S. Collart-Dutilleul
In model driven engineering, model transformation is the “heart and soul ”. The purpose of using a model transformation is to save efforts and reduce errors by automatically building the models that conform to different modelling languages. In the French railway industry, the Petri nets and the B method are two recognized formal methods for safety critical systems, having their own successful applications. The Petri nets are a mathematical modelling language for describing the distributed systems, and they offer superior graphical notations for stepwise processes. The B method is a software development method based on abstract machine notations and the concept of refinement. There are already some tools supporting B language. The Petri nets are accepted by the French railway specialists, because they have user-friendly notations. Consequently, various railway systems and key components have been specified by Petri nets and have been validated by railway experts. For a better model representation, the “prioritized transitions” can be a useful mechanism in such models. In order to produce the final executable codes and to make use of all the existing valid models, this paper introduces a transformation method, which could take advantage of both formal languages and transform a valid Petri net model to an abstract B machine. This transformation is presented with a systematic mapping process and illustrated by a case study.
{"title":"Model transformation from coloured Petri nets with prioritized transitions to B machines","authors":"P. Sun, P. Bon, S. Collart-Dutilleul","doi":"10.1109/IESM.2015.7380131","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380131","url":null,"abstract":"In model driven engineering, model transformation is the “heart and soul ”. The purpose of using a model transformation is to save efforts and reduce errors by automatically building the models that conform to different modelling languages. In the French railway industry, the Petri nets and the B method are two recognized formal methods for safety critical systems, having their own successful applications. The Petri nets are a mathematical modelling language for describing the distributed systems, and they offer superior graphical notations for stepwise processes. The B method is a software development method based on abstract machine notations and the concept of refinement. There are already some tools supporting B language. The Petri nets are accepted by the French railway specialists, because they have user-friendly notations. Consequently, various railway systems and key components have been specified by Petri nets and have been validated by railway experts. For a better model representation, the “prioritized transitions” can be a useful mechanism in such models. In order to produce the final executable codes and to make use of all the existing valid models, this paper introduces a transformation method, which could take advantage of both formal languages and transform a valid Petri net model to an abstract B machine. This transformation is presented with a systematic mapping process and illustrated by a case study.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131760521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-21DOI: 10.1109/IESM.2015.7380183
Anne Francine Martins, Roberta Costa Affonso, Simon Tamayo, S. Lamouri, Christine Baldy Ngayo
In an increasingly volatile, globalized, and demanding market, Lean is the differential factor that could increase companies' competitiveness and efficiency. In spite of the abundant literature addressing Lean system's technical aspects, there has been little discussion on the importance of national culture in Lean's implementation process. It has been proven that the implementation of lean practices do not always produce the intended results and national culture has been highlighted as one of the contextual variables that may explain the success or failure of Lean practices. Since companies are influenced by the culture of the country where they're located, some comparative advantages may occur due to their location, making it necessary to adjust Lean's implementation process to national culture. The purpose of this article is to propose a literature review to examine the relationship between national culture and Lean Management. This study explores the assertions and/or contradictions found in the literature regarding the cultural dimensions that may act as enablers or withholders to the lean principles and practices.
{"title":"Relationships between national culture and Lean Management: A literature Review","authors":"Anne Francine Martins, Roberta Costa Affonso, Simon Tamayo, S. Lamouri, Christine Baldy Ngayo","doi":"10.1109/IESM.2015.7380183","DOIUrl":"https://doi.org/10.1109/IESM.2015.7380183","url":null,"abstract":"In an increasingly volatile, globalized, and demanding market, Lean is the differential factor that could increase companies' competitiveness and efficiency. In spite of the abundant literature addressing Lean system's technical aspects, there has been little discussion on the importance of national culture in Lean's implementation process. It has been proven that the implementation of lean practices do not always produce the intended results and national culture has been highlighted as one of the contextual variables that may explain the success or failure of Lean practices. Since companies are influenced by the culture of the country where they're located, some comparative advantages may occur due to their location, making it necessary to adjust Lean's implementation process to national culture. The purpose of this article is to propose a literature review to examine the relationship between national culture and Lean Management. This study explores the assertions and/or contradictions found in the literature regarding the cultural dimensions that may act as enablers or withholders to the lean principles and practices.","PeriodicalId":308675,"journal":{"name":"2015 International Conference on Industrial Engineering and Systems Management (IESM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122555803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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