Pub Date : 2021-12-01DOI: 10.46254/j.ieom.20210204
K. Vimal, A. Kulatunga, Lakshmanakumar Veeraragavan, M. Ravichandran, Jayakrishna Kandasamy
The continuous increase in production, lack of flexibility of organizations, and lack of knowledge on sustainability have led to the depletion of raw materials and increased waste generation. Industrial symbiosis now has become a very effective solution and an essential strategy for responsible consumption and waste utilization. This strategy helps different organizations to blend their resources, share information, logistics, and waste materials to solve their problems by forming a network to increase profits. This study was directed towards identifying the barriers towards applying Industrial Symbiosis in an organization with probable solutions to them. ISM modeling and MICMAC analysis were used to visualize the impact of different barriers for implementing Industrial symbiosis in an organization and improve efficiency in terms of eco-innovation. The results of this study give experiences and rules to practicing managers in medium and small-scale industries to effectively execute Industrial Symbiosis. The study also adds to the improvement of a basic model for examining the barriers affecting IS with regards to eco-innovation and sustainable frameworks and contributes to ongoing researches on this eco-friendly idea of Industrial Symbiosis.
{"title":"Prioritization of barriers in industrial symbiosis implementation in automotive industry - Using ISM and MICMAC Analysis","authors":"K. Vimal, A. Kulatunga, Lakshmanakumar Veeraragavan, M. Ravichandran, Jayakrishna Kandasamy","doi":"10.46254/j.ieom.20210204","DOIUrl":"https://doi.org/10.46254/j.ieom.20210204","url":null,"abstract":"The continuous increase in production, lack of flexibility of organizations, and lack of knowledge on sustainability have led to the depletion of raw materials and increased waste generation. Industrial symbiosis now has become a very effective solution and an essential strategy for responsible consumption and waste utilization. This strategy helps different organizations to blend their resources, share information, logistics, and waste materials to solve their problems by forming a network to increase profits. This study was directed towards identifying the barriers towards applying Industrial Symbiosis in an organization with probable solutions to them. ISM modeling and MICMAC analysis were used to visualize the impact of different barriers for implementing Industrial symbiosis in an organization and improve efficiency in terms of eco-innovation. The results of this study give experiences and rules to practicing managers in medium and small-scale industries to effectively execute Industrial Symbiosis. The study also adds to the improvement of a basic model for examining the barriers affecting IS with regards to eco-innovation and sustainable frameworks and contributes to ongoing researches on this eco-friendly idea of Industrial Symbiosis.","PeriodicalId":268888,"journal":{"name":"International Journal of Industrial Engineering and Operations Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129633806","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 : 2021-10-01DOI: 10.46254/j.ieom.20210103
Hassan Hijry, Richard Olawoyin
Many hospitals consider the length of time waiting in queue to be a measure of emergency room (ER) overcrowding. Long waiting times plague many ER departments, hindering the ability to effectively provide medical attention to those in need and increasing overall costs. Advanced techniques such as machine learning and deep learning (DL) have played a central role in queuing system applications. This study aims to apply DL algorithms for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). We applied four optimization algorithms, including SGD, Adam, RMSprop, and AdaGrad. The algorithms were compared to find the best model with the lowest mean absolute error (MAE). A traditional mathematical simulation was used for additional comparisons. The results showed that the DL model is applicable using the SGD algorithm by activating a lowest MAE of 10.80 minutes (24% error reduction) to predict patients' waiting times. This work presents a theoretical contribution of predicting patients’ waiting time with alternative techniques by achieving the highest performing model to better prioritize patients waiting in the queue. Also, this study offers a practical contribution by using real-life data from ERs. Furthermore, we proposed models to predict patients' waiting time with more accurate results than a traditional mathematical method. Our approach can be easily implemented for the queue system in the healthcare sector using electronic health records (EHR) data.
{"title":"Predicting Patient Waiting Time in the Queue System Using Deep Learning Algorithms in the Emergency Room","authors":"Hassan Hijry, Richard Olawoyin","doi":"10.46254/j.ieom.20210103","DOIUrl":"https://doi.org/10.46254/j.ieom.20210103","url":null,"abstract":"Many hospitals consider the length of time waiting in queue to be a measure of emergency room (ER) overcrowding. Long waiting times plague many ER departments, hindering the ability to effectively provide medical attention to those in need and increasing overall costs. Advanced techniques such as machine learning and deep learning (DL) have played a central role in queuing system applications. This study aims to apply DL algorithms for historical queueing variables to predict patient waiting time in a system alongside, or in place of, queueing theory (QT). We applied four optimization algorithms, including SGD, Adam, RMSprop, and AdaGrad. The algorithms were compared to find the best model with the lowest mean absolute error (MAE). A traditional mathematical simulation was used for additional comparisons. The results showed that the DL model is applicable using the SGD algorithm by activating a lowest MAE of 10.80 minutes (24% error reduction) to predict patients' waiting times. This work presents a theoretical contribution of predicting patients’ waiting time with alternative techniques by achieving the highest performing model to better prioritize patients waiting in the queue. Also, this study offers a practical contribution by using real-life data from ERs. Furthermore, we proposed models to predict patients' waiting time with more accurate results than a traditional mathematical method. Our approach can be easily implemented for the queue system in the healthcare sector using electronic health records (EHR) data.","PeriodicalId":268888,"journal":{"name":"International Journal of Industrial Engineering and Operations Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121890237","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 : 2021-10-01DOI: 10.46254/j.ieom.20210102
Swarnakar Vikas
In the present scenario manufacturing industries have been facing problem-related to cost, quality, and customer satisfaction. To overcome such problems, the organizations are ready to adopt continuous improvement (CI) approaches such as Lean Six Sigma (LSS) which keeps them stable when the demand for products or services fluctuates. LSS is a breakthrough improvement approach that helps to improve the bottom-line result of the company by utilizing its tools and techniques. The successful adaptation of the LSS approach provides a significant improvement in key metrics but deficiency of proper implementation shows a negative effect. To prevent such a situation, need to know about their failure factors. The objective of the present study is to assess the critical failure factors (CFFs) for LSS framework implementation in manufacturing organizations. The leading CFFs for LSS have been identified and selected through a structured literature review and expert opinion. The CFFs based model for LSS implementation has been developed using the Interpretative Structural Modelling and Matrice d’ Impacts Croises Multiplication Appliquee a un Classement (ISM-MICMAC) approach. Previous studies related to such concerns have not developed a structural hierarchical model that is necessary to tackle CFFs towards the LSS implementation process. Such an interrelation helps decision-makers, planners to systematically guide about the barriers that affect the implementation process and help for further implementation success. The developed structured model will also help LSS practitioners, consultants, researchers to anticipate the potential CFFs to implement the LSS framework in their industry for continuous improvement and achieve a leading position in a competitive market.
{"title":"Assessment of Critical Failure Factors for Implementing Lean Six Sigma in Manufacturing Industry: A case study","authors":"Swarnakar Vikas","doi":"10.46254/j.ieom.20210102","DOIUrl":"https://doi.org/10.46254/j.ieom.20210102","url":null,"abstract":"In the present scenario manufacturing industries have been facing problem-related to cost, quality, and customer satisfaction. To overcome such problems, the organizations are ready to adopt continuous improvement (CI) approaches such as Lean Six Sigma (LSS) which keeps them stable when the demand for products or services fluctuates. LSS is a breakthrough improvement approach that helps to improve the bottom-line result of the company by utilizing its tools and techniques. The successful adaptation of the LSS approach provides a significant improvement in key metrics but deficiency of proper implementation shows a negative effect. To prevent such a situation, need to know about their failure factors. The objective of the present study is to assess the critical failure factors (CFFs) for LSS framework implementation in manufacturing organizations. The leading CFFs for LSS have been identified and selected through a structured literature review and expert opinion. The CFFs based model for LSS implementation has been developed using the Interpretative Structural Modelling and Matrice d’ Impacts Croises Multiplication Appliquee a un Classement (ISM-MICMAC) approach. Previous studies related to such concerns have not developed a structural hierarchical model that is necessary to tackle CFFs towards the LSS implementation process. Such an interrelation helps decision-makers, planners to systematically guide about the barriers that affect the implementation process and help for further implementation success. The developed structured model will also help LSS practitioners, consultants, researchers to anticipate the potential CFFs to implement the LSS framework in their industry for continuous improvement and achieve a leading position in a competitive market.","PeriodicalId":268888,"journal":{"name":"International Journal of Industrial Engineering and Operations Management","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116819607","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 : 2021-10-01DOI: 10.46254/j.ieom.20210101
D. Gebisa, Tika Ram
The objective of this paper is to investigate empirically the effect of information sharing and inventory management practice on firms’ performance. To achieve the stated objective the study targeted supply chain practices of some companies operating in Ethiopia. Data were collected from 170 respondents including employees, suppliers, and distributors of the companies under investigation. Before the analysis of data, the accuracy of data entry, the existence of missing values, normality of data distribution and outliers checked and proved the nonexistence of serious issues. The specified objective and proposed hypotheses in this study tested by structural equation modelling (SEM). The result shows information sharing and inventory management practices have a direct and significant effect on the firm’s performance. Similarly, the higher share of information leads to better inventory management practice, which in turn leads to a greater performance of firms. The study concludes that information sharing has both direct and indirect effects on a firm's performance in the supply chain practices; whereas inventory management practices have a direct effect on the firm's performance. Generally, the results of the study have major theoretical and practical implications. Theoretically, it offers concrete evidence on the significant effects of information sharing and inventory management in the supply chain practices on firm’s performance in developing countries; and hence contributes to the scarce body of literature and reduces the gaps of knowledge in the developing countries on the specified area of study. Besides the theoretical implication, practically the study allows the companies and industries under the considerations to recognize the significant effects of information sharing and inventory management practices on firm’s performance and to use this information to develop and enhance culture of information sharing and usage of sound inventory management techniques in the supply chain practices for the enhancement of organizational performance.
{"title":"The Effect of Information sharing and Inventory Management in the Supply Chain Practices on Firms’ Performance: Empirical Evidence from Some Selected Companies of Ethiopia","authors":"D. Gebisa, Tika Ram","doi":"10.46254/j.ieom.20210101","DOIUrl":"https://doi.org/10.46254/j.ieom.20210101","url":null,"abstract":"The objective of this paper is to investigate empirically the effect of information\u0000sharing and inventory management practice on firms’ performance. To achieve the\u0000stated objective the study targeted supply chain practices of some companies operating\u0000in Ethiopia. Data were collected from 170 respondents including employees,\u0000suppliers, and distributors of the companies under investigation. Before the analysis\u0000of data, the accuracy of data entry, the existence of missing values, normality of data\u0000distribution and outliers checked and proved the nonexistence of serious issues. The\u0000specified objective and proposed hypotheses in this study tested by structural equation\u0000modelling (SEM). The result shows information sharing and inventory management\u0000practices have a direct and significant effect on the firm’s performance. Similarly, the\u0000higher share of information leads to better inventory management practice, which in\u0000turn leads to a greater performance of firms. The study concludes that information\u0000sharing has both direct and indirect effects on a firm's performance in the supply chain\u0000practices; whereas inventory management practices have a direct effect on the firm's\u0000performance. Generally, the results of the study have major theoretical and practical\u0000implications. Theoretically, it offers concrete evidence on the significant effects of\u0000information sharing and inventory management in the supply chain practices on firm’s\u0000performance in developing countries; and hence contributes to the scarce body of\u0000literature and reduces the gaps of knowledge in the developing countries on the\u0000specified area of study. Besides the theoretical implication, practically the study\u0000allows the companies and industries under the considerations to recognize the\u0000significant effects of information sharing and inventory management practices on\u0000firm’s performance and to use this information to develop and enhance culture of\u0000information sharing and usage of sound inventory management techniques in the\u0000supply chain practices for the enhancement of organizational performance.","PeriodicalId":268888,"journal":{"name":"International Journal of Industrial Engineering and Operations Management","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463575","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 : 2021-10-01DOI: 10.46254/j.ieom.20210104
Babedi Kufigwa, N. Gwangwava, R. Addo-Tenkorang
In recent time workplace organization and easy information retrieval help in achieving optimum productivity through maximum utilization of the resources available, significantly reducing industrial lead-time and waste thus resulting in low production cost and increase return-on-investment (ROI). This paper is a study of the effective and efficient implementation of 5S processes in a beef abattoir. Thus, the paper employs both qualitative (case study) and quantitative (statistical analysis like 5S scorecard and 5S audit performance) methods. This research identifies and outlines 5S “best practice” issues overlooked such as unneeded items lying around, torn sign displays, labels and shelves not partitioned, bins not clearly stored in demarcated areas and storage tools not clearly shown with sign panels and labels. Furthermore, budget constraints and the abattoir unreadiness to adopt the 5S system inhibits the smooth implementation of the 5S phases required. Therefore, this research managed to map out a 5S lean-system implementation framework for the case company X beef abattoirs. Finally, the research recommended effective process on how 5S can efficiently save the industry on planning to reduce waste in processes such as lead-time in effective information retrieval system, safety issues to mitigate non-value adding activities and space utilization, for improved productivity.
{"title":"Strategic and Sustainable Implementation of 5S in a Beef Abattoir","authors":"Babedi Kufigwa, N. Gwangwava, R. Addo-Tenkorang","doi":"10.46254/j.ieom.20210104","DOIUrl":"https://doi.org/10.46254/j.ieom.20210104","url":null,"abstract":"In recent time workplace organization and easy information retrieval help in achieving optimum productivity through maximum utilization of the resources available, significantly reducing industrial lead-time and waste thus resulting in low production cost and increase return-on-investment (ROI). This paper is a study of the effective and efficient implementation of 5S processes in a beef abattoir. Thus, the paper employs both qualitative (case study) and quantitative (statistical analysis like 5S scorecard and 5S audit performance) methods. This research identifies and outlines 5S “best practice” issues overlooked such as unneeded items lying around, torn sign displays, labels and shelves not partitioned, bins not clearly stored in demarcated areas and storage tools not clearly shown with sign panels and labels. Furthermore, budget constraints and the abattoir unreadiness to adopt the 5S system inhibits the smooth implementation of the 5S phases required. Therefore, this research managed to map out a 5S lean-system implementation framework for the case company X beef abattoirs. Finally, the research recommended effective process on how 5S can efficiently save the industry on planning to reduce waste in processes such as lead-time in effective information retrieval system, safety issues to mitigate non-value adding activities and space utilization, for improved productivity.","PeriodicalId":268888,"journal":{"name":"International Journal of Industrial Engineering and Operations Management","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131275704","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 : 2020-10-01DOI: 10.46254/j.ieom.20200102
Yahel Giat
Deliveries in global supply chains are often made through lengthy shipping routes that are subject to many delays such as border crossings, inspections and so forth. Consequently, orders frequently crossover, that is, their order of arrival is not the same as the order that they were issued. In this paper we model a multiple location inventory system with Poisson demand and periodic review in which orders may crossover. The system’s performance measure is the window fill rate, i.e., the probability that a customer arriving to the system is served within her tolerable wait. We show that when spares are scarce the system’s overall performance decreases if spares are allotted equitably. Additionally, we show that there is a linear tradeoff between the tolerable wait and the number of spares needed to maintain a given performance level. The observations have practical implications to inventory mangers. First, that when resources are scarce it is optimal to cluster spares to only few locations. In contrast, when resources are abundant, then a more equitable solution is optimal. Second, that it is possible to design simple contracts that reward customers for their patience, or alternatively, that charge customers a premium for expedited service.
{"title":"The Window Fill Rate in a Multiple Location Inventory System with Periodic Review and Order Crossover","authors":"Yahel Giat","doi":"10.46254/j.ieom.20200102","DOIUrl":"https://doi.org/10.46254/j.ieom.20200102","url":null,"abstract":"Deliveries in global supply chains are often made through lengthy shipping routes that are subject to many delays such as border crossings, inspections and so forth. Consequently, orders frequently crossover, that is, their order of arrival is not the same as the order that they were issued. In this paper we model a multiple location inventory system with Poisson demand and periodic review in which orders may crossover. The system’s performance measure is the window fill rate, i.e., the probability that a customer arriving to the system is served within her tolerable wait. We show that when spares are scarce the system’s overall performance decreases if spares are allotted equitably. Additionally, we show that there is a linear tradeoff between the tolerable wait and the number of spares needed to maintain a given performance level. The observations have practical implications to inventory mangers. First, that when resources are scarce it is optimal to cluster spares to only few locations. In contrast, when resources are abundant, then a more equitable solution is optimal. Second, that it is possible to design simple contracts that reward customers for their patience, or alternatively, that charge customers a premium for expedited service.","PeriodicalId":268888,"journal":{"name":"International Journal of Industrial Engineering and Operations Management","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114356487","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 : 2020-10-01DOI: 10.46254/j.ieom.20200105
In this paper, the food waste valorization alternatives are evaluated from a sustainability point of view. Using food waste characteristics as input data, we estimate the sustainable benefits such as energy utilization and GHG emission reduction for each potential food waste processing technique. Additionally, the sustainable benefits of reverse logistics of food waste are quantified based upon geographic distance and valorization characteristics. We formulate the food waste network framework as a strategic linear programming (LP) model that aims to minimize total food waste management costs while satisfying emissions and energy use constraints. Given the recent regulations of the commercial food material disposal ban, we test the efficiency of the proposed framework by designing a sustainable food waste treatment network for the state of Massachusetts. Results show that with a marginal increase in the treatment cost of food waste, the model has achieved zero net emissions, zero net energy use, and a competitive overall sustainability impact. Thus, by utilizing the food waste network model, policymakers can achieve the best sustainable strategies for food waste management. The paper contributes theoretically to the assessment of the food waste recovery alternatives by expanding the system boundary and presenting additional key performance measures of sustainability. Practically, this study provides case studies based on real-life data and generates multiple scenarios to better analyze the results and select the best recovery options from a sustainability perspective.
{"title":"Carbon Emissions and Energy Balance in the Design of a Sustainable Food Waste Network","authors":"","doi":"10.46254/j.ieom.20200105","DOIUrl":"https://doi.org/10.46254/j.ieom.20200105","url":null,"abstract":"In this paper, the food waste valorization alternatives are evaluated from a sustainability point of view. Using food waste characteristics as input data, we estimate the sustainable benefits such as energy utilization and GHG emission reduction for each potential food waste processing technique. Additionally, the sustainable benefits of reverse logistics of food waste are quantified based upon geographic distance and valorization characteristics. We formulate the food waste network framework as a strategic linear programming (LP) model that aims to minimize total food waste management costs while satisfying emissions and energy use constraints. Given the recent regulations of the commercial food material disposal ban, we test the efficiency of the proposed framework by designing a sustainable food waste treatment network for the state of Massachusetts. Results show that with a marginal increase in the treatment cost of food waste, the model has achieved zero net emissions, zero net energy use, and a competitive overall sustainability impact. Thus, by utilizing the food waste network model, policymakers can achieve the best sustainable strategies for food waste management. The paper contributes theoretically to the assessment of the food waste recovery alternatives by expanding the system boundary and presenting additional key performance measures of sustainability. Practically, this study provides case studies based on real-life data and generates multiple scenarios to better analyze the results and select the best recovery options from a sustainability perspective.","PeriodicalId":268888,"journal":{"name":"International Journal of Industrial Engineering and Operations Management","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127933753","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 : 2020-10-01DOI: 10.46254/j.ieom.20200103
Assembly processes can be optimized with various methods. However, it is difficult to evaluate the effectiveness and interaction of these methods. To date, shop floor improvement methods in series production, such as the methods under investigation 5S, Poka Yoke, Kanban, and Standard Work Sheet, have not been scientifically analyzed using a business simulation. This study is aimed at closing this research gap by conducting a business simulation and analyzing the generated data using the design of experiments. This combination represents a new form of research. For the design of experiments, full factorial design with four factors was used. Lead time is selected as the KPI. By analyzing the lean methods that were investigated in this study, both main effects and interactions were found. Results show that it is useful to apply at least one optimization method, whereby Poka Yoke has the most significant impact on the lead time. Researchers in the field of optimization methods can base their investigations on this study.
{"title":"Optimization on the shop floor - A business simulation Approach","authors":"","doi":"10.46254/j.ieom.20200103","DOIUrl":"https://doi.org/10.46254/j.ieom.20200103","url":null,"abstract":"Assembly processes can be optimized with various methods. However, it is difficult\u0000to evaluate the effectiveness and interaction of these methods. To date, shop floor\u0000improvement methods in series production, such as the methods under investigation\u00005S, Poka Yoke, Kanban, and Standard Work Sheet, have not been scientifically\u0000analyzed using a business simulation. This study is aimed at closing this research gap\u0000by conducting a business simulation and analyzing the generated data using the design\u0000of experiments. This combination represents a new form of research. For the design\u0000of experiments, full factorial design with four factors was used. Lead time is selected\u0000as the KPI. By analyzing the lean methods that were investigated in this study, both\u0000main effects and interactions were found. Results show that it is useful to apply at\u0000least one optimization method, whereby Poka Yoke has the most significant impact\u0000on the lead time. Researchers in the field of optimization methods can base their\u0000investigations on this study.","PeriodicalId":268888,"journal":{"name":"International Journal of Industrial Engineering and Operations Management","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116710793","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 : 2019-05-15DOI: 10.46254/j.ieom.20190102
A. Elwerfalli, M. K. Khan, J. E. Munive-Hernandez
Many oil and gas companies have suffered major production losses, and higher cost of maintenance due to the total shutdown of their plants to conduct TAM event during a certain period and according to scope of work. Therefore, TAM is considered the biggest maintenance activity in oil and gas plant in terms of manpower, material, time and cost. These plants usually undergo other maintenance strategies during normal operation of plants such as preventive, corrective and predictive maintenance. However, some components or units cannot be inspected or maintained during normal operation of plant unless plant facilities are a totally shut downed due to operating risks. These risks differ from a company to another due to many factors such as fluctuated temperatures and pressures, corrosion, erosion, cracks and fatigue caused by operating conditions, geographical conditions and economic aspects. The aim of this paper is to develop a TAM model to optimize the TAM scheduling associated with decreasing duration and increasing interval of the TAM of the gas plant. The methodology that this paper presents has three stages based on the critical and non-critical pieces of equipment. At the first stage, identifying and removing Non-critical Equipment pieces (NEs) from TAM activity to proactive maintenance types. During the second stage, the higher risk of each selected equipment is assessed in order to prioritize critical pieces of equipment based on Risk Based Inspection (RBI). At the third stage, failure probability and reliability function for those selected critical pieces of equipment are assessed. The results of development of the TAM model is led to the real optimization of TAM scheduling of gas plants that operated continuously around the clock in order to achieve a desired performance of reliability and availability of the gas plant, and reduce cost of TAM resulting from the production shutdown and cost of inspection and maintenance.
{"title":"Developing Turnaround Maintenance (TAM) Model to Optimize TAM Performance Based on the Critical Static Equipment (CSE) of GAS Plants","authors":"A. Elwerfalli, M. K. Khan, J. E. Munive-Hernandez","doi":"10.46254/j.ieom.20190102","DOIUrl":"https://doi.org/10.46254/j.ieom.20190102","url":null,"abstract":"Many oil and gas companies have suffered major production losses, and higher cost of maintenance due to the total shutdown of their plants to conduct TAM event during a certain period and according to scope of work. Therefore, TAM is considered the biggest maintenance activity in oil and gas plant in terms of manpower, material, time and cost. These plants usually undergo other maintenance strategies during normal operation of plants such as preventive, corrective and predictive maintenance. However, some components or units cannot be inspected or maintained during normal operation of plant unless plant facilities are a totally shut downed due to operating risks. These risks differ from a company to another due to many factors such as fluctuated temperatures and pressures, corrosion, erosion, cracks and fatigue caused by operating conditions, geographical conditions and economic aspects. The aim of this paper is to develop a TAM model to optimize the TAM scheduling associated with decreasing duration and increasing interval of the TAM of the gas plant. The methodology that this paper presents has three stages based on the critical and non-critical pieces of equipment. At the first stage, identifying and removing Non-critical Equipment pieces (NEs) from TAM activity to proactive maintenance types. During the second stage, the higher risk of each selected equipment is assessed in order to prioritize critical pieces of equipment based on Risk Based Inspection (RBI). At the third stage, failure probability and reliability function for those selected critical pieces of equipment are assessed. The results of development of the TAM model is led to the real optimization of TAM scheduling of gas plants that operated continuously around the clock in order to achieve a desired performance of reliability and availability of the gas plant, and reduce cost of TAM resulting from the production shutdown and cost of inspection and maintenance.","PeriodicalId":268888,"journal":{"name":"International Journal of Industrial Engineering and Operations Management","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116409502","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 : 2019-05-15DOI: 10.46254/j.ieom.20190103
Farzaneh Mansourifard, Parisa Mansourifard, B. Krishnamachari
This paper studies the Newsvendor problem for a setting in which (i) the demand is temporally correlated, (ii) the demand is censored, (iii) the distribution of the demand is unknown. The correlation is modeled as a Markovian process. The censoring means that if the demand is larger than the action (selected inventory), only a lower bound on the demand can be revealed. The uncertainty set on the demand distribution is given by only the upper and lower bound on the amount of the change from a time to the next time. We propose a robust approach to minimize the worst-case total cost and model it as a min-max zero-sum repeated game. We prove that the worst-case distribution of the adversary at each time is a two-point distribution with non-zero probabilities at the extrema of the uncertainty set of the demand. And the optimal action of the decision-maker can have any of the following structures: (i) a randomized solution with a two-point distribution at the extrema, (ii) a deterministic solution at a convex combination of the extrema. Both above solutions balance over-utilization and under-utilization costs. Finally, we extend our results to uni-model cost functions and present numerical results to study the solution.
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