M. Kaladhar, Vss Sameer Chakravarthy, P. S. Chowdary
Surface quality is a technical prerequisite in the field of manufacturing industries and can be treated as a quality index for machined parts. Attainment of appropriate surface finish plays a key role during functional performance of machined part. The machining parameters typically influence it. Consequently, a highly focused task is to enumerate the good relation between surface roughness (Ra) and machining parameters. In the current work, response surface methodology (RSM) based regression models and flower pollination algorithm (FPA) based sparse data model were developed to predict the minimum value of surface roughness. The model is developed for hard turning of AISI 4340 steel (35 HRC) using a single nanolayer of TiSiN-TiAlN PVD-coated cutting insert. The results obtained from this approach had good harmony with experimental results, as the standard deviation of the estimated values was simply 0.0804 (for whole) and 0.0289 (for below 1 μm Ra). Compared with RSM models, the proposed FPA based model showed a minuscule percentage of mean absolute error. The model obtained asubstantial correlation coefficient value of 99.75% among the other model’s values. The behavior of machining parameters and its interaction against surface roughness in the developed models were discussed with Pareto chart. It was observed that the feed rate was highly significant parameter in swaying machining surface roughness. In inference, the FPA sparse data model is better than the RSMbased regression models for prognosis of surface roughness in hard turning of AISI 4340 steel (35 HRC). The model developed using FPA based sparse data for surface roughness during hard turning operation in the current work is not reported to the best of author’s knowledge. This model disclosed a more dependable estimation over the multiple regression models.
{"title":"Prediction of surface roughness using a novel approach","authors":"M. Kaladhar, Vss Sameer Chakravarthy, P. S. Chowdary","doi":"10.22068/IJIEPR.32.3.4","DOIUrl":"https://doi.org/10.22068/IJIEPR.32.3.4","url":null,"abstract":"Surface quality is a technical prerequisite in the field of manufacturing industries and can be treated as a quality index for machined parts. Attainment of appropriate surface finish plays a key role during functional performance of machined part. The machining parameters typically influence it. Consequently, a highly focused task is to enumerate the good relation between surface roughness (Ra) and machining parameters. In the current work, response surface methodology (RSM) based regression models and flower pollination algorithm (FPA) based sparse data model were developed to predict the minimum value of surface roughness. The model is developed for hard turning of AISI 4340 steel (35 HRC) using a single nanolayer of TiSiN-TiAlN PVD-coated cutting insert. The results obtained from this approach had good harmony with experimental results, as the standard deviation of the estimated values was simply 0.0804 (for whole) and 0.0289 (for below 1 μm Ra). Compared with RSM models, the proposed FPA based model showed a minuscule percentage of mean absolute error. The model obtained asubstantial correlation coefficient value of 99.75% among the other model’s values. The behavior of machining parameters and its interaction against surface roughness in the developed models were discussed with Pareto chart. It was observed that the feed rate was highly significant parameter in swaying machining surface roughness. In inference, the FPA sparse data model is better than the RSMbased regression models for prognosis of surface roughness in hard turning of AISI 4340 steel (35 HRC). The model developed using FPA based sparse data for surface roughness during hard turning operation in the current work is not reported to the best of author’s knowledge. This model disclosed a more dependable estimation over the multiple regression models.","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80185294","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}
Faisal Rasool, P. Koomsap, Emérancia Raharisoa, A. Qayoom
In the last decade, customers’ active involvement during product development, commonly referred to as co-creation, has emerged as an effective tool to overcome barriers that keep firms from understanding customer needs. Still in its infancy, many co-creation aspects are under-researched; this may present difficulties in aligning firm goals with their co-creators, often leading to project failure. To make the co-creation process more systematic, a framework is presented in this paper that will allow firms to analyse product attributes before engaging in co-creation, concerning firm capabilities and interests and the capabilities and interests of their co-creators. The results of this analysis will help firms to align their goals with the goals of co-creators. Two exploratory case studies were conducted for illustration.
{"title":"A Framework of Product Attributes Analysis for Co-Creation","authors":"Faisal Rasool, P. Koomsap, Emérancia Raharisoa, A. Qayoom","doi":"10.22068/IJIEPR.32.3.3","DOIUrl":"https://doi.org/10.22068/IJIEPR.32.3.3","url":null,"abstract":"In the last decade, customers’ active involvement during product development, commonly referred to as co-creation, has emerged as an effective tool to overcome barriers that keep firms from understanding customer needs. Still in its infancy, many co-creation aspects are under-researched; this may present difficulties in aligning firm goals with their co-creators, often leading to project failure. To make the co-creation process more systematic, a framework is presented in this paper that will allow firms to analyse product attributes before engaging in co-creation, concerning firm capabilities and interests and the capabilities and interests of their co-creators. The results of this analysis will help firms to align their goals with the goals of co-creators. Two exploratory case studies were conducted for illustration.","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88880616","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}
This study proposes an outsourcing mechanism for marketing plans in small and medium-sized enterprises (SMEs) using knowledge sharing. SMEs may not be able to establish a marketing department due to operational expenditures. Therefore, organizing a marketing agency to handle marketing concerns of SMEs is significant. First, SMEs are clustered regarding their activity area, products, services, and etc. Then, for SMEs in a same cluster, the marketing agency should collect the required information to process marketing actions. The challenge is how to gather and deposit information in common among SMEs in a cluster. Knowledge sharing is one of the stages of knowledge management helping to distribute information among elements of a system. Thus, the process of knowledge sharing is investigated in outsourcing marketing activities. Accordingly, a questionnaire was prepared based on research hypotheses. After confirmation of validity and reliability, the questionnaire was given to managers and employees of furniture companies in Tehran province, Iran. The collected questionnaires were analyzed using SPSS software version 24.0. According to the statistical sample of the research, descriptive statistics, and inferential statistics were analyzed. Descriptive statistics were used to describe the demographic characteristics of respondents. The inferential statistics, Kolmogorov-Smirnov test was used first for the test of normality of data. Considering normality of the data, T-student test was used to obtain the relationship between variables. Finally, the results of the research showed that there is a positive and significant relationship between outsourcing marketing in SMEs using knowledge sharing. Therefore, it is suggested that SMEs pay particular attention to outsourcing their marketing activities using knowledge sharing.
{"title":"Outsourcing Marketing Plans for Small and Medium Enterprises using Knowledge Sharing Process Case study: Tehran Wood and Furniture Companies","authors":"Yaser Hosseini, H. Fazlollahtabar, M. Ashoori","doi":"10.22068/IJIEPR.1161","DOIUrl":"https://doi.org/10.22068/IJIEPR.1161","url":null,"abstract":"This study proposes an outsourcing mechanism for marketing plans in small and medium-sized enterprises (SMEs) using knowledge sharing. SMEs may not be able to establish a marketing department due to operational expenditures. Therefore, organizing a marketing agency to handle marketing concerns of SMEs is significant. First, SMEs are clustered regarding their activity area, products, services, and etc. Then, for SMEs in a same cluster, the marketing agency should collect the required information to process marketing actions. The challenge is how to gather and deposit information in common among SMEs in a cluster. Knowledge sharing is one of the stages of knowledge management helping to distribute information among elements of a system. Thus, the process of knowledge sharing is investigated in outsourcing marketing activities. Accordingly, a questionnaire was prepared based on research hypotheses. After confirmation of validity and reliability, the questionnaire was given to managers and employees of furniture companies in Tehran province, Iran. The collected questionnaires were analyzed using SPSS software version 24.0. According to the statistical sample of the research, descriptive statistics, and inferential statistics were analyzed. Descriptive statistics were used to describe the demographic characteristics of respondents. The inferential statistics, Kolmogorov-Smirnov test was used first for the test of normality of data. Considering normality of the data, T-student test was used to obtain the relationship between variables. Finally, the results of the research showed that there is a positive and significant relationship between outsourcing marketing in SMEs using knowledge sharing. Therefore, it is suggested that SMEs pay particular attention to outsourcing their marketing activities using knowledge sharing.","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73937397","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}
Today, one of the most important concerns of production units is the evaluation, analysis and risk management in the production process. In this research, based on the fuzzy control approach, a scientific and logical method for evaluating, analyzing and managing risk in the production process is presented. Based on the proposed method of this research, after identifying the risks in the production process of products, according to the three criteria of failure severity, probability of failure and detectability, as well as using the best worst method, evaluation and determining the importance of these risks, is done. Then, with the fuzzy rules, fuzzy inference system is designed. The final result is the classification and prioritization of identified risks. Finally, the proposed research model for an applied sample is used and its final results are analyzed.
{"title":"Assessment, analysis and risk management in the production process of products with a fuzzy control approach","authors":"M. R. Meybodi","doi":"10.22068/IJIEPR.32.3.2","DOIUrl":"https://doi.org/10.22068/IJIEPR.32.3.2","url":null,"abstract":"Today, one of the most important concerns of production units is the evaluation, analysis and risk management in the production process. In this research, based on the fuzzy control approach, a scientific and logical method for evaluating, analyzing and managing risk in the production process is presented. Based on the proposed method of this research, after identifying the risks in the production process of products, according to the three criteria of failure severity, probability of failure and detectability, as well as using the best worst method, evaluation and determining the importance of these risks, is done. Then, with the fuzzy rules, fuzzy inference system is designed. The final result is the classification and prioritization of identified risks. Finally, the proposed research model for an applied sample is used and its final results are analyzed.","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90288958","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}
Mohammad Esfehani Zanjani, A. Najafi, A. Naghilou, N. Mohammadi
Sustainability is now increasingly recognized as an effective strategy to deal with the current challenges of global supply chains. Supply chains of the lead and zinc industries are most important. Because these two industries not only are among the high-risk in different countries, including Iran, but also can affect economic, social, and environmental sustainability. On the other hand, identifying and assessing the critical risks of supply chains have been less addressed in recent studies. This study aimed to identify and assess critical risks of sustainable supply chains (SSCs) in the Iranian lead and zinc industry. This study was a mixed-method (qualitative and quantitative) descriptive survey. Based on the literature, 24 risk factors that affect supply chain sustainability were identified, out of which 20 critical risk factors were confirmed in two steps by reviewing experts’ comments and the data obtained from in-depth interviews and questionnaires. The validity of questionnaires is verified based on the opinions of a group of 5 experts in the first step and another group of 17 experts and professionals of the lead and zinc industry in the second. The Cronbach’s alpha coefficient of the questionnaires was calculated to be 0.837, indicating the reliability of the questionnaires. The risk factors were analyzed using the Risk Priority Number (RPN), fuzzy DEMATEL, and risk matrices. Based on the results, “lack of technological/knowledge sustainability”, “price and cost fluctuations”, “inflation and exchange rates” and “environmental pollution” were the most important risk factors in the supply chain of the Iranian lead and zinc industry.
{"title":"Identification and Assessment of Critical Risks of Sustainable Supply Chain in the Iranian Lead and Zinc Industry","authors":"Mohammad Esfehani Zanjani, A. Najafi, A. Naghilou, N. Mohammadi","doi":"10.22068/IJIEPR.32.3.8","DOIUrl":"https://doi.org/10.22068/IJIEPR.32.3.8","url":null,"abstract":"Sustainability is now increasingly recognized as an effective strategy to deal with the current challenges of global supply chains. Supply chains of the lead and zinc industries are most important. Because these two industries not only are among the high-risk in different countries, including Iran, but also can affect economic, social, and environmental sustainability. On the other hand, identifying and assessing the critical risks of supply chains have been less addressed in recent studies. This study aimed to identify and assess critical risks of sustainable supply chains (SSCs) in the Iranian lead and zinc industry. This study was a mixed-method (qualitative and quantitative) descriptive survey. Based on the literature, 24 risk factors that affect supply chain sustainability were identified, out of which 20 critical risk factors were confirmed in two steps by reviewing experts’ comments and the data obtained from in-depth interviews and questionnaires. The validity of questionnaires is verified based on the opinions of a group of 5 experts in the first step and another group of 17 experts and professionals of the lead and zinc industry in the second. The Cronbach’s alpha coefficient of the questionnaires was calculated to be 0.837, indicating the reliability of the questionnaires. The risk factors were analyzed using the Risk Priority Number (RPN), fuzzy DEMATEL, and risk matrices. Based on the results, “lack of technological/knowledge sustainability”, “price and cost fluctuations”, “inflation and exchange rates” and “environmental pollution” were the most important risk factors in the supply chain of the Iranian lead and zinc industry.","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81203180","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}
One of the basic assumptions of classical production-inventory models is that all products are of perfect quality. However, in real manufacturing situations, the production of defective items is inevitable, and a fraction of the items produced may be naturally imperfect. In fact, items may be damaged due to production and/or transportation conditions in the manufacturing process. On the other hand, some reworkable items exist among imperfect items that can be made perfect by additional processing. In addition, the classical production-inventory models assume that there is only one product in the system and that there is an unlimited amount of resources. However, in many practical situations, several products are produced and there are some constraints related to various factors such as machine capacity, storage space, available budget, number of allowable setups, etc. Therefore, we propose new constrained production-inventory models for multiple products where the manufacturing process is defective and produces a fraction of imperfect items. A percentage of defective items can be reworked, and these products go through the rework process to become perfect and return to the consumption cycle. The goal is to determine economic production quantities to minimize the total cost of the system. The analytical solutions are each derived separately by Lagrangian relaxation method, and a numerical example is presented to illustrate and discuss the procedure. A sensitivity analysis is performed to investigate how the variation in the inputs of the models affects the total cost of the inventory system. Finally, some research directions for future works are discussed.
{"title":"Multi-Product Constrained Economic Production Quantity Models for Imperfect Quality Items with Rework","authors":"H. Mokhtari, A. Hasani, A. Fallahi","doi":"10.22068/IJIEPR.950","DOIUrl":"https://doi.org/10.22068/IJIEPR.950","url":null,"abstract":"One of the basic assumptions of classical production-inventory models is that all products are of perfect quality. However, in real manufacturing situations, the production of defective items is inevitable, and a fraction of the items produced may be naturally imperfect. In fact, items may be damaged due to production and/or transportation conditions in the manufacturing process. On the other hand, some reworkable items exist among imperfect items that can be made perfect by additional processing. In addition, the classical production-inventory models assume that there is only one product in the system and that there is an unlimited amount of resources. However, in many practical situations, several products are produced and there are some constraints related to various factors such as machine capacity, storage space, available budget, number of allowable setups, etc. Therefore, we propose new constrained production-inventory models for multiple products where the manufacturing process is defective and produces a fraction of imperfect items. A percentage of defective items can be reworked, and these products go through the rework process to become perfect and return to the consumption cycle. The goal is to determine economic production quantities to minimize the total cost of the system. The analytical solutions are each derived separately by Lagrangian relaxation method, and a numerical example is presented to illustrate and discuss the procedure. A sensitivity analysis is performed to investigate how the variation in the inputs of the models affects the total cost of the inventory system. Finally, some research directions for future works are discussed.","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80142006","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}
Mohsen Khezeli, E. Najafi, Mohammad Haji Molana, M. Seidi
One of the most important fields of logistic network is transportation network design that has an important effect on strategic decisions in supply chain management. It has recently attracted the attention of many researchers. In this paper, a multi-stage and multi-product logistic network design is considered. This paper presents a hybrid approach based on simulation and optimization (Simulation based optimization), the model is formulated and presented in three stages. At first, the practical production capacity of each product is calculated using the Overall Equipment Effectiveness (OEE) index, in the second stage, the optimization of loading schedules is simulated. The layout of the loading equipment, the number of equipment per line, the time of each step of the loading process, the resources used by each equipment were simulated, and the output of the model determines the maximum number of loaded vehicles in each period. Finally, a multi-objective model is presented to optimize the transportation time and cost of products. A mixed integer nonlinear programming (MINLP) model is formulated in such a way as to minimize transportation costs and maximize the use of time on the planning horizon. We have used Arena simulation software to solve the second stage of the problem, the results of which will be explained. It is also used GAMS software to solve the final stage of the model and optimize the transporting cost and find the optimal solutions. Several test problems were generated and it showed that the proposed algorithm could find good solutions in reasonable time spans.
{"title":"Simulation based optimization model for logistic network in a multi-stage supply chain network with considering operational production planning \"truck loading system and transportation network\"","authors":"Mohsen Khezeli, E. Najafi, Mohammad Haji Molana, M. Seidi","doi":"10.22068/IJIEPR.1109","DOIUrl":"https://doi.org/10.22068/IJIEPR.1109","url":null,"abstract":"One of the most important fields of logistic network is transportation network design that has an important effect on strategic decisions in supply chain management. It has recently attracted the attention of many researchers. In this paper, a multi-stage and multi-product logistic network design is considered. This paper presents a hybrid approach based on simulation and optimization (Simulation based optimization), the model is formulated and presented in three stages. At first, the practical production capacity of each product is calculated using the Overall Equipment Effectiveness (OEE) index, in the second stage, the optimization of loading schedules is simulated. The layout of the loading equipment, the number of equipment per line, the time of each step of the loading process, the resources used by each equipment were simulated, and the output of the model determines the maximum number of loaded vehicles in each period. Finally, a multi-objective model is presented to optimize the transportation time and cost of products. A mixed integer nonlinear programming (MINLP) model is formulated in such a way as to minimize transportation costs and maximize the use of time on the planning horizon. We have used Arena simulation software to solve the second stage of the problem, the results of which will be explained. It is also used GAMS software to solve the final stage of the model and optimize the transporting cost and find the optimal solutions. Several test problems were generated and it showed that the proposed algorithm could find good solutions in reasonable time spans.","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83505855","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}
One of the challenging issues in today’s competitive world for servicing companies is uncertainty in some factors or parameters that they often derive from fluctuations of market price and other reasons. With regard to this subject, it would be essential to provide robust solutions in uncertain situations. This paper addresses an open vehicle routing problem with demand uncertainty and cost of vehicle uncertainty. Bertsimas and Sim’s method has been applied to deal with uncertainty in this paper. In addition, a deterministic model of open vehicle routing problem is developed to present a robust counterpart model. The deterministic and the robust model is solved by GAMS software. Then, the mean and standard deviations of obtained solutions were compared in different uncertainty levels in numerous numerical examples to investigate the performance of the developed robust model and deterministic model. The computational results show that the robust model has a better performance than the solutions obtained by the deterministic model.
{"title":"Open Vehicle Routing Problem with Robust Optimization Approach","authors":"N. Hamta, S. Rabiee","doi":"10.22068/IJIEPR.32.3.10","DOIUrl":"https://doi.org/10.22068/IJIEPR.32.3.10","url":null,"abstract":"One of the challenging issues in today’s competitive world for servicing companies is uncertainty in some factors or parameters that they often derive from fluctuations of market price and other reasons. With regard to this subject, it would be essential to provide robust solutions in uncertain situations. This paper addresses an open vehicle routing problem with demand uncertainty and cost of vehicle uncertainty. Bertsimas and Sim’s method has been applied to deal with uncertainty in this paper. In addition, a deterministic model of open vehicle routing problem is developed to present a robust counterpart model. The deterministic and the robust model is solved by GAMS software. Then, the mean and standard deviations of obtained solutions were compared in different uncertainty levels in numerous numerical examples to investigate the performance of the developed robust model and deterministic model. The computational results show that the robust model has a better performance than the solutions obtained by the deterministic model.","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85534140","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}
This paper develops an approximate cost function for a three-echelon supply chain that has two suppliers, a central warehouse and an arbitrary number of retailers. It takes an integrated approach to multi-echelon inventory control and order-splitting problems. It assumes that all facilities apply continuous review policy for replenishment, demand at the retailers follows a Poisson process, and lead times are stochastic with no predetermined probability distribution. Unsatisfied demand is considered as lost sales at the retailers and backlogged at the warehouse and suppliers. Due to information sharing between the existing echelons, order quantity at each higher level is assumed to be an integer multiple of the lower level. Order placed by the warehouse gets divided between the two suppliers and re-order point is not restricted at the warehouse or suppliers. The main contribution of this paper is its integrated approach and the practical assumption that it uses for the order arrival sequence and the unsatisfied demands. It adds two suppliers as the third echelon to the traditional twoechelon supply chain and considers dynamic sequence of orders arrival to the warehouse at each cycle. The fact that inventory control and sourcing decisions are interdependent and act as the main challenge of supply chain management, considering them in an integrated model can significantly influence operating costs and supply chain’s efficiency. Such approach can even have greater impact when blended with practical assumptions that consider lead-time as unpredictable and unsatisfied demand as lost sales. Total cost of the three-echelon inventory system is approximated based on the average unit cost and its accuracy is assessed through simulation. Numerical results with relatively low errors confirms the accuracy of the model. It also shows how to further enhance its accuracy by either increasing the holding cost at all echelons or the penalty cost at the retailers.
{"title":"Cost Approximation of a Three-echelon Inventory System with Order Splitting and Information Sharing","authors":"A. Ghahghaei, M. Seifbarghy, D. Pishva","doi":"10.22068/IJIEPR.32.3.6","DOIUrl":"https://doi.org/10.22068/IJIEPR.32.3.6","url":null,"abstract":"This paper develops an approximate cost function for a three-echelon supply chain that has two suppliers, a central warehouse and an arbitrary number of retailers. It takes an integrated approach to multi-echelon inventory control and order-splitting problems. It assumes that all facilities apply continuous review policy for replenishment, demand at the retailers follows a Poisson process, and lead times are stochastic with no predetermined probability distribution. Unsatisfied demand is considered as lost sales at the retailers and backlogged at the warehouse and suppliers. Due to information sharing between the existing echelons, order quantity at each higher level is assumed to be an integer multiple of the lower level. Order placed by the warehouse gets divided between the two suppliers and re-order point is not restricted at the warehouse or suppliers. The main contribution of this paper is its integrated approach and the practical assumption that it uses for the order arrival sequence and the unsatisfied demands. It adds two suppliers as the third echelon to the traditional twoechelon supply chain and considers dynamic sequence of orders arrival to the warehouse at each cycle. The fact that inventory control and sourcing decisions are interdependent and act as the main challenge of supply chain management, considering them in an integrated model can significantly influence operating costs and supply chain’s efficiency. Such approach can even have greater impact when blended with practical assumptions that consider lead-time as unpredictable and unsatisfied demand as lost sales. Total cost of the three-echelon inventory system is approximated based on the average unit cost and its accuracy is assessed through simulation. Numerical results with relatively low errors confirms the accuracy of the model. It also shows how to further enhance its accuracy by either increasing the holding cost at all echelons or the penalty cost at the retailers.","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86081515","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}
R. Mor, A. Bhardwaj, Vishal Kharka, Manjeet Kharub
{"title":"Spare parts inventory management in warehouse: a lean approach","authors":"R. Mor, A. Bhardwaj, Vishal Kharka, Manjeet Kharub","doi":"10.22068/IJIEPR.1110","DOIUrl":"https://doi.org/10.22068/IJIEPR.1110","url":null,"abstract":"","PeriodicalId":52223,"journal":{"name":"International Journal of Industrial Engineering and Production Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81255051","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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