Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse function...This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.展开更多
Inventory management(e.g.lost sales)is a central problem in supply chain management.Lost sales inventory systems with lead times and complex cost function are notoriously hard to optimize.Deep reinforcement learning(D...Inventory management(e.g.lost sales)is a central problem in supply chain management.Lost sales inventory systems with lead times and complex cost function are notoriously hard to optimize.Deep reinforcement learning(DRL)methods can learn optimal decisions based on trails and errors from the environment due to its powerful complex function representation capability and has recently shown remarkable successes in solving challenging sequential decision-making problems.This paper studies typical lost sales and multi-echelon inventory systems.We first formulate inventory management problem as a Markov Decision Process by taking into account ordering cost,holding cost,fixed cost and lost-sales cost and then develop a solution framework DDLS based on Double deep Q-networks(DQN).In the lost-sales scenario,numerical experiments demonstrate that increasing fixed ordering cost distorts the ordering behavior,while our DQN solutions with improved state space are flexible in the face of different cost parameter settings,which traditional heuristics find challenging to handle.We then study the effectiveness of our approach in multi-echelon scenarios.Empirical results demonstrate that parameter sharing can significantly improve the performance of DRL.As a form of information sharing,parameter sharing among multi-echelon suppliers promotes the collaboration of agents and improves the decisionmaking efficiency.Our research further demonstrates the potential of DRL in solving complex inventory management problems.展开更多
This paper develops a theoretical model to describe how exporting firms manage their inventory stocks in response to an exogenous trade policy uncertainty shock.Using firm-level data from China Industrial Enterprise D...This paper develops a theoretical model to describe how exporting firms manage their inventory stocks in response to an exogenous trade policy uncertainty shock.Using firm-level data from China Industrial Enterprise Database and China Customs Database on inventory and exports over the period around China's WTO accession,we show that a reduction in trade policy uncertainty significantly increased exporting firms'inventory holdings.The result wasrobust tovarious robustness checks.This effect was found to be stronger for private and foreign firms than state-owned enterprises and mainly driven by firms from the coastal region.We also found that the reduction in trade policy uncertainty increased the frequency and the average volume of export transactions,and that this was the mechanism behind the effect.This paper helps to understand exporting firms'optimal inventory problem arising from tradepolicyuncertaintyand shocks.展开更多
The <strong><em>purpose</em></strong> of this research was to ascertain causes of Restocking Delays in a Distributor Company of Airtel Airtime (<em>AA</em>) that give justification ...The <strong><em>purpose</em></strong> of this research was to ascertain causes of Restocking Delays in a Distributor Company of Airtel Airtime (<em>AA</em>) that give justification for benefits of using Real Time Inventory Tracking (<em>R.T.I.T</em>) in an attempt to mitigate Restocking Delays. From a study out at the Private Marketing and Trading Services (<em>PMTS</em>) an Authorized Distributor of Airtel Products undertaken in 2017 evidenced by Airtime scratch card and Electronic, <strong><em>E-Recharge</em></strong> Airtime among other forms to encourage <em>R.T.I.T</em> among other products in Telecom Companies and other Business Enterprises. The research comprises of the following areas among which included a detailed focus on a <strong><em>Qualitative</em></strong> and <strong><em>Quantitative approach</em></strong> in obtaining different <strong><em>categories</em></strong> of Restocking Delays in form of <strong><em>Themes</em></strong> and <strong><em>Sub Themes</em></strong> encountered in the Distribution Supply Chain (<em>SC</em>) of <em>AA</em> that is contained in this paper. This research continues to capture an in-depth explanation of the <strong><em>Managerial</em></strong> and <strong><em>Operational</em></strong> causes of restocking delays in respect to <em>AA</em>. Similarly, fast consumer products and services other than <em>AA</em> require a solution to <strong><em>Restocking Delays</em></strong> through implementation of Real Time Inventory Tracking Model (<em>R.T.I.T.M</em>) of <em>AA</em> among Distributor Companies (<em>DCs</em>). This paper also elaborated on Literature, Methodology and Findings obtained from the study. The <strong><em>results</em></strong> were obtained from <strong><em>regression analysis</em></strong> by using the Statistical Package for Social Sciences (<em>SPSS</em>) that showed a higher significance of <strong><em>Stock Turnover Period</em></strong> and<strong><em> Airtime Denomination</em></strong> as a contributor to Restocking Delays whereas <strong><em>Messages from Airtel</em></strong> Head office to the Distributor had a non-significant contribution to restocking Delays as in Figure 9. The research recommends a Model for <em>R.T.I.T</em> in Telecom Distribution <em>SC</em> of <em>AA </em>and Omnichannel Inventory Management (<em>OIM</em>) as a significant contributor to timely reliable inventory restocking and promotes higher sales among <em>DCs</em> and retailers through minimized Restocking Delays. It shows that the forces of Demand and Supply change over time with different tastes and preferences of customers. The imbalance in <em>AA</em> stock levels changes at given times due to unforeseen forces of consumer demand experienced by <em>DCs</em>, explained by the “<strong><em>Bullwhip Effect</em></strong>” due to information distortion in the Supply Chain (<em>SC</em>).展开更多
In the field of the construction industry, logistics management has become an issue of concern which needs to be focused on in order to ensure effective, successful and timely execution of projects in the construction...In the field of the construction industry, logistics management has become an issue of concern which needs to be focused on in order to ensure effective, successful and timely execution of projects in the construction industry. Logistics management in construction is a multidisciplinary process which makes sure things are done at the right time, cost and quality as well as material supply and handling, schedule control, site infrastructure and equipment location, site physical flow management, traffic arrangements on and around the construction site, and information management related to all physical and services flow. This paper seeks to identify and evaluate the possible factors that affect logistics management in the construction industry and also to ascertain the current practices of inventory management and the effect on logistics management envisage by construction personnel’s and stake holders. A descriptive survey research was conducted with both structured and unstructured questionnaire and administered both via e-mail and by sending questionnaire link onto a WhatsApp platform groups of the respondents, and out of the 187 respondents that took part in the survey, 22 respondents feedback were incomplete so 165 member respondents results were analyzed representing 88.2% response rate. The survey results were analyzed, displayed and interpreted in the form of tables, bar charts, column chart and pie chart with the use of SPSS software. The research survey respondents </span><span style="font-family:Verdana;">were </span><span style="font-family:Verdana;">comprised of Project Managers, Project Coordinators, Foremen, Site Managers/</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">Officers, Logistics Managers, Inventory Managers and Laborers. The survey results revealed that majority of the total respondents are in the capacity or position as Logistics Managers and also have from 5 to 10 years working experience, and as well majority of the respondents have also attained Master’s degree qualification. It was also revealed that logistics management is greatly affected by the inability or the failure to embrace, adopt and integrate new and emerging technology and its approach into the logistics management system. Majority of the respondents also indicated that in general the adequacy of inventory management system was dissatisfied representing 63.8% of the total respondents. The construction industry not only needed to improve its inventory management systems but also to ensure appropriate inventory management techniques for enhancing high degree of accuracy, reduced cycle times and replenishment of stock. This usually accounts for the poor delivery and quality of work, accompanied with delays and other associated losses in project construction. For effective Logistics management to be achieved in the construction industry, the introduction of modern technology to carry out logistics functions in the construction industry should be paramount to the industry players.展开更多
Transshipment is an effective method for reducing mismatches between supply and demand among retailers.Consumers attempting to purchase out-of-stock items may wait for transshipment,purchase at another store,or choose...Transshipment is an effective method for reducing mismatches between supply and demand among retailers.Consumers attempting to purchase out-of-stock items may wait for transshipment,purchase at another store,or choose not to buy.In this paper,the consumer behavioral heterogeneity is characterized using the transshipment request rate and consumer switching rate.It affects the replenishment and transshipment decisions,as well as the system profits.The inventory replenishment and transshipment decisions are studied in both centralized and decentralized two-location inventory systems with consumer behavioral heterogeneity.We characterize optimal replenishment decisions in a centralized system,prove the existence of a unique Nash equilibrium in a decentralized system with a specific demand distribution,and determine the coordinating transshipment price for some decentralized systems.In numerical studies,the performance of various systems is analyzed for consumers with identical(symmetric)or differing(asymmetric)behavior between retailers.For scenarios with symmetric consumers,a higher transshipment request rate and consumer switching rate resulted in increased total profit in all systems.For scenarios with asymmetric consumers,the retailer with the higher consumer switching rate should reduce ordering in a centralized system but increase ordering in a decentralized system.Moreover,the retailer with the higher transshipment request rate reduces ordering to increase profit,whereas the other retailer increases order quantity yet earns less profit.展开更多
Purpose-The proposed IT2FMOVMI model intends to concurrently minimize total cost and warehouse space for the single vendor-retailer,multi-item and a consolidated vendor store.Regarding demand and order quantities with...Purpose-The proposed IT2FMOVMI model intends to concurrently minimize total cost and warehouse space for the single vendor-retailer,multi-item and a consolidated vendor store.Regarding demand and order quantities with the deterministic and type-1 fuzzy numbers,we have also formulated the classic/crisp MOVMI model and type-1 fuzzy MOVMI(T1FMOVMI)model.The suggested solution technique can solve both crisp MOVMIand T1FMOVMIproblems.By finding the optimal ordered quantities and backorder levels,the Paretofronts are constructed to form the solution sets for the three models.Design/methodology/approach-A multi-objective vendor managed inventory(MOVMI)is the most recognized marketing and delivery technique for the service provider and the retail in the supply chain in Industry 4.0.Due to the evolving market conditions,the characteristics of the individual product,the delivery period and the manufacturing costs,the demand rate and order quantity of the MOVMI device are highly unpredictable.In such a scenario,a MOVMI system with a deterministic demand rate and order quantity cannot be designed to estimate the highly unforeseen cost of the problem.This paper introduces a novel interval type-2 fuzzy multi-objective vendor managed inventory(IT2FMOVMI)system,which uses interval type-2 fuzzy numbers(IT2FNs)to represent demand rate and order quantities.As the model is an NP-hard,the well-known meta-heuristic algorithm named NSGA-II(Non-dominated sorted genetic algorithm-II)with EKM(Enhanced Karnink-Mendel)algorithm based solution method has been established.Findings-The experimental simulations for the five test problems that demonstrated distinct conditions are considered from the real-datasets of SAPCO company.Experimental study concludes that T1FMOVMI and crisp MOVMI schemes are outclassed by IT2FMOVMI model,offering more accurate Pareto-Fronts and efficiency measurement values.Originality/value-Using fuzzy sets theory,a significant amount of work has been already done in past decades from various points of views to model the MOVMI.However,this is the very first attempt to introduce type-2 fuzzy modelling for the problem to address the realistic implementation of the imprecise parameters.展开更多
As one of the critical issues in operations management, spare parts planning classification systems based only on cost may not withstand the test of time because of continuing technological advancement or environmenta...As one of the critical issues in operations management, spare parts planning classification systems based only on cost may not withstand the test of time because of continuing technological advancement or environmental degradation. This study, however, emphasizes using failure mode effect and criticality analysis(FMECA) as a basis for designing an ABC classification system that is capable of more accurately determining critical equipment and making maintenance more efficient. In the proposed methodology, risk priority numbers(RPN) and criticality as an index of safety and reliability can be obtained using a structured failure analysis technique. These indexes can be added to the economic indicator of maintenance cost, and then a classification model of spare parts can be established according to a comprehensive scoring method. The model is calibrated by using case data from a bus braking system, and the components of the braking system are compared and analyzed. The results show that the improved ABC classification method reduces the proportion of key and main components. This method can make maintenance work more efficient, targeting the most critical components, and can reduce administrative costs for enterprises.展开更多
文摘Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
文摘This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.
基金supported in part by the National Natural Science Foundation of China,under grant Nos.72022001,92146003,,71901003CAAI-Huawei MindSpore,CCF-Tencent Open Research Fund。
文摘Inventory management(e.g.lost sales)is a central problem in supply chain management.Lost sales inventory systems with lead times and complex cost function are notoriously hard to optimize.Deep reinforcement learning(DRL)methods can learn optimal decisions based on trails and errors from the environment due to its powerful complex function representation capability and has recently shown remarkable successes in solving challenging sequential decision-making problems.This paper studies typical lost sales and multi-echelon inventory systems.We first formulate inventory management problem as a Markov Decision Process by taking into account ordering cost,holding cost,fixed cost and lost-sales cost and then develop a solution framework DDLS based on Double deep Q-networks(DQN).In the lost-sales scenario,numerical experiments demonstrate that increasing fixed ordering cost distorts the ordering behavior,while our DQN solutions with improved state space are flexible in the face of different cost parameter settings,which traditional heuristics find challenging to handle.We then study the effectiveness of our approach in multi-echelon scenarios.Empirical results demonstrate that parameter sharing can significantly improve the performance of DRL.As a form of information sharing,parameter sharing among multi-echelon suppliers promotes the collaboration of agents and improves the decisionmaking efficiency.Our research further demonstrates the potential of DRL in solving complex inventory management problems.
文摘This paper develops a theoretical model to describe how exporting firms manage their inventory stocks in response to an exogenous trade policy uncertainty shock.Using firm-level data from China Industrial Enterprise Database and China Customs Database on inventory and exports over the period around China's WTO accession,we show that a reduction in trade policy uncertainty significantly increased exporting firms'inventory holdings.The result wasrobust tovarious robustness checks.This effect was found to be stronger for private and foreign firms than state-owned enterprises and mainly driven by firms from the coastal region.We also found that the reduction in trade policy uncertainty increased the frequency and the average volume of export transactions,and that this was the mechanism behind the effect.This paper helps to understand exporting firms'optimal inventory problem arising from tradepolicyuncertaintyand shocks.
文摘The <strong><em>purpose</em></strong> of this research was to ascertain causes of Restocking Delays in a Distributor Company of Airtel Airtime (<em>AA</em>) that give justification for benefits of using Real Time Inventory Tracking (<em>R.T.I.T</em>) in an attempt to mitigate Restocking Delays. From a study out at the Private Marketing and Trading Services (<em>PMTS</em>) an Authorized Distributor of Airtel Products undertaken in 2017 evidenced by Airtime scratch card and Electronic, <strong><em>E-Recharge</em></strong> Airtime among other forms to encourage <em>R.T.I.T</em> among other products in Telecom Companies and other Business Enterprises. The research comprises of the following areas among which included a detailed focus on a <strong><em>Qualitative</em></strong> and <strong><em>Quantitative approach</em></strong> in obtaining different <strong><em>categories</em></strong> of Restocking Delays in form of <strong><em>Themes</em></strong> and <strong><em>Sub Themes</em></strong> encountered in the Distribution Supply Chain (<em>SC</em>) of <em>AA</em> that is contained in this paper. This research continues to capture an in-depth explanation of the <strong><em>Managerial</em></strong> and <strong><em>Operational</em></strong> causes of restocking delays in respect to <em>AA</em>. Similarly, fast consumer products and services other than <em>AA</em> require a solution to <strong><em>Restocking Delays</em></strong> through implementation of Real Time Inventory Tracking Model (<em>R.T.I.T.M</em>) of <em>AA</em> among Distributor Companies (<em>DCs</em>). This paper also elaborated on Literature, Methodology and Findings obtained from the study. The <strong><em>results</em></strong> were obtained from <strong><em>regression analysis</em></strong> by using the Statistical Package for Social Sciences (<em>SPSS</em>) that showed a higher significance of <strong><em>Stock Turnover Period</em></strong> and<strong><em> Airtime Denomination</em></strong> as a contributor to Restocking Delays whereas <strong><em>Messages from Airtel</em></strong> Head office to the Distributor had a non-significant contribution to restocking Delays as in Figure 9. The research recommends a Model for <em>R.T.I.T</em> in Telecom Distribution <em>SC</em> of <em>AA </em>and Omnichannel Inventory Management (<em>OIM</em>) as a significant contributor to timely reliable inventory restocking and promotes higher sales among <em>DCs</em> and retailers through minimized Restocking Delays. It shows that the forces of Demand and Supply change over time with different tastes and preferences of customers. The imbalance in <em>AA</em> stock levels changes at given times due to unforeseen forces of consumer demand experienced by <em>DCs</em>, explained by the “<strong><em>Bullwhip Effect</em></strong>” due to information distortion in the Supply Chain (<em>SC</em>).
文摘In the field of the construction industry, logistics management has become an issue of concern which needs to be focused on in order to ensure effective, successful and timely execution of projects in the construction industry. Logistics management in construction is a multidisciplinary process which makes sure things are done at the right time, cost and quality as well as material supply and handling, schedule control, site infrastructure and equipment location, site physical flow management, traffic arrangements on and around the construction site, and information management related to all physical and services flow. This paper seeks to identify and evaluate the possible factors that affect logistics management in the construction industry and also to ascertain the current practices of inventory management and the effect on logistics management envisage by construction personnel’s and stake holders. A descriptive survey research was conducted with both structured and unstructured questionnaire and administered both via e-mail and by sending questionnaire link onto a WhatsApp platform groups of the respondents, and out of the 187 respondents that took part in the survey, 22 respondents feedback were incomplete so 165 member respondents results were analyzed representing 88.2% response rate. The survey results were analyzed, displayed and interpreted in the form of tables, bar charts, column chart and pie chart with the use of SPSS software. The research survey respondents </span><span style="font-family:Verdana;">were </span><span style="font-family:Verdana;">comprised of Project Managers, Project Coordinators, Foremen, Site Managers/</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">Officers, Logistics Managers, Inventory Managers and Laborers. The survey results revealed that majority of the total respondents are in the capacity or position as Logistics Managers and also have from 5 to 10 years working experience, and as well majority of the respondents have also attained Master’s degree qualification. It was also revealed that logistics management is greatly affected by the inability or the failure to embrace, adopt and integrate new and emerging technology and its approach into the logistics management system. Majority of the respondents also indicated that in general the adequacy of inventory management system was dissatisfied representing 63.8% of the total respondents. The construction industry not only needed to improve its inventory management systems but also to ensure appropriate inventory management techniques for enhancing high degree of accuracy, reduced cycle times and replenishment of stock. This usually accounts for the poor delivery and quality of work, accompanied with delays and other associated losses in project construction. For effective Logistics management to be achieved in the construction industry, the introduction of modern technology to carry out logistics functions in the construction industry should be paramount to the industry players.
基金supported by the National Natural Science Foundation of China(71901170,72102174,71874133,72001164,171871177)Natural Science Basic Research Program of Shaanxi(2021JM-144)+1 种基金Ministry of Educationof Humanities and Social Science Project(21XJC630004)Fundamental Research Funds for the Central Universities(RW210103JBF220602).
文摘Transshipment is an effective method for reducing mismatches between supply and demand among retailers.Consumers attempting to purchase out-of-stock items may wait for transshipment,purchase at another store,or choose not to buy.In this paper,the consumer behavioral heterogeneity is characterized using the transshipment request rate and consumer switching rate.It affects the replenishment and transshipment decisions,as well as the system profits.The inventory replenishment and transshipment decisions are studied in both centralized and decentralized two-location inventory systems with consumer behavioral heterogeneity.We characterize optimal replenishment decisions in a centralized system,prove the existence of a unique Nash equilibrium in a decentralized system with a specific demand distribution,and determine the coordinating transshipment price for some decentralized systems.In numerical studies,the performance of various systems is analyzed for consumers with identical(symmetric)or differing(asymmetric)behavior between retailers.For scenarios with symmetric consumers,a higher transshipment request rate and consumer switching rate resulted in increased total profit in all systems.For scenarios with asymmetric consumers,the retailer with the higher consumer switching rate should reduce ordering in a centralized system but increase ordering in a decentralized system.Moreover,the retailer with the higher transshipment request rate reduces ordering to increase profit,whereas the other retailer increases order quantity yet earns less profit.
基金The authors gratefully acknowledge the helpful comments/feedback received from the reviewers and the editors that have significantly helped enhance the paper.The first author would like to thanks Prof.Pranab K.Muhuri and Dr.Q.M.Danish Lohani for their generous support.
文摘Purpose-The proposed IT2FMOVMI model intends to concurrently minimize total cost and warehouse space for the single vendor-retailer,multi-item and a consolidated vendor store.Regarding demand and order quantities with the deterministic and type-1 fuzzy numbers,we have also formulated the classic/crisp MOVMI model and type-1 fuzzy MOVMI(T1FMOVMI)model.The suggested solution technique can solve both crisp MOVMIand T1FMOVMIproblems.By finding the optimal ordered quantities and backorder levels,the Paretofronts are constructed to form the solution sets for the three models.Design/methodology/approach-A multi-objective vendor managed inventory(MOVMI)is the most recognized marketing and delivery technique for the service provider and the retail in the supply chain in Industry 4.0.Due to the evolving market conditions,the characteristics of the individual product,the delivery period and the manufacturing costs,the demand rate and order quantity of the MOVMI device are highly unpredictable.In such a scenario,a MOVMI system with a deterministic demand rate and order quantity cannot be designed to estimate the highly unforeseen cost of the problem.This paper introduces a novel interval type-2 fuzzy multi-objective vendor managed inventory(IT2FMOVMI)system,which uses interval type-2 fuzzy numbers(IT2FNs)to represent demand rate and order quantities.As the model is an NP-hard,the well-known meta-heuristic algorithm named NSGA-II(Non-dominated sorted genetic algorithm-II)with EKM(Enhanced Karnink-Mendel)algorithm based solution method has been established.Findings-The experimental simulations for the five test problems that demonstrated distinct conditions are considered from the real-datasets of SAPCO company.Experimental study concludes that T1FMOVMI and crisp MOVMI schemes are outclassed by IT2FMOVMI model,offering more accurate Pareto-Fronts and efficiency measurement values.Originality/value-Using fuzzy sets theory,a significant amount of work has been already done in past decades from various points of views to model the MOVMI.However,this is the very first attempt to introduce type-2 fuzzy modelling for the problem to address the realistic implementation of the imprecise parameters.
基金supported by the Opening Project of the Key Laboratory of Operation Safety Technology on Transportation Vehicles,Ministry of Transport,PRC (No.KFKT2016-03)the Basic Science Research Plan of Shaanxi Province (No.2020JM225)the Fundamental Research Funds for the Central Universities (No.310822173702)。
文摘As one of the critical issues in operations management, spare parts planning classification systems based only on cost may not withstand the test of time because of continuing technological advancement or environmental degradation. This study, however, emphasizes using failure mode effect and criticality analysis(FMECA) as a basis for designing an ABC classification system that is capable of more accurately determining critical equipment and making maintenance more efficient. In the proposed methodology, risk priority numbers(RPN) and criticality as an index of safety and reliability can be obtained using a structured failure analysis technique. These indexes can be added to the economic indicator of maintenance cost, and then a classification model of spare parts can be established according to a comprehensive scoring method. The model is calibrated by using case data from a bus braking system, and the components of the braking system are compared and analyzed. The results show that the improved ABC classification method reduces the proportion of key and main components. This method can make maintenance work more efficient, targeting the most critical components, and can reduce administrative costs for enterprises.
