模具制造企业备件库存分类模型研究
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摘要
在市场经济中,成本和效率是一个企业能够生存壮大的关键。对于制造型企业而言,备件是企业生产所需要的重要物资,备件库存有利于维持企业的正常运行,促进生产过程的连续性,减少企业因备件短缺而造成的损失。备件管理应该做到把备件供应不及时所造成的损失减少到最低限度,同时把备件库存储备资金压缩到合理供应的最低水平。
本文研究了模具制造企业的生产特征及备件、备件管理特征,结合某模具制造企业现状说明了传统ABc分类法在其备件库存管理中的局限性,分析了这种局限性产生的原因,借此提出了一种适合模具制造企业现状的备件库存ABCD分类模型,该模型把影响备件库存的六个重要因素作为分类指标,以A类、B类、C类、D类作为四种分类类别,同时给出了相应的库存管理策略。
结合BP神经网络的优点求解备件库存ABCD分类模型,提取样本数据集,借助MATLAB软件仿真设计,通过大量网络训练,初步建立基于BP神经网络的备件库存ABCD分类模型。
针对BP神经网络训练过程中出现的收敛慢、学习效率低、易陷入局部最小值的不足,引用遗传算法对BP神经网络算法进行优化改进,弥补神经网络的不足,取长补短,建立了基于遗传算法的BP神经网络备件库存ABCD分类模型,最后进行精度测试,通过精度比较,得出基于遗传算法的BP神经网络备件分类模型的优异性。
以某模具制造企业的实际数据为依托,引入现有库存量作为判断条件,实例运算、对比验证了基于遗传算法的BP神经网络备件库存ABCD分类模型的合理性。
最后,对本论文进行了总结,对模具制造企业备件库存管理的发展做出展望。
Cost and efficiency are the key for an enterprise to survive and thrive in market-oriented economy. As these manufacturing enterprises, it is very important to maintain the normal operation for spare parts inventory. Spare parts inventory can keep manufacturing enterprises operation, promote the continuity of the production, and reduce the losses caused by shortages of spare parts. Spare parts inventory management should minimize the losses, and simultaneously reduce stock-out cost to the reasonable lowest level.
The dissertation, researched characteristics of production, spare parts and spare parts management in these mold manufacturing enterprises, combined with current situation of a mold manufacturing enterprises, explained the limitations of the traditional ABC classification in the spare parts inventory management, analyzed the reasons. For this purpose, it proposed a suitable ABCD classification of model spare parts inventory for the mold manufacturing enterprises, set the six important influence factors as classification index of model and took Class A, class B, class C, class D as the four spare parts category of model, provided corresponding inventory management strategy.
Combining with the advantages of BP neural network, the dissertation solved the ABCD classification model of spare parts inventory, though the sample data which was extracted and MATLAB software, designed the simulation, and made lots of network training, preliminary built the spare parts inventory classification model based on BP neural network.
Because of the slow convergence and learning efficiency of BP neural network, it is easy to fall into the local minimum and make BP neural network invalid, The dissertation, put forward to apply genetic algorithm to optimize and improve BP neural network, made up the insufficient of neural network, learned from each other, built the spare parts inventory classification model based on genetic algorithm and BP neural network, though testing and comparing the precision, proved the advantage of the spare parts inventory ABCD classification model that is based on GA-BP.
Though computing and contrasting the actual data of the mold manufacturing enterprises, the dissertation, introduced the existing inventory as the judgment condition, and verified the rationality of the spare parts inventory classification model based on GA-BP.
Finally, the dissertation made the summary and made the prospect for inventory management development of spare parts in the mold manufacturing enterprises.
本文研究了模具制造企业的生产特征及备件、备件管理特征,结合某模具制造企业现状说明了传统ABc分类法在其备件库存管理中的局限性,分析了这种局限性产生的原因,借此提出了一种适合模具制造企业现状的备件库存ABCD分类模型,该模型把影响备件库存的六个重要因素作为分类指标,以A类、B类、C类、D类作为四种分类类别,同时给出了相应的库存管理策略。
结合BP神经网络的优点求解备件库存ABCD分类模型,提取样本数据集,借助MATLAB软件仿真设计,通过大量网络训练,初步建立基于BP神经网络的备件库存ABCD分类模型。
针对BP神经网络训练过程中出现的收敛慢、学习效率低、易陷入局部最小值的不足,引用遗传算法对BP神经网络算法进行优化改进,弥补神经网络的不足,取长补短,建立了基于遗传算法的BP神经网络备件库存ABCD分类模型,最后进行精度测试,通过精度比较,得出基于遗传算法的BP神经网络备件分类模型的优异性。
以某模具制造企业的实际数据为依托,引入现有库存量作为判断条件,实例运算、对比验证了基于遗传算法的BP神经网络备件库存ABCD分类模型的合理性。
最后,对本论文进行了总结,对模具制造企业备件库存管理的发展做出展望。
Cost and efficiency are the key for an enterprise to survive and thrive in market-oriented economy. As these manufacturing enterprises, it is very important to maintain the normal operation for spare parts inventory. Spare parts inventory can keep manufacturing enterprises operation, promote the continuity of the production, and reduce the losses caused by shortages of spare parts. Spare parts inventory management should minimize the losses, and simultaneously reduce stock-out cost to the reasonable lowest level.
The dissertation, researched characteristics of production, spare parts and spare parts management in these mold manufacturing enterprises, combined with current situation of a mold manufacturing enterprises, explained the limitations of the traditional ABC classification in the spare parts inventory management, analyzed the reasons. For this purpose, it proposed a suitable ABCD classification of model spare parts inventory for the mold manufacturing enterprises, set the six important influence factors as classification index of model and took Class A, class B, class C, class D as the four spare parts category of model, provided corresponding inventory management strategy.
Combining with the advantages of BP neural network, the dissertation solved the ABCD classification model of spare parts inventory, though the sample data which was extracted and MATLAB software, designed the simulation, and made lots of network training, preliminary built the spare parts inventory classification model based on BP neural network.
Because of the slow convergence and learning efficiency of BP neural network, it is easy to fall into the local minimum and make BP neural network invalid, The dissertation, put forward to apply genetic algorithm to optimize and improve BP neural network, made up the insufficient of neural network, learned from each other, built the spare parts inventory classification model based on genetic algorithm and BP neural network, though testing and comparing the precision, proved the advantage of the spare parts inventory ABCD classification model that is based on GA-BP.
Though computing and contrasting the actual data of the mold manufacturing enterprises, the dissertation, introduced the existing inventory as the judgment condition, and verified the rationality of the spare parts inventory classification model based on GA-BP.
Finally, the dissertation made the summary and made the prospect for inventory management development of spare parts in the mold manufacturing enterprises.
引文
[1]罗百辉.冲压模具技术问答[M].北京：化学工业出版社,2008.
[2]施良才.金融危机下中国模具制造业如何调整并抓住机遇[J].模具工业,2009,35(7)： 67—69.
[3]程惠云.广东模具企业模具项目管理研究[D].北京：北京交通大学,2009.
[4]李明裕,袁雄飞,李从心.面向制造企业的ERP系统库存管理解决方案[J].机床与液压,2003,3：346-348.
[5]Janne Huiskonen. Maintenance Spare Parts Logistics:Special Characteristics and Strategic Choices [J]. Int. J. Production Economic,2001,71:125-133.
[6]Guvenir HA, Erel E. Multicriteria Inventory Classification Using A Genetic Algorithm[J]. European Journal of Operational Research,1998,105:29-37.
[7]Nieola Saceani, Mareo Perona, Fredrik Persson. Spare Parts Classification And Inventory Decisions A Preliminary Study [J]. European Journal of Operational Research,2004,154:703-739.
[8]Flores B.E, Olson DL, Dorai VK. Management of Multicriteria Inventory Classification[J]. Mathematical And Computer Modeling,1992,16(12):71-82.
[9]Cohen MA, Ernst R. Multi-Item Classification and Generic Inventory Stock Control Policies[J]. Production and Inventory Management Journal,1988,29(3):6-8.
[10]金锡万,方承武等.备件管理发展趋势与促进备件管理现代化的对策[J].华东冶金学院学报,1997,14(1)：77-80.
[11]方承武,包菊芳,丑纪岳等.备件库存结构分析研究[J].华东冶金学院学报,1997,14(1)：495.
[12]崔南方,罗雪.维修备件基于AHP的ABC分类模型[J].工业工程与管理,2004,6： 33-36.
[13]刘志焘.基于支持向量机的不常用备件分类模型研究[D].武汉：华中科技大学,2008.
[14]熊君星.基于IDS的备件库存管理系统研究[D].南昌：南昌大学,2009.
[15]郭智敏,严洪森等.备件库存控制方法研究[J].计算机集成制造系统,2003, 9(11)：1028—1032.
[16]赵振峰.流程式生产企业备件供应管理研究[D].武汉：华中科技大学,2004.
[17]Kalkowska, Joanna, Trzcielinski. Some conditions of implementing CE in advanced manufacturing systems [J]. Proceedings of the Fourth International Workshop on Robot Motion and Control, RoMoCo'04,2004,16:299-309.
[18]Christine Harland. International comparison of supply chain relationship[J]. Logistics Information Management,1996,9(4):35-38.
[19]Claudia H.Pragman. JIT II:A purchasing concept for reducing lead times in time-based competition[J]. BusinessH orizons,1996,7(8):54-58.
[20]Takeshi Shibuya, Tadashi Dohi, Shunji Osaki. Optimal continuous review policies for spare part provisioning with random lead times[J]. Int.J.Production Economics, 1998,55:257-271.
[21]王永川,蔡金燕,曹宏炳.基于遗传算法的雷达功能板备件优化模型[J].现代雷达,2002,4：1-4.
[22]张志斌,赵建民,王著.基于影响图理论的备件消耗因素分析因数[J].工业工程,2001,4：39-41.
[23]赵宇,伏洪勇,张坚等.航空电子设备备件需求量分析[J].系统工程与电子技术,2002,3：1—3.
[24]王都,李金华.欧美模具企业技术发展综述[J].中国机电工业,2001,16：27-28.
[25]马鹏霄.面向模具企业的库存管理系统关键技术研究[D].武汉：华中科技大学,2009.
[26]Kamath K, Rajashree, T.P.M. Pakkala, A Bayesian approach to a dynamic inventory model under an unknown demand distributiona[J]. Computers&Operations Research, 2002,29:403-422.
[27]赵嵩正.备件管理现状的调查分析与探讨[J].西北工业大学学报,1996,19(6)：27—29.
[28]曲立,徐远.国外备件库存管理新趋势企业经济[J].2006,313(9),24—26.
[29]Allen S.G, D.A.D'esopo, An ordering policy for repairable stocktems[J]. Operations Research,1968.16(3):482-489.
[30]赵风多.ABC分类法在备件管理中的应用[J].设备管理与维修,2009,6：11—12.
[31]胡从旭.ABC分类法在库存管理中的实施及改进[J].物流管理,2010,9(32)：62-63.
[32]AHC Eavesl, BG Kingsman, Forecasing for the ordering and stock-holding of spare parts[J]. Journal of Operational Research Society,2004,55:431-437.
[33]曲立,张群.备件库存管理综述[J].实验室研究与探索,2006,25(7),875-880.
[34]黄圣官.面向进销存流程的管理信息系统的研究与开发[D].南京：南京理工大学,2006.
[35]TockGN, GreisNP, KasardaJD. Enterprises logistics and supply chain structure[J]. Journal of Operations Management,2000,18:531-547.
[36]刘文博.供应链库存管理之VMI管理[J].物流科技,2009,3：78-79.
[37]李建民.供应链管理环境下的联合库存管理[J].铁路采购与物流,2009,1：40-41.
[38]甄文祥.CPFR原理及实施[J].工业工程与管理,2001,5：6-9.
[39]王振华.设备维修模式和备件管理储备的合理性[J].中国设备工程,2001,9：7—8.
[40]李国勇.智能控制及其MATLAB实现[M].北京：电子工业出版社,2005.
[41]韩力群.人工神经网络理论、设计及应用[M].北京：化学工业出版社,2007.
[42]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京：电子工业出版社,2005.
[43]Hopfield J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceedings of the Nmional Academy of Science.1982,2: 554-558.
[44]雷英杰.MATLAB遗传算法工具箱及应用[M].西安：西安电子科技大学出版社,2005.
[45]谢小山.基于遗传算法和BP神经网络的铁路客运量预测研究[D].成都：西南交通大学,2005.
[46]Alexandre H.F.Dias. Multiobjective Genetic Algorithms Applied to Solve Optimization Problems[J]. IEEE Transactions on Magnetics,Vol.2002,2(38): 1133-1136.
[2]施良才.金融危机下中国模具制造业如何调整并抓住机遇[J].模具工业,2009,35(7)： 67—69.
[3]程惠云.广东模具企业模具项目管理研究[D].北京：北京交通大学,2009.
[4]李明裕,袁雄飞,李从心.面向制造企业的ERP系统库存管理解决方案[J].机床与液压,2003,3：346-348.
[5]Janne Huiskonen. Maintenance Spare Parts Logistics:Special Characteristics and Strategic Choices [J]. Int. J. Production Economic,2001,71:125-133.
[6]Guvenir HA, Erel E. Multicriteria Inventory Classification Using A Genetic Algorithm[J]. European Journal of Operational Research,1998,105:29-37.
[7]Nieola Saceani, Mareo Perona, Fredrik Persson. Spare Parts Classification And Inventory Decisions A Preliminary Study [J]. European Journal of Operational Research,2004,154:703-739.
[8]Flores B.E, Olson DL, Dorai VK. Management of Multicriteria Inventory Classification[J]. Mathematical And Computer Modeling,1992,16(12):71-82.
[9]Cohen MA, Ernst R. Multi-Item Classification and Generic Inventory Stock Control Policies[J]. Production and Inventory Management Journal,1988,29(3):6-8.
[10]金锡万,方承武等.备件管理发展趋势与促进备件管理现代化的对策[J].华东冶金学院学报,1997,14(1)：77-80.
[11]方承武,包菊芳,丑纪岳等.备件库存结构分析研究[J].华东冶金学院学报,1997,14(1)：495.
[12]崔南方,罗雪.维修备件基于AHP的ABC分类模型[J].工业工程与管理,2004,6： 33-36.
[13]刘志焘.基于支持向量机的不常用备件分类模型研究[D].武汉：华中科技大学,2008.
[14]熊君星.基于IDS的备件库存管理系统研究[D].南昌：南昌大学,2009.
[15]郭智敏,严洪森等.备件库存控制方法研究[J].计算机集成制造系统,2003, 9(11)：1028—1032.
[16]赵振峰.流程式生产企业备件供应管理研究[D].武汉：华中科技大学,2004.
[17]Kalkowska, Joanna, Trzcielinski. Some conditions of implementing CE in advanced manufacturing systems [J]. Proceedings of the Fourth International Workshop on Robot Motion and Control, RoMoCo'04,2004,16:299-309.
[18]Christine Harland. International comparison of supply chain relationship[J]. Logistics Information Management,1996,9(4):35-38.
[19]Claudia H.Pragman. JIT II:A purchasing concept for reducing lead times in time-based competition[J]. BusinessH orizons,1996,7(8):54-58.
[20]Takeshi Shibuya, Tadashi Dohi, Shunji Osaki. Optimal continuous review policies for spare part provisioning with random lead times[J]. Int.J.Production Economics, 1998,55:257-271.
[21]王永川,蔡金燕,曹宏炳.基于遗传算法的雷达功能板备件优化模型[J].现代雷达,2002,4：1-4.
[22]张志斌,赵建民,王著.基于影响图理论的备件消耗因素分析因数[J].工业工程,2001,4：39-41.
[23]赵宇,伏洪勇,张坚等.航空电子设备备件需求量分析[J].系统工程与电子技术,2002,3：1—3.
[24]王都,李金华.欧美模具企业技术发展综述[J].中国机电工业,2001,16：27-28.
[25]马鹏霄.面向模具企业的库存管理系统关键技术研究[D].武汉：华中科技大学,2009.
[26]Kamath K, Rajashree, T.P.M. Pakkala, A Bayesian approach to a dynamic inventory model under an unknown demand distributiona[J]. Computers&Operations Research, 2002,29:403-422.
[27]赵嵩正.备件管理现状的调查分析与探讨[J].西北工业大学学报,1996,19(6)：27—29.
[28]曲立,徐远.国外备件库存管理新趋势企业经济[J].2006,313(9),24—26.
[29]Allen S.G, D.A.D'esopo, An ordering policy for repairable stocktems[J]. Operations Research,1968.16(3):482-489.
[30]赵风多.ABC分类法在备件管理中的应用[J].设备管理与维修,2009,6：11—12.
[31]胡从旭.ABC分类法在库存管理中的实施及改进[J].物流管理,2010,9(32)：62-63.
[32]AHC Eavesl, BG Kingsman, Forecasing for the ordering and stock-holding of spare parts[J]. Journal of Operational Research Society,2004,55:431-437.
[33]曲立,张群.备件库存管理综述[J].实验室研究与探索,2006,25(7),875-880.
[34]黄圣官.面向进销存流程的管理信息系统的研究与开发[D].南京：南京理工大学,2006.
[35]TockGN, GreisNP, KasardaJD. Enterprises logistics and supply chain structure[J]. Journal of Operations Management,2000,18:531-547.
[36]刘文博.供应链库存管理之VMI管理[J].物流科技,2009,3：78-79.
[37]李建民.供应链管理环境下的联合库存管理[J].铁路采购与物流,2009,1：40-41.
[38]甄文祥.CPFR原理及实施[J].工业工程与管理,2001,5：6-9.
[39]王振华.设备维修模式和备件管理储备的合理性[J].中国设备工程,2001,9：7—8.
[40]李国勇.智能控制及其MATLAB实现[M].北京：电子工业出版社,2005.
[41]韩力群.人工神经网络理论、设计及应用[M].北京：化学工业出版社,2007.
[42]飞思科技产品研发中心.神经网络理论与MATLAB7实现[M].北京：电子工业出版社,2005.
[43]Hopfield J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceedings of the Nmional Academy of Science.1982,2: 554-558.
[44]雷英杰.MATLAB遗传算法工具箱及应用[M].西安：西安电子科技大学出版社,2005.
[45]谢小山.基于遗传算法和BP神经网络的铁路客运量预测研究[D].成都：西南交通大学,2005.
[46]Alexandre H.F.Dias. Multiobjective Genetic Algorithms Applied to Solve Optimization Problems[J]. IEEE Transactions on Magnetics,Vol.2002,2(38): 1133-1136.