不常用备件需求预测模型与方法研究
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摘要
备件管理是设备管理工作的一项重要内容,它与企业的生产运行绩效和经济效益密切相关。既满足设备维护检修需要,又合理占用资金是备件库存管理的基本原则,准确的备件需求预测对于备件库存控制与优化十分重要。
不常用备件具有使用频次低、间隔期长且需求不确定等特征,这使得该类备件需求的预测非常困难。目前在不常用备件需求预测上常用的指数平滑法、Croston方法等均不能提供令人满意的预测准确度。本文在对不常用备件需求数据特征和现有预测方法进行分析之后,引入Syntetos的需求分类规则,将不常用备件需求定量的分为不稳定需求、间断性需求和“块状”需求三类。针对这三类需求,本文引入多项信号处理技术,分别设计了以下预测方法：
首先,针对不稳定需求,本文设计了EEMD-SVM预测模型。该方法引入集合经验模态分解(EEMD)方法,将数据量变化剧烈的不稳定需求分解为多个相对稳定的需求序列,然后使用SVM方法对分解得到的分量进行预测,最后设计整合模型整合各预测值,得到最终预测结果。通过数据实验,证明对于不稳定需求序列,该方法的预测准确度高于目前常用的其他基于时间序列分析的预测方法。
其二,对间断性需求的需求发生时刻的预测,在以往的文献中几乎没有有效的方法。本文在分析间断性需求发生时刻序列的数据特征之后,引入数字调制技术,设计出针对该类型数据的调制预测模型。文中设计了一种能将间断性的(0-1)序列数据转换成连续需求的受调载波,对调制后的数据采用EEMD-SVM模型进行预测,并设计出有效的检测方法对预测值进行检测判断,最终得到需求发生时刻的预测值。通过对大量人工数据和真实备件需求数据的数据实验,证明该调制预测模型对间断性需求发生时刻的预测是准确有效的。
其三,在分别针对不稳定需求和间断性需求发生时刻设计出预测模型之后,本文将EEMD-SVM模型、调制预测模型和SVM回归预测模型结合,设计出针对一般性不常用备件需求的组合预测模型。该模型能对“块状”需求和普通间断性需求进行较为准确的预测。通过在大量人工数据和真实备件数据上的数据实验,证明该方法与包括移动平均(MA),指数平滑(SES), Croston方法和支持向量机回归(SVR)在内的目前最常见的基于时序分析的不常用备件需求预测方法相比,准确率有了显著提升。
最后,在单一不常用备件需求预测模型的基础上,本文借鉴混合专家系统结构的思想,建立起一套集成多预测方法的不常用备件预测支持系统(NDFSS)的原型。对该NDFSS系统的结构组成、各子系统的功能和实现以及系统的算法逻辑流程进行了详细设计。该NDFSS系统可以完成对大量不常用备件的分类,自动选择最优预测模型并进行自适应模型参数寻优,实现对不常用备件需求的准确预测,以指导企业生产运作。
Spare parts management plays an important role in industry equipment management. Spare parts management aims to cut down occupied capital and related cost so as to improve reliability, maintainability and economy of equipments, and is closely related with the manufacturing schedule and overall profit. Accurate forecast on spare parts demand is crucial to optimize spare parts management.
Rarely used spare parts demand with limited history demand data samples is difficult to forecast with traditional statistic forecast methods, due to its appearance at random with many time periods having no demand. Based on the analysis of rarely used spare parts' data characteristics and the current methods which is used to forecast rarely used spare parts demands, the thesis introduces Syntetos' categorization scheme on demand patterns to classify rarely used spare parts demand into following patterns:erratic demand, internmittent demand and lumpy demand. Then, this dissertation designs new forecast methods for each demand pattern as follows:
Firstly, to improve the forecasting accuracy of erratic demand, an ensemble empirical mode decomposition (EEMD) based hybrid modeling framework is proposed. This approach is under a "decomposition-and-ensemble" principal to decompose the original erratic demand series into several independent smooth subseries including a small number of intrinsic mode functions (IMFs) and a residue by EEMD technique. Then support vector machine regression (SVR) based forecasting methods are used to model each of the subseries so as to achieve more accurate forecast respectively. Finally, the forecasts of all subseries are aggregated to formulate an ensemble forecast for the original erratic demand series. This approach is called "EEMD-SVM" forecasting method.
Secondly, a Modulation Forecasting method was designed to forecast the time when the intermittent demand occurs. An intermittent demand series could be decomposed into two subseries:one is demand size subseries, the other is a "0-1" subseries with "1" denote a demand occurs. A carry wave was designed to transform the "0-1" series into a continuous and smooth series. This series is forecasted by EEMD-SVM method. Finally a detector was designed to detect the "0" and "1" from the forecast. An example is raised to verify the rightness and the effectiveness of the method. The result shows that this method can forecast the demand occurs time at an ideal accuracy.
Thirdly, a combination forecast method based on EEMD-SVM method and Modulation Frecasting method was designed to forecast intermittent demand and lumpy demand. Some artificial data and real spare parts demand data were used to compare this combination forecast method with other common used methods such as moving average, single exponential smoothing, Croston method, SVR etc. The result shows that the accuracy of this method is fairly better than other methods.
Lastly, a forecasting support system assembling multiple methods is designed for rarely used spare parts demand. It is called as "Non-normal Demand Forecasting Support System" (NDFSS). NDFSS can automatically classify the rarely used spare parts' demand, choose the fit forecasting method and optimize the parameters of chosen method intelligently.
不常用备件具有使用频次低、间隔期长且需求不确定等特征,这使得该类备件需求的预测非常困难。目前在不常用备件需求预测上常用的指数平滑法、Croston方法等均不能提供令人满意的预测准确度。本文在对不常用备件需求数据特征和现有预测方法进行分析之后,引入Syntetos的需求分类规则,将不常用备件需求定量的分为不稳定需求、间断性需求和“块状”需求三类。针对这三类需求,本文引入多项信号处理技术,分别设计了以下预测方法：
首先,针对不稳定需求,本文设计了EEMD-SVM预测模型。该方法引入集合经验模态分解(EEMD)方法,将数据量变化剧烈的不稳定需求分解为多个相对稳定的需求序列,然后使用SVM方法对分解得到的分量进行预测,最后设计整合模型整合各预测值,得到最终预测结果。通过数据实验,证明对于不稳定需求序列,该方法的预测准确度高于目前常用的其他基于时间序列分析的预测方法。
其二,对间断性需求的需求发生时刻的预测,在以往的文献中几乎没有有效的方法。本文在分析间断性需求发生时刻序列的数据特征之后,引入数字调制技术,设计出针对该类型数据的调制预测模型。文中设计了一种能将间断性的(0-1)序列数据转换成连续需求的受调载波,对调制后的数据采用EEMD-SVM模型进行预测,并设计出有效的检测方法对预测值进行检测判断,最终得到需求发生时刻的预测值。通过对大量人工数据和真实备件需求数据的数据实验,证明该调制预测模型对间断性需求发生时刻的预测是准确有效的。
其三,在分别针对不稳定需求和间断性需求发生时刻设计出预测模型之后,本文将EEMD-SVM模型、调制预测模型和SVM回归预测模型结合,设计出针对一般性不常用备件需求的组合预测模型。该模型能对“块状”需求和普通间断性需求进行较为准确的预测。通过在大量人工数据和真实备件数据上的数据实验,证明该方法与包括移动平均(MA),指数平滑(SES), Croston方法和支持向量机回归(SVR)在内的目前最常见的基于时序分析的不常用备件需求预测方法相比,准确率有了显著提升。
最后,在单一不常用备件需求预测模型的基础上,本文借鉴混合专家系统结构的思想,建立起一套集成多预测方法的不常用备件预测支持系统(NDFSS)的原型。对该NDFSS系统的结构组成、各子系统的功能和实现以及系统的算法逻辑流程进行了详细设计。该NDFSS系统可以完成对大量不常用备件的分类,自动选择最优预测模型并进行自适应模型参数寻优,实现对不常用备件需求的准确预测,以指导企业生产运作。
Spare parts management plays an important role in industry equipment management. Spare parts management aims to cut down occupied capital and related cost so as to improve reliability, maintainability and economy of equipments, and is closely related with the manufacturing schedule and overall profit. Accurate forecast on spare parts demand is crucial to optimize spare parts management.
Rarely used spare parts demand with limited history demand data samples is difficult to forecast with traditional statistic forecast methods, due to its appearance at random with many time periods having no demand. Based on the analysis of rarely used spare parts' data characteristics and the current methods which is used to forecast rarely used spare parts demands, the thesis introduces Syntetos' categorization scheme on demand patterns to classify rarely used spare parts demand into following patterns:erratic demand, internmittent demand and lumpy demand. Then, this dissertation designs new forecast methods for each demand pattern as follows:
Firstly, to improve the forecasting accuracy of erratic demand, an ensemble empirical mode decomposition (EEMD) based hybrid modeling framework is proposed. This approach is under a "decomposition-and-ensemble" principal to decompose the original erratic demand series into several independent smooth subseries including a small number of intrinsic mode functions (IMFs) and a residue by EEMD technique. Then support vector machine regression (SVR) based forecasting methods are used to model each of the subseries so as to achieve more accurate forecast respectively. Finally, the forecasts of all subseries are aggregated to formulate an ensemble forecast for the original erratic demand series. This approach is called "EEMD-SVM" forecasting method.
Secondly, a Modulation Forecasting method was designed to forecast the time when the intermittent demand occurs. An intermittent demand series could be decomposed into two subseries:one is demand size subseries, the other is a "0-1" subseries with "1" denote a demand occurs. A carry wave was designed to transform the "0-1" series into a continuous and smooth series. This series is forecasted by EEMD-SVM method. Finally a detector was designed to detect the "0" and "1" from the forecast. An example is raised to verify the rightness and the effectiveness of the method. The result shows that this method can forecast the demand occurs time at an ideal accuracy.
Thirdly, a combination forecast method based on EEMD-SVM method and Modulation Frecasting method was designed to forecast intermittent demand and lumpy demand. Some artificial data and real spare parts demand data were used to compare this combination forecast method with other common used methods such as moving average, single exponential smoothing, Croston method, SVR etc. The result shows that the accuracy of this method is fairly better than other methods.
Lastly, a forecasting support system assembling multiple methods is designed for rarely used spare parts demand. It is called as "Non-normal Demand Forecasting Support System" (NDFSS). NDFSS can automatically classify the rarely used spare parts' demand, choose the fit forecasting method and optimize the parameters of chosen method intelligently.
引文
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