支持向量机在装备维修备件需求量预测中的应用

利用支持向量机回归算法建立备件需求模型,对未来备件需求进行了预测,并结合实例将支持向量回归算法与传统的最小二乘拟合方法作比较。结果表明,支持向量回归算法在预测精度上具有明显的优势,该方法能够较好地适应样本数量较少、需求呈非线性特征的备件预测问题。     

慢速流动备件库存管理研究综述

主要研究了国内外慢速流动备件库存管理的相关文献,介绍了备件库存及备件分类的相关知识,主要针对慢速流动备件的需求预测方法及其库存模型算法的研究进展进行了综述。针对需求预测,重点分析了当前所使用的几种预测方法;在库存模型方面研究了慢速流动备件库存管理模式的发展历程,从单级库存模式到多级库存模式,再到联合库存模式和协同库存模式。最后指出了研究存在的不足,明确了慢速流动备件库存管理在未来研究中应加以重视的几个研究方向,为进一步的研究奠定了基础。     

战时装备维修备件品种确定步骤与方法研究

着眼于战时特点,阐述准确确定战时装备维修备件需求的重要性,对我军在确定战时装备维修备件需求方面存在的不足进行了分析。针对现代战争“精确化”保障的特殊要求,结合我军现有的理论成果和实践经验,探讨系统科学地确定战时装备维修备件品种的程序和方法。     

主成分分析—BP神经网络在备件需求预测中的应用

备件需求预测在装备维修保障中占据重要的地位,针对当前主要以经验为主进行估计,与实际需求相差较大,提出基于主成分分析—BP神经网络模型的备件需求预测方法。首先利用主成分分析方法去除原始输入数据的相关性,降低数据维度,减小网络规模,选择合适的隐含层的BP神经网络。最后通过结合实例进行分析,取得较好的效果。     

备件需求预测模型研究

文中提出了基于寿命分布函数、时间序列和成组技术的备件需求预测模型的一般性原则,并研究了它们的特点和适用的范围,对于提高备件需求预测的科学性有实际的指导意义。     

基于灰色系统理论的间断性需求备件预测模型

针对间断性需求备件历史需求数据缺乏、含有大量零值,以致难以进行预测的问题,应用灰色系统理论,先后建立了间断性需求备件预测的非等间隔GM(1,1)模型和包络GM(1,1)模型,并得到了需求预测灰区间,最后进行了计算与实例仿真。     

GM(1,1)模型在航材消耗预测中的应用

传统的预测方法很难对部分消耗数据少、影响因素不明确的航材进行准确预测。运用灰色GM(1,1)模型,可以对"小样本"、贫信息"这类航材进行更为准确的消耗预测。     

Stability in the inefficient use of forecasting systems: A case study in a supply chain company

Computer-based demand forecasting systems have been widely adopted in supply chain companies, but little research has studied how these systems are actually used in the forecasting process. We report the findings of a case study of demand forecasting in a pharmaceutical company over a 15-year period. At the start of the study, managers believed that they were making extensive use of their forecasting system that was marketed based on the accuracy of its advanced statistical methods. Yet most forecasts were obtained using the system’s facility for judgmentally overriding the automatic statistical forecasts. Carrying out the judgmental interventions involved considerable management effort as part of a sales & operations planning (S&OP) process, yet these often only served to reduce forecast accuracy. This study uses observations of the forecasting process, interviews with participants and data on the accuracy of forecasts to investigate why the managers continued to use non-normative forecasting practices for many years despite the potential economic benefits that could be achieved through change. The reasons for the longevity of these practices are examined both from the perspective of the individual forecaster and the organization as a whole.     

降低ASC的售后配件库存方法

为切实降低特约售后服务中心的配件库存,提出了基于多层次多维度的ABC分类法,并对不同类型配件采取不同库存策略,同时提出针对快速流动配件的预测优化方法和针对呆滞件的信息共享平台的建设。     

Design and development of inventory knowledge discovery system

Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.     

浅谈电力负荷预测

电力负荷预测是供电部门的重要工作之一,准确的负荷预测,可以在保障电网的安全前提下,经济合理的安排电网内部发电机组的启停,合理安排机组检修计划,减少不必要的旋转备用容量,降低电网公司的运营成本,提高经济和社会效益。本文主要介绍了电力负荷预测的步骤以及经常采用的负荷预测方法。     

设备维修备件采购模型

根据维修备件采购的要求和特点,在对维修备件进行分类的基础上,提出了适用于维修备件采购的两种模型,专用备件以经济订购批量为基础的采购模型、通用备件以重要度为比例系数的采购模型．并通过算例分析说明提供的模型或方法具有一定的通用性和指导作用。     

基于信息融合技术的备件库存控制决策模型研究

备件在保证企业正常生产发挥着巨大作用。本文通过对备件库存决策信息的特征进行分析的基础上,构建了基于多源信息融合技术的备件库存控制决策模型,并对采用何种信息融合方法与策略进行备件库存决策分析做了一定的研究。     

备件需求预测技术综述

通过对国内外关于备件需求预测的文献进行研究,总结了当前备件需求的预测方法,揭示了备件需求预测技术的发展趋势。     

Retail forecasting: Research and practice

This paper reviews the research literature on forecasting retail demand. We begin by introducing the forecasting problems that retailers face, from the strategic to the operational, as sales are aggregated over products to stores and to the company overall. Aggregated forecasting supports strategic decisions on location. Product-level forecasts usually relate to operational decisions at the store level. The factors that influence demand, and in particular promotional information, add considerable complexity, so that forecasters potentially face the dimensionality problem of too many variables and too little data. The paper goes on to evaluate evidence on comparative forecasting accuracy. Although causal models outperform simple benchmarks, adequate evidence on machine learning methods has not yet accumulated. Methods for forecasting new products are examined separately, with little evidence being found on the effectiveness of the various approaches. The paper concludes by describing company forecasting practices, offering conclusions as to both research gaps and barriers to improved practice.     

A hybrid system-identification method for forecasting telecommunications product demands

A crucial challenge for telecommunications companies is how to forecast changes in demand for specific products over the next 6 to 18 months—the length of a typical short-range capacity-planning and capital-budgeting planning horizon. The problem is especially acute when only short histories of product sales are available. This paper presents a new two-level approach to forecasting demand from short-term data. The lower of the two levels consists of adaptive system-identification algorithms borrowed from signal processing, especially, Hidden Markov Model (HMM) methods [Hidden Markov Models: Estimation and Control (1995) Springer Verlag]. Although they have primarily been used in engineering applications such as automated speech recognition and seismic data processing, HMM techniques also appear to be very promising for predicting probabilities of individual customer behaviors from relatively short samples of recent product-purchasing histories. The upper level of our approach applies a classification tree algorithm to combine information from the lower-level forecasting algorithms. In contrast to other forecast-combination algorithms, such as weighted averaging or Bayesian aggregation formulas, the classification tree approach exploits high-order interactions among error patterns from different predictive systems. It creates a hybrid, forecasting algorithm that out-performs any of the individual algorithms on which it is based. This tree-based approach to hybridizing forecasts provides a new, general way to combine and improve individual forecasts, whether or not they are based on HMM algorithms. The paper concludes with the results of validation tests. These show the power of HMM methods to forecast what individual customers are likely to do next. They also show the gain from classification tree post-processing of the predictions from lower-level forecasts. In essence, these techniques enhance the limited techniques available for new product forecasting.     

现代战争航材保障分析

高技术条件下的现代战争对航材保障工作带来了新的挑战,掌握战时航材保障特点和原则,才能更好地完成战时航材保障任务。     

U公司设备维修备件的分类方法研究

简单阐述了设备维修备件管理的目的与内容,结合U公司在设备维修备件的管理方法,分析了U公司在设备维修备件分类管理上存在的问题。针对现存的问题点,以ABC分类方法为基础,结合了实际的工作经验,为U公司的设备维修备件分类管理制定了一套新的方法。该方法设定了比较明确的标准和简单易行的打分方法,给U公司的设备维修备件的分类带来了一定的指导意义,便得U公司设备维修备件的分类管理在良性的状态下运行。     

基于产品生命周期的备件物流的采购策略研究

阐述了备件采购在备件物流系统中的重要地位及其特殊的属性,着重从产品生命周期的角度来研究备件物流采购的特点和策略,提出了备件物流在产品生命周期的各个阶段的采购策略,为备件采购操作的进行提供了依据,同时对定价策略和库存策略的制定也提供了一定的参考.     

Use and misuse of information in supply chain forecasting of promotion effects

Demand forecasting is critical to sales and operations planning (S&OP), but the effects of sales promotions can be difficult to forecast. Typically, a baseline statistical forecast is judgmentally adjusted on receipt of information from different departments. However, much of this information either has no predictive value or its value is unknown. Research into base rate discounting has suggested that such information may distract forecasters from the average uplift and reduce accuracy. This has been investigated in situations in which forecasters were able to adjust the statistical forecasts for promotions via a forecasting support system (FSS). In two ecologically valid experiments, forecasters were provided with the mean level of promotion uplift, a baseline statistical forecast, and quantitative and qualitative information. However, the forecasters were distracted from the base rate and misinterpreted the information available to them. These findings have important implications for the design of organizational S&OP processes, and for the implementation of FSSs.