Intermittent demand forecasting in the Enterprise: Empirical verification

Forecasting methods are often valued by means of simulation studies. For intermittent demand items there are often very few non–zero observations, so it is hard to check any assumptions, because statistical information is often too weak to determine, for example, distribution of a variable. Therefore, it seems important to verify the forecasting methods on the basis of real data. The main aim of the article is an empirical verification of several forecasting methods applicable in case of intermittent demand. Some items are sold only in specific subperiods (in given month in each year, for example), but most forecasting methods (such as Croston's method) give non–zero forecasts for all periods. For example, summer work clothes should have non–zero forecasts only for summer months and many methods will usually provide non–zero forecasts for all months under consideration. This was the motivation for proposing and testing a new forecasting technique which can be applicable to seasonal items. In the article six methods were applied to construct separate forecasting systems: Croston's, SBA (Syntetos–Boylan Approximation), TSB (Teunter, Syntetos, Babai), MA (Moving Average), SES (Simple Exponential Smoothing) and SESAP (Simple Exponential Smoothing for Analogous subPeriods). The latter method (SESAP) is an author's proposal dedicated for companies facing the problem of seasonal items. By analogous subperiods the same subperiods in each year are understood, for example, the same months in each year. A data set from the real company was used to apply all the above forecasting procedures. That data set contained monthly time series for about nine thousand products. The forecasts accuracy was tested by means of both parametric and non–parametric measures. The scaled mean and the scaled root mean squared error were used to check biasedness and efficiency. Also, the mean absolute scaled error and the shares of best forecasts were estimated. The general conclusion is that in the analyzed company a forecasting system should be based on two forecasting methods: TSB and SESAP, but the latter method should be applied only to seasonal items (products sold only in specific subperiods). It also turned out that Croston's and SBA methods work worse than much simpler methods, such as SES or MA. The presented analysis might be helpful for enterprises facing the problem of forecasting intermittent items (and seasonal intermittent items as well).     

中国的出口扩张是否影响了健康?——基于Grossman模型的拓展分析

在全球经济复苏乏力的背景下,"逆全球化"浪潮正以不可思议的速度席卷全球,并顺势引导着我们重新审视"全球化"的益处及其隐形成本.鉴于目前并没有相关文献从理论上探讨出口扩张对健康的影响,本文以中国改革开放以来迅猛的出口扩张为背景进行了首次探索.本文构建了一个工人们与其雇主讨价还价的企业出口模型框架.厂商依据利润最大化原理选择最优的资本、劳动和原材料的投入之后,每个工人也会选择"最优的工作努力程度"使得努力的边际收益等于努力的边际成本.然后.依据Grossman健康需求模型.本文系统地探讨了由外生性原因引致的出口扩张影响工人健康的五个路径,包括工作强度,工资率,收入差距,公共服务和环境污染.最后,依据文章结论提出相应的政策建议.     

大规模定制模式下的顾客需求管理：获取、识别和聚类分析

为了有效处理不同客户的需求以保证大规模定制生产的规模性和经济性,提出了一种以聚类分析思想为核心的定制需求管理方法。该方法在获取客户需求信息并进行有效化处理的基础上,采取模糊聚类的方法将数量众多的单个客户转化为具有相似需求的客户群体,指导企业产品的标准化、生产的模块化以及产品族的规划设计并提升客户满意度。文章最后,结合笔记本电脑的应用实例说明了该方法的可行性和有效性。     

需求分布含未知参数时报童模型的实施方法

考虑需求分布未知时的报童模型,假设需求分布的类型已知,但分布函数中含未知参数.证明了直接使用参数的估计量代替参数真实值的实施方法将使报童的期望利润偏离理论上的最优水平,只能得到一个次优结果.给出了两种修正的实施方法:一是基于大样本理论,通过收集足够多的需求样本数据,不断修正未知参数的相合估计值;另是在小样本下,直接修正未知参数的估计量.     

弹性需求用户平衡分配模型及其应用

对具有弹性需求的交通网络随机用户平衡分配问题进行了分析研究,将其描述为一个等价的变分不等式。最后将模型应用于拥护公交网络系统的平衡分配问题,并针对公交网络系统的特殊性,提出相应的求解算法 法。实例表明了模型的合理性和算法的有效性。     

非线性需求下三种市场二度价格歧视的比较

在一类典型非线性需求函数条件下,就一个垄断厂商、二个寡头厂商和三个寡头厂商3种市场情形在实施二度价格歧视时的总收益、总收益增长率、消费者剩余和获取的消费者剩余率进行了比较分析.结果表明:在这类非线性需求函数条件下实施二度价格歧视,垄断性强的市场总能提高生产者利润,而竞争性强的市场总能提高消费者剩余.     

需求价格弹性理论在价格策略中的应用

需求弹性在企业经营中是一个非常重要的问题。本文首先论述了需求弹性的一般含义,由此分析需求弹性在市场预测中的应用和在企业价格决策中的重要性,以及如何制定企业的价格策略。     

基于PHP的流媒体视频点播网站的设计与实现

在阐述流媒体技术、流式传输协议以及PHP技术的基础上,以甘肃科技电视网为例,详细介绍了三层结构下基于PHP技术的视频点播网站实现的软硬件环境、关键技术和具体步骤,为实现大规模多媒体实时点播系统的设计提供参考.     

化学化工实验废水排放的监测分析与治理实践

以广西大学化学化工实验大楼排放的实验废水为例,对其水质进行为期半年的监测.监测结果表明:该水的有机物含量不高但排水量大;有代表性的重金属污染中,Hg的平均含量约为0.015 m g/L、C r平均含量低于0.1 m g/L,两者均低于《城市污水综合排放标准》(GB 8978-1996).根据监测结果,提出采用厌氧生物滤池对实验废水进行初步处理,处理后的废水可直接排入市政管道,不会对环境造成不良的影响.     

运用属性论方法加强供应链牛鞭效应防范

提高预测的准确性是缓解供应链牛鞭效应的关键。目前预测的方法往往很难适应订购量历史数据的变动,导致预测结果的偏差。运用属性论方法,就制造商—零售商—客户三级供应链,提出了一个需求信息预测的方法,由此来提高对这种波动性的适应能力,增强预测的准确性,帮助供应链上各节点企业优化决策过程,最终防范了牛鞭效应,并且还提供了此预测方法的算法流程。