论网络环境下高校图书馆用户服务的六大发展趋势

分析了网络环境下高校图书馆用户信息需求的变化,总结了网络环境下高校图书馆用户服务的六大发展趋势。     

价格竞争下基于报童问题的需求模型分析

当前市场条件下,企业间的竞争通常只考虑库存竞争,而忽略了价格竞争.针对这一问题,在需求不确定且与价格具有相关性的前提下,建立了基于报童问题的需求模型.该模型面向包含多个企业的横向市场.同时,这些企业间采用联合定价/存货,并利用价格来竞争市场需求.分析了一个基于报童问题的定价博弈,通过增加不确定性延伸了需求模型.模型中各个企业通过自身的价格弹性影响自身的市场需求,同时也影响着其他企业的市场需求.     

货架摆放量影响需求的库存管理

建立某些商品的需求与库存管理模型,从商品自身的销售弹性与替代品的销售弹性出发,设定摆放量充足时货架摆放量与需求量是指数函数关系;当摆放量小于一定量时,顾客的需求为一常数,并且允许顾客流失和延迟销售;在此基础上建立货架摆放量与需求量的模型.并通过解析解的情况,求出最优解.     

文献信息的合理配购与提高读者满意度的关系

介绍了网络环境下图书馆文献信息的构成,分析了网络环境下读者需求发生的变化,从3个方面论述了合理配购与提高读者满意度的关系。     

基于用户心理的网络信息组织思考

从心理学的角度出发,探讨了用户的信息需求心理,分析了现行网络信息的组织方式与用户信息需求心理之间的矛盾,并从用户信息需求心理的角度出发,为优化现行网络信息的组织方式提出了若干合理化建议。     

信息需求的研究概况

阐述了研究信息需求的现实意义,介绍了对信息需求特点的各种认识及关于信息需求的实证研究,在此基础上总结了信息需求研究的现状及发展趋势。     

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。     

对反向Chebyshev不等式的推广

推广了反向Chebyshev不等式,得到了对应分量的单调性相反的连续向量函数的积分不等式和模的单调性相反的连续向量函数的积分不等式.     

需求推断差异下供应链成员的运营决策机制

供应链终端随机需求的分布不确定时,制造商和零售商将根据各自掌握信息推断需求分布。若零售商不与制造商分享历史需求数据,制造商将根据零售商的历史订单数据进行需求推断。分析了制造商是否有历史需求数据时的价格决策、零售商相应的订购决策,以及供应链成员之间需求数据分享决策。结论显示：制造商根据零售商订单数据推断的需求过高时,将制定较高的批发价格;零售商的期望利润将比与制造商分享需求数据时少,零售商与制造商共享终端需求数据的可能性较大。进一步分析了激励供应链成员分享需求信息并增加供应链利润的协调机制。     

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).