需求不确定的供应链两阶段订货模型

销售商如何在不确定需求的市场环境下根据制造商提供的订货条件进行合理订货是供应链管理的一个核心问题。本文利用信号博弈的原理从销售商的角度研究在不确定需求且传统需求预测方法失效的情况下，允许调整订货量的短生命周期产品两阶段订货模型，得到了在两次订货条件下销售商应该采取的最优订货量与调整策略以及制造商对契约灵活性限制的成本函数。     

考虑消费动机和动态竞争的电影日需求预测模型

提出并验证考虑消费动机和动态竞争的电影日需求预测模型。考虑非粉丝及粉丝型的消费动机,构建电影消费两阶段过程模型;融合该模型和Bass模型,考虑竞争导致市场潜量的动态性,考虑映前被关注度、口碑、节假日对票房的影响,提出电影日需求预测模型。利用2016~2017年上映的电影数据验证该模型,并与Bass模型对比分析。结果显示,该模型预测效果优于Bass模型。因考虑竞争导致的动态市场潜量,考虑粉丝型消费者由续集效应及改编效应导致的动态市场潜量提升,该模型能显著提高预测准确度。利用映前被关注度和电影口碑数据,该模型能实现映前及上映早期的预测。该模型可推广至存在消费动机不同、市场动态竞争的其它短生命周期体验品的需求预测,是对Bass模型的改进。     

需求受库存水平影响的短生命周期物品延迟订货的库存管理

像计算机、电视机、空调等这类具有物理变质的可能性很小,但生命周期较短、不断更新换代、价值不断贬值的电子产品会发生无形变质的现象.在假设无形变质率与需求率负相关,同时在产品的存储过程中,考虑库存水平对销售量的影响情况下,研究需求受库存水平影响、且缺货时存在延迟订货的短生命周期物品的库存管理问题.创新之处在于考虑了产品的缺货问题;在允许缺货的条件下,建立了短生命周期物品的库存模型;运用数值算例进行了求解和验证;并对各参数进行了敏感性分析.     

顾客搜索强度影响下短生命周期产品订货决策

根据短生命周期产品的特点,考虑与需求相关的顾客搜索强度,在假设溢出需求为顾客搜索强度函数的情况下,建立了考虑顾客搜索强度因素的斜坡型需求模型,分两种情形对模型最优解进行了存在性证明和求解.然后通过数值算例分析了主要参数变化对缺货时间、订货量、库存成本的影响,发现订货量与顾客搜索强度同方向变动,缺货时间与需求变化临界点出现先后的不同,缺货成本、持有成本和变质成本对库存总成本的影响不同.     

融合Bass模型和三阶段过程模型的续集电影需求扩散研究

续集电影与母电影之间存在品牌溢出效应但不存在替代效应。现有多代扩散模型主要考虑替代效应,难以适用于续集电影。构建了一个融合Bass模型和三阶段过程模型的续集电影需求扩散模型,以预测续集电影的市场潜量和上映期间每日需求量。以2011年至2016年国内上映的续集电影相关数据对模型进行了验证,并与Marshall模型和SBM进行了比较。结果表明1)母电影品牌溢出效应、两代电影特征差距和市场扩张显著影响续集电影的市场潜量;2)电影需求过程存在显著的季节性波动;3)所建模型在拟合优度和预测精度方面均优于两个对比模型。所建模型一方面更适用于预测续集电影的需求扩散,另一方面研究对象拓展至不存在替代效应的多代短生命周期体验品,考虑了多代产品品牌溢出效应,是对已有多代扩散模型的补充。     

考虑季末调整的季节性产品(Qi,Ri)库存模型与管理

考虑季节性产品需求的季节性周期变化、季节内需求不确定性双重波动对产品需求的影响,为季节性产品进行综合需求预测,制定库存管理策略提供参考.本研究综合Holt-Winter需求预测模型和(Q,R)库存模型进行订货点和订货量的决策,并针对季节性产品特点提出了季节间转换时期的订货策略.最后,利用上述模型和某化工企业多年实际数据,进行实证研究,对化工产品的订货量和订货点进行优化,验证了本研究提出的模型与方法可以大幅降低季节性产品库存成本和缺货率.研究对季节性产品库存管理有着重要的学术与实践意义.     

大数据背景下的商品需求预测与分仓规划

商品需求预测对于电商企业意义重大,对阿里电商平台的交易数据进行挖掘以获取有效特征,利用特征建立模型对未来两周这些商品的需求进行动态预测,并基于预测结果和成本最小的原则提出分仓规划建议.预测模型选择随机森林做回归,然后在残差分析的基础上建立报童模型求解分仓的库存规划.对特征数量众多的电商交易数据挖掘所建立的模型有助于电商企业进行有效的商品需求预测并据此制定成本更低的分仓规划.     

基于时态约束贝叶斯网的电信产品生命周期预测研究

针对通信企业多产品同时运营的特点,在获取通信市场发展阶段时态区间分布的基础上,构造带时态约束广义朴素贝叶斯网电信产品生命周期主值分类预测模型,并提出禁忌搜索-蚁群优化算法学习产品属性节点间有向边..在禁忌搜索过程中利用贝叶斯网MDL评分函数结构信息熵、模型复杂度度量交互影响,避免贝叶斯网学习陷入局部最优.在实证部分,应用多层交叉验证对产品运营数据进行测试,并对比NB网、TAN网分类法,结果表明:时态约束GNB网分类预测方法具有准确度高、稳定性好的优势,并能提供产品属性节点对产品生命周期主值预测影响程度实证分析手段,为电信产品生命周期预测研究提供了一种有效的新途径.     

基于组合预测模型的物流园区物流需求预测——以重庆空港物流园为例

物流需求预测是物流园区整体规划的重要前提,准确的物流需求预测可以大大提高物流园区规划的科学性.首先建立了趋势曲线预测模型、回归预测模型及灰色预测模型的物流需求单项预测模型,鉴于单项预测模型的局限性,然后以Shapley值为权重确定方法,建立了组合预测模型,并以重庆空港物流园为例进行应用,最后得出了2015年和2020年重庆空港物流园物流需求的预测值.研究表明,组合预测比单项预测具有更高的精度和稳定性,方法在物流园区物流需求预测中具有推广应用价值.     

基于产品生命周期的闭环供应链回收量预测方法

针对马尔科夫预测方法存在的误差,根据产品在生命周期不同阶段的特点,提出了产品生命周期转化系数和状态转移矩阵,并利用该系数和矩阵对马尔科夫预测方法进行了改进:当相邻两期的产品处于不同的生命周期阶段,用产品生命周期转化系数修正预测值.在此基础上,结合闭环供应链的物品回收特点,提出了相应的预测方法,通过算例对模型的验证与分析得出:改进后的预测方法在一定程度上提高了预测精度.     

Real options approach-based demand forecasting method for a range of products with highly volatile and correlated demand

To achieve a competitive edge needed for marketing highly competitive products, modern enterprises have actively sought to provide the marketplace with an expansive range of products with high random volatility of demand and correlations between demands of product. Consequently, traditional forecasting methods for separately forecasting demand for these products are likely to yield significant deviations. Therefore, this study develops a real options approach-based forecasting model to accurately predict future demand for a given range of products with highly volatile and correlated demand. Additionally, this study also proposes using Monte Carlo simulation to solve the demand forecasting model. The real options approach associated with Monte Carlo simulation not only deals effectively with random variation involving a particular demand stochastic diffusion process, but can handle the correlations in product demand.     

The effect of forecasting and information sharing in SCM for multi-generation products

Proper selection of information sharing policy and forecasting method has a significant impact on supply chain performance, especially in the high-tech industry where the product life cycle is short and multiple generations of products coexist. This paper evaluates the value of information sharing with various forecasting methods where two generations of high-tech products compete with each other in the same market. We consider two market environmental factors and two supply chain factors for the Monte Carlo Simulation and find out the most ideal combination of information sharing policy and forecasting method producing the maximum profits and service level.     

On the identification of sales forecasting models in the presence of promotions

Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially.     

Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions

New product development involves several critical decisions. A key decision making area in new product development is the evaluation of the viability and the market potentials of a new product. In the absence of any relevant historical data, companies ask the potential buyers of their products about their intentions to buy those products when assessing their viability. Despite the popularity of the use of behavioral intentions in predicting the market acceptance of new product ideas, both survey and empirical studies suggest that the accuracy of such predictions is usually very low. Although earlier case-based studies suggest that a number of factors can affect the quality of new product decisions, it is still empirically unclear how product knowledge and the type of new products might impact the predictive accuracy of intentions-based new product forecasting. This study utilized a longitudinal research design and empirically tested the hypotheses across two new products. The study first collected purchase intentions data about the new products. Second, it collected subsequent actual purchase data about the new products. The results of series of hierarchical regression analyses comparing the initial purchase intentions and subsequent actual behaviors showed that while product knowledge is positively related to the predictive accuracy and consistency of intentions-based new product forecasting, product type is negatively related to them.     

An inventory model for increasing demand under two levels of trade credit linked to order quantity

During the growth stage of a product life cycle especially for high-tech products, the demand function increases with time. In this paper, we extend the constant demand to a linear non-decreasing demand function of time and incorporate a permissible delay in payment under two levels of trade credit into the model. The supplier offers a permissible delay linked to order quantity, and the retailer also provides a downstream trade credit period to its customers. The objective is to find the optimal replenishment cycle that minimizes the retailer’s annual total relevant cost per unit time. The condition for an optimal solution to the generalized model is presented and some fundamental theoretical results are established. Finally, numerical examples to illustrate the proposed model are provided. Sensitivity analysis is performed and some relevant managerial insights are obtained.     

Initial supply and re-order level of new service parts

When a new product is introduced to the market, it may be necessary to estimate in advance the demand for service parts. Historical demand data are by definition lacking, so that standard forecasting techniques cannot be applied. In this paper we present a case study in which the failure rate of the parts and the number of end products in operation were used to estimate the demand and the re-order level during the first few months. Both the applied theoretical model and a few practical results have been included, whereby it is assumed that the number of products in operation grows linearly.     

Incorporating demand uncertainty and forecast error in supply chain planning models

This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.     

考虑维修时间的总质保需求预测模型

激烈的市场竞争迫使制造商们逐渐向以顾客需求为中心的公司转变。在近 20 年内,作为影响顾客满意度的主要因素,产品的质保服务管理的相关研究开始成为学术界的焦点。良好的质保服务会给企业节省较多的运营成本,故对于刚投入市场的新产品而言,准确地预测质保需求对制造商合理分配资金等具有重要意义。以往对质保需求的预测模型都局限于分析长期意义上一个产品的总质保成本,忽略了产品的维修时间和动态销售过程对准确预测产品的总质保需求及成本的影响。为此,以销售期内的产品所产生的维修需求为主要的研究对象,深入探讨维修时间对预测质保需求的影响。模型中,利用非齐次泊松过程模拟产品的动态销售过程,并利用复合随机过程中的交错更新理论来刻画维修时间对总质保需求的影响。最后的参数分析,为企业更好地管理质保服务提供了重要的现实依据。     

A mega-trend-diffusion grey forecasting model for short-term manufacturing demand

Accurate short-term demand forecasting is critical for developing effective production plans; however, a short forecasting period indicates that the product demands are unstable, rendering tracking of product development trends difficult. Determining the actual developing data patterns by using forecasting models generated using historical observations is difficult, and the forecasting performance of such models is unfavourable, whereas using the latest limited data for forecasting can improve management efficiency and maintain the competitive advantages of an enterprise. To solve forecasting problems related to a small data set, this study applied an adaptive grey model for forecasting short-term manufacturing demand. Experiments involving the monthly demand data for thin film transistor liquid crystal display panels and wafer-level chip-scale packaging process data showed that the proposed grey model produced favourable forecasting results, indicating its appropriateness as a short-term forecasting tool for small data sets.