A dynamic model for optimal design quality and return policies

A clearly explained and generous return policy has been established as a competitive weapon to enhance sales. From the firm’s point of view, a generous return policy will increase sales revenue, but will also increase cost due to increased likelihood of return. Design quality of the product has been used as a competitive weapon for a long time. This paper recognizes the relationship between design quality and price of the product, and the firm’s return policy. Quality level in the product would influence the amount of return directly. When the product quality is higher, the customer satisfaction rate will increase and the probability of return will decrease. We develop a profit-maximization model to jointly obtain optimal policies for the product quality level, price and the return policy over time. The model presented in this paper is dynamic in nature and considers the decisions as the product moves through the life cycle. We obtain a number of managerial guidelines for using marketing and operational strategy variables to obtain the maximum benefit from the market. We mention several future research possibilities.     

Fashion Forecasting for a Mail Order Company Using a Bayesian Approach

Traditional forecasting methods assume a large amount of product history. New product launches take place in the absence of any product sales statistics and initially it is necessary to formulate beliefs as to the product future and then to combine these with sales data as it becomes available. The particular situation considered in this paper concerns a mail order company which sells a different group of ladies dresses on each of its catalogues. The life of a catalogue is of the order of 6 months and material ordering decisions tend to be relevant only at the beginning and during the first few weeks of the catalogue. The task of the Distribution Department is not helped by the presence of a large number of returns from customers. This paper describes a Bayesian approach to the forecasting problem and the "live" performance of the method on one catalogue is discussed.     

‘Model selection for generalized linear models with factor‐augmented predictors’

This paper considers generalized linear models in a data‐rich environment in which a large number of potentially useful explanatory variables are available. In particular, it deals with the case that the sample size and the number of explanatory variables are of similar sizes. We adopt the idea that the relevant information of explanatory variables concerning the dependent variable can be represented by a small number of common factors and investigate the issue of selecting the number of common factors while taking into account the effect of estimated regressors. We develop an information criterion under model mis‐specification for both the distributional and structural assumptions and show that the proposed criterion is a natural extension of the Akaike information criterion (AIC). Simulations and empirical data analysis demonstrate that the proposed new criterion outperforms the AIC and Bayesian information criterion. Copyright © 2009 John Wiley & Sons, Ltd.     

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.     

Forecasting the demand for banknotes: some empirical results for the Netherlands

The desired production of banknotes is the product of the demand for banknotes at any particular time, and the average lifetime. While the latter might be relatively constant, the first shows a steady increase over time. Various techniques are available to generate forecasts of banknote demand. They have in common that they are extrapolative methods which assume that the future may be derived from the pattern of the past. One major class of forecasting methods of this type are time series models, which are particularly useful in a limited information environment. Causal or regression models provide another class of forecasting methods and are particularly valuable for annual or medium-term prediction, and in cases where explanatory variables are controlled by the policy maker. Contrary to these quantitative models, qualitative forecasting methods do not require as many data but are sometimes very beneficial in guiding intuitive thinking about future developments.The present paper presents some examples of forecasting models studied at the Netherlands Bank as well as some results obtained from qualitative research undertaken in the recent past.     

Forecasting the Development of the Market for Business Telephones in the UK

The problem of producing medium- to long-term forecasts of the market for business telephones is examined. Growth curves are generally appropriate for forecasting developing markets. However, this market is particularly sensitive to the state of business confidence and the feasibility of incorporating explanatory economic variables into the forecasting model is investigated. Three different model types are compared: growth curves with a fixed saturation level, multivariate linear models and growth curves with saturation levels determined by explanatory variables. The initial promise of models using explanatory variables is considerably diminished, once forecast rather than actual values of these variables are used. The market development model implicit in the growth curve is shown to be more robust than the linear model. Although the variable saturation level growth curve grants more insight into the maturity of the market, it does not produce significantly better forecasts than that with the fixed saturation level.     

Fuzzy linear regression and its applications to forecasting in uncertain environment

Linear regression has been used for many years in developing mathematical models for application in marketing, management, and sales forecasting. In this paper, two different sales forecasting techniques are discussed. The first technique involves non-fuzzy abstract methods of linear regression and econometrics. A study of the international market sales of cameras, done in 1968 by John Scott Armstrong, utilized these non-fuzzy forecasting techniques. The second sales forecasting technique uses fuzzy linear regression introduced by H. Tanaka, S. Uejima, and K. Asai, in 1980. In this paper, a study of the computer and peripheral equipment sales in the United States is discussed using fuzzy linear regression. Moreover, fuzzy linear regression is applied to forecasting in an uncertain environment. Finally, some possible improvements and suggestions for further study are mentioned.     

Forecasting newspaper demand with censored regression

Newspaper circulation has to be determined at the level of the individual retail outlets for each of the editions to be sold through such outlets. Traditional forecasting methods provide no insight into the impact of the service level: defined as the probability that no out-of-stock will occur. The service level results in out-of-stock situations, causing missed sales and oversupply or returns. In our application management sets a policy aiming at a 97% service level. The forecasting system developed provides estimates for excess deliveries and for the expected shortages. The results compare favourably to the traditional moving average approach previously employed by the publisher. Censored regression is a natural approach to the newspaper problem. It provides information on key policy variables and it is relatively simple to integrate into the distribution policy, with only small adaptations to the existing forecasting and distribution policy.     

Improving forecasting performance by employing the Taguchi method

To satisfy the volatile nature of today’s markets, businesses require a significant reduction in product development lead times. Consequently, the ability to develop precise product sales forecasts is of fundamental importance to decision-makers. Over the years, many forecasting techniques of varying capabilities have been introduced. The precise extent of their influences, and the interactions between them, has never been fully clarified, although various forecasting factors have been explored in previous studies. Accordingly, this study adopts the Taguchi method to calibrate the controllable factors of a forecasting model. An L₉(3⁴) inner orthogonal array is constructed for the controllable factors of data period, horizon length, and number of observations required. An experimental design is then performed to establish the appropriate levels for each factor. At the same time, an L₄(2³) outer orthogonal array is used to consider the inherited parameters of forecasting method as the noise factors of Taguchi method simultaneously. An illustrated example, employing data from a power company, serves to demonstrate the thesis. The results show that the proposed model permits the construction of a highly efficient forecasting model through the suggested data collection method.     

A new approach of forecasting intermittent demand for spare parts inventories in the process industries

Accurate demand forecasting is of vital importance in inventory management of spare parts in process industries, while the intermittent nature makes demand forecasting for spare parts especially difficult. With the wide application of information technology in enterprise management, more information and data are now available to improve forecasting accuracy. In this paper, we develop a new approach for forecasting the intermittent demand of spare parts. The described approach provides a mechanism to integrate the demand autocorrelated process and the relationship between explanatory variables and the nonzero demand of spare parts during forecasting occurrences of nonzero demands over lead times. Two types of performance measures for assessing forecast methods are also described. Using data sets of 40 kinds of spare parts from a petrochemical enterprise in China, we show that our method produces more accurate forecasts of lead time demands than do exponential smoothing, Croston's method and Markov bootstrapping method.     

多产品公司创建平台邀请竞争对手进行联合销售的策略研究

由于资金分配或生产规模的限制,多产品公司的某类产品与专门销售此类产品的专业产品公司相比,会有一定的不足.以两个产品公司为对象,研究了两个竞争性公司的联合销售模式,即多产品公司投资建设平台,邀请销售单一产品的专业公司在平台上共同销售某类产品.运用主从博弈建立联合销售的基础模型,探讨不同销售模式下的相关投资,并利用数值计算进行决策分析.研究表明,通过创建平台进行联合销售,一方面,消除了消费者的额外购物成本;另一方面,在平台进行联合销售使得两个公司由单纯的竞争关系转化为相互补充,不仅能够吸引更多有不同产品需求的客户,而且能够在一定程度上提高两个产品公司的利润.     

2000年厦门港货物吞吐量预测

本文是《厦门港及附近水域交管系统应用研究》课题中关于港口货物吞吐量预测的部分。这一课题已通过专家鉴定。文中应用回归模型预测2000年厦门港货物吞吐量。通过从多个解释变量中选择合适的解释变量，可获得较好的预测结果。其结果说明在应用数学模型预测时，最为关键的是模型、变量和数据三者之间的相互适应，而不在于模型的复杂程度，特别是在历史数据不多的情况下更是如此。     

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。     

Finding market structure by sales count dynamics—Multivariate structural time series models with hierarchical structure for count data—

In this paper, we propose a multivariate time series model for sales count data. Based on the fact that setting an independent Poisson distribution to each brand’s sales produces the Poisson distribution for their total number, characterized as market sales, and then, conditional on market sales, the brand sales follow a multinomial distribution, we first extend this Poisson–multinomial modeling to a dynamic model in terms of a generalized linear model. We further extend the model to contain nesting hierarchical structures in order to apply it to find the market structure in the field of marketing. As an application using point of sales time series in a store, we compare several possible hypotheses on market structure and choose the most plausible structure by using several model selection criteria, including in-sample fit, out-of-sample forecasting errors, and information criterion.     

A short and mean-term automatic forecasting system––application to textile logistics

In order to reduce their stocks and to limit stock out, textile companies require specific and accurate sale forecasting systems. More especially, textile distribution involves different forecast lead times: mean-term (one year) and short-term (one week in average). This paper presents two new complementary forecasting models, appropriate to textile market requirements. The first model (AHFCCX) allows to automatically obtain mean-term forecasting by using fuzzy techniques to quantify influence of explanatory variables. The second one (SAMANFIS), based on a neuro-fuzzy method, performs short-term forecasting by readjusting mean-term model forecasts from load real sales. To evaluate forecasts accuracy, our models and classical ones are compared to 322 real items sales series of an important ready to wear distributor.     

Likelihood-Based Boosting in Binary and Ordinal Random Effects Models

This article presents a likelihood-based boosting approach for fitting binary and ordinal mixed models. In contrast to common procedures, this approach can be used in high-dimensional settings where a large number of potentially influential explanatory variables are available. Constructed as a componentwise boosting method, it is able to perform variable selection with the complexity of the resulting estimator being determined by information criteria. The method is investigated in simulation studies both for cumulative and sequential models and is illustrated by using real datasets. The supplementary materials for the article are available online.     

An application of support vector machines to sales forecasting under promotions

This paper deals with sales forecasting of a given commodity in a retail store of large distribution. For many years statistical methods such as ARIMA and Exponential Smoothing have been used to this aim. However the statistical methods could fail if high irregularity of sales are present, as happens for instance in case of promotions, because they are not well suited to model the nonlinear behaviors of the sales process. In recent years new methods based on machine learning are being employed for forecasting applications. A preliminary investigation indicates that methods based on the support vector machine (SVM) are more promising than other machine learning methods for the case considered. The paper assesses the application of SVM to sales forecasting under promotion impacts, compares SVM with other statistical methods, and tackles two real case studies.     

The multiple-partners assignment game with heterogeneous sales and multi-unit demands: competitive equilibria

A multiple-partners assignment game with heterogeneous sales and multi-unit demands consists of a set of sellers that own a given number of indivisible units of potentially many different goods and a set of buyers who value those units and want to buy at most an exogenously fixed number of units. We define a competitive equilibrium for this generalized assignment game and prove its existence by using only linear programming. In particular, we show how to compute equilibrium price vectors from the solutions of the dual linear program associated to the primal linear program defined to find optimal assignments. Using only linear programming tools, we also show (i) that the set of competitive equilibria (pairs of price vectors and assignments) has a Cartesian product structure: each equilibrium price vector is part of a competitive equilibrium with all optimal assignments, and vice versa; (ii) that the set of (restricted) equilibrium price vectors has a natural lattice structure; and (iii) how this structure is translated into the set of agents?? utilities that are attainable at equilibrium.     

Time Series Forecasting Based on the Logistic Curve

This paper presents a class of models which are designed for forecasting the net sales of a product when the stock of that product is believed to be subject to a saturation level. The forecast function for the stock takes the form of a general modified exponential, a family which includes the logistic as a special case. However, framing the model in terms of the net increase in the product enables a link to be made between the traditional approach to forecasting based on non-linear trend curves and the approach based on ARIMA models.