Forecasting commercial electricity sales

An important component of the New England Electric System Companies' (the 'System') total electricity sales is attributable to commercial customers. Commercial growth has recently been strong; moreover the System's peak demand is highly sensitive to commercial load. In a typical month this class represents 33 per cent of total System sales. Accurate short-run forecasts of total kWh sales are important for rate making, budgeting, fuel cause proceedings, and corporate planning. In this study we use a variety of econometric and time-series techniques to produce short-run forecasts of commercial sales for two geographical areas served by two separate retail companies;.     

Long‐term sales forecasting using holt–winters and neural network methods

The problem of medium to long‐term sales forecasting raises a number of requirements that must be suitably addressed in the design of the employed forecasting methods. These include long forecasting horizons (up to 52 periods ahead), a high number of quantities to be forecasted, which limits the possibility of human intervention, frequent introduction of new articles (for which no past sales are available for parameter calibration) and withdrawal of running articles. The problem has been tackled by use of a damped‐trend Holt–Winters method as well as feedforward multilayer neural networks (FMNNs) applied to sales data from two German companies. Copyright © 2005 John Wiley & Sons, Ltd.     

Judgemental revision of sales forecasts: A longitudinal eetension

When quantitative models are used for short-term multi-item sales forecasts it is possible that the managers who use such forecasts may disagree with at least some of the estimates obtained, and wish to change them so that they become more consistent with their own (subjective) evaluation of the marketplace. This study reports on an analysis of the effectiveness of judgemental revision of sales forecasts over six quarterly forecasting periods. The results give general support for the practice of forecast manipulation as a means of improving forecasting accuracy. It is also observed that the effectiveness of revision activity varies across different time periods.     

Practitioners' forum. Sales forecasting by forsys

This paper describes a sales forecasting system widely used by European companies. The system, known as FORSYS, includes several unique characteristics which increase its use and applicability among practitioners. FORSYS is simple to use; its underlying rationale is clear to the user; it is adaptive, and it allows the incorporation of special events into the model in order to determine their influence on forecasting.     

A comparison of adaptive structural forecasting methods for electricity sales

This paper presents the results of a study to determine whether new forecasting technologies might be of use to electric utilities for sales forecasting up to 3 years into the future. The methods considered included ordinary least squares on dynamic structural models, autocorrelated error models, adaptive variance and adaptive parameter models. Overall, the more adaptive models performed best, but most of the methods proved vastly superior to simple least squares models which do not take dynamics into account.     

An aggregate sales model for consumer durables incorporating a time‐varying mean replacement age

Forecasting category or industry sales is a vital component of a company's planning and control activities. Sales for most mature durable product categories are dominated by replacement purchases. Previous sales models which explicitly incorporate a component of sales due to replacement assume there is an age distribution for replacements of existing units which remains constant over time. However, there is evidence that changes in factors such as product reliability/durability, price, repair costs, scrapping values, styling and economic conditions will result in changes in the mean replacement age of units. This paper develops a model for such time‐varying replacement behaviour and empirically tests it in the Australian automotive industry. Both longitudinal census data and the empirical analysis of the replacement sales model confirm that there has been a substantial increase in the average aggregate replacement age for motor vehicles over the past 20 years. Further, much of this variation could be explained by real price increases and a linear temporal trend. Consequently, the time‐varying model significantly outperformed previous models both in terms of fitting and forecasting the sales data. Copyright © 2001 John Wiley & Sons, Ltd.     

Forecasting technique familiarity,satisfaction, usage,and application

A ten-year retrospective study of Mentzer and Cox (1984) was undertaken to answer the question 'Have sales forecasting practices changed over the past ten years?' A mail survey of 207 forecasting executives was employed to investigate this important question. Findings revealed both discrepancies and similarities between today's sales forecasting practices and those of ten years ago. One particular finding indicated greater reliance on and satisfaction with quantitative forecasting techniques today versus ten years ago. Another indicated that forecasting accuracy has not improved over the past ten years, even though the familiarity and usage of various sophisticated sales forecasting techniques have increased. Future research and managerial implications are discussed based on these and other findings.     

基于PLS及时间序列法的卷烟销量组合预测方法

以A省及17个地市约七年间的销售面板数据为研究对象，首先建立三个单项预测模型，即Hoher—Winter季节乘积模型、时间序列分解法模型和偏最小二乘回归模型。在得到三个单项模型预测值之后，再运用组合模型方法，对三种模型的预测结果进行优化。实证结果显示，本组合预测方法更进一步的提高了预测精度，同时对卷烟销量预测实际工作具有借鉴意义。     

Modelling the advertising-sales relationship through use of multiple time series techniques

When time series data are available for both advertising and sales, it may be worth while to model the two series jointly. Such an analysis may contribute to our understanding of the dynamic relationships among the series and may improve the accuracy of forecasts. Multiple time series techniques are applied to the well-known Lydia Pinkham data to illustrate their use in modelling the advertising-sales relationship. In analysing the Lydia Pinkham data the need for a joint model is established and a bivariate model is identified, estimated and checked. Its forecasting properties are discussed and compared to other time series approaches.     

Long-term forecasting with innovation diffusion models: The impact of replacement purchases

The model presented in this paper integrates two distinct components of the demand for durable goods: adoptions and replacements. The adoption of a new product is modeled as an innovation diffusion process, using price and population as exogenous variables. Adopters are expected to eventually replace their old units of the product, with a probability which depends on the age of the owned unit, and other random factors such as overload, style-changes etc. It is shovn that the integration of adoption and replacement demand components in our model yields quality sales forecasts, not only under conditions where detailed data on replacement sales is available, but also when the forecaster's access is limited to total sales data and educated guesses on certain elements of the replacement process.     

Multivariate Time Series Model with Hierarchical Structure for Over‐Dispersed Discrete Outcomes

In this paper, we propose a multivariate time series model for over‐dispersed discrete data to explore the market structure based on sales count dynamics. We first discuss the microstructure to show that over‐dispersion is inherent in the modeling of market structure based on sales count data. The model is built on the likelihood function induced by decomposing sales count response variables according to products' competitiveness and conditioning on their sum of variables, and it augments them to higher levels by using the Poisson–multinomial relationship in a hierarchical way, represented as a tree structure for the market definition. State space priors are applied to the structured likelihood to develop dynamic generalized linear models for discrete outcomes. For the over‐dispersion problem, gamma compound Poisson variables for product sales counts and Dirichlet compound multinomial variables for their shares are connected in a hierarchical fashion. Instead of the density function of compound distributions, we propose a data augmentation approach for more efficient posterior computations in terms of the generated augmented variables, particularly for generating forecasts and predictive density. We present the empirical application using weekly product sales time series in a store to compare the proposed models accommodating over‐dispersion with alternative no over‐dispersed models by several model selection criteria, including in‐sample fit, out‐of‐sample forecasting errors and information criterion. The empirical results show that the proposed modeling works well for the over‐dispersed models based on compound Poisson variables and they provide improved results compared with models with no consideration of over‐dispersion. Copyright © 2014 John Wiley & Sons, Ltd.     

A diffusion model for products with indirect network externalities

This paper develops a new diffusion model that incorporates the indirect network externality. The market with indirect network externalities is characterized by two‐way interactive effects between hardware and software products on their demands. Our model incorporates two‐way interactions in forecasting the diffusion of hardware products based on a simple but realistic assumption. The new model is parsimonious, easy to estimate, and does not require more data points than the Bass diffusion model. The new diffusion model was applied to forecast sales of DVD players in the United States and in South Korea, and to the sales of Digital TV sets in Australia. When compared to the Bass and NSRL diffusion models, the new model showed better performance in forecasting long‐term sales. Copyright © 2008 John Wiley & Sons, Ltd.     

Development of a multifunctional sales response model with the diagnostic aid of artificial neural networks

This paper proposes an approach that models and forecasts sales through a flexible parametric response function (multifunctional), allowing for differentiated behavioural assumptions of the response determinants to be specified, and uses neural network modelling as a re‐specification tool for the response model in order to improve forecasting performance. An initial experiment on a sample of sales data demonstrates feasibility and gives comparative insights via alternative model specifications. Copyright © 2005 John Wiley & Sons, Ltd.     

Measuring large‐scale market responses and forecasting aggregated sales: Regression for sparse high‐dimensional data

In this article, we propose a regression model for sparse high‐dimensional data from aggregated store‐level sales data. The modeling procedure includes two sub‐models of topic model and hierarchical factor regressions. These are applied in sequence to accommodate high dimensionality and sparseness and facilitate managerial interpretation. First, the topic model is applied to aggregated data to decompose the daily aggregated sales volume of a product into sub‐sales for several topics by allocating each unit sale (“word” in text analysis) in a day (“document”) into a topic based on joint‐purchase information. This stage reduces the dimensionality of data inside topics because the topic distribution is nonuniform and product sales are mostly allocated into smaller numbers of topics. Next, the market response regression model for the topic is estimated from information about items in the same topic. The hierarchical factor regression model we introduce, based on canonical correlation analysis for original high‐dimensional sample spaces, further reduces the dimensionality within topics. Feature selection is then performed on the basis of the credible interval of the parameters' posterior density. Empirical results show that (i) our model allows managerial implications from topic‐wise market responses according to the particular context, and (ii) it performs better than do conventional category regressions in both in‐sample and out‐of‐sample forecasts.     

基于专利引文数据的混合动力汽车创新扩散研究

以巴斯扩散模型为理论基础，利用1995～2011年的专利引文数据，对混合动力汽车的技术扩散模型进行了非线性最小二乘法估计。结果显示：①各参数均为统计上显著的，且可决系数与调整可决系数均较大；②该技术的扩散路径符合巴斯模型，且处于成熟阶段。同时，以普锐斯为例，对其1997～2010年的销售数据进行拟合与预测，结果显示，该产品的扩散过程符合巴斯模型。文章认为新技术的扩散与新产品之间存在时滞。     

Evaluating forecasts: a look at aggregate bias and accuracy measures

In this paper an investigation is made of the properties and use of two aggregate measures of forecast bias and accuracy. These are metrics used in business to calculate aggregate forecasting performance for a family (group) of products. We find that the aggregate measures are not particularly informative if some of the one‐step‐ahead forecasts are biased. This is likely to be the case in practice if frequently employed forecasting methods are used to generate a large number of individual forecasts. In the paper, examples are constructed to illustrate some potential problems in the use of the metrics. We propose a simple graphical display of forecast bias and accuracy to supplement the information yielded by the accuracy measures. This support includes relevant boxplots of measures of individual forecasting success. This tool is simple but helpful as the graphic display has the potential to indicate forecast deterioration that can be masked by one or both of the aggregate metrics. The procedures are illustrated with data representing sales of food items. Copyright © 2005 John Wiley & Sons, Ltd.     

Human judgments in New York state sales and use tax forecasting

Human judgments have become quite important in revenue forecasting processes. This paper centres on human judgments in New York state sales and use tax by examining the actual practices of information integration. Based on the social judgment theory (i.e., the lens model), a judgment analysis exercise was designed and administered to a person from each agency (the Division of the Budget, Assembly Ways and Means Committee Majority and Minority, and the Senate Finance Committee) to understand how information integration is processed among different agencies. The results of the judgment analysis exercise indicated that revenue forecasters put different weight on cues. And, in terms of relative and subjective weights, the cues were used differently, although they were presented with the same information. Copyright © 2004 John Wiley & Sons, Ltd.     

Judgemental revision of sales forecasts: The relative performance of judgementally revised versus non-revised forecasts

The judgemental revision of sales forecasts is an issue which is receiving increasing attention in the forecasting literature. This paper compares the performance of forecasts after revision by managers with that of the forecasts which were accepted by them without revision. The data set consists of sales forecasting data from an industrial company, spanning six quarterly periods and relating to some 900 individual products. The findings show that, in general, the improvements made by managers bring the forecast errors of revised forecasts more into line with non-revised forecasts, but the change is often marginal, and the best result is equivalence between revised and non-revised forecasts.     

Sales revenues: Time-series properties and predictions

This paper compares the predictive ability of ARIMA models in forecasting sales revenue. Comparisons were made at both industry and firm levels. With respect to the form of the ARIMA model, a parsimonious model of the form (0, 1, 1) (0, 1, 1) was identified most frequently for firms and industries. This model was identified previously by Griffin and Watts for the earnings series, and by Moriarty and Adams for the sales series. As a parsimonious model, its predictive accuracy was quite good. However, predictive accuracy was also found to be a function of the industry. Out of the eleven industry classifications, ‘metals’ had the lowest predictive accuracy using both firmspecific and industry-specific ARIMA models.     

New product forecasting

Forecasting new-product performance has been called ‘one of the most difficult and critical management tasks’. It has attracted considerable attention because of the magnitude of the resources devoted to product development and because of the sizeable risks involved in making the go–no-go decisions. In comparison with forecasting sales for established products, there is no sales history, or more generally, the company has no product specific experience related to consumer acceptance, trade support and competitive reactions. This article first presents a review of new product forecasting techniques with an emphasis given to the more recent developments in forecasting models. Then, forecasting procedures are assessed by discussing their benefits and their costs. The third part of the article discusses trends in new product forecasting.