The evolution of sales forecasting management: a 20‐year longitudinal study of forecasting practices

This paper presents results of a survey designed to discover how sales forecasting management practices have changed over the past 20 years as compared to findings reported by Mentzer and Cox (1984) and Mentzer and Kahn (1995). An up‐to‐date overview of empirical studies on forecasting practice is also presented. A web‐based survey of forecasting executives was employed to explore trends in forecasting management, familiarity, satisfaction, usage, and accuracy among companies in a variety of industries. Results revealed decreased familiarity with forecasting techniques, and decreased levels of forecast accuracy. Implications for managers and suggestions for future research are presented. Copyright © 2006 John Wiley & Sons, Ltd.     

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.     

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.     

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.     

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.     

Some practical aspects of forecasting in organizations

This paper presents the writer's experience, over a period of 25 years, in analysing organizational systems and, in particular, concentrates on the overall forecasting activity. The paper first looks at the relationship between forecasting and decision taking–with emphasis on the fact that forecasting is a means to aid decision taking and not an end in itself. It states that there are many types of forecasting problems, each requiring different methods of treatment. The paper then discusses attitudes which are emerging about the relative advantages of different forecasting techniques. It suggests a model building process which requires‘experience’and‘craftsmanship’, extensive practical application, frequent interaction between theory and practice and a methodology that eventually leads to models that contain no detectable inadequacies. Furthermore, it argues that although models which forecast a time series from its past history have a very important role to play, for effective policy making it is necessary to augment the model by introducing policy variables, again in a systematic not an ‘ad hoc’ manner. Finally, the paper discusses how forecasting systems can be introduced into the management process in the first place and how they should be monitored and updated when found wanting.     

Forecasting Stock Market Volatility in Central and Eastern European Countries

In recent years, considerable attention has focused on modelling and forecasting stock market volatility. Stock market volatility matters because stock markets are an integral part of the financial architecture in market economies and play a key role in channelling funds from savers to investors. The focus of this paper is on forecasting stock market volatility in Central and East European (CEE) countries. The obvious question to pose, therefore, is how volatility can be forecast and whether one technique consistently outperforms other techniques. Over the years a variety of techniques have been developed, ranging from the relatively simple to the more complex conditional heteroscedastic models of the GARCH family. In this paper we test the predictive power of 12 models to forecast volatility in the CEE countries. Our results confirm that models which allow for asymmetric volatility consistently outperform all other models considered. Copyright © 2011 John Wiley & Sons, Ltd.     

Towards a taxonomy of forecast error measures a factor-comparative investigation of forecast error dimensions

This study explores the nature of information conveyed by 14 error measures drawn from the literature, using real-life forecasting data from 691 individual product items over six quarterly periods. Principal components analysis is used to derive factor solutions that are subsequently compared for two forecasting methods, a version of Holt's exponential smoothing, and the random walk model (Naive 1). The results reveal four underlying forecast error dimensions that are stable across the two factor solutions. The potentially confounding influence of sales volume on the derived error dimensions is also explored via correlation analysis.     

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;.     

Traditional versus novel forecasting techniques: how much do we gain?

This article applies two novel techniques to forecast the value of US manufacturing shipments over the period 1956–2000: wavelets and support vector machines (SVM). Wavelets have become increasingly popular in the fields of economics and finance in recent years, whereas SVM has emerged as a more user‐friendly alternative to artificial neural networks. These two methodologies are compared with two well‐known time series techniques: multiplicative seasonal autoregressive integrated moving average (ARIMA) and unobserved components (UC). Based on forecasting accuracy and encompassing tests, and forecasting combination, we conclude that UC and ARIMA generally outperform wavelets and SVM. However, in some cases the latter provide valuable forecasting information that it is not contained in the former. Copyright © 2008 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.     

Mortality forecasting using neural networks and an application to cause‐specific data for insurance purposes

Mortality forecasting is important for life insurance policies, as well as in other areas. Current techniques for forecasting mortality in the USA involve the use of the Lee–Carter model, which is primarily used without regard to cause. A method for forecasting morality is proposed which involves the use of neural networks. A comparative analysis is done between the Lee–Carter model, linear trend and the proposed method. The results confirm that the use of neural networks performs better than the Lee–Carter and linear trend model within 5% error. Furthermore, mortality rates and life expectancy were formulated for individuals with a specific cause based on prevalence data. The rates are broken down further into respective stages (cancer) based on the individual's diagnosis. Therefore, this approach allows life expectancy to be calculated based on an individual's state of health. Copyright © 2008 John Wiley & Sons, Ltd.     

On Forecasting Conflict in the Sudan: 2009–2012

This paper considers univariate and multivariate models to forecast monthly conflict events in the Sudan over the out‐of‐sample period 2009–2012. The models used to generate these forecasts were based on a specification from a machine learning algorithm fit to 2000–2008 monthly data. The model that includes previous month's wheat price performs better than a similar model which does not include past wheat prices (the univariate model). Both models did not perform well in forecasting conflict in a neighborhood of the 2012 ‘Heglig crisis’. Copyright © 2015 John Wiley & Sons, Ltd.     

Nonlinear identification of judgmental forecasts effects at SKU level

Prediction of demand is a key component within supply chain management. Improved accuracy in forecasts directly affects all levels of the supply chain, reducing stock costs and increasing customer satisfaction. In many application areas, demand prediction relies on statistical software which provides an initial forecast subsequently modified by the expert's judgment. This paper outlines a new methodology based on state‐dependent parameter (SDP) estimation techniques to identify the nonlinear behaviour of such managerial adjustments. This non‐parametric SDP estimate is used as a guideline to propose a nonlinear model that corrects the bias introduced by the managerial adjustments. One‐step‐ahead forecasts of stock‐keeping unit sales sampled monthly from a manufacturing company are utilized to test the proposed methodology. The results indicate that adjustments introduce a nonlinear pattern, undermining accuracy. This understanding can be used to enhance the design of the forecasting support system in order to help forecasters towards more efficient judgmental adjustments. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

Short‐term salmon price forecasting

This study establishes a benchmark for short‐term salmon price forecasting. The weekly spot price of Norwegian farmed Atlantic salmon is predicted 1–5 weeks ahead using data from 2007 to 2014. Sixteen alternative forecasting methods are considered, ranging from classical time series models to customized machine learning techniques to salmon futures prices. The best predictions are delivered by k‐nearest neighbors method for 1 week ahead; vector error correction model estimated using elastic net regularization for 2 and 3 weeks ahead; and futures prices for 4 and 5 weeks ahead. While the nominal gains in forecast accuracy over a naïve benchmark are small, the economic value of the forecasts is considerable. Using a simple trading strategy for timing the sales based on price forecasts could increase the net profit of a salmon farmer by around 7%.     

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.     

Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Bias‐corrected bootstrap prediction intervals for autoregressive model: new alternatives with applications to tourism forecasting

This paper proposes the use of the bias‐corrected bootstrap for interval forecasting of an autoregressive time series with an arbitrary number of deterministic components. We use the bias‐corrected bootstrap based on two alternative bias‐correction methods: the bootstrap and an analytic formula based on asymptotic expansion. We also propose a new stationarity‐correction method, based on stable spectral factorization, as an alternative to Kilian's method exclusively used in past studies. A Monte Carlo experiment is conducted to compare small‐sample properties of prediction intervals. The results show that the bias‐corrected bootstrap prediction intervals proposed in this paper exhibit desirable small‐sample properties. It is also found that the bootstrap bias‐corrected prediction intervals based on stable spectral factorization are tighter and more stable than those based on Kilian's stationarity‐correction. The proposed methods are applied to interval forecasting for the number of tourist arrivals in Hong Kong. Copyright © 2010 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.