Advances in Business and Management Forecasting: Volume 6

Successful revenue management programs are found in industries where managers can accurately forecast customer demand. Airlines, rental car agencies, cruise lines, and hotels are all examples of industries that have been associated with revenue management. All of these industries have applied revenue management, whether it be complex overbooking models in the airline industry or simple price discrimination (i.e., having a tiered price system for those making reservations ahead of time versus walk-ups) for hotels.

A one-year-ahead price change forecasting model is proposed based on the fundamental analysis to examine the relationship between equity market value and financial performance measures. By including book value and six financial statement items in the valuation model, current firm value can be determined and the estimation error can predict the direction and magnitude of future returns of a given portfolio. The six financial performance measures represent both cash flows – cash flows from operations (CFO), cash flows from investing (CFI), and cash flows from financing (CFF) – as well as net income – R&D expenditures (R&D), operating income (OI), and adjusted nonoperating income (ANOI). This study uses a 10-year sample of the Taiwan information electronic industry (1995–2004 with 2,465 firm-year observations). We find hedge portfolios (consisting of a long position in the most underpriced portfolio and an offsetting short position in the most overpriced portfolio) provide an average annual return of 43%, more than three times the average annual stock return of 12.6%. The result shows the estimation error can be a good stock return predictor; however, the return of hedge portfolios generally decreases as the market matures.

We propose a novel method of forecasting the level of informed trading at merger announcements. Informed traders typically take advantage of their knowledge of the forthcoming merger by trading heavily at announcement. They trade on positive volume or informed buys for cash mergers and negative volume or informed sells for stock mergers. In response, market makers set wider spreads and raise prices for informed buys and lower prices for informed sells. As liquidity traders trade on these prices, our vector autoregressive framework establishes the link between informed trading and liquidity trading through price changes. As long as the link holds, informed trading may be detected by measuring levels of liquidity trading. We observe the link during the −1 to +1 period for cash mergers and −1 to +5 period for stock mergers.

Forecasting is an important tool used by businesses to plan and evaluate their operations. One of the most commonly used techniques for forecasting is regression analysis. Often forecasts are produced for a set of comparable units which could be individuals, groups, departments, or companies that perform similar activities such as a set of banks, a group of mangers, and so on. We apply a methodology that includes a new variable, the comparable unit's data envelopment analysis relative efficiency, into the regression analysis. This chapter presents the results of applying this methodology to the performance of commercial banks.

This chapter uses advance order data from an actual manufacturing shop to develop and test a forecast model for total demand. The proposed model made direct use of historical time series data for total demand and time series data for advance orders. Comparison of the proposed model to commonly used approaches showed that the proposed model exhibited greater forecast accuracy.

Forecasting sales for an innovation before the product's introduction is a necessary but difficult task. Forecasting is a crucial analytic tool when assessing the business case for internal or external investments in new technologies. For early stage investments or internal business cases for new products, it is essential to have some understanding of the likely diffusion of the technology. Diffusion of innovation models are important tools for effectively assessing the merits of investing in technologies that are new or novel and do not have prima facie, predictable patterns of user uptake. Most new product forecasting models require the estimation of parameters for use in the models. In this chapter, we evaluate three techniques to determine the parameters of the Bass diffusion model by using an example of a new movie.

Much attention has been given to adoption and diffusion, defined as the degree of market penetration, of Information and Communications Technologies (ICT) in recent years (Carter, Jambulingam, Gupta, & Melone, 2001; Kiiski & Pohjola, 2002; Milner, 2003; Benhabib & Spiegel, 2005). The theory of diffusion of innovations considers how a new idea spreads throughout the market over time. The ability to accurately predict new product diffusion is of concern to designers, marketers, managers, and researchers alike. However, although the diffusion process of new products is generally accepted as following an s-curve pattern, where diffusion starts slowly, grows exponentially, peaks, and then declines (as shown in Fig. 1), there is considerable disagreement about what factors affect diffusion and how to measure diffusion rates (Bagchi, Kirs, & Lopez, 2008).

Database marketers often select households for individual marketing contacts using information on past purchase behavior. One of the most common methods, known as RFM variables approach, ranks households according to three criteria: the recency of the latest purchase event, the long-run frequency of purchases, and the cumulative dollar expenditure. We argue that RFM variables approach is an indirect measure of the latent purchase propensity of the customer. In addition, the use of RFM information in targeting households creates major statistical problems (selection bias and RFM endogeneity) that complicate the calibration of forecasting models. Using a latent trait approach to capture a household's propensity to purchase a product, we construct a methodology that not only measures directly the latent propensity value of the customer, but also avoids the statistical limitations of the RFM variables approach. The result is a general household response forecasting and scoring approach that can be used on any database of customer transactions. We apply our methodology to a database from a charitable organization and show that the forecasting accuracy of the new methodology improves upon the traditional RFM variables approach.

Assume that we generate forecasts from a model y=cx+d+ξ. The constants “c” and “d” are placement parameters estimated from observations on x and y, and ξ is the residual error variable.

Our objective is to develop a method for accurately measuring and evaluating the risk profile of a forecasted variable y. To do so, it is necessary to first obtain an accurate representation of the histogram of a forecasting model's residual errors. That is not always so easy because the histogram of the residual ξ may be symmetric, or it may be skewed to either the left of or to the right of its mode. We introduce the probability density function (PDF) family of functions because it is versatile enough to fit any residual's locus be it skewed to the left, symmetric about the mean, or skewed to the right. When we have measured the residual's density, we show how to correctly calculate the risk profile of the forecasted variable y from the density of the residual using the PPD function. We achieve the desired and accurate risk profile for y that we seek. We conclude the chapter by discussing how a universally followed paradigm leads to misstating the risk profile and to wrongheaded decisions by too freely using the symmetric Gauss–normal function instead of the PPD function. We expect that this chapter will open up many new avenues of progress for econometricians.

Much forecasting is done by experts, who either make the forecasts themselves or who do opinion research to gather such forecasts. This is consistent with previous knowledge management research that typically has focused on directly soliciting knowledge from those with greater recognized expertise.

However, recent research has found that in some cases, electronic markets, whose participants are not necessarily individual experts, often have been found to be more effective aggregated forecasters. This suggests that knowledge management take a similar tact and expand the perspective to include internal markets. As a result, this chapter extends the use of internal markets to be included in knowledge management, thus expanding the base of knowledge to gathering from nonexperts.

In particular, in this paper I examine the use of human expertise and opinion as a basis to forecast a range of different events. This chapter uses a “knowledge distribution grid” as a basis for understanding which kind of forecasting tool is appropriate for particular forecasting situations. We examine a number of potential sources of forecast information, including knowledge acquisition, Delphi techniques, and internal markets. Each is seen as providing forecasting information for unique settings.

The forecasting needs for inventory control purposes are hierarchical. For stock keeping units (SKUs) in a product family or a SKU stored across different depot locations, forecasts can be made from the individual series’ history or derived top–down. Many discussions have been found in the literature, but it is not clear under what conditions one approach is better than the other. Correlation between demands has been identified as a very important factor to affect the performance of the two approaches, but there has been much confusion on whether it is positive or negative correlation. This chapter summarises the conflicting discussions in the literature, argues that it is negative correlation that benefits the top–down or grouping approach, and quantifies the effect of correlation through simulation experiments.

The challenge of truckload routing is increased in complexity by the introduction of stochastic demand. Typically, this demand is generalized to follow a Poisson distribution. In this chapter, we cluster the demand data using data mining techniques to establish the more acceptable distribution to predict demand. We then examine this stochastic truckload demand using an econometric discrete choice model known as a count data model. Using actual truckload demand data and data from the bureau of transportation statistics, we perform count data regressions. Two outcomes are produced from every regression run, the predicted demand between every origin and destination, and the likelihood that that demand will occur. The two allow us to generate an expected value forecast of truckload demand as input to a truckload routing formulation. The negative binomial distribution produces an improved forecast over the Poisson distribution.

Warranty policies for certain products, such as automobiles, often involve consideration of two attributes, for example, time and usage. Since consumers are not necessarily homogeneous in their use of the product, such policies provide protection to users of various categories. In this chapter, product usage at a certain time is linked to the product age through a variable defined as usage rate. This variable, usage rate, is assumed to be a random variable with a specified probability distribution, which permits modeling of a variety of customer categories. Another feature of the chapter is to model the propensity to execute the warranty, in the event of a failure within specified parameter values (say time or usage). In a competitive market, alternative product/warranty offerings may reduce the chances of exercising the warranty. This chapter investigates the impact of warranty policy parameters with the goal of maximizing market share, subject to certain constraints associated with expected warranty costs per unit not exceeding a desirable level.

A dual transportation analysis is considered as a strategic matter for plant facility expansion/contraction decision making in manufacturing operations. The primal-dual problem is presented in a generalized mathematical form. A practical technique of generating the dual solution is illustrated with a plant facility expansion/contraction example as a tutorial. Demand forecasting is performed based on the time series data with seasonal variation adjustments. The dual solution helps facilitate operations decision making by providing useful information.

Demand forecasting has long been an imperative tenet in production planning especially in a make-to-order environment where a typical manufacturer has to balance the issues of holding excessive safety stocks and experiencing possible stockout. Many studies provide pragmatic paradigms to generate demand forecasts (mainly based on smoothing forecasting models.) At the same time, artificial neural networks (ANNs) have been emerging as alternatives. In this chapter, we propose a two-stage forecasting approach, which combines the strengths of a neural network with a more conventional exponential smoothing model. In the first stage of this approach, a smoothing model estimates the series of demand forecasts. In the second stage, general regression neural network (GRNN) is applied to learn and then correct the errors of estimates. Our empirical study evaluates the use of different static and dynamic smoothing models and calibrates their synergies with GRNN. Various statistical tests are performed to compare the performances of the two-stage models (with error correction by neural network) and those of the original single-stage models (without error-correction by neural network). Comparisons with the single-stage GRNN are also included. Statistical results show that neural network correction leads to improvements to the forecasts made by all examined smoothing models and can outperform the single-stage GRNN in most cases. Relative performances at different levels of demand lumpiness are also examined.