Advances in Business and Management Forecasting: Volume 7

Some consumer durables, such as automobiles, involve warranties involving two attributes. These are time elapsed since the sale of the product and the usage of the product at a given point in time. Warranty may be invoked by the customer if both time and usage are within the specified warranty parameters and product failure occurs. In this chapter, we assume that usage and product age are related through a random variable, the usage rate, which may have a certain probabilistic distribution as influenced by consumer behavior pattern. Further, product failure rate is influenced by the usage rate and product age. Of importance to the organization is to contain expected warranty costs and select appropriate values of the warranty parameters accordingly. An avenue to impact warranty costs is through research on product development. This has the potential to reduce the failure rate of the product. The objective then becomes to determine warranty parameters, while constraining the sum of the expected unit warranty costs and research and development (R&D) costs per unit sales, under a limited R&D budget.

Seasoned equity offerings (SEOs) are sales of stock after the initial public offering. They are a means to raise funds through the sale of stock rather than the issuance of additional debt. We propose a method to predict the characteristics of firms that undertake this form of financing. Our procedure is based on logistic regression where firm-specific variables are obtained from the perspective of the firm's need to raise cash such as high debt ratios, high current liabilities, reduction and changes in current debt, significant increase in capital expenditure, and cash flows in terms of cash as a percentage of assets.

The components of earnings or cash flows have different implications for the assessment of the firm's value. We extend the research for value-relevant fundamentals to examine which financial performance measures convey more information to help investors evaluate the performance and value for firms in different life cycle stages in the high-tech industry. Six financial performance measures are utilized to explain the difference between market value and book value. Cross-sectional data from firms in Taiwanese information electronics industry are used. We find all the six performance measures which are taken from Income Statement and Cash Flow Statement are important value indicators but the relative degrees of value relevance of various performance measures are different across the firm's life cycle stages. The empirical results support that capital markets react to various financial performance measures in different life cycle stages and are reflected on the stock price.

The article is a description of the real-life experience based on the implementation of a financial forecasting model to inform budgeting and strategic planning. The organization is a charity-based health system that has hospitals and medical centers that provide care to the community. The health system performs a central budgeting process which is typically based on aggregation of individual budgets from the various hospitals and medical centers within the system. All financial data are reported to a central financial information system. Traditionally budgeting was done based on prior year's financial performance with a slight adjustment based on the hospital or medical center finance department's educated guess. This article describes the new forecasting method instituted to predict revenue and expenses, and to improve the budget planning process. Finally, the forecasts from the model are compared with real data to demonstrate accuracy of the financial forecasts. The model is since then being used in the budgeting process.

This chapter presents an Excel-based regression analysis to forecast seasonal demand for U.S. Imported Beer sales data. The following seasonal regression models are presented and interpreted including a simple yearly model, a quarterly model, a semi-annual model, and a monthly model. The results of the models are compared and a discussion of each model's efficacy is provided. The yearly model does the best at forecasting U.S. Import Beer sales. However, the yearly does not provide a window into shorter-term (i.e., monthly) forecasting periods and subsequent peaks and valleys in demand. Although the monthly seasonal regression model does not explain as much variance in the data as the yearly model it fits the actual data very well. The monthly model is considered a good forecasting model based on the significance of the regression statistics and low mean absolute percentage error. Therefore, it can be concluded that the monthly seasonal model presented is doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

This study examines the use of forecast combination to improve the accuracy of forecasts of cumulative demand. A forecast combination methodology based on least absolute value (LAV) regression analysis is developed and is applied to partially accumulated demand data from an actual manufacturing operation. The accuracy of the proposed model is compared with the accuracy of common alternative approaches that use partial demand data. Results indicate that the proposed methodology outperforms the alternative approaches.

The direct marketing retailers have traditionally provided mail order and call center channels. In the emergence of Internet channel, the direct marketing retailers have reported a large increase in the use of Internet channel, and some have encouraged the customers to use the Internet channel more than other channels due to potential cost savings for the firm. However, over a decade of Internet usage, the traditional Call Center channel has not disappeared in the direct marketing industry. This study is motivated by this observation and incorporates the variables that capture the benefits of using different channels in the multi-channel choice model.

We apply the proposed model to a transactional database from a direct marketing retailer that operates multiple channels. Our empirical result shows that the multi-channel choice model that incorporates the channel benefits has stronger channel share prediction power than the model without. It further shows that consumers are more likely to choose the Internet channel when the consumer has low perceived risk and high experience and familiarity with the purchase, but they are more likely to choose the Call Center when the consumers have high perceived risk and low experience and familiarity.

A company is developing a new product and wants an accurate estimate of the investment's ROI. For that the money in-flows and out-flows for the project have to be forecasted. And to develop those forecasts, the resulting product's life cycle must first be forecasted.

In this chapter, we are considering a real company. The company was in the process of developing a new product – a special purpose computer. In June of a year, the company wished to predict the product's future life cycle before the product had been fully developed. The product would be introduced into the market in January of the following year. However, to predict the locus of a product's future life cycle before the product has been fully developed is known to be very difficult.

This chapter presents a method for predicting a new brand's life cycle trajectory from its beginning to its end before the brand is introduced into the markets. The chapter also presents a combination of two methods to use current information to revise the entire predicted trajectory so it comes closer and closer to the true life cycle trajectory. The true trajectory is not known till the product is pulled from the market. The two methods are the Delphi method and Kalman filtering tracking method.

The company, which this application originates with, and the problem we discuss are real. However, we are prepared to identify neither the company nor the product. This chapter discusses the approach, but the data and the time scale have been masked, so that no identification from the data is possible.

Producing good forecast is a vital aspect of a business. The accuracy of these forecasts could have a critical impact on the organization. We introduce a new, practical, and meaningful forecast performance measure called percentage forecast error (PFE). The results of comparing and evaluating this new measure to traditional forecasting performance measures under several different simulation scenarios are presented in this chapter.

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based on a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. We incorporate soft constraints and preemptive additive weights into a mathematical programming approach to improve our forecasting accuracy. We compare the results of our approach with the preemptive MOLP approach. A financial example is used to illustrate the efficacy of the proposed forecasting methodology.

Several prominent data-mining studies have evaluated the performance of neural networks (NNs) against traditional statistical methods on the two-group classification problem in discriminant analysis. Although NNs often outperform traditional statistical methods, their performance can be hindered because of failings in the use of training data. This problem is particularly acute when using NNs on smaller data sets. A heuristic is presented that utilizes Mahalanobis distance measures (MDM) to deterministically partition training data so that the resulting NN models are less prone to overfitting. We show this heuristic produces classification results that are more accurate, on average, than traditional NNs and MDM.

The 2008 U.S. presidential election was of great interest nationally and internationally. Interest in the 2008 election was sufficient to drive a $2.8 million options market by a U.K.-based company INTRADE. The options in this market are priced as European style fixed return options (FRO). In 2008, the Security and Exchanges Commission approved, and both the American Stock Exchange and the Chicago Board Options Exchange began to trade FROs. Little research is available on trading in FROS because these markets are very new. This chapter uses the INTRADE options market data to construct exponential smoothing forecasts, which are then compared under a hypothetical trading strategy. The trading returns indicate that this market is relatively efficient at least in the short term but that because of the all or nothing payout structure of a FRO, there may exist small arbitrage opportunities.

Most setup management techniques associated with electronic assembly operations focus on component similarity in grouping boards for batch processing. These process planning techniques often minimize setup times. On the contrary, grouping with respect to component geometry and frequency has been proved to further minimize assembly time. Thus, we propose the Placement Location Metric (PLM) algorithm to recognize and measure the similarity between printed circuit board (PCB) patterns. Grouping PCBs based on the geometric and frequency patterns of components in boards will form clusters of locations and, if these clusters are common between boards, similarity among layouts can be recognized. Hence, placement time will decrease if boards are grouped together with respect to the geometric similarity because the machine head will travel less. Given these notions, this study develops a new technique to group PCBs based on the essences of both component commonality and the PLM. The proposed pattern recognition method in conjunction with the Improved Group Setup (IGS) technique can be viewed as an extended enhancement to the existing Group Setup (GS) technique, which groups PCBs solely according to component similarity. Our analysis indicates that the IGS performs relatively well with respect to an array of existing setup management strategies. Experimental results also show that the IGS produces a better makespan than its counterparts over a low range of machine changeover times. These results are especially important to operations that need to manufacture quickly batches of relatively standardized products in moderate to larger volumes or in flexible cell environments. Moreover, the study provides justification to adopt different group management paradigms by electronic suppliers under a variety of processing conditions.

The purpose of this chapter is to investigate the notions of “Public Image Monitoring and Forecasting” done using media available in digital formats, such as blogs, discussion groups, and news articles, referred to in the aggregate as “digital scuttlebutt.” This chapter analyzes the purposes behind development of such a system and the different kinds of information that such a system would draw from. The chapter also investigates construction and extensions of a public image monitoring system designed to troll through various digital media to better understand a firm's public image.

Palliative care concentrates on reducing the severity of disease symptoms, rather than providing a cure. The goal is to prevent and relieve suffering and to improve the quality of life for people facing serious, complex illness. It is therefore critical in the palliative environment that caregivers are able to make recommendations to patients and families based on reasonable assessments of amount of suffering and quality of life. This research uses statistical methods of evaluation and prediction as well as simulation to create a multiple criteria model of survival rates, survival likelihoods, and quality of life assessments. The results have been reviewed by caregivers and are seen to provide a solid analytical base for patient recommendations.