Advances in Business and Management Forecasting: Volume 11

In this chapter, inventory and sales data from a small business with seven showrooms are evaluated to forecast future sales and maximize total profits. In each showroom, three major brands of ceiling fans are sold and a limited amount of products from each brand are displayed. Each showroom varies in their sales volume, display capacity, and profit margins. Using historical data, the optimal display configuration was determined for each showroom; that is, the proportion of products from each brand to display in the limited display grid, while acknowledging existing constraints. Next using the optimal displays, profit for the next year is forecasted. Finally a comparison is made between actual and forecasted results and profits pre and post the optimal product display.

The objective of this research is to explore the relationship between brand experience and customer equity (value equity, brand equity, and relationship equity). We examine the impacts of different contact points’ experiences (media contact, physical environment contact, people contact, and product usage contact) and different dimensions of brand experience on customer equity. Further we investigate the possible moderating effects of different brand positioning and strategies – hedonic and utilitarian, on this relationship. The data which are collected via online survey includes 410 observations with brand experience and 83 without brand experience, 493 valid samples in total. We found that positive and strong brand experience is the key factor for building strong customer equity. Although the impacts of all four contact points’ brand experiences are significant, product usage contact has the most powerful influence on customer equity and its individual drivers. The results also indicate that the different brand positioning strategies do have moderating effects. For utilitarian brand, only brand experience at product usage contact point has significant impact on customer equity and its three drivers. For hedonic brand, all four contact points’ experiences have significant relationships with customer equity. Finally, the four experience dimensions (sensory, affective, intellectual, and behavioral) have different impacts on customer equity and its three drivers at different experience contact points.

Research in the area of forecasting and stock inventory control for intermittent demand is designed to provide robust models for the underlying demand which appears at random, with some time periods having no demand at all. Croston’s method is a popular technique for these models and it uses two single exponential smoothing (SES) models which involve smoothing constants. A key issue is the choice of the values due to the sensitivity of the forecasts to changes in demand. Suggested selections of the smoothing constants include values between 0.1 and 0.3. Since an ARIMA model has been illustrated to be equivalent to SES, an optimal smoothing constant can be selected from the ARIMA model for SES. This chapter will conduct simulations to investigate whether using an optimal smoothing constant versus the suggested smoothing constant is important. Since SES is designed to be an adapted method, data are simulated which vary between slow and fast demand.

This is the third in a series of papers aimed at providing models effective in predicting the degree of pain and discomfort in canines. The first two papers provided benchmarking and examination of dogs suffering from osteoarthritis (OA). In this chapter, we extend the study to include dogs suffering from OA, sarcoma, and oral mucositis (a side effect of chemotherapy and radiation treatments). The R programming language and SAS JMP are used to clean data, generate ANOVA, LSR regression, decision tree, and nominal logistic regression models to predict changes in activity levels associated with the progression of arthritis. The predictive models provide a diagnostic basis for determining the degree of disease in a dog (based on demographics and activity levels) and provide forecasts that assist in establishing appropriate medication dosages for suffering dogs.

Forecasting the number of bed days (NBD) needed within a large hospital network is extremely challenging, but it is imperative that management find a predictive model that best estimates the calculation. This estimate is used by operational managers for logistical planning purposes. Furthermore, the finance staff of a hospital would require an expected NBD as input for estimating future expenses. Some hospital reimbursement contracts are on a per diem schedule, and expected NBD is useful in forecasting future revenue.

This chapter examines two ways of estimating the NBD for a large hospital system, and it builds from previous work comparing time regression and an autoregressive integrated moving average (ARIMA). The two approaches discussed in this chapter examine whether using the total or combined NBD for all the data is a better predictor than partitioning the data by different types of services. The four partitions are medical, maternity, surgery, and psychology. The partitioned time series would then be used to forecast future NBD by each type of service, but one could also sum the partitioned predictors for an alternative total forecaster. The question is whether one of these two approaches outperforms the other with a best fit for forecasting the NBD. The approaches presented in this chapter can be applied to a variety of time series data for business forecasting when a large database of information can be partitioned into smaller segments.

Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

Business schools are tasked with matching curriculum to techniques that industry practitioners rely on for profitability. Forecasting is a significant part of what many firms use to try to predict budgets and to provide guidance as to the direction the business is headed. This chapter focuses on forecasting and how well business schools match the requirements of industry professionals. Considering its importance to achieving successful business outcomes, forecasting is increasingly becoming a more complex endeavor. Firms must be able to forecast accurately to gain an understanding of the direction the business is taking and to prevent potential setbacks before they occur. Our results suggest that, although techniques vary, in large part business schools are introducing students to the forecasting tools that graduates will need to be successful in an industry setting. The balance of our chapter explores the forecasting tools used by business schools and firms, and the challenge of aligning the software learning curve between business school curriculum and industry expectations.

The research is directed toward the prediction of operating income within the MetLife Insurance Company. The operating income of the firm is the amount of profit realized from a firm’s own operation, as opposed to net income. The econometric model is based on 10 years of quarterly data (2004–2014). The explanatory variables used in this modeling effort are (1) stock price, (2) long-term borrowing, (3) capital surplus, (4) free cash flow, (5), S&P average, (6) GDP, and (7) CPI.

As an important carrier of sustainable economy and social development, population is the foundation of the whole society. Scientific predictions of future population growth will bring great reference to macro-economic and social planning. For China, as a country of the biggest population, the research on its population policy is worthwhile.

Previous literatures on population growth prediction are generally based on time-series analysis. However, the new two-child policy in China provides us an opportunity to predict the population growth from the perspective of the welfare efficiency, since each family is able to determine whether to have the second child on account of the family’s utility. The welfare efficiency is calculated through the database of newborn babies, disposable income per capita, living resource per capita, and health expenditure pre capital. These are the main factors by which each family decides whether to bear additional babies. In this chapter, we perform the micro-economic analysis on a new policy and propose a Data Envelopment Analysis (DEA) method to predict the population growth. Under the condition of policy adjustment, we successfully predict the population growth with this method. We also propose some suggestions concerning the implementation of the new policy.

Los Angeles, California, is facing record drought conditions. As a result, there is interest in all things related to building and maintaining water capabilities. Leaks in the infrastructure can lead to costly losses of water resources. Accordingly, attention increasingly is being devoted to water leak management. Using data available through the City of Los Angeles’ open data movement, the number of leaks is analyzed in order to study both the impact of temperature and whether the number of leaks is decreasing over time. Three different approaches for modeling the number of leaks, including regression, time series, and neural networks, are compared.

Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission media such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks. Important aspects include the content and its presentation, the network topology, and transmission dynamics. Theoretical models, algorithms, and case studies of viral marketing will be explored.

This chapter analyzes the aggregate performance of Home Run Derby competitors’ performance both before and after the Home Run Derby for the time period 1999–2013. Regression to the mean suggests that in general, those players with outstanding performances in the first half of the season will regress to the mean. The findings here are consistent with regression to the mean, and the mean performance along four key analytics is statistically significantly worse for the competitors. However, the winners’ mean performance both before and after the Home Run Derby are not statistically significantly different. Thus, the results are consistent with previous research, but the results also find so-called “winner and loser” effects in Major League Baseball.