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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.

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.

We investigate whether smooth robust methods for forecasting can help mitigate pronounced and persistent failure across multiple forecast horizons. We demonstrate that naive predictors are interpretable as local estimators of the long-run relationship with the advantage of adapting quickly after a break, but at a cost of additional forecast error variance. Smoothing over naive estimates helps retain these advantages while reducing the costs, especially for longer forecast horizons. We derive the performance of these predictors after a location shift, and confirm the results using simulations. We apply smooth methods to forecasts of UK productivity and US 10-year Treasury yields and show that they can dramatically reduce persistent forecast failure exhibited by forecasts from macroeconomic models and professional forecasters.

When forecasting intermittent demand the method derived by Croston (1972) is often cited. Previous research favorably compared Croston's forecasting method for demand with simple exponential smoothing assuming a nonzero demand occurs as a Bernoulli process with a constant probability. In practice, however, the assumption of a constant probability for the occurrence of nonzero demand is often violated. This research investigates Croston's method under violation of the assumption of a constant probability of nonzero demand. In a simulation study, forecasts derived using single exponential smoothing (SES) are compared to forecasts using a modification of Croston's method utilizing double exponential smoothing to forecast the time between nonzero demands assuming a normal distribution for demand size with different standard deviation levels. This methodology may be applicable to forecasting intermittent demand at the beginning or end of a product's life cycle.

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.

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.

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.

Forecasting is a vital part of hospitality operations because it allows businesses to make imperative decisions, such as pricing, promotions, distribution, scheduling, and arranging facilities, based on the predicted demand and supply. This chapter covers three main concepts related to forecasting: it provides an understanding of hospitality demand and supply, it introduces several forecasting methods for practical application, and it explains yield management as a function of forecasting. In the first section, characteristics of hospitality demand and supply are described and several techniques for managing demand and supply are addressed. In the second section, several forecasting methods for practical application are explored. In the third section, yield management is covered. Additionally, examples of yield management applications from airlines, hotels, and restaurants are presented.

This research examines the use of a number of time series model structures of a moderate allocation mutual fund, PRWCX. PRWCX was rated as the top fund in its category during the past five years. The fund invests at least 50% of its total assets that the fund manager believes that have above average potential for capital growth. The remaining assets are generally invested in convertible securities, corporate and government debt bank loans, and foreign securities. Forecasting the total NAV of such a moderate allocation mutual fund, composed of an extremely large number of investments, requires a method that produces accurate results. These models are exponentially smoothing (single, double, and Winter’s Method), trend models (linear, quadratic, and exponential) are Box-Jenkins models.

In contrast to recent forecasting developments, “Old School” forecasting techniques, such as exponential smoothing and the Box–Jenkins methodology, do not attempt to explicitly model or estimate breaks in a time series. Adherents of the “New School” methodology argue that once breaks are well estimated, it is possible to control for regime shifts when forecasting. We compare the forecasts of monthly unemployment rates in 10 OECD countries using various Old School and New School methods. Although each method seems to have drawbacks and no one method dominates the others, the Old School methods often outperform the New School methods for forecasting the unemployment rates.