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This paper presents a mathematical programming model to reduce bias for both aggregate demand forecasts and lower echelon forecasts comprising a hierarchical forecasting system. Demand data from an actual service operation are used to illustrate the model and compare its accuracy with a standard approach for hierarchical forecasting. Results show that the proposed methodology outperforms the standard approach.

This chapter examines the use of mathematical programming to remove systematic bias from demand forecasts. A debiasing methodology is developed and applied to demand data from an actual service operation. The accuracy of the proposed methodology is compared to the accuracy of a well-known approach that utilizes ordinary least squares regression. Results indicate that the proposed method outperforms the least squares approach.

Recently, it has been suggested that macroeconomic forecasts from estimated dynamic stochastic general equilibrium (DSGE) models tend to be more accurate out-of-sample than random walk forecasts or Bayesian vector autoregression (VAR) forecasts. Del Negro and Schorfheide (2013) in particular suggest that the DSGE model forecast should become the benchmark for forecasting horse-races. We compare the real-time forecasting accuracy of the Smets and Wouters (2007) DSGE model with that of several reduced-form time series models. We first demonstrate that none of the forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting output growth, while for inflation forecasts the results are reversed. Moreover, the relative accuracy of all models tends to evolve over time. Third, we show that there is no support to the common practice of using large-scale Bayesian VAR models as the forecast benchmark when evaluating DSGE models. Indeed, low-dimensional unrestricted AR and VAR forecasts may forecast more accurately.

In the last few decades, there has been growing interest in forecasting with computer intelligence, and both fuzzy time series (FTS) and artificial neural networks (ANNs) have gained particular popularity, among others. Rather than the conventional methods (e.g., econometrics), FTS and ANN are usually thought to be immune to fundamental concepts such as stationarity, theoretical causality, post-sample control, among others. On the other hand, a number of studies significantly indicated that these fundamental controls are required in terms of the theory of forecasting, and even application of such essential procedures substantially improves the forecasting accuracy. The aim of this paper is to fill the existing gap on modeling and forecasting in the FTS and ANN methods and figure out the fundamental concepts in a comprehensive work through merits and common failures in the literature. In addition to these merits, this paper may also be a guideline for eliminating unethical empirical settings in the forecasting studies.

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.

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 chapter will present a goal programming model which simultaneously generates forecasts for the aggregate level and for lower echelons in a multilevel forecasting context. Data from an actual service firm will be used to illustrate and test the proposed model against a standard forecast technique based on the bottom-up/top-down approach.

Purpose – To provide strategic management scholars, particularly graduate students and new faculty members, a novel approach, the lens model, to investigate emerging economies phenomena.

Design/methodology/approach – Based on a review of the strategic management literature and a search of the strategy databases and journals, I propose the lens model approach and discuss its origins, development, and designs since its introduction. It has been used extensively in such fields as cognitive psychology, social psychology, medicine, agriculture, human resources management, and organizational behavior. Besides the wide application, it has relevance for strategic management research.

Findings – An illustrative study and a summary of the approach from a previous study in one prominent journal are also provided as guides. I conclude by providing recommendations on what to consider in using the approach for the study of emerging economies.

Research limitations/implications – In addition to the strengths of the approach, its weaknesses are also discussed. Suggestions on maximizing the potential of the approach are also discussed.

Practical implications – The approach is an invaluable source particularly for graduate students of strategy who often are unfamiliar with microlevel approaches. They can use it to supplement approaches for strategic management.

Originality/value – To my knowledge, this chapter is the first to discuss the lens model approach in the strategic management literature. In that regard, it fills a gap in the research methodology literature. It can therefore help graduate students improve their careers.

Past research has shown that forecast combination typically improves demand forecast accuracy even when only two component forecasts are used; however, systematic bias in the component forecasts can reduce the effectiveness of combination. This study proposes a methodology for combining demand forecasts that are biased. Data from an actual manufacturing shop are used to develop the methodology and compare its accuracy with the accuracy of the standard approach of correcting for bias prior to combination. Results indicate that the proposed methodology outperforms the standard approach.