Search results

During recent years a number of techniques have been developed to aid in the forecasting of corporate sales, individual product demand, economic indicators, and other related series. These techniques have included classical time series analysis, multiple regression and adaptive forecasting procedures. As a result of these developments, the individual company and decision maker is faced with the task of selecting the forecasting technique that is most appropriate for his situation. This article reports research conducted at INSEAD on how simulation can be used to compare and evaluate alternative forecasting techniques for a specific application.

Planning, both “operational” and “strategic”, relies on accurate forecasting. Planning in tourism is no less dependent on accurate forecasts. However, tourism demand forecasting has been dominated by the application of regression/econometric techniques. Past studies on the forecasting accuracy of econometric/regression models suggest that forecasts generated by these models are not necessarily superior to forecasts generated by simple time series techniques. Seven time series forecasting techniques were used to generate forecasts of international tourist arrivals from Thailand to Hong Kong. The results confirm that simple techniques may be just as accurate and often more time‐and cost‐effective than more complex ones. Practitioners in the tourism industry may confidently use any of the forecasting techniques demonstrated here for their short‐term planning activities.

This article seeks to (1) identify forecasting techniques used to estimate taxable sales in California counties; (2) analyze which of these produces the most accurate estimate; (3) document what prevented officials from using the most accurate forecasting technique in California counties; and (4) determine what forecasting approach would work best for individual counties. This research generally confirms previous research findings that judgmental approaches are the most commonly used method of revenue forecasting in smaller localities. In terms of accuracy, econometric models outperform other quantitative methods, particularly compared to trend line fitting and extrapolation-by-average approaches. The “not now but later” perception in the use of econometric models can be ascribed to California county forecasters’ discomfort and lack of preparation for using this sophisticated technique. Once the critical prerequisites for the use of econometric models are provided -- such as statewide training, timely inter-governmental data sharing, easy access to economic data, and user-friendly forecasting formats with automated procedures -- econometric models can serve the needs of California counties.

The purpose of this paper is to examine the performance of the vector error‐correction (VEC) econometric modelling technique in predicting short‐ to medium‐term construction manpower demand.

The VEC modelling technique is evaluated with two conventional forecasting methods: the Box‐Jenkins approach and the multiple regression analysis, based on the forecasting accuracy on construction manpower demand.

While the forecasting reliability of the VEC modelling technique is slightly inferior to the multiple log‐linear regression analysis in terms of forecasting accuracy, the error correction econometric modelling technique outperformed the Box‐Jenkins approach. The VEC and the multiple linear regression analysis in forecasting can better capture the causal relationship between the construction manpower demand and the associated factors.

Accurate predictions of the level of manpower demand are important for the formulation of successful policy to minimise possible future skill mismatch.

The accuracy of econometric modelling technique has not been evaluated empirically in construction manpower forecasting. This paper unveils the predictability of the prevailing manpower demand forecasting modelling techniques. Additionally, economic indicators that are significantly related to construction manpower demand are identified to facilitate human resource planning, and policy simulation and formulation in construction.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

It is widely believed that the construction industry is more volatile than other sectors of the economy. Accurate predictions of the level of aggregate demand for construction are of vital importance to all sectors of this industry (e.g. developers, builders and consultants). Empirical studies have shown that accuracy performance varies according to the type of forecasting technique and the variable to be forecast. Hence, there is a need to gain useful insights into how different techniques perform, in terms of accuracy, in the prediction of demand for construction. In Singapore, the residential sector has often been regarded as one of the most important owing to its large percentage share in the total value of construction contracts awarded per year. In view of this, there is an increasing need to objectively identify a forecasting technique which can produce accurate demand forecasts for this vital sector of the economy. The three techniques examined in the present study are the univariate Box‐Jenkins approach, the multiple loglinear regression and artificial neural networks. A comparison of the accuracy of the demand models developed shows that the artificial neural network model performs best overall. The univariate Box‐Jenkins model is the next best, while the multiple loglinear regression model is the least accurate. Relative measures of forecasting accuracy dealing with percentage errors are used to compare the forecasting accuracy of the three different techniques.

In recent times there has been a change of emphasis in business forecasting. The shift has been away from the technical and statistical aspects. More thought is now being given to the way in which techniques are used and the context in which they are applied. This article is the first in a series of two which deal with these issues. It describes the role of the manager in forecasting. In particular, it discusses the tasks in designing and planning a forecasting system which are the key to its success and which fall within a manager's responsibility. The second article is concerned with the link between forecasts and the decisions they support.

If there is any one function managers most despise, it is the art of forecasting. By its very nature it concerns guessing the outcome of future events. Do all firms forecast the same? Compares forecasting behaviour between large and small firms and examines questions such as who does the forecasting, how often do they do forecasts, what areas are forecasted, what techniques are used, why they do it, what results are like from forecasting effort, and are they satisfied or dissatisfied. Examines significant differences in forecasting behaviour and makes conclusions.

Fluctuations in the tender price index have an adverse effect on the construction sector and the economy at large. This is largely due to the positive relationship that exists between the construction industry and economic growth. The consequences of these variations include cost overruns and schedule delays, among others. An accurate forecast of the tender price index is good for controlling the uncertainty associated with its variation. In the present study, the efficacy of using an adaptive neuro-fuzzy inference system (ANFIS) for tender price forecasting is investigated. In addition, the Box–Jenkins model, which is considered a benchmark technique, was used to evaluate the performance of the ANFIS model. The results demonstrate that the ANFIS model is superior to the Box–Jenkins model in terms of the accuracy and reliability of the forecast. The ANFIS could provide an accurate and reliable forecast of the tender price index in the medium term (i.e. over a three-year period). This chapter provides evidence of the advantages of applying nonlinear modelling techniques (such as the ANFIS) to tender price index forecasting. Although the proposed ANFIS model is applied to the tender price index in this study, it can also be applied to a wider range of problems in the field of construction engineering and management.

Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy.

A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry.

The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments.

So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.