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The Concept of Usefulness Usefulness in forecasting implies two critical issues. Firstly, there is a concern to relate forecasting to its decision making context. This covers the vast majority of forecasting that is actually done although it is possible that a very limited number of instances are genuinely independent of a decision making process.

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.

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.

In order to predict the future we must examine the past in order to observe trends over periods of time and establish the degree of probability with which these trends are likely to repeat themselves in the future. All forecasts are wrong, and management must be aware of this fact and decide upon the degree of inexactitude that can be tolerated when planning for the future.

Discusses the development and evaluation of a forecasting model for inventory management in an advanced technology batch production environment. Traditional forecasting and inventory management do not adequately address issues relating to a short life cycle and to non‐seasonal products with a relatively long lead time. Limited historical data (fewer than 100 observations) is also a problem in predicting short‐term dynamic or unstable time series. A Bayesian dynamic linear time series model is proposed as an alternative technique for forecasting demand in a dynamically changing environment. Provides details of the important characteristics and development process of the forecasting model. A case study is then presented to illustrate the application of the model based on data from a multinational company in Singapore. It also compares the Bayesian dynamic linear time series model with a classical forecasting model (auto‐regressive integrated moving average (ARIMA) model).

The purpose of this paper is to review the current literature in the field of tourism demand forecasting.

Published papers in the high quality journals are studied and categorized based their used forecasting method.

There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods.

This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.

In many industrial contexts, firms are encountering increasingly uncertain demand. Numerous factors are driving this phenomenon; however, a major change that is spreading among different sectors is the ever‐growing attention to customers. Companies have identified that customers are critical not only because they directly influence the success of specific products or firms, but also because they play a fundamental role in many internal processes. Although the role of customers in business processes has been deeply analysed, the issue of demand forecasting and the role of customers has not been fully explored. The present study aims to examine the impact of heterogeneity of customer requests on demand forecasting approaches, based on three action research cases. Based on the analysis of customer behaviour, an appropriate methodology for each case is designed based on clustering customers according to their demand patterns.

Objectives are achieved by means of three action research case studies, developed in cooperation with three different companies. The paper structures a general methodology based on these three experiences to help managers in better dealing with uncertain demand.

By means of proper analysis of customers' heterogeneity and by using simple statistical techniques such as cluster analysis, forecasting performance can significantly improve. In these terms, this work claims that focusing on customers' heterogeneity is a relevant topic both for practitioners and researchers.

The paper proposes some specific guidelines to forecast demand where customers' differences impact significantly on demand variability. In these terms, results are relevant for practitioners. Moreover, the paper claims that this issue should be better analysed in future researches and proposes some guidelines for future works.

Stresses the importance of forecasting in essential supply industries such as the gas industry with regard to storing the product. Examines some of the problems encountered in this area and how they are dealt with.

The research objective is to increase the computational efficiency of the automated teller machine (ATM) cash demand forecasting problem. It proposes a practical decision-making process that uses aggregated time series of a bank's ATM network. The purpose is to decrease ATM numbers that will be forecasted by individual models, by finding the machines’ cluster where the forecasting results of the aggregated series are appropriate to use.

A comparative statistical forecasting approach is proposed in order to reduce the calculation complexity of an ATM network by using the NN5 competition data set. Integrated autoregressive moving average (ARIMA) and its seasonal version SARIMA are fitted to each time series. Then, averaged time series are introduced to simplify the forecasting process carried out for each ATM. The ATMs that are forecastable with the averaged series are identified by calculating the forecasting accuracy change in each machine.

The proposed approach is evaluated by different error metrics and is compared to the literature findings. The results show that the ATMs that have tolerable accuracy loss may be considered as a cluster and can be forecasted with a single model based on the aggregated series.

The research is based on the public data set. Financial institutions do not prefer to share their ATM transactions data, therefore accessible data are limited.

The proposed practical approach will be beneficial for financial institutions to use, that hold an excessive number of ATMs because it reduces the computational time and resources allocated for the forecasting process.

This study offers an effective simplified methodology to the challenging cash demand forecasting process by introducing an aggregated time series approach.

The literature on forecasting makes hardly any distinction between domestic and export sales forecasting. Based on in‐depth interviews with exporting firms, suggests that companies face additional problems when preparing export sales forecasts compared to forecasts for the domestic market. More specifically, using a qualitative data analysis methodology, offers insights into actual export sales forecasting practices and forecast performance. Also links company and export characteristics to forecasting practices, developing a typology of the latter, and offers suggestions for future research in the area.