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This is a study of forecasting models that aggregate monthly times series into bimonthly and quarterly models using the 1,428 seasonal monthly series of the M3 competition of Makridakis and Hibon (2000). These aggregating models are used to answer the question of whether aggregation models of monthly time series significantly improve forecast accuracy. Through aggregation, the forecast mean absolute deviations (MADs) and mean absolute percent errors (MAPEs) were found to be statistically significantly lower at a 0.001 level of significance. In addition, the ratio of the forecast MAD to the best forecast model MAD was reduced from 1.066 to 1.0584. While those appear to be modest improvements, a reduction in the MAD affects a forecasting horizon of 18 months for 1,428 time series, thus the absolute deviations of 25,704 forecasts (i.e., 18*1,428 series) were reduced. Similar improvements were found for the symmetric MAPE.

The purpose of this article is to examine empirically the impact of aggregation on forecasting accuracy; specifically, whether more accurate forecasts are obtained by forecasting a number of disaggregated tourist flows and summing the forecasts to obtain the aggregate forecast, or by summing the disaggregated tourist flows and forecasting the aggregate series directly. On the one hand, it may be easier to produce accurate forecasts from disaggregated series as the latter allow for differing behavioural patterns which may be more readily recognisable and hence easier to model and extrapolate. On the other hand, more aggregate series may be less susceptible to “noise” and therefore easier to forecast.

Forecasting is considered as a key element in formulating manufacturing systems. There is a wealth of information about forecasting methods but little has been written about forecasting systems. An outline of an approach to build Decision Support Systems (DSS) for forecasting in manufacturing is given. A strategic forecasting framework is provided. To integrate the forecasting system into the manufacturing information systems, guidelines for manufacturers are suggested.

The field of supply chain management (SCM) evolves dramatically due to factors of globalization, innovation, sustainability, and technology. These changes raise challenges not only to higher education institutions, but also to students, employing organizations, and third parties like SCM-related professional bodies. To understand the challenge, the purpose of this paper is to examine the gap between demand and supply of SCM-related knowledge areas, answer-related design questions, and make recommendations to close the gaps.

To compare the demand and supply of SCM-related knowledge areas, demand data is collected from a professional career website and supply data is gathered from operations management (OM) and SCM course syllabi from AACSB-accredited business schools in the USA. Cluster analysis identifies how supply and demand are matched on the data collected.

First, gaps exist between SCM talent requirements from industry and the knowledge/skill training by US business schools. This paper identifies matching, under-supplying, and over-supplying knowledge areas. Under-supply in emerging areas such as SCM information technology and certain logistics management topics are found. Some traditional OM topics are over-supplied due to out-of-date industry applications and should be revised to reflect the field’s transition from an OM to SCM view. Last, this paper makes recommendations to different stakeholders in this matching supply with demand process.

This study contributes to the literature in two ways. First, it provides an up-to-date understanding on demand and supply of SCM talent in USA. Second, it provides insights and recommendations not only to educators on curriculum design, but also to potential candidates interested in SCM careers, to companies’ job recruiters, and to professional organizations (such as APICS and Council of Supply Chain Management Professionals) to reduce the gaps between demand and supply.

In a manufacturing system, time performances are measures of system response speed to external influences. This speed depends on the resource allocation process (materials, equipment, labour) which is driven by finished‐product forecasts. Describes two essential steps, in order to develop a model for evaluating time performances which is able to detect crucial resources. The first step is represented by analysing forecast characteristics; the second step is expressed by a definition of the environment of manufacturing resources. The model, depicted in its structure and in its relationships with the most common business tools, has been tested in a number of manufacturing firms and the results are also shown.

Demand forecasting models are past the point of academic curiosity, and although they are still in the early stages of their life cycle, they are well beyond the development stage. The modelling of demand phenomena may be viewed as having two main thrusts: the first is a scientific one that leads to a greater understanding of the phenomena. Here, the goal is to build either normative or descriptive models which advance knowledge. The second is a pragmatic thrust concerned with the capability of management science to aid decision makers. A model is demonstrated and its future potential assessed.

This paper aims to use Australian analysts' forecast data to compare the relative accuracy of consensus and the most recent forecast in the month before the earnings announcement.

Cross‐sectional regression is used on a sample of 4,358 company‐year observations of annual analyst forecasts to examine whether the number of analysts following and the timeliness of an individual analyst's forecast is more strongly associated with the superior forecast measure.

The results suggest that whilst in the late 1980s the most recent forecast was more accurate than the consensus, since the early 1990s the accuracy of the consensus forecast has outperformed the most recent forecast in 15 out of 17 years, and the differences are significant for nine out of 15 years. The forecasting superiority of the consensus can be attributed to the aggregating value of the consensus outweighing the small timing advantage of the most recent forecast over the short forecast horizon examined in this paper.

Given the consistent use of analysts' forecasts as proxies for expected earnings in Australian research, this paper provides insights to what extent the expected level of forecast accuracy is realised and the reasons for the greater accuracy in the superior forecast measure.

The findings confirm market practitioners' views that the consensus forecast is a better measure of the market's earnings expectations.

This paper provides direct evidence of the accuracy of alternative forecast measures and the importance of diversifying idiosyncratic individual error across analyst forecasts.

This paper aims to improve the accuracy of hotel daily occupancy forecasts – an essential element in the revenue management cycle – by proposing and testing a novel approach. The authors add the hotel competitive-set’s predicted occupancy as an input of the individual property forecast and, using a recursive approach, demonstrate that there is a potential for significant reduction in the forecasting error.

The paper outlines the theoretical justification and the mechanism for this new approach. It applies a simulation for exploring the potential to improve the accuracy of the hotel’s daily occupancy forecasts, as well as analysis of data from a field study of two hotel clusters’ daily forecasts to provide empirical support to the procedure’s viability.

The results provide strong support to the notion that the accuracy could be enhanced. Incorporating the competitive set prediction by using either a genetic algorithm or the simple linear regression model improves the accuracy of the forecast using either the absolute or the absolute percentage as the error measure.

The proliferation of data sharing practices in the hotel industry reveals that the timely data sharing-aggregation-dissemination mechanism required for implementing this forecasting paradigm is feasible.

Given the crucial role of accurate forecasts in revenue management and recent changes in the hotels’ operating environment which made it harder to achieve or maintain high levels of accuracy, this study’s proposed novel approach has the potential to make a unique contribution in the realm of forecasting daily occupancies.