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Joanne S. Utley and J. Gaylord May

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…

Abstract

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.

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Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-201-3

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Article

Leslie Bernard Trustrum, F. Robert Blore and William James Paskins

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…

Abstract

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.

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Marketing Intelligence & Planning, vol. 5 no. 3
Type: Research Article
ISSN: 0263-4503

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Article

BEE‐HUA GOH

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…

Abstract

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.

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Engineering, Construction and Architectural Management, vol. 5 no. 3
Type: Research Article
ISSN: 0969-9988

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Article

Sanjita Jaipuria and Siba Sankar Mahapatra

The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering…

Abstract

Purpose

The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).

Design/methodology/approach

A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.

Findings

A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).

Research limitations/implications

This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.

Practical implications

The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.

Originality/value

This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.

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Journal of Modelling in Management, vol. 14 no. 2
Type: Research Article
ISSN: 1746-5664

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Article

Elizabeth Agyeiwaah and Raymond Adongo

– The purpose of this paper is to identify the core factors that determine tourism demand in four inbound markets of Hong Kong.

Abstract

Purpose

The purpose of this paper is to identify the core factors that determine tourism demand in four inbound markets of Hong Kong.

Design/methodology/approach

The general-to-specific approach was adopted as a step-by-step approach to identify the major determinants of tourism demand in Hong Kong.

Findings

The study revealed word of mouth and income of source market are core determinants of tourism demand in all four inbound markets.

Originality/value

Knowledge of core determinants of tourism demand is useful to destination management organizations and tourism business owners for strategic planning and decision making to increase total revenues.

Details

International Journal of Tourism Cities, vol. 2 no. 1
Type: Research Article
ISSN: 2056-5607

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Article

Rick L. Andrews and Peter Ebbes

This paper aims to investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models. Endogeneity problems in demand models

Abstract

Purpose

This paper aims to investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models. Endogeneity problems in demand models occur when certain factors, unobserved by the researcher, affect both demand and the values of a marketing mix variable set by managers. For example, unobserved factors such as style, prestige or reputation might result in higher prices for a product and higher demand for that product. If not addressed properly, endogeneity can bias the elasticities of the endogenous variable and subsequent optimization of the marketing mix. In practice, instrumental variables (IV) estimation techniques are often used to remedy an endogeneity problem. It is well-known that, for linear regression models, the use of IV techniques with poor-quality instruments can produce very poor parameter estimates, in some circumstances even worse than those that result from ignoring the endogeneity problem altogether. The literature has not addressed the consequences of using poor-quality instruments to remedy endogeneity problems in non-linear models, such as logit-based demand models.

Design/methodology/approach

Using simulation methods, the authors investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models applied to finite-sample data sets. The results show that, even when the conditions for lack of parameter identification due to poor-quality instruments do not hold exactly, estimates of price elasticities can still be quite poor. That being the case, the authors investigate the relative performance of several non-linear IV estimation procedures utilizing readily available instruments in finite samples.

Findings

The study highlights the attractiveness of the control function approach (Petrin and Train, 2010) and readily available instruments, which together reduce the mean squared elasticity errors substantially for experimental conditions in which the theory-backed instruments are poor in quality. The authors find important effects for sample size, in particular for the number of brands, for which it is shown that endogeneity problems are exacerbated with increases in the number of brands, especially when poor-quality instruments are used. In addition, the number of stores is found to be important for likelihood ratio testing. The results of the simulation are shown to generalize to situations under Nash pricing in oligopolistic markets, to conditions in which cross-sectional preference heterogeneity exists and to nested logit and probit-based demand specifications as well. Based on the results of the simulation, the authors suggest a procedure for managing a potential endogeneity problem in logit-based demand models.

Originality/value

The literature on demand modeling has focused on deriving analytical results on the consequences of using poor-quality instruments to remedy endogeneity problems in linear models. Despite the widespread use of non-linear demand models such as logit, this study is the first to address the consequences of using poor-quality instruments in these models and to make practical recommendations on how to avoid poor outcomes.

Details

Journal of Modelling in Management, vol. 9 no. 3
Type: Research Article
ISSN: 1746-5664

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Article

Syed Asif Raza and Mohd. Nishat Faisal

This paper aims to develop efficient decision support tools for a firm’s environment protection by using greening effort while yet improving profitability by utilizing…

Abstract

Purpose

This paper aims to develop efficient decision support tools for a firm’s environment protection by using greening effort while yet improving profitability by utilizing pricing and inventory decisions with discount consideration.

Design/methodology/approach

This study proposed a mathematical model for price- and greening effort-dependent demand rate with discount considerations. Later, the mathematical model is extended to the situation in which the demand rate is also dependent on the stock level, in addition to the price and greening effort. Efficient solution methodologies are developed for finding the optimal solution to the proposed models.

Findings

Simple yet elegant models are proposed to mimic real-life applications. Structural properties of the models are explored to outline efficient algorithms with quantity discounts.

Research limitations/implications

The paper considers monopoly and assumes deterministic demand. Only a more commonly observed all-units discount scheme is studied.

Practical implications

The models provide decision support tools for firms in pursuit of joint profit maximization and environment consciousness goals.

Social implications

The study develops environment-friendly approaches for inventory management and improving the profitability alike.

Originality/value

This study is among the first to consider environmental protection with an investment in greening effort along with inventory management and pricing decision. The study also explored the effect of all-unit quantity discounts.

Details

Journal of Modelling in Management, vol. 13 no. 1
Type: Research Article
ISSN: 1746-5664

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Book part

Tihana Škrinjarić

This chapter analyses potentials of including online search volume data in modeling the demand series of consumer products. Forecasting future demand for products of a…

Abstract

This chapter analyses potentials of including online search volume data in modeling the demand series of consumer products. Forecasting future demand for products of a company represents one of the important parts of planning and conducting business in general. Thus, the purpose of this chapter is twofold. The first purpose is to give a critical overview of the existing research on the topic of forecasting and nowcasting demand and consumption. The other purpose is to fill the gap in the literature by empirically comparing several approaches of modeling and forecasting demand and consumption on real data. Results of the empirical analysis show that including online search volume data can enhance modeling and forecasting of demand series, especially in times of economic downturns. Thus, it is advised to use such an approach in modeling of consumer demand in a business so that better business performance in terms of profits could be obtained.

Abstract

Details

Panel Data Econometrics Theoretical Contributions and Empirical Applications
Type: Book
ISBN: 978-1-84950-836-0

Abstract

Details

Functional Structure Inference
Type: Book
ISBN: 978-0-44453-061-5

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