From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.

/pdf/forecasting-and-operational-research-a-review-2hjrde95rz.pdf

Forecasting and operational research : a review

The concept of market segmentation has been widely accepted and warmly embraced both by tourism industry and academia. In tourism research, this increased interest in segmentation studies has led to the emergence of a standard research approach. Most notably a concept referred to as “factor–cluster segmentation” has been broadly adopted. The aim of this article is to demonstrate that this approach is not generally the best procedure to identify homogeneous groups of individuals (market segments).

/pdf/challenging-factor-cluster-segmentation-4ujnzsqiuu.pdf

Challenging “Factor–Cluster Segmentation”

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score It then outlines 10 challenges for Operational Research to support modelling in consumer finance Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance

/pdf/consumer-finance-challenges-for-operational-research-vbau3k7two.pdf

Consumer finance: challenges for operational research

http://ebape.fgv.br/sites/ebape.fgv.br/files/REVISION%20A%20picture%20is%20worth%20a%20thousand%20words.pdf

A picture is worth a thousand words: translating product reviews into a product positioning map

In data-rich marketing environments (e.g., direct marketing or new product design), managers face an ever-growing need to reduce the number of variables effectively. To accomplish this goal, the authors introduce a new method called sliced inverse regression (SIR), which finds factors by taking into account the information contained in both the dependent and independent variables. Sliced inverse regression objectively identifies appropriate factors through simple statistical tests for determining the number of factors to retain and for assessing the significance of factor-loading coefficients. The authors make conceptual connections between SIR and several existing approaches, including principal components regression (PCR) and partial least squares regression (PLSR). Using Monte Carlo experiments, the authors demonstrate that SIR performs better than these approaches. Two empirical examples—designing a new executive business program and direct marketing by a catalog company—are presented to illu...

A New Dimension Reduction Approach for Data-Rich Marketing Environments: Sliced Inverse Regression

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