Estimation of retail sales under competitive location in Mexico

Merino, María; Ramírez–Nafarrate, Adrián

doi:10.1016/j.jbusres.2015.06.050

Cited by 22 publications

(12 citation statements)

References 23 publications

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“…Furthermore, spatial data mining techniques and GIS simulation have been applied in retail location planning. These new techniques have proved to outperform the traditional modeling approach with regard to predictive accuracy (Lv, Bai, Yin, and Dong, 2008;Merino and Ramirez-Nafarrate, 2016).…”

Section: Store Level Aggregate Sales Forecastingmentioning

confidence: 99%

Retail forecasting: Research and practice

Fildes

Kolassa

2022

International Journal of Forecasting

188

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This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice.

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Section: Store Level Aggregate Sales Forecastingmentioning

confidence: 99%

Retail forecasting: Research and practice

Fildes

Kolassa

2022

International Journal of Forecasting

188

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“…The models derived from spatial interaction theory are the most widely used by firms when deciding where to locate new outlets (Merino, Ramirez-Nafarrate 2015;De Beule et al 2014). The multiplicative competitive interaction (MCI) model, which conceptualizes consumers' spatial behaviour, is part of spatial interaction theory.…”

Section: Retail Location Theory: MCI Modelmentioning

confidence: 99%

Geomarketing Models in Supermarket Location Strategies

Baviera‐Puig

Buitrago‐Vera

Escribá-Pérez

2016

Journal of Business Economics and Management

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Abstract.Choosing where to open a new outlet is a critical decision for retail firms. Building on the multiplicative competitive interaction model from retail location theory, this paper develops a geomarketing model that can be used to devise supermarket location strategies. First, attributes that explain a supermarket's pull on consumers were determined. These attributes included objective (taken from databases and empirical observation) and subjective (based on managerial judgements) variables relating to the supermarket and its trade area. Then, geographic information system tools were used to analyse real data at a highly detailed level (road section). From a geomarketing viewpoint, the model shows that sociodemographic characteristics of the supermarket's trade area affect firms' location strategies. The paper also discusses improvements for calibrating and validating this model. Adding the spatial organization of supermarkets to the model yields a different consumer behaviour pattern. This geomarketing model can help managers to design supermarket location strategies according to shop features, competitors and environment, whilst estimating supermarket sales.

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“…Time‐series modelling and prediction is a fundamental task in business research and analytics (Armstrong et al ., 2015; Goodwin, 2015; Fildes and Petropoulos, 2015). Indeed, it is receiving growing interest in different business applications, including retail sales (Merino and Ramirez‐Nafarrate, 2016), inventory demand (Syntetos et al ., 2015), market size (Qian and Soopramanien, 2014), and accounting (Brown and Fernando, 2011). In particular, analysing and forecasting stock market prices have been the subject of many empirical studies in academia since these are one of the primary inputs to a wide range of financial applications, including portfolio optimization and derivatives pricing.…”

Section: Introductionmentioning

confidence: 99%

A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra‐day data

Lahmiri

2020

Intell Sys Acc Fin Mgmt

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Summary There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.

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Estimation of retail sales under competitive location in Mexico

Cited by 22 publications

References 23 publications

Retail forecasting: Research and practice

Retail forecasting: Research and practice

Geomarketing Models in Supermarket Location Strategies

A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra‐day data

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