Gray relation analysis and multilayer functional link network sales forecasting model for perishable food in convenience store

Chen, F. L.; Ou, Tsung-Yin

doi:10.1016/j.eswa.2008.08.034

Cited by 51 publications

(22 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Franchise strategy refers to the outcome of the decision to operate and expand a business by franchising versus companyownership (Chen & Ou, 2009;Falbe & Welsh, 1998).…”

Section: Introductionmentioning

confidence: 99%

Strategic group identification using evolutionary computation

Torres¹,

Toral²

2010

Expert Systems with Applications

View full text Add to dashboard Cite

a b s t r a c tThis paper proposes to identify strategic groups among franchisors from a big set of franchisor variables. Genetic evolutionary computation was used to reduce a set of variables efficiently, and factor analysis was used to make up the strategic groups. Franchise 500 was used as database. The results suggest both that the general map of franchisor has changed since Carney and Gedajlovic's study, and that genetic evolutionary computation is a valid way to extract knowledge from a huge set of data. This paper proposes useful information for those retail firms considering internationalization via franchising. The originality of this paper is in the use of Genetic Algorithm to discriminate the final set of variables to be used for the identification of strategic groups instead of evaluating one by one the adequacy of each variable theoretically. The ability of evolutionary computation to create new knowledge is good to produce new insights into this topic.

show abstract

“…Franchise strategy refers to the outcome of the decision to operate and expand a business by franchising versus companyownership (Chen & Ou, 2009;Falbe & Welsh, 1998).…”

Section: Introductionmentioning

confidence: 99%

Strategic group identification using evolutionary computation

Torres¹,

Toral²

2010

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…A large number of sales forecasting papers have been reported, which involves a wide variety of real-world applications in numerous industries, mainly including print circuit board industry (Chang et al 2005;Hadavandi et al 2011), tourism and lodging industry (Andrew et al 1990;Smith et al 1994;Kulendran and Wong 2011), airline industry (Oberhausen and Koppelman 1982;Saab and Zouein 2001;Lin 2006;Jing et al 2010), foodservice industry (Miller et al 1991;Chen and Ou 2009;Tsai and Kimes 2009), and apparel industry (Sztandera et al 2004;Thomassey et al 2005;Au et al 2008;Guo et al 2013). Danese and Kalchschmidt (2011) examined the impact of multivariate forecasting on companies' performance by analyzing the sample data from 343 manufacturing companies in six different countries.…”

Section: Sales Forecastingmentioning

confidence: 98%

“…Jing et al (2010) investigated an airline demand forecasting problem based on period-decoupled booking data, by constructing an ARIMA model and forecasting for one typical period-decoupled booking data. Chen and Ou (2009) investigated a perishable food forecasting problem with the consideration of sales data of target store and neighboring stores as well as weather data. Guo et al (2013) addressed a sales forecasting problem in the retail industry based on early sales and proposed an effective multivariate intelligent decision-making model to provide effective forecasts for this problem.…”

Section: Sales Forecastingmentioning

confidence: 99%

New Developments and Decision-makings in Production and Retailing

Guo¹

2016

Intelligent Decision-Making Models for Production and Retail Operations

View full text Add to dashboard Cite

This chapter introduces firstly the new developments in production and retail operations. These developments bring new challenges to daily operations and decision-makings because some new decision-making problems and problem features emerge and have to be handled. This chapter then reviews previous studies on decision-making problems in production and retailing, including production planning, production scheduling, assembly line balancing, and sales forecasting. Finally, this chapter reviews commonly used techniques for typical decision-making problems, including production optimization and sales forecasting.

show abstract

“…However, for periods with promotions, regression trees with explicit features improve accuracy substantially. Chen et al (2009) developed the GMFLN forecasting model by integrating GRA and MFLN neural networks. The experimental results indicated that the proposed forecasting model outperforms the MA, ARIMA and GARCH forecasting models of the retail goods.…”

Section: Literaturementioning

confidence: 99%

“…Empirical findings in general show that combining improves forecasting accuracy and reduces the variance of post-sample forecasting errors (Makridakis and Winkler, 1983) and this holds true in statistical forecasting, judgmental estimates 4 and when averaging statistical and subjective predictions (Clemen, 1989). In this regard, most studies on retail sales forecasting has attempted to improve forecasts from single models by combining forecasts from two or atmost three ANN models, primarily (see Chang and Wang 2006;Doganis et al, 2006;Aburto and Weber, 2007;Chen et al 2009;Ou, 2011a, 2011b;Ni and Fan, 2011).…”

Section: Introductionmentioning

confidence: 99%

Forecasting aggregate retail sales: The case of South Africa

Aye

Balcılar

Gupta

et al. 2015

International Journal of Production Economics

View full text Add to dashboard Cite

Forecasting aggregate retail sales may improve portfolio investors" ability to predict movements in the stock prices of the retailing chains. Therefore, this paper uses 26 (23 single and 3 combination) forecasting models to forecast South Africa"s aggregate seasonal retail sales. We use data from 1970: 01 -2012:05, with 1987:01-2012:05 as the out-of-sample period. Unlike, the previous literature on retail sales forecasting, we not only look at a wider array of linear and nonlinear models, but also generate multi-steps-ahead forecasts using a real-time recursive estimation scheme over the out-of-sample period, to mimic better the practical scenario faced by agents making retailing decisions. In addition, we deviate from the uniform symmetric quadratic loss function typically used in forecast evaluation exercises, by considering loss functions that overweight forecast error in booms and recessions. Focusing on the single models alone, results show that their performances differ greatly across forecast horizons and for different weighting schemes, with no unique model performing the best across various scenarios. However, the combination forecasts models, especially the discounted mean-square forecast error method which weighs current information more than past, produced not only better forecasts, but were also largely unaffected by business cycles and time horizons. This result, along with the fact that individual nonlinear models performed better than linear models, led us to conclude that theoretical research on retail sales should look at developing dynamic stochastic general equilibrium models which not only incorporates learning behaviour, but also allows the behavioural parameters of the model to be state-dependent, to account for regime-switching behaviour across alternative states of the economy.

show abstract

Gray relation analysis and multilayer functional link network sales forecasting model for perishable food in convenience store

Cited by 51 publications

References 16 publications

Strategic group identification using evolutionary computation

Strategic group identification using evolutionary computation

New Developments and Decision-makings in Production and Retailing

Forecasting aggregate retail sales: The case of South Africa

Contact Info

Product

Resources

About