Demand forecasting based on natural computing approaches applied to the foodstuff retail segment

Veiga, Claudimar Pereira da; Veiga, Cássia Rita Pereira da; Puchalski, Weslly; Coelho, Leandro dos Santos; Tortato, Ubiratã

doi:10.1016/j.jretconser.2016.03.008

Cited by 29 publications

(21 citation statements)

References 46 publications

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“…Neural Networks (Kuo, 2001); Regression Trees (Gür Ali et al, 2009), Gray relation analysis and multilayer functional link networks Ou, 2009, 2011), a two level switching model selecting between a simple moving average and a non-linear predictor (e.g., k-nearest neighbor, decision trees) based on the characteristics of the time series (Žliobaitė, Bakker, and Pechenizkiy, 2012), Support Vector Machines (Gür Ali and Yaman, 2013;Pillo, Latorre, Lucidi, and Procacci, 2016), Wavelets Neural Networks (Veiga, Veiga, Puchalski, Coelho, and Tortato, 2016), and Bayesian P-splines (Lang et al, 2015). An exception where non-linearities led to poor performance is van Donselaar et al (2016) who analyzed the impact of relative price discounts on product sales during a promotion but did not find conclusive evidence for the presence of threshold and/or saturation levels for price discounts for perishable products.…”

Section: Nonlinear and Machine Learning Methodsmentioning

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: Nonlinear and Machine Learning Methodsmentioning

confidence: 99%

Retail forecasting: Research and practice

Fildes

Kolassa

2022

International Journal of Forecasting

188

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“…Failure to meet these assumptions when applying control charts can result in a significant increase in false alarms, an unwanted factor that not only increases control costs but also leads to wrong conclusions and causes the operator to lose credibility as a consequence (Costa et al 2004;Del Castillo 2002). So, the alternative used in an autocorrelated process is to fit an ARIMA model and use the residues produced for this model to evaluate the process (Veiga et al 2016;Kalavani et al 2019).…”

Section: Figmentioning

confidence: 99%

Control chart based on residues: Is a good methodology to detect outliers?

et al. 2019

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The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models.

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“…As the present study used the exhaustive systematic literature review technique in its field of study, some limitations remain to be pursued: (a) the definition of the search strings was limited to the results of publications in the English language; and (b) only journals published within the citation quartiles of the Scimago Journal Ranking (SJR) were considered, and consequently there may be other relevant publications published in other formats such as books and conference proceedings; and (c) the study was limited to a 10‐year coverage. For future research, we suggest an SLR for demand prediction models in the retail context (Mou, Robb, & DeHoratius, ; Veiga et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…In the literature, it has been firmly established that there is a need for further studies on forecasting models. Demand forecasting aids strategic production planning in an industry, as it allows managers to anticipate the future and plan joint activities with the functional areas (Veiga, Veiga, Puchalski, Coelho, & Tortato, ). Forecasting provides the best evaluation of available information.…”

Section: Introductionmentioning

confidence: 99%

Forecasting models in the manufacturing processes and operations management: Systematic literature review

Agostino

Silva

Veiga

et al. 2020

Journal of Forecasting

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The purpose of this paper is to present the result of a systematic literature review regarding the application and development of forecasting models in the industrial context, especially the context of manufacturing processes and operations management. The study was conducted considering the preparation of an established research protocol to know, discuss, and analyze the main approaches adopted by researchers in the field. To achieve this objective, we analyzed 354 recent papers published in periodicals between 2008 and 2018. This paper makes three main contributions to the field: (i) it presents an updated portfolio of prediction models in the industrial context, providing a reference point for researchers and industrial managers; (ii) it presents a characterization of the field of study through the identification of publication vehicles, frequency, and the principal authors and countries related to the development of research on the theme; (iii) it proposes a unified framework, listing the characteristics of the prediction models with their respective application contexts, identifying the current research directions to provide theoretical aids for the development of new approaches to forecasting in industry. The results of this study provide an empirical base for further discussions on studies that focus on forecasting in the industrial context.

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Demand forecasting based on natural computing approaches applied to the foodstuff retail segment

Cited by 29 publications

References 46 publications

Retail forecasting: Research and practice

Retail forecasting: Research and practice

Control chart based on residues: Is a good methodology to detect outliers?

Forecasting models in the manufacturing processes and operations management: Systematic literature review

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