Post-script—Retail forecasting: Research and practice

Fildes, Robert; Kolassa, Stephan; Ma, Shaohui

doi:10.1016/j.ijforecast.2021.09.012

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…For the medium to large retailer, forecasting at store level for product replenishment poses major problems which are extensively discussed in [3,34]. For retailers the problem falls naturally into a hierarchical structure mapped on three dimensions: time (e.g., day, week), product (e.g., SKU, Category) and supply chain (e.g., Store, Distribution Centre), although depending on the retailer more secondary levels may be relevant.…”

Section: Retail Forecastingmentioning

confidence: 99%

Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?

Ramos

Oliveira

Kourentzes

et al. 2022

ASI

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Retailers depend on accurate forecasts of product sales at the Store × SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model’s parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast retailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives.

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Section: Retail Forecastingmentioning

confidence: 99%

Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?

Ramos

Oliveira

Kourentzes

et al. 2022

ASI

Self Cite

View full text Add to dashboard Cite

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“…Similarly, Quiñones-Rivera et al [4] found that, in the context of the manufacturing of electrical products in Colombia, it is difficult for companies to adequately forecast demand due to its volatility and its dependence on various non-linear exogenous factors. Fildes et al [5] found that due to the rise of electronic commerce, demand forecasting in the retail sector faces, on the one hand, the need to model the complex competition and complementarity of online sales in an increasingly omnichannel context and, on the other hand, the challenge of foreseeing the impact of sectoral and global crises such as those experienced, for example, with the COVID 19 pandemic. Along the same lines, Viverit et al [6], points out that the aforementioned pandemic has had short and long-term consequences on the hotel industry, plunging it into an unprecedented situation where its historical demand has lost its value, making forecasting activities very complex.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of the Literature on the Use of Artificial Intelligence in Forecasting the Demand for Products and Services in Various Sectors

Villar,

Lengua

2024

IJACSA

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This systematic review, carried out under the PRISMA methodology, aims to identify the recently proposed artificial intelligence models for demand forecasting, distinguishing the problems they try to overcome, recognizing the artificial intelligence methods used, detailing the performance metrics used, recognizing the performance achieved by these models and identifying what is new in them. Studies in the manufacturing, retail trade, tourism and electric energy sectors were considered in order to facilitate the transfer of knowledge from different sectors. 33 articles were analyzed, with the main results being that the proposed models are generally ensembles of various artificial intelligence methods; that the complexity of data and its scarcity are the main problems addressed; that combinations of simple machine learning, "bagging", "boosting" and deep neural networks, are the most used methods; that the performance of the proposed models surpasses the classic statistical methods and other reference models; and that, finally, the proposed novelties cover aspects such as the type of data used, the pattern extraction techniques used, the assembly forms of the applied models and the use of algorithms for automating the adjustment of the models. Finally, a forecast model is proposed that includes the most innovative aspects found in this research.

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