Model of Multiple Artificial Neural Networks Oriented on Sales Prediction and Product Shelf Design

Abstract

In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving supermarket product facing. The models are related to the prediction of different attributes concerning mainly shelf product allocation applying times series forecasting approach. The study highlights the range validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows the correct procedures able to analyse most guaranteed results, by describing how test and train datasets can be processed. The prediction results are useful in order to design monthly a planogram by taking into account the shelf allocations, the general sales trend, and the promotion activities. The preliminary correlation analysis provided an innovative key reading of the predicted outputs. The testing has been performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning process of the model. The proposed study is developed within a framework of an industry project.

Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector

“…When the training took place over a very long period, i.e., over 120 days, the error grew as the products suffer the effect of seasonality and because the habits of customers can change over a long period. Moreover, other studies using other algorithms are oriented on the prediction of few days [61], thus confirming that there could be a lot of variables for long periods that cannot be controlled for the forecasting.…”

A Case Study of Innovation in the Implementation of a DSS System for Intelligent Insurance Hub Services

“…The DSS can be constituted by different data mining algorithms into a single information system based on big data connections [12], [13]. AI is adopted for the improvement of the visual merchandising in Global Distribution [14], for predictive maintenance in industrial production [15], for sales prediction [16], [17], and for driver behaviour estimation [18]. All the listed application fields enhance the importance of the DSS embedding AI, which must be constructed by analysing production processes and defining accurately the variables which are different for each case of study.…”