%0 Journal Article %1 noauthororeditor %A Massaro, Alessandro %A Vitti, Valeria %A Galiano, Angelo %D 2018 %J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) %K ANN Artificial Facing Forecasting Hybrid Merchandising Model. Multiple Networks Neural Prediction Product RapidMiner Sales Series Times Visual Weka %N 3 %P 19 %R 10.5121/ijscai.2018.7301 %T Model of Multiple Artificial Neural Networks Oriented on Sales Prediction and Product Shelf Design %U http://aircconline.com/ijscai/V7N3/7318ijscai01.pdf %V 7 %X 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

@article{noauthororeditor, 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}, added-at = {2018-09-11T13:43:40.000+0200}, author = {Massaro, Alessandro and Vitti, Valeria and Galiano, Angelo}, biburl = {https://www.bibsonomy.org/bibtex/2b98c7b11d11861cbd703a4a47b52ae14/leninsha}, doi = {10.5121/ijscai.2018.7301}, interhash = {1606010cf74394b9bf6902ee5c98c980}, intrahash = {b98c7b11d11861cbd703a4a47b52ae14}, issn = {2319-1015}, journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)}, keywords = {ANN Artificial Facing Forecasting Hybrid Merchandising Model. Multiple Networks Neural Prediction Product RapidMiner Sales Series Times Visual Weka}, language = {English}, month = {August}, number = 3, pages = 19, timestamp = {2018-09-11T13:50:53.000+0200}, title = {Model of Multiple Artificial Neural Networks Oriented on Sales Prediction and Product Shelf Design}, url = {http://aircconline.com/ijscai/V7N3/7318ijscai01.pdf}, volume = 7, year = 2018 }