Artificial Intelligence to support retail sales optimization

  • Agostino G. Bruzzone ,
  • Kirill Sinelshchikov ,
  • Marina Massei ,
  • Wolfhard Schmidt
  • a,c SIM4Future, Simulation Team, via Trento 34, 16145, Genova, Italy
  • a,b,c  Simulation Team, DIME, University of Genoa, via Opera Pia 15, 16145 Genova, Italy
  • Simulation Team, via Magliotto, 17100, Savona, Italy
Cite as
(a)Bruzzone A.G., (b)Sinelshchikov K., (c)Massei M., (d)Schmidt W. (2020). Artificial Intelligence to support retail sales optimization. Proceedings of the 32nd European Modeling & Simulation Symposium (EMSS 2020), pp. 430-434. DOI: https://doi.org/10.46354/i3m.2020.emss.061

Abstract

Presented study focuses on utilization of Artificial Intelligence (AI) in order to support data integration, sales forecasting and process optimization in retail. In particular, use of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in order to support decision makers from sales departments has evaluated.

References

  1. Ansari, M. E., & Joloudar, S. Y. E. (2011). An investigation of TV advertisement effects on customers' purchasing and their satisfaction. International Journal of Marketing Studies, 3(4), 175.
  2. d'Astous, A., & Landreville, V. (2003). An experimental investigation of factors affecting consumers' perceptions of sales promotions. European Journal of Marketing.
  3. Bogue, R. (2019). Strong prospects for robots in retail. Industrial Robot: the international journal of robotics research and application.
  4. Brill, T. M., Munoz, L., & Miller, R. J. (2019). Siri, Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications. Journal of Marketing Management, 35(15-16), pp. 1401-1436.
  5. Bruzzone, A. G., Di Matteo, R., & Sinelshchikov, K. (2018) Strategic Engineering & Innovative Modeling Paradigms. In Workshop on Applied Modelling & Simulation, Praha, Czech Republic, October 17-19
  6. Bruzzone, A. G., & Longo, F. (2010). An advanced system for supporting the decision process within large-scale retail stores. Simulation, 86(12), 742-762.
  7. Bruzzone, A. G., Bocca, E., & Poggi, S. (2009). Renovating Intelligent Operations in Supermarket Chains. Proceedings of Third IEEE Asia International Conference on Modelling & Simulation, Bandung, Indonesia, May, pp. 425-430
  8. Bruzzone, A., Massei, M., & Bocca, E. (2009). Fresh-food supply chain. In Simulation-based case studies in logistics (pp. 127-146). Springer, London.
  9. Bruzzone, A. G., Bocca, E., Pierfederici, L., & Massei, M. (2009). Multilevel forecasting models in manufacturing systems. In Proceedings of Summer Computer Simulation Conference, Istanbul, Turkey, July, pp. 239-245
  10. Bruzzone, A. G., Massei, M., & Poggi, S. (2007). Simulation based analysis on different logistics solutions for fresh food supply chain. In Proceedings of Spring Simulation Multiconference, Norfolk, VA, March, Volume 3 (pp. 84-89).
  11. Chung, M., Ko, E., Joung, H., & Kim, S. J. (2018). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research.
  12. Darwin, C. (1859), On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. London: John Murray, p. 502
  13. Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing & Service Operations Management, 18(1), pp. 69-88.
  14. Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media.
  15. McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. " O'Reilly Media, Inc.".
  16. Pak, O., Ferguson, M., Perdikaki, O., & Wu, S. M. (2020). Optimizing stock‐keeping unit selection for promotional display space at grocery retailers. Journal of Operations Management, 66(5), 501-533.
  17. Kengpol, A., & Wangananon, W. (2006). The expert system for assessing customer satisfaction on fragrance notes: Using artificial neural networks. Computers & Industrial Engineering, 51(4), pp. 567-584.
  18. Kumar, S., & Zymbler, M. (2019). A machine learning approach to analyze customer satisfaction from airline tweets. Journal of Big Data, 6(1), 62.
  19. Setiabudi, D. H., & Tjahyana, L. J. (2011). Mobile Phone as a Personal Digital Shopping Assistant to Help Customers Shop in Shopping Center. International Journal of Information and Education Technology, 1(2), pp. 126-131.
  20. Verstraete, G., Aghezzaf, E. H., & Desmet, B. (2019). A data-driven framework for predicting weather impact on high-volume low-margin retail products. Journal of Retailing and Consumer Services, 48, 169-177.
  21. Wallace, M., Maglogiannis, I., Karpouzis, K., Kormentzas, G., & Kollias, S. (2003). Intelligent one-stop-shop travel recommendations using an adaptive neural network and clustering of history. Information Technology & Tourism, 6(3), pp. 181-193.