Determination of Marketing Parameters for Building a Demand Forecasting Model using Neural Networks

Authors

  • Victor Sineglazov National Aviation University, Kyiv, Ukraine https://orcid.org/0000-0002-3297-9060
  • Mikhaylo Novikov National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,”

DOI:

https://doi.org/10.18372/1990-5548.78.18263

Keywords:

determination of marketing parameters, forecasting, neural networks, regression models, multilayer perceptron

Abstract

This article is dedicated to the discovery of marketing parameters for the implementation of a forecasting model using additional neural measures using data from real data. The robot looks at the problem in the field of modeling the delivery of goods to the market in marketing using additional methods of artificial intelligence and machine learning. The main features of the main approaches to introducing product models to the market, their advantages and disadvantages are shown. It has been revealed that they will require thoroughness. A new methodology for solving the problem is presented. The model's ability to accurately predict lasting demand based on a variety of marketing parameters has been demonstrated, which helps businesses effectively plan inventory, inventory, and personnel, and can lead to significant cost savings. and increased efficiency.

Author Biographies

Victor Sineglazov , National Aviation University, Kyiv, Ukraine

Doctor of Engineering Science

Professor

Head of the Department Aviation Computer-Integrated Complexes

Faculty of Air Navigation Electronics and Telecommunications

Mikhaylo Novikov , National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,”

Graduate Student

Department of Artificial Intelligence

Institute of Applied System Analysis

References

Armstrong J. Scott, and Kesten C. Green. "Demand forecasting II: Evidence-based methods and checklists," 2017. URL: https://faculty.wharton.upenn.edu/wp-content/uploads/2017/05/JSA-Demand-Forecasting-89-clean.pdf

Silveira Netto, Carla Freitas & Brei, Vinicius Andade, Demand Forecasting in Marketing: Methods, Types of Data, and Future Research, 2017.

C. Ingle, D. Bakliwal, J. Jain, P. Singh, P. Kaleand V. Chhajed, "Demand Forecasting: Literature Review on Various Methodologies," 2021, 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2021, pp. 1–7, https://doi.org/10.1109/ICCCNT51525.2021.9580139.

eWorldFulfillment, "Demand Forecasting: Methods, Models, and Examples." eWorldFulfillment, 2021. URL: https://eworldfulfillment.com/blog/demand-forecasting-methods/

E. S. Gardner, “Exponential smoothing: The state of the art—Part II”, International Journal of Forecasting, vol. 22, no. 4, pp. 637–666, Oct. 2006. [Online]. Available: https://doi.org/10.1016/j.ijforecast.2006.03.005

A. Mitra, A. Jain, A. Kishore, and et al. “A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach,” Oper. Res. Forum, 3, 58 (2022). https://doi.org/10.1007/s43069-022-00166-4

M. Seyedan, & F. Mafakheri, “Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities,” Journal of Big Data, vol. 7, Article number: 53, 2020. https://doi.org/10.1186/s40537-020-00329-2.

A. Aktepe, E. Yanık, & S. Ersöz, “Demand forecasting application with regression and artificial intelligence methods in a construction machinery company,” J Intell Manuf, 32, 1587–1604, 2021. https://doi.org/10.1007/s10845-021-01737-8

A. Mitra, A. Jain, A. Kishore, and et al., “A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach,” Oper. Res. Forum, 3, 58, 2022. https://doi.org/10.1007/s43069-022-00166-4

Guoping Xu, Hanqiang Cao, Youli Dong, Chunyi Yue, Kexin Li, and Yubing Tong, “Focal Loss Function based DeepLabv3+ for Pathological Lymph Node Segmentation on PET/CT,” Proceedings of the 2020 2nd International Conference on Intelligent Medicine and Image Processing, 2020 pp. 24–28. https://doi.org/10.1145/3399637.3399651.

G. Behera, A. Bhoi,, A. K. Bhoi, “A Comparative Analysis of Weekly Sales Forecasting Using Regression Techniques,” In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 431, 2022. Springer, Singapore. https://doi.org/10.1007/978-981-19-0901-6_4

T. Gopalakrishnan, Ritesh Choudhary, and Sarada Prasad, "Prediction of Sales Value in Online shopping using Linear Regression," 2018 4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, 2018, pp. 1–6, https://doi.org/10.1109/CCAA.2018.8777620.

D. Schiessl, H. B. A. Dias, & J. C. Korelo, “Artificial intelligence in marketing: a network analysis and future agenda.” J Market Anal, 10, 207–218, 2022. https://doi.org/10.1057/s41270-021-00143-6

Jun Zhu, Jianfei Chen, Wenbo Hu, and Bo Zhang, “Big Learning with Bayesian methods,” National Science Review, vol. 4, Issue 4, July 2017, pp. 627–651, https://doi.org/10.1093/nsr/nwx044

G.M. Allenby, E.T. Bradlow, E.I. George, et al. “Perspectives on Bayesian Methods and Big Data,” Cust. Need. And Solut. 1, 169–175, 2014. https://doi.org/10.1007/s40547-014-0017-9

M. Scutari, C. Vitolo, & A. Tucker, “Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation,” Stat Comput, 29, 1095–1108, 2019. https://doi.org/10.1007/s11222-019-09857-1

D. G. Rasines & G. A. Young, “Empirical Bayes and Selective Inference,” J Indian Inst Sci, 102, 1205–1217, 2022. https://doi.org/10.1007/s41745-022-00286-0

L. Alzubaidi, J. Zhang, A. J. Humaidi, and et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, futuredirections,” J Big Data, 8, 53, 2021. https://doi.org/10.1186/s40537-021-00444-8

Nan Zheng and Pinaki Mazumder, "Fundamentals and Learning of Artificial Neural Networks," in Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design, IEEE, 2020, pp. 11–60, https://doi.org/10.1002/9781119507369.ch2.

R. Zese, E. Bellodi, M. Fraccaroli, F. Riguzzi, E. Lamma, 2022, “Neural Networks and Deep Learning Fundamentals,” In: Micheloni, R., Zambelli, C. (eds) Machine Learning and Non-volatile Memories. Springer, Cham. https://doi.org/10.1007/978-3-031-03841-9_2

O. A. Montesinos López, A. Montesinos López, & J. Crossa, “Fundamentals of Artificial Neural Networks and Deep Learning,” In: Multivariate Statistical Machine Learning Methods for Genomic Prediction. 2022, Springer, Cham. https://doi.org/10.1007/978-3-030-89010-0_10

Wei Wang & Ruyi Yang, “Enterprise Network Marketing Prediction Using the Optimized GA-BP Neural Network,” Complexity, 2020, Article No. 6682296. https://doi.org/10.1155/2020/6682296.

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Published

2023-12-27

Issue

Vol. 4 No. 78 (2023)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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