Intelligent Demand Forecasting in the Bakery Industry Using  Convolutional Neural Network Regressors

K Parameswari, S.Yeshwanth, K.Yamini, G.Sudeep, S.Saicharan

Intelligent Demand Forecasting in the Bakery Industry Using Convolutional Neural Network Regressors

Authors

K Parameswari, S.Yeshwanth, K.Yamini, G.Sudeep, S.Saicharan

Keywords:

Food Supply Chain Management, Demand Forecasting, Convolutional Neural Network, CNN Regressor, Bakery Sector, Inventory Optimization, Food Wastage Reduction, Machine Learning, Logistics Optimization.

Abstract

Food supply chain management plays a vital role in ensuring the efficient production, distribution,and delivery of food products, particularly in developing economies such as India. However, thesector faces persistent challenges including demand–supply mismatches, inefficient logistics, and excessive food wastage

References

Gonçalves, J.N.; Cortez, P.; Carvalho, M.S.; Frazao, N.M. A multivariate approach for multistep demand forecasting in assembly industries: Empirical evidence from an automotive supply chain. Decis. Support Syst. 2021, 142, 113452.

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Published

2025-05-25

How to Cite

K Parameswari, S.Yeshwanth, K.Yamini, G.Sudeep, S.Saicharan. (2025). Intelligent Demand Forecasting in the Bakery Industry Using Convolutional Neural Network Regressors. Journal of Computational Analysis and Applications (JoCAAA), 34(5), 446–457. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4623