Abstract
In India, food sales are emerging to be a major revenue generator for multiplex operators currently amounting to over $367 million a year. Efficient food sales forecasting techniques are the need of the hour as they help minimize the wastage of resources for the multiplex operators. In this paper, the authors propose a model to make a day-ahead prediction of food sales in one of the top multiplexes in India. Online learning and feature engineering by data correlative analysis in conjecture with a densely connected Neural Network, address the concept drifts and latent time correlations present in the data respectively. A scale independent metric, \(\eta _{comp}\) is also introduced to measure the success of the models across all food items from the business perspective. The proposed model performs better than the traditional time-series models, and also performs better than the corporate’s currently existing model by a factor of 7.7%. This improved performance also leads to a saving of 170 units of food everyday.
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Adithya Ganesan, V., Divi, S., Moudhgalya, N.B., Sriharsha, U., Vijayaraghavan, V. (2020). Forecasting Food Sales in a Multiplex Using Dynamic Artificial Neural Networks. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_8
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