Predictive and Comparative Analysis on Products Demand in Supply Chain and Management
Journal Title: Journal of Independent Studies and Research - Computing - Year 2018, Vol 16, Issue 1
Abstract
Retailers industry normally involve big investment as their products have many categories with different options. To increase the profit margin, retailers need to identify the right products otherwise cost and stock of their products would increase significantly. The efficient demand forecast system is a useful method to accomplish prior goals, improve customer satisfaction and reduce out of stock conditions for products. The main idea behind this study is to predict the demand of products for future and increase the sales revenue of grocery retailing industry by using two machine learning algorithms namely support vector machine (SVM) and artificial neural network (ANN). In this study, the dataset of a supermarket located in Pakistan is used which comprises of the actual demand of the past year. The results specified that SVM ensured a better-forecasted quality rather than ANN.
Authors and Affiliations
Fahad Hussain, Muhammad Shahzad Haroon
Keywords
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- EP ID EP626366
- DOI 10.31645/jisrc/(2018).16.1.05
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