Predictive and Comparative Analysis on Products Demand in Supply Chain and Management

  • Fahad Hussain
  • Shahzad Haroon

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.

References

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Published
2018-06-30
How to Cite
HUSSAIN, Fahad; HAROON, Shahzad. Predictive and Comparative Analysis on Products Demand in Supply Chain and Management. Journal of Independent Studies and Research-Computing, [S.l.], v. 16, n. 1, p. 37-43, june 2018. ISSN 1998-4154. Available at: <http://jisrc.szabist.edu.pk/index.php/jisrc/article/view/49>. Date accessed: 17 oct. 2019. doi: https://doi.org/10.31645/jisrc/(2018).16.1.05.
Section
Articles