Table of Contents
Journal of Industrial Engineering
Volume 2017, Article ID 2061260, 8 pages
Research Article

A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study

1Department of Industrial and Production Engineering, Jessore University of Science and Technology, Jessore 7408, Bangladesh
2School of Engineering, Royal Melbourne Institute of Technology University, Melbourne, VIC 3001, Australia
3Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh

Correspondence should be addressed to P. K. Halder; moc.liamg@redlah.artibop

Received 23 February 2017; Accepted 22 June 2017; Published 27 July 2017

Academic Editor: Gabor Szederkenyi

Copyright © 2017 C. L. Karmaker et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In today’s competitive environment, predicting sales for upcoming periods at right quantity is very crucial for ensuring product availability as well as improving customer satisfaction. This paper develops a model to identify the most appropriate method for prediction based on the least values of forecasting errors. Necessary sales data of jute yarn were collected from a jute product manufacturer industry in Bangladesh, namely, Akij Jute Mills, Akij Group Ltd., in Noapara, Jessore. Time series plot of demand data indicates that demand fluctuates over the period of time. In this paper, eight different forecasting techniques including simple moving average, single exponential smoothing, trend analysis, Winters method, and Holt’s method were performed by statistical technique using Minitab 17 software. Performance of all methods was evaluated on the basis of forecasting accuracy and the analysis shows that Winters additive model gives the best performance in terms of lowest error determinants. This work can be a guide for Bangladeshi manufacturers as well as other researchers to identify the most suitable forecasting technique for their industry.