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

Karmaker, C, Halder, P and Sarker, E 2017, 'A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study', Journal of Industrial Engineering, vol. 2017, pp. 1-8.

Document type: Journal Article
Collection: Journal Articles

Title A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study
Author(s) Karmaker, C
Halder, P
Sarker, E
Year 2017
Journal name Journal of Industrial Engineering
Volume number 2017
Start page 1
End page 8
Total pages 8
Publisher Hindawi
Abstract In todays 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 Holts 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.
Subject Mechanical Engineering not elsewhere classified
DOI - identifier 10.1155/2017/2061260
Copyright notice 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.
ISSN 2314-4890
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