INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Machine Learning For Intermittent Demand Forecasting
Authors Name:
Zainab Assaghir
Unique Id:
IJSDR1701017
Published In:
Volume 2 Issue 1, January-2017
Abstract:
Forecasting demand is a crucial step in inventory control; its accuracy affects the management of storage and materials, and therefore the profitability of the company. When the demand is intermittent, infrequent, and highly variable when it occurs, it causes problems when using traditional statistical models and forecasting techniques (constant forecasts, zero forecasts, unsuitable accuracy metrics, etc). This work is a comparison between several methods, including the Croston's method specific for intermittent demand, and other important machine learning techniques such as artificial neural networks, using a database of different spare part products. Our objective is to forecast the demand of these products in the most accurate way possible, and to apply methods that can outperform the conventional methods.
"Machine Learning For Intermittent Demand Forecasting", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.2, Issue 1, page no.99 - 102, January-2017, Available :http://www.ijsdr.org/papers/IJSDR1701017.pdf
Downloads:
000346979
Publication Details:
Published Paper ID: IJSDR1701017
Registration ID:170019
Published In: Volume 2 Issue 1, January-2017
DOI (Digital Object Identifier):
Page No: 99 - 102
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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