Open AccessSupply Chain Modelling Based on Twelve Related Features: A Novel Iteration Feature Selection Method
Author(s) -
Hussein Alsteif,
Murat Akkaya
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.h9271.09101121
Subject(s) - supply chain , computer science , selection (genetic algorithm) , noise (video) , feature (linguistics) , supply chain management , feature selection , volume (thermodynamics) , order (exchange) , data mining , artificial intelligence , economics , finance , linguistics , philosophy , physics , quantum mechanics , image (mathematics) , political science , law
Real-time prediction of hour-based order entry has been lacking in literature. Compared to previous research on supply chain problems, our proposed approach overcomes the constraints of operations management with longer time periods such as weekly and monthly by developing a novel iteration model. We performed experiments on 100 products with high cumulative volume over time. Using 3 different dataset, our proposed model proved efficient in forecasting skewed demand signals with lot of noise in supply chains.