Supply Chain Modelling Based on Twelve Related Features: A Novel Iteration Feature Selection Method
Hussein Alsteif1, Murat Akkaya2
1Hussein Alsteif, Girne American University, Girne, North Cyprus.
2Murat Akkaya, Girne American University, Girne, North Cyprus.
Manuscript received on June 21, 2021. | Revised Manuscript received on September 05, 2021. | Manuscript published on September 30, 2021. | PP: 1-5 | Volume-10 Issue-11, September 2021. | Retrieval Number: 100.1/ijitee.H92710610821 | DOI: 10.35940/ijitee.H9271.09101121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Supply Chain, Machine learning, Noise reduction, Iteration Model, Learning algorithm.
Scope of the Article: Machine learning