Predictive analytics for fast moving item using nonlinear regresssion models

A supply chain and logistics company are trying to realign their stock placement in the warehouse based on the type of movement of the stock keeping units (SKU), fast moving or slow moving. This project is executed to construct a nonlinear regression models for order frequency per month of fast movi...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohd. Azhar, Nur Arisha
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/96404/1/NurArishaMSC2021.pdf.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.96404
record_format	uketd_dc
spelling	my-utm-ep.964042022-07-18T11:13:30Z Predictive analytics for fast moving item using nonlinear regresssion models 2021 Mohd. Azhar, Nur Arisha QA75 Electronic computers. Computer science A supply chain and logistics company are trying to realign their stock placement in the warehouse based on the type of movement of the stock keeping units (SKU), fast moving or slow moving. This project is executed to construct a nonlinear regression models for order frequency per month of fast moving items using Python programming language. The variables used for this prediction model is the median order frequency per month for each warehouse, total quantity of item, total volume of item and total value of item. The project framework has been set up with the inclusion of data visualization for the type of movement of each SKU for each warehouse using Tableau software. SKU are segmented by comparing the average frequency of order for each SKU in the span of 33 months with the median frequency of order for each respective warehouse the SKU resides in. Three nonlinear regression based models are used to construct the predictive model which are Decision Tree Regression, Random Forest Regression and Extreme Gradient Boosting Algorithms. Parameters tuning for the model carried out by using RandomizedSearchCV from scikit-learn library. Random forest produce the smallest error rate for prediction by using mean square error with an average value of 1.2608 and mean absolute error with an average value of 0.4496 as model evaluation and holdout method as model validation in this study. 2021 Thesis http://eprints.utm.my/id/eprint/96404/ http://eprints.utm.my/id/eprint/96404/1/NurArishaMSC2021.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:143459 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mohd. Azhar, Nur Arisha Predictive analytics for fast moving item using nonlinear regresssion models
description	A supply chain and logistics company are trying to realign their stock placement in the warehouse based on the type of movement of the stock keeping units (SKU), fast moving or slow moving. This project is executed to construct a nonlinear regression models for order frequency per month of fast moving items using Python programming language. The variables used for this prediction model is the median order frequency per month for each warehouse, total quantity of item, total volume of item and total value of item. The project framework has been set up with the inclusion of data visualization for the type of movement of each SKU for each warehouse using Tableau software. SKU are segmented by comparing the average frequency of order for each SKU in the span of 33 months with the median frequency of order for each respective warehouse the SKU resides in. Three nonlinear regression based models are used to construct the predictive model which are Decision Tree Regression, Random Forest Regression and Extreme Gradient Boosting Algorithms. Parameters tuning for the model carried out by using RandomizedSearchCV from scikit-learn library. Random forest produce the smallest error rate for prediction by using mean square error with an average value of 1.2608 and mean absolute error with an average value of 0.4496 as model evaluation and holdout method as model validation in this study.
format	Thesis
qualification_level	Master's degree
author	Mohd. Azhar, Nur Arisha
author_facet	Mohd. Azhar, Nur Arisha
author_sort	Mohd. Azhar, Nur Arisha
title	Predictive analytics for fast moving item using nonlinear regresssion models
title_short	Predictive analytics for fast moving item using nonlinear regresssion models
title_full	Predictive analytics for fast moving item using nonlinear regresssion models
title_fullStr	Predictive analytics for fast moving item using nonlinear regresssion models
title_full_unstemmed	Predictive analytics for fast moving item using nonlinear regresssion models
title_sort	predictive analytics for fast moving item using nonlinear regresssion models
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2021
url	http://eprints.utm.my/id/eprint/96404/1/NurArishaMSC2021.pdf.pdf
_version_	1747818664145453056

Predictive analytics for fast moving item using nonlinear regresssion models

Similar Items