Please use this identifier to cite or link to this item: https://bspace.buid.ac.ae1234/1248
Title: A Study on Applying Data Mining in Airline Industry for Demand Forecasting by predicting Item Criticality
Authors: KARAM, ZAINAB HAMDAN
Keywords: inventory forecasting
data mining
classification algorithm
supply chain
Issue Date: Mar-2018
Publisher: The British University in Dubai (BUiD)
Abstract: Organizations inventory forecasting plays an important role for supply chain management. It is very important for an organization to be able to identify the inventory demand required in future and this can be achieved by using the data stored in the company’s data warehouse and with the help of data mining, future inventory demand can be predicted using specific data mining techniques. Several forecasting techniques have been developed for different businesses and each has its own advantages and disadvantages. In this research, the focus is in applying data mining technique to predict the item criticality for Expandable items (E-Class) which will support the organization to plan future demand. This research is highlighting the use of data mining – predictive analysis using specific data mining classification methodologies to predict item criticality. This report is structured as following: introduction, Literature Review, Experimentation, Data Understanding, Data Preparation, Methodology, Results & Finding, Discussion and a conclusion.
URI: https://bspace.buid.ac.ae1234/1248
Appears in Collections:Dissertations for Informatics (Knowledge and Data Management)

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