Inventory record analysis using deviation analysis / Muda Wali Abdullah

Abdullah, Muda Wali (2015) Inventory record analysis using deviation analysis / Muda Wali Abdullah. Degree thesis, Universiti Teknologi MARA Cawangan Melaka.


This project is about the analysis of inventory records based on data mining technique which is deviation analysis. Deviation analysis refers to the identifying of anomalies which are different things from normal. Every retail store are not excluded from suffering inventory loss which could cause to inventory shrinkage. Retail store, Koperasi Perlop 2 had difficulties in identifying whether there are item missing or not and which item has been missing due to the actual quantity of items are not updated. Deviation analysis is used to identifying unusual pattern between inventory and sale data in order to detect the missing items and to generate knowledge for future prediction. The aim of this project is to study the inventory and sale records pattern using deviation analysis technique. To achieve the project aim, there are three objectives need to be accomplished. First objective are to gather information as well as data regarding the inventory and sale records. Second objective is to analyze the inventory records using deviation analysis and the final objective is to manage the analyzed inventory data into database. The methodology used in this project consists of four phases which are Problem Identification and Data Gathering, Analysis, Design and Implementation. The outcome from this project are the pattern of analyzed data by deviation analysis and anomalies data related to missing items are detected.


Item Type: Thesis (Degree)
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Abdullah, Muda Wali
Subjects: Q Science > QA Mathematics > Mathematical statistics. Probabilities
Q Science > QA Mathematics > Analysis
Divisions: Universiti Teknologi MARA, Melaka > Jasin Campus > Faculty of Computer and Mathematical Sciences
Programme: Bachelor of Information Technology (Hons) Information Systems Engineering
Keywords: Inventory record analysis; Deviation analysis; Data mining technique
Date: 2015
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