Data Warehouse Design with Kimball Method: Case Study of Fahrenheit Manufacturing Systems

Authors

  • Widianty Widianty Bina Nusantara University

DOI:

https://doi.org/10.21512/comtech.v6i4.2200

Keywords:

Kimball method, datawarehouse design

Abstract

Competition in the business world that increasingly stringent requires management to make decisions accurately and quickly. It is also felt by Farhenheit as entrepreneurs. To achieve thier forte in competing in the
global competition, he needs systems like strategic decision makers which is up-to-date reliable and fast. Then the solution of area manufatur datawarehouse design is necessary to support the above objectives,i.e. : the right and fast decisions. However, Fahrenheit only has ERP as their core system currently and they do not have management support system. They have some difficulty to understand some problems and do better analysis. There are some datawarehouse development methodologies. Methods that will be used for the development of datawarehouse design is Kimball Method. Kimball method was chosen because of its development process that follows the business process is very suitable for the development of datawarehouse gradually to a company.
Kimball's method gives a mart for the related business processes.This study is conducted by interview and survey from several senior managers and directors in Fahrenheit to know about their requirement and how they do analysis currently.

Dimensions

Plum Analytics

References

Breslin, Mary. (2004). Datawarehousing Battle of The Giant: Comparing the Basic of the Kimball and Inmon Models . Business Intelligent Journal, 9 (1).

Kim, Aekyung., Kim, Kyuri. (2012). Business Process Warehouse For Manufacturing Collaboration. 41th International Conference on Computers & Industrial Engineering.

Kimball, R., Margy, R. (2002). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modelling. Second edition. New York: John Wiley & Sons.

Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., & Becker, B. (2008). The Data Warehouse Lifecycle Toolkit. Second edition. New York: John Wiley & Sons.

Marketa, H., Hana. (2013). Business Intelligence and Implementation in a Small Enterprise. Journal of System Information, 4(2):1804-2724.

Saxena, S., Mathur, S. (2014). A Lifecycle based Testing of Data Warehouse. International Journal of Advanced Research in Computer Science and Software Engineering, 4(12), 518-523.

Downloads

Published

2015-12-01

Issue

Section

Articles
Abstract 888  .
PDF downloaded 491  .