Enterprise Resource Planning & Data Mining
企業資源規劃與資料探勘
Enterprise Resource Planning & Data Mining
企業資源規劃與資料探勘
Instructor: 徐立群 (LihChyun Shu, shulc@mail.ncku.edu.tw)
Office: 63323研究室
Course Description:
Enterprise resource planning (ERP) is already an indispensable part of modern e-Business. ERP systems track business resources—cash, raw materials, production capacity—and the status of business commitments: orders, purchase orders, and payroll. An ERP system covers many common functional areas, and they are typically grouped together as ERP modules, including financial accounting, management accounting, human resources, manufacturing, order processing, etc. In this course, we will look into core business processes and interactions between ERP modules that manage business processes.
With the increasing use of business software including ERP systems, many companies have accumulated huge amount of operational data, and it has been well recognized that discovering the hidden meaning behind these data (sometimes along with external data) will give business valuable competitive advantage. Data mining is the exploration and analysis of large quantities of data in order to discover valid, novel, potentially useful, and ultimately understandable patterns in data. In this course, students will learn cutting-edge data mining techniques. In addition, students will learn to use a commercial data mining software. By using the tool, students will be able to mine useful information from a few data sets.
Course Objectives:
Students will have a good understanding of the conceptual foundations of enterprise resource planning. Students will learn cutting-edge data mining technologies and applications and learn to apply them to a few practical problems.
Content Summary:
Accounting information systems: an overview
Introduction to business processes
Introduction to enterprise systems
Introduction to data-analytic thinking
Business problems and data science solutions
Introduction to predictive modeling
Fitting a model to data
Overfitting and its avoidance
Decision analytic thinking: what is a good model?
Text:
Foster Provost and Tom Fawcett. Data science for business, O’Reilly, 2013.
References:
M.B. Romney and P.J. Steinbart. Accounting Information Systems, Pearson International Ed.
R. Kalakota and M. Robinson. e-Business 2.0: Roadmap for success, Addison Wesley, 2000.
S. R. Magal and J. Word. Integrated business processes with ERP Systems, John Wiley & Sons, 2012.
Course Requirement:
Students are expected and required to attend most, if not all, classes. Random roll call will be conducted to check class attendance. Students are required to write their homework assignments themselves. Discussions among fellow students are allowed though.
Course slides:
SAP: Module and Business Overview
Accounting information systems: an overview
Overview of business processes
Evaluation (subject to change):
Quizzes/attendance: 10%
HWs 45%
Exams 45%