Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2019 258880 Demand Forecasting & Inventory Management 1 3.0 3 ★ Shu-Kai Fan IMBA
IMFI 6 0 IMBA & IMFI

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2019	258880	Demand Forecasting & Inventory Management	1	3.0	3	★	Shu-Kai Fan	IMBA IMFI	6	0	IMBA & IMFI

Course Plan：

Instructor Shu-Kai Fan

Email morrisfan@ntut.edu.tw

Last Updated 2019-01-04 13:58:24

Course Objective Analyzing time-oriented data and forecasting future values of a time series are among the most important problems that analysts face in many fields, ranging from finance and economics to managing production operations, to the analysis of political and social policy sessions, to investigating the impact of humans and the policy decisions that they make on the environment. This course is specially designed for graduate students who are focused on a few specific forecasting techniques. This course will address time series modeling, regression analysis, exponential smoothing methods, autoregressive integrated moving average models, transfer functions and intervention models, and other related forecasting topics.

Course Schedule 1 Process monitoring (SPC) vs. process control (EPC/APC) Chapter 1 2 Process monitoring (SPC) vs. process control (EPC/APC); Chapter 1 and Chapter 2 3 ARIMA (Box-Jenkins) time series models Chapter 3 4 ARIMA (Box-Jenkins) time series models Chapter 3 5 ARIMA (Box-Jenkins) time series models Chapter 3 6 ARIMA (Box-Jenkins) time series models Chapter 3 7 Transfer function (TF, input/output) models Chapter 4 8 Transfer function (TF, input/output) models Chapter 4 9 Midterm Exam in class 10 Review of Midterm Exam 11 Optimal stochastic controller design Chapter 5 12 Optimal stochastic controller design Chapter 5 13 EWMA controllers and the run-to-run (R2R) control problem Chapter 7 14 EWMA controllers and the run-to-run (R2R) control problem Chapter 7 15 Recursive estimation & self-tuning (ST) quality control Chapter 8 16 Recursive estimation & self-tuning (ST) quality control Chapter 8 17 Term Project Presentations in class

Evaluation and grading policy Grading Policy: Homework (30%), Term Project (60%) due in part throughout the semester, participation (10%). A set of journal papers & class notes might be provided. We will also make use of the SAS, EXCEL, Matlab, and Minitab software packages. Additional book references can be accessed at the University Library.

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Textbook & References [Textbook] E. del Castillo, Statistical Process Adjustment for Quality Control, John Wiley, 2002. [References] (1) Box, G. E. P., Jenkins, G. M., and Reinsel, G. C., Time Series Analysis, Forecasting, and Control, 4th ed., 2008. (2) Montgomery, D. C., Forecasting & Time Series Analysis, 2nd ed., 1990. (3) Box, G. E. P., and Luceo, A., Statistical Control by Monitoring and Feedback Adjustment, 1997.

The access to curricular consultation

Note
　

Instructor	Shu-Kai Fan
Email	morrisfan@ntut.edu.tw
Last Updated	2019-01-04 13:58:24
Course Objective	Analyzing time-oriented data and forecasting future values of a time series are among the most important problems that analysts face in many fields, ranging from finance and economics to managing production operations, to the analysis of political and social policy sessions, to investigating the impact of humans and the policy decisions that they make on the environment. This course is specially designed for graduate students who are focused on a few specific forecasting techniques. This course will address time series modeling, regression analysis, exponential smoothing methods, autoregressive integrated moving average models, transfer functions and intervention models, and other related forecasting topics.
Course Schedule	1 Process monitoring (SPC) vs. process control (EPC/APC) Chapter 1 2 Process monitoring (SPC) vs. process control (EPC/APC); Chapter 1 and Chapter 2 3 ARIMA (Box-Jenkins) time series models Chapter 3 4 ARIMA (Box-Jenkins) time series models Chapter 3 5 ARIMA (Box-Jenkins) time series models Chapter 3 6 ARIMA (Box-Jenkins) time series models Chapter 3 7 Transfer function (TF, input/output) models Chapter 4 8 Transfer function (TF, input/output) models Chapter 4 9 Midterm Exam in class 10 Review of Midterm Exam 11 Optimal stochastic controller design Chapter 5 12 Optimal stochastic controller design Chapter 5 13 EWMA controllers and the run-to-run (R2R) control problem Chapter 7 14 EWMA controllers and the run-to-run (R2R) control problem Chapter 7 15 Recursive estimation & self-tuning (ST) quality control Chapter 8 16 Recursive estimation & self-tuning (ST) quality control Chapter 8 17 Term Project Presentations in class
Evaluation and grading policy	Grading Policy: Homework (30%), Term Project (60%) due in part throughout the semester, participation (10%). A set of journal papers & class notes might be provided. We will also make use of the SAS, EXCEL, Matlab, and Minitab software packages. Additional book references can be accessed at the University Library.
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Textbook & References [Textbook] E. del Castillo, Statistical Process Adjustment for Quality Control, John Wiley, 2002. [References] (1) Box, G. E. P., Jenkins, G. M., and Reinsel, G. C., Time Series Analysis, Forecasting, and Control, 4th ed., 2008. (2) Montgomery, D. C., Forecasting & Time Series Analysis, 2nd ed., 1990. (3) Box, G. E. P., and Luceo, A., Statistical Control by Monitoring and Feedback Adjustment, 1997.
The access to curricular consultation
Note