Dokuz Eylül University Information Package / Courses Catalog

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type Of Course	D	U	L	ECTS
QMT 3001	BUSINESS FORECASTING	ELECTIVE	4	0	0	6

Offered By

BUSINESS ADMINISTRATION

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR AYSUN KAPUÇUGIL IKIZ

Offered to

International Business and Trade
International Trade and Business (English)
BUSINESS ADMINISTRATION

Course Objective

The course aims to provide students the core issues of generating and implementing business forecasts. Focus is on modern statistical methods that are widely used to generate business forecasts. Specific applications to business include forecasting sales, production, inventory, macroeconomic factors such as interest rates and exchange rates, and other aspects of both short- and long-term business planning.

Learning Outcomes of the Course Unit

1	Have a knowledge understanding of the use of basic tools of forecasting and basic time series analysis techniques.
2	Have a knowledge understanding of the significance of data analysis and model selection criteria.
3	Demonstrate a good understanding of averaging-based forecasting and smoothing techniques, and modeling time series with regression.
4	Apply their skills and knowledge to forecast real economic, business and financial time series using a statistical package program.
5	Demonstrate their ability to analyze time series in the business environment using the appropriate methods with a high level of confidence.
6	Have experience developing a complete forecast and apply their skills in interpreting computer output and report writing.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

IBS 4489 - ROAD TRANSPORTATION

Recomended Optional Programme Components

None

Course Contents

Week	Subject	Description
1	Introduction to Forecasting
2	An overview of Basic Statistical Concepts and Forecasting Techniques
3	Exploring Time Seris Data Patterns
4	Forecasting Process and Accuracy Measures
5	Moving Averages and Smoothing Methods
6	Moving Averages and Smoothing Methods
7	Time Series Decomposition
8	Time Series Decomposition
9	Simple Regression
10	Multiple Regression Analysis
11	Multiple Regression Analysis
12	Regression with Time Series Data
13	Judgmental Forecasting and Forecast Adjustments
14	Managerial Implications of Forecasts

Recomended or Required Reading

1.Text Book:
Business Forecasting. John E. Hanke and Dean W. Wichern, 9th Edition (2014) or later, Pearson Education.

2. References
Business Forecasting. J. Holton Wilson and Barry Keating, 6th Edition, Irwin/McGraw-Hill, 2009.
Forecasting Methods and Applications. Spyros Makridais, Steven C. Wheelwright and Rob. J. Hyndman, 3th Edition or later. John Wiley and Sons Inc.

3. Software:
Minitab
IBM SPSS
Excel

4. Calculator:
Students will need a scientific calculator for various calculation problems in and out of class, and during exams.

Planned Learning Activities and Teaching Methods

1. Lectures
Class lecture is highly interactive and format is direct. The instructor prompts students for response to questions posed and solicits their thoughts on issues discussed. Lectures will focus on the transfer of basic tools of forecasting and basic time series analysis techniques where comprehension is substantially enhanced by additional elaboration and illustration.
2. Text Readings
Each week, readings from the text will introduce new forecasting concepts and quantitative techniques. Readings provide both the theoretical background and technical skills necessary to generate and interpret business forecasts at an advanced level.
3. Review Sessions and Class Discussions
Review sessions will be handled frequently by the instructor. In-class exercises are the basis in these sessions and individual participation by students in classroom discussion is strongly encouraged.
4. Computer Applications
In the laboratory component, a particular statistical package will be introduced to perform analyses of real economic, business and financial time series data.

Assessment Methods

SORTING NUMBER	SHORT CODE	LONG CODE	FORMULA
1	MT	Midterm
2	TP	TermProject
3	FCG	FINAL COURSE GRADE	MT * 0.40 + TP * 0.60

*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

The midterm exam consists of questions that will measure the ability to analyze and interpret each stage of the estimation process within the framework of the topics covered in the course.

The term project is a group effort to implement the steps of the forecasting process from end-to-end using the (hypothetical) data of an organization. The guidelines and template files for the term project are presented to students during the course.

Assessment Criteria

In this course, each student is working on a forecast project as a team member. The term project aims to improve the students' problem-solving and written communication skills. Depending on their preferences, students form their teams. Maximum number of team members are limited to 5.

The team is required to prepare a report that analyzes past data, compare several forecast methods, present forecasts with the best method, and explain managerial implications of these forecasts and errors of the best forecasting model. For completing this project, real datasets regarding specific industries, guidelines and a template file are supplied by the instructor and the students are informed about the project requirements.

The project work is requested to be completed by adhering the following structural elements (including 22 traits in total):

* Introduction (company background, forecasting problem, important factors affecting the primary variable in the problem)

* Preliminary analysis of past data (statistical summaries and graphs, exploration of patterns in time series data)

* Analysis of different forecasting models
- Implementing averaging-based methods (Naïve Method, Moving Average Method, Single Exponential Smoothing, Holt s Method, Winter s Method), checking diagnostics, and model selection
- Implementing decomposition methods (identifying decomposition model structure, interpretation of components, checking diagnostics, forecasting next time periods with best performing model)
- Implementing regression analysis (Model Specification, Model Building, checking Diagnostics, Forecasting with Regression model

* Model selection and forecasting (a summary comparison table, a discussion of best model and forecast of next year's sales)

* Managerial Implications (explaining what the generated forecasts imply for the business and how these forecasts can facilitate the decision making process.)

The report quality of the term project is also assessed based on the following six traits: Logic & Organization, Language, Spelling and Grammar, Development of Ideas, Purpose and Audience, Format.

The 22 structural requirements (which account for 80% of the score) and the six report quality attributes (20% of the score) combine to determine the overall project grade.

In project work, it is critical that all team members contribute together as well as provide quality content. The Peer Evaluation form is used to assess each team member's individual contribution by allowing them to evaluate both their own performance and that of other team members. Based on these peer evaluation scores, a multiplier coefficient is calculated to reflect each group member's contribution to the project. Therefore, students get their term project grade individually according to their contributions.

Language of Instruction

English

Course Policies and Rules

1. It is obligatory to attend at least 70% of the classes.
2. Violations of Plagiarism of any kind will result in disciplinary steps being taken.
3. Students are required to have their own calculator for this course. It will not be allowed to share a calculator during exams. Cellular phones cannot be used as a calculator during an exam.

Contact Details for the Lecturer(s)

Prof. Dr. Aysun KAPUCUGIL IKIZ
aysun.kapucugil@deu.edu.tr
Office No: 125/A
Office Phone #: 0.232.3018226

Office Hours

To be announced.

Work Placement(s)