| Code | QUA350 |
| School | EBSL |
| Module Level | 3 |
| Module Code | QUA350 |
| Class | GE|EL|MA|MI| |
| Availability | DE|SA| |
| Semester | 1 |
| Credits | 15 |
| ECTS Credits | 7.5 |
| Contact Hours | 48 |
| Self Study Hours | 102 |
| Course Aims | Businesses operate in an uncertain environment and to enable optimal planning forecasts for their future requirements information is needed as to how uncertain factors, such as customer demand, are likely to vary. There are many instances where a 'best guess' approach has produced significant inaccuracy with the resulting impact on the business planning process. What is required is an objective, method based approach to produce such forecasts. This module introduces some of the major techniques used in business today and builds on the student knowledge base achieved through the study of the pre-requisite module listed above. |
| Course Content | The subject matter covered in this module will endeavour to focus on the most widely utilised techniques in this area: - Basic Forecasting Tools (graphical representation of data, basic statistics commonly used in forecasting)
- Techniques for time series data (decomposition methods, methods of exponential smoothing)
- Techniques for causal / explanatory data (single variable regression, multiple regression)
- Issues in the collection of forecasting data (how data is collected; data reliability)
- Long-term Forecasting (cycles versus long-term trends)
- Qualitative & Judgemental Forecasting (issues of accuracy, dealing with bias), Delphi Forecasting, Historical Analogy, Expert Judgement Approaches)
- Use of Forecasting in Practice (practical cases)
- The Uses, Advantages, and Limitations of Forecasting.
The listing above indicates the subject matter dealt with during the course. The allocated workshop periods are essential to the working of this material. Lecture periods are used to present and discuss the underlying concepts dealt with in the class notes supplied. Selected topics are discussed in detail to establish the underlying principles of what is being done and other individual ones enlarged upon as and when the need for this arises.
Core Skills ContentThe teaching and learning strategy has been designed to help students in developing the following skills - Knowledge - Has a detailed knowledge of the area of Business Forecasting and an awareness of the provisional nature of this knowledge
- Cognitive - Can critically evaluate evidence to support conclusions and recommendations made in Business Forecasting
- Problem Solving - Can identify key areas of complex problems in the field of Business Forecasting and confidently apply appropriate methods for their resolution
- Can communicate effectively, and report results of findings in a detailed, coherent and professional manner in the field of Business Forecasting
- Autonomy - Can act autonomously, with minimal supervision within agreed guidelines
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| Learning Outcomes | A12, B9, B10, B11, B12, C11, C15, D9, D10 |
| Presentation | The teaching and learning strategy combines classroom based sessions, computing sessions and self-directed learning. The classroom sessions will be used to discuss key concepts in choosing and implementing forecasting methods. The computing workshops will permit students to learn by implementation. Additional problems will be provided for students to work under self-direction. |
| Reading Recommended | Anderson, Sweeney and Williams (2004) An Introduction to Management Science - Quantitative Approaches to Decision Making, South Western College Publishing.
Silver, M. (1997) Business Statistics, 2nd Edition, McGraw-Hill Publishing Company.
Useful Website
http://www.ibforecast.com (Website of the Institute of Business Forecasting) |
| Reading Required | Class Lecture notes supplied.
Makridakis, S. & Wheelwright, S.C., & Hyndman, R.J. (1998) Forecasting Methods and Applications, 3rd Edition., John Wiley and Son Ltd. |
| Asseseement Methods | Assessment will be based entirely on coursework for which three equally weighted assignments will be set highlighting different types of business data from which forecasts are to be produced and reported on. |