Course Information
SchoolMechanical Engineering
Cycle / Level1st / Undergraduate
Teaching PeriodSpring
CoordinatorGeorgios Tagaras
Course ID20002161

Programme of Study: UPS of School of Mechanical Engineering

Registered students: 31
OrientationAttendance TypeSemesterYearECTS
Industrial ManagementElective Course belonging to the selected specialization (Elective Specialization Course)1055

Class Information
Academic Year2019 – 2020
Class PeriodSpring
Instructors from Other Categories
Weekly Hours4
Class ID
Course Category
Knowledge Deepening / Consolidation
Mode of Delivery
  • Face to face
  • Distance learning
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
Learning Outcomes
1.Understand the importance of forecasting 2.Understand the theory of different forecasting methods (exponential smoothing models, simple and multiple linear regression and non-linear regression models) 3.Classify exponential smoothing methods (Holt, Brown, Holt-Winter Gardner-McKenzie) by Pegel and by Gardner-McKenzie 4.Apply computer software (Data analysis, Solver, Minitab) to calculate model parameters 5.Evaluate forecast accuracy 6.Combine statistical and critical forecasting methods to solve business problems
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Importance of forecasting, forecasting categories, key forecasting steps. Data preparation: missing values - calendar adjustments - price indices - chi-squared goodness-of-fit test. Evaluating forecast accuracy. Confidence intervals for forecasting. Moving average methods. Exponential smoothing methods (Holt, Brown, Holt-Winter models). Exponential smoothing with correction parameter (Gardner-McKenzie model). Classification of exponential smoothing methods by Gardner-McKenzie and Pegel. Simple and multiple linear regression. Non-linear regression. Computer forecasting programs. Time series decomposition methods. Intermittent demand forecasting models (Croston, SBA). Critical methods: the Delphi method and the forecast by analogy method.
forecasting, timeseries analysis
Educational Material Types
  • Notes
  • Slide presentations
  • Book
  • Computer software
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Utilization of specialized computer software for problem solving. Posting of announcements, notes and information regarding the course at eLearning (
Course Organization
Reading Assigment752.5
Interactive Teaching in Information Center60.2
Written assigments120.4
Student Assessment
The final grade Β is a combination of the grades in the final written examination (T), the midterm examination (Π) and the project/homework (E) as follows: • If either Τ < 4,5 or (Τ+Π)/2 < 4, then the final grade is Β = (0,8)Τ. • In every other case the final grade is Β = max {(0,6)Τ + (0,3)Π + (0,2)Ε, (0,8)T}.
Student Assessment methods
  • Written Exam with Short Answer Questions (Summative)
  • Written Assignment (Summative)
  • Written Exam with Problem Solving (Summative)
Course Bibliography (Eudoxus)
1. Πετρόπουλος, Φ., Ασημακόπουλος Β., 2013. Επιχειρησιακές προβλέψεις, Εκδόσεις Συμμετρία. 2. Αγιακλόγλου, Χ., Οικονόμου Γ., 2004. Μέθοδοι Προβλέψεων και Ανάλυσης Αποφάσεων, Εκδόσεις Μπένου.
Additional bibliography for study
- Hanke, J., Wichern, D., 2009. Business Forecasting, 9th edition, Pearson Education Inc. - Makridakis, S., Wheelwright, S., Hyndman, R., 1998. Forecasting: Methods and Applications, 3rd edition, Wiley. - Mendenjall, W., Sincich, T., 1996. A second course in statistics: regression analysis, 5th edition, Prentice Hall. - Montgomery, D., Runger, G., Hubele, N., 2007. Engineering Statistics, Wiley.
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