IE301 Production Planning I
IE301 Production Planning I
Syllabus | International University of Sarajevo - Last Update on Mar 03, 2026
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Industrial Engineering
TBA
Course Lecturer
Course Objectives
This course equips students with knowledge of fundamental issues in production and inventory planning and control in manufacturing firms, at the same time, developing the students’ modeling and analytical skills. The course is targeted toward engineering students planning careers in technical consulting, business analysis in operations, logistics and supply-chain, positions in general management and future entrepreneurs. The students will be able to apply the techniques using MS EXCEL.
Learning Outcomes
After successful completion of the course, the student will be able to:
Course Materials
Required Textbook
“Production and Operation Analysis”, S. Nahmias, McGraw Hill, 2009
Additional Literature
Teaching Methods
Class discussions with examples
Active tutorial sessions for engaged learning and continuous feedback on progress
Computer analysis and interpretation
Weekly Topics
| Week | Topic | Readings / References |
|---|---|---|
| 1 | Introduction | i |
| 2 | Strategy and cometition | 1.1, 1.2, 1.3, 1.4 |
| 3 | Learning and experience curves. Make or bay decision | 1.9, 1.10, 1.11 |
| 4 | Forecasting | 2.1. - 2.7 |
| 5 | Forecasting, methods, Quiz 1 | 2.8, 2.9 |
| 6 | Aggregate planning/Constant workforce case | 3.1, 3.2, 3.3 |
| 7 | Midterm exam | |
| 8 | Aggregate planning/Zero inventory case | 3.4, 3.5, 3.6 |
| 9 | Inventory control subject to known demand | 4.1, 4.2, 4.3, 4.4 |
| 10 | The EOQ model | 4.5, 4.6 |
| 11 | Quantity doscount models | 4.7 |
| 12 | Inventory control subject to uncertain demand | 5.1, 5.2 |
| 13 | The newsboy model, Quiz 2 | 5.3 |
| 14 | Lot size reorder point system | 5.4 |
| 15 | Preparation for Final Exam |
Course Schedule (All Sections)
Office Hours & Room
Assessment Methods and Criteria
Assessment Components
Final Exam
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Active tutorials and Homeworks
AI: Not AllowedAlignment with Learning Outcomes : 1 2 3 4
Midterm
AI: Not AllowedAlignment with Learning Outcomes : 1 2
Quiz
AI: Not AllowedAlignment with Learning Outcomes : 1 3
IUS Grading System
| Grading Scale | IUS Grading System | IUS Coeff. | Letter (B&H) | Numerical (B&H) |
|---|---|---|---|---|
| 0 - 44 | F | 0 | F | 5 |
| 45 - 54 | E | 1 | ||
| 55 - 64 | C | 2 | E | 6 |
| 65 - 69 | C+ | 2.3 | D | 7 |
| 70 -74 | B- | 2.7 | ||
| 75 - 79 | B | 3 | C | 8 |
| 80 - 84 | B+ | 3.3 | ||
| 85 - 94 | A- | 3.7 | B | 9 |
| 95 - 100 | A | 4 | A | 10 |
Late Work Policy
Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.
ECTS Credit Calculation
📚 Student Workload
This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:
Lecture hours
30 hours ⏳ (15 week × 2 h)
Active tutorials
28 hours ⏳ (14 week × 2 h)
Quiz 1
16 hours ⏳ (2 week × 8 h)
Quiz 2
16 hours ⏳ (2 week × 8 h)
In-term exam study
20 hours ⏳ (1 week × 20 h)
Final exam study
40 hours ⏳ (2 week × 20 h)
150 Total Workload Hours
6 ECTS Credits
Course Policies
Academic Integrity
All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.
Attendance Policy
Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.
Technology & AI Policy
Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.
Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.
Communication Policy
All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [IE301] in the subject line.
Academic Quality Assurance Policy
Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
Learning Tips
Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.
Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.
Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.
Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.
Syllabus Last Updated on Mar 03, 2026 | International University of Sarajevo
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Referencing Curricula Print this page
| Course Code | Course Title | Weekly Hours* | ECTS | Weekly Class Schedule | ||||||
| T | P | |||||||||
| IE301 | Production Planning I | 3 | 2 | 6 | ||||||
| Prerequisite | MATH203 | It is a prerequisite to | IE302 | |||||||
| Lecturer | TBA | Office Hours / Room / Phone | Currently not available |
|||||||
| TBA | ||||||||||
| Assistant | Erna Omerašević | Assistant E-mail | ekeskinovic@ius.edu.ba | |||||||
| Course Objectives | This course equips students with knowledge of fundamental issues in production and inventory planning and control in manufacturing firms, at the same time, developing the students’ modeling and analytical skills. The course is targeted toward engineering students planning careers in technical consulting, business analysis in operations, logistics and supply-chain, positions in general management and future entrepreneurs. The students will be able to apply the techniques using MS EXCEL. |
|||||||||
| Textbook | “Production and Operation Analysis”, S. Nahmias, McGraw Hill, 2009 | |||||||||
| Additional Literature | ||||||||||
| Learning Outcomes | After successful completion of the course, the student will be able to: | |||||||||
|
||||||||||
| Teaching Methods | Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. Computer analysis and interpretation. | |||||||||
| Teaching Method Delivery | Teaching Method Delivery Notes | |||||||||
| WEEK | TOPIC | REFERENCE | ||||||||
| Week 1 | Introduction | i | ||||||||
| Week 2 | Strategy and cometition | 1.1, 1.2, 1.3, 1.4 | ||||||||
| Week 3 | Learning and experience curves. Make or bay decision | 1.9, 1.10, 1.11 | ||||||||
| Week 4 | Forecasting | 2.1. - 2.7 | ||||||||
| Week 5 | Forecasting, methods, Quiz 1 | 2.8, 2.9 | ||||||||
| Week 6 | Aggregate planning/Constant workforce case | 3.1, 3.2, 3.3 | ||||||||
| Week 7 | Midterm exam | |||||||||
| Week 8 | Aggregate planning/Zero inventory case | 3.4, 3.5, 3.6 | ||||||||
| Week 9 | Inventory control subject to known demand | 4.1, 4.2, 4.3, 4.4 | ||||||||
| Week 10 | The EOQ model | 4.5, 4.6 | ||||||||
| Week 11 | Quantity doscount models | 4.7 | ||||||||
| Week 12 | Inventory control subject to uncertain demand | 5.1, 5.2 | ||||||||
| Week 13 | The newsboy model, Quiz 2 | 5.3 | ||||||||
| Week 14 | Lot size reorder point system | 5.4 | ||||||||
| Week 15 | Preparation for Final Exam | |||||||||
| Assessment Methods and Criteria | Evaluation Tool | Quantity | Weight | Alignment with LOs | AI Usage |
| Final Exam | 1 | 40 | 1,2,3,4 | Not Allowed | |
| Semester Evaluation Components | |||||
| Active tutorials and Homeworks | 15 | 10 | 1,2,3,4 | Not Allowed | |
| Midterm | 1 | 30 | 1,2 | Not Allowed | |
| Quiz | 2 | 20 | 1,3 | Not Allowed | |
| *** ECTS Credit Calculation *** | |||||
| Activity | Hours | Weeks | Student Workload Hours | Activity | Hours | Weeks | Student Workload Hours | |||
| Lecture hours | 2 | 15 | 30 | Active tutorials | 2 | 14 | 28 | |||
| Quiz 1 | 8 | 2 | 16 | Quiz 2 | 8 | 2 | 16 | |||
| In-term exam study | 20 | 1 | 20 | Final exam study | 20 | 2 | 40 | |||
| Total Workload Hours = | 150 | |||||||||
| *T= Teaching, P= Practice | ECTS Credit = | 6 | ||||||||
| Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 27/03/2026 | |||||||||