GRA 6753 Operations Management
GRA 6753 Operations Management
Operations management and logistics require a good knowledge of both methods and concepts for optimizing cost and service level. In this course, through learning predictive and prescriptive methods and models, students will learn about operational decisions in logistics and their impacts.
The emphasis will be on various methods for optimising supply chains, and how they can be implemented in the real world.
To identify, interpret and analyze various cases of optimization, planning and decision making in the context of supply chain management
- To formulate and solve optimization decision making models in Excel Solver
- To analyse capacity and find bottlenecks in sequential processes
- To predict future demand and sales in supply chains
- To formulate, create and optimize aggregate plans for production planning
- To generate pricing and overbooking plans for multiple market segments
- To create and optimize inventory control plans for cycle and safety stock
- To understand the role of decision support systems in Supply Chain Management
- To appreciate and understand possibilities and limitations of optimisation models
- To understand the day to day challenges of logistics and supply chain management
- To understand the use of models in business analytics, in particular with regard to operational decisions
- To reflect on the limitations of models used in the course
- Introduction to operations management and optimization
- Understanding the supply process and capacity evaluation process
- Estimating and reducing labour cost:
- A process perspective
- Batching and flow interruptions:
- Optimising the flow through a supply chain:
- Forecasting
- Requirements of forecasting
- Static forcasting
- Adaptive forecasting
- Cycle stock
- Inventory management with multiple products.
- Discount schemes
- Joint optimisation for supplier and customer
- Safety stock
- Optimal service levels
- Aggregate planning
- Pricing and revenue management
The course is mainly delivered in a learning-by-doing manner. Students will make different models in Excel during the class and do exercises on extensions of those models outside of the class.
It is necessary that the students have a computer with them (with Excel installed on it) during the lectures.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Covid-19
Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.
Teaching
Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.
It is necessary that the students are familiar with the basics of Microsoft Excel, especially, how to enter and copy formulas and use relative and absolute addresses.
Basic Excel courses are offered at BI, but the students can also find free online tutorials including videos and exercises here: https://excelexposure.com/lesson-guide/.
| Assessments |
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Exam category: Submission Form of assessment: Written submission Invigilation Weight: 100 Grouping: Individual Support materials:
Duration: 5 Hour(s) Exam code: GRA67531 Grading scale: ECTS Resit: Examination when next scheduled course |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.
The exam is digital, majorly carried out on Excel.