IS421
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Enterprise Analytics for Decision Support
Course (UG/PG)
Undergraduate
Offering Unit/Department
Course Description
In a fast-paced business world, enterprise systems must adapt dynamically to environmental uncertainties. Existing plans and schedules need to be constantly updated to accommodate new requests and events under severe time constraints. Such requirements are becoming increasingly common in the service industry (transport and logistics, health-care, hospitality, to name a few).
In this course, we discuss the inner working of decision analytics embedded in enterprise systems for managerial decision making and decision support. Students will acquire skills for understanding, modeling and solving such decision problems. We will cover both foundational as well as emerging methodologies. This course, together with other “enterprise” courses (such as EI, EIS and EBS) provides a holistic picture on the functioning of information systems in intelligent enterprises today and into the future.
It is preferred that students are familiar with Excel (particularly the use of solver and sensitivity analysis) and have background on Analytics (eg. have taken ANLY104).”
In this course, we discuss the inner working of decision analytics embedded in enterprise systems for managerial decision making and decision support. Students will acquire skills for understanding, modeling and solving such decision problems. We will cover both foundational as well as emerging methodologies. This course, together with other “enterprise” courses (such as EI, EIS and EBS) provides a holistic picture on the functioning of information systems in intelligent enterprises today and into the future.
It is preferred that students are familiar with Excel (particularly the use of solver and sensitivity analysis) and have background on Analytics (eg. have taken ANLY104).”
Course Learning Outcomes
Master the process to think about resource management problems arising in enterprises (both government and industry), and frame these problems as optimization problems.
Formulate mathematical models for these optimization problems, building on what has been briefly covered in CAT (IS102).
Apply computationally efficient methods to design and implement solutions for such problems, building on what has been covered in Computational Thinking (IS103).
As an SMU-X course, use the learnt skills to solve a problem sponsored by a real company.
Grading Basis
GRD - Graded
Course Units
1