Performance Analysis of Manufacturing Systems
Performance Analysis of Manufacturing Systems examines stochastic network theory to develop performance models of modern manufacturing systems.
Summary
Recent recorded grades — Fall 2014: 3.62 GPA, 84.6% A/AB (n=13 letter grades); Fall 2015: 3.75 GPA, 85.0% A/AB (n=20 letter grades); Spring 2018: 3.62 GPA, 75.0% A/AB (n=12 letter grades).
What you’ll be able to do
Model a variety of manufacturing problems as stochastic models using Markov Chain and Process theory
Identify the basic assumptions underlying stochastic models and understand what can happen when these assumptions do not hold
Apply queueing theory to model manufacturing systems
Apply the line balancing method for assembly systems design
Perform cost analysis for manufacturing systems
Apply the analytical approaches of performance analysis for manufacturing systems to real industry cases
Apply advanced Markov process method to solve complicated performance evaluation problems encountered in manufacturing production systems
Source: UW Guide 1· UW Guide 2
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course map(I SY E 624 or STAT/I SY E/MATH/OTM 632) and (COMP SCI 200, 220, 300, 301, 400, or placement into COMP SCI 300), graduate/professional standing, or member of Engineering Guest Students
- take one
- COMPSCI 200 · unspecified
- COMPSCI 220 · unspecified
- COMPSCI 300 · unspecified
- 301
- COMPSCI 400 · unspecified
- placement into COMP SCI 300
- graduate/professional standing
- member of Engineering Guest Students
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- All of
- All of
- Any of
- Any of
- COMPSCI 200 · unspecified
- COMPSCI 220 · unspecified
- COMPSCI 300 · unspecified
- 301
- COMPSCI 400 · unspecified
- placement into COMP SCI 300
- Any of
- graduate/professional standing
- member of Engineering Guest Students
- All of
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Recent recorded grades — Fall 2014: 3.62 GPA, 84.6% A/AB (n=13 letter grades); Fall 2015: 3.75 GPA, 85.0% A/AB (n=20 letter grades); Spring 2018: 3.62 GPA, 75.0% A/AB (n=12 letter grades).
difficulty & workload
No workload feedback recorded.
Topics
- Stochastic network theory
- Performance models of modern manufacturing systems
- Queueing theory
- Line balancing method for assembly systems
Skills
- Modeling manufacturing problems as stochastic models using Markov Chain and Process theory
- Applying queueing theory to model manufacturing systems
- Applying the line balancing method for assembly systems design
- Performing cost analysis for manufacturing systems
- Applying advanced Markov process methods to solve performance evaluation problems
Grades
Latest available · Spring 2018— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2018
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "9709f8dafcf50977ae9903a6e3e8ae36b51e254c4f981b4e114be82175c07230",
"requirements_status": "needs_review",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}