An important goal of this class is to arm students with a fundamental understanding of next-generation tools and approaches to business process management, analytics, and improvement.  These include:
1.	Notation: BPMN 2.0 – the notation that has emerged as the standard for BPM. Students will gain hands-on experience with the notation for both human and machine-readable applications. Evidence: Homework assignments and exams testing knowledge of notation.
2.	Simulation: Since BPMN is machine-readable, the simulation of process models has become never been more accessible. The course will involve gaining the ability to simulate process models in Signavio. Evidence: Assignment testing simulation skills as applied to process redesign. In-class cases using simulation software.
3.	Mining: Process mining is a next-generation technique for process discovery and other applications. Students will gain hands-on experience using Celonis, a popular industry tool for process mining. Evidence: Homework assignments and exams testing mining skills. In-class cases using mining software.
4.	Execution and Automation: Students will understand concepts regarding the automation of process components. We will discuss recent advances in robotic process automation and tools such as UiPath. Evidence: Case study building process automation tool.
5.	Analysis: Students will understand how to assess the performance and efficiency of business processes. Analytic methods such as cycle time analysis, queuing, and capacity analysis will be covered. Evidence: Homework assignments and exams testing analytics skills.

1. BPM Notation
a.	Basic process elements 
b.	Level 1 and Level 2 models
c.	The decision model
2. Process Analysis
a.	Introduction to lean and six sigma concepts
b.	Cycle time analysis
c.	Cost and capacity of processes
d.	Queueing analysis
3. Process Simulation and Redesign
a.	Implementing process simulations
b.	Redesign heuristics
c.	Simulation as a tool for experimentation and testing
4. Process Mining
a.	Concepts of process mining and data requirements
b.	Overview of algorithms and approaches
c.	Software for conducting process mining
d.	Building dashboards and monitoring performance
e.	Conformance checking
5. Process Automation
a.	Identifying automation opportunities
b.	Introduction to RPA

The University of Georgia has an Honor Code and Academic 
Honesty Policy that governs student academic performance both 
in and out of the classroom.  The Honor Code appears in both 
the Student Handbook and in the UGA Catalog.  The 
responsibilities of students, instructors, and judiciary 
personnel are spelled out in the Honor Code, as are potential 
penalties for plagiarism and cheating.  As a student at UGA, 
you are expected to abide by the Honor Code for this class and 
for all others in which you are enrolled.  Please understand 
that portraying others' work as your own will result in 
appropriate sanctions.

All academic work must meet the standards contained in A 
Culture of Honesty, the University's policy and procedures for 
handling cases of suspected dishonesty, can be found at 
www.uga.edu/ovpi.  Each student is responsible to inform 
themselves about those standards before performing any academic 
work.