ÌìÃÀ´«Ã½

Lean Six Sigma Green Belt  (self-paced, online)

Next Course Offering: December 17th, 2025 to January 25th, 2026

Registration Deadline: TBD

The Green Belt course will provide a deeper understanding of Lean and Six Sigma tools within the context of the DMAIC methodology. This training enables participants to analyze and solve quality-related issues and teaches how to achieve process excellence through continuous improvement. For more background on LSS, click here.

Delivery format: This is an online, self-paced course with twelve 90-minute video lectures and an extensive take-home exam (which takes about 30-40 hours to complete). A Statistics refresher is available, and those who register for the Statistics refresher will get access to the statistics course materials 2 weeks before the Green Belt course starts.

Instructor: Mohammad Khasawneh

Credentials: At successful completion, you will earn the Green Belt Certification, and the Lean Six Sigma Green Belt badge will be issued. A ÌìÃÀ´«Ã½-issued course completion certificate is also available for successful participants. A grade of 70% or higher is required to pass the course and earn the certification and badge.

Who can take this course: This course has no prerequisites, although knowledge/experience/education in probability & statistics is required. Students without this background are strongly encouraged to consider the statistics refresher. This course is intended for all engineers as well as non-engineers, professionals, faculty, and students, and is appropriate for both technical and non-technical audiences. The Green Belt certification or similar training is a prerequisite for the Black Belt certification.

Registration deadlines:

• NOTE: Please be aware that after you register, it can take 1-5 days for you to be enrolled in the course if you are an external (non-BU) registrant. If it should happen that you are enrolled in the course late, we can make it up to you by giving you additional time to complete the course. 

ABOUT THE COURSE

This course is offered by the Systems Science and Industrial Engineering (SSIE) Department at ÌìÃÀ´«Ã½.  Pre-requisite statistics instruction is also offered. This course includes: 

• Twelve 90-minute pre-recorded lectures include soft copies of presentation slides. (A prerequisite statistics day of instruction, which is comprised of four additional 90-minute pre-recorded lectures, is available to those without experience with probability, statistics, and/or quality control.)
• A take-home examination with a 70-percent minimum passing grade within 4 weeks of the start of the course.
• Support via access to virtual office hours and email contacts to the professor and teaching assistant.
• An open-book take-home exam will be distributed by the end of the first week of the course.
• Allow 30-40 hours to complete the exam.
• The exam will be due by July 2, 2025, at 11:59 p.m.
•  A Micro-credential in the form of a Lean Six Sigma Green Belt digital badge and Green Belt Certificate will be provided to those who complete the course.

Open to

• Current ÌìÃÀ´«Ã½ and non-ÌìÃÀ´«Ã½ students
• ÌìÃÀ´«Ã½ alumni
• Members of industry, non-profit, and government agencies 

Requirements

• Knowledge/experience/education in probability & statistics is required.  Students without this background are strongly encouraged to consider the statistics refresher.
• ÌìÃÀ´«Ã½ students: Some examples of courses that fulfill this prerequisite include ISE 261, ISE 362, SSIE 505, and CSQ 112.
• A computer with a high-speed internet connection and audio. Microsoft Excel is also required.
• A trial version (free for 30 days) of the  (for statistical analysis) is provided at the start of the training program. Minitab is for all ÌìÃÀ´«Ã½ students.
• A headset with a microphone may be useful, but it is not necessary.

Statistics Refresher: Four 90-minute lectures

For those who do not have prerequisite coursework in statistics

• Session 1 - Course Introduction, Course Outline, Fundamentals of Problem Solving, Introduction to Statistics, Sampling Process, Introduction to Minitab
• Session 2 - Basic Statistics: Measures of Location, Measures of Variability, Data Visualization, Coefficient of Variation, Dot Plot, Histogram, Stem And Leaf, Box Plot
• Session 3 - Basic Statistics: Random Distribution, Variable Types, T-test, Z-test, Statistical Tables, Confidence Intervals for Mean, Confidence Interval for Proportions, Confidence Interval for Standard Deviation
• Session 4 - Advanced Statistics: Compare Means, Compare Variances, Compare Proportions, Rejection Region, Fail to Reject, Type 1 error and Type 2 error, Power and Sample Size, P-value

Green Belt: Twelve 90-minute lectures

Several case studies and applications of Lean Six Sigma concepts will be presented throughout the course. 

• Continuous process improvement (CPI) with emphasis on both lean and six sigma concepts and methodologies
• Lean concepts and their applications, such as 5S, waste reduction, value stream mapping, and error proofing
• DMAIC (Define, Measure, Analyze, Improve, and Control) to solve issues and transition CPI projects from one phase to another
• Basic statistical analysis methods, tools, and control charts are used to determine key relationships between inputs and outputs.
• The integration of both lean and Six Sigma for achieving data-driven process improvement results
• Team dynamics and leadership are required to provide effective and successful projects.

Course Outline

• Session 1 - Course Introduction, Meet the Instructor, Training Objectives, Training Outline, Fundamental Concepts, Flow Charts, Process Maps
• Session 2 - Pareto Charts, Cause, and Effect Diagram or Fishbone (Ishikawa), Optimizing Process Flow, Bottlenecks, Forecasting Demand
• Session 3 - Capacity, Wait Times, Continuous Improvement, Kaizen, Deming, PDSA Cycle, Quality Circles, Quality Certifications and Awards - ISO 9000 and Malcolm Baldrige, Statistical Process Control
• Session 4 - Lean, Six Sigma, TPS, Value Added vs. Non-Value Added, 7 Wastes/Muda (TIM WOOD), Production Systems (Craft, Mass, Lean), Lean Tools, House of Lean, Push/Pull, Visual Control, Kanban
• Session 5 - Time & Motion, Takt Time, Throughput, Value-Added Time, Heijunka (Leveling), Standardized Work, Jikoda & Andon, Mistake Proofing (Poke-Yoke), SMED, 5Ss
• Session 6 - Spaghetti Diagram, Value Stream Mapping, SIPOC, Projects, and Case Studies, Intro to Six Sigma
• Session 7 - Six Sigma, Lean Six Sigma, Kaizen Event, Six Sigma Model, Key Players in Six Sigma Program – Role and Responsibilities, DMAIC, Project Management, Decision Criteria, and Decision Matrix
• Session 8 - Project Scoping, Project Charter, Project Planning, 7 Basic Quality Tools, 7 New Quality Tools, Critical to Quality, Data Collection
• Session 9 - Data Collection Methods, Sampling Methods, Basic Statistics, Measurement System Analysis, Gauge R&R
• Session 10 - Process Capability, Benchmarking, Correlation Coefficient, Regression Analysis
• Session 11 - Hypothesis Testing, Design of Experiment, ANOVA
• Session 12 - FMEA, House of Quality, Quality Function Deployment, Control Charts: X-bar Chart, R Chart, Process Control Plan, Case Studies on DMAIC

Instructor

Mohammad T. Khasawneh

SUNY Distinguished Prof; School Director; Healthcare Systems Engineering / Health Systems / Manhattan Graduate Program Director; SUNY Distinguished Professor; Director

School of Systems Science and Industrial Engineering; Watson Institute for Systems Excellence (WISE)

mkhasawn@binghamton.edu
607-777-4408
EB R18

Background

Mohammad Khasawneh is a SUNY distinguished professor and chair of Systems Science and Industrial Engineering at ÌìÃÀ´«Ã½. He received his PhD in industrial engineering from Clemson University in August 2003 and his BS and MS in mechanical engineering from Jordan University of Science and Technology, in 1998 and 2000, respectively. 

Khasawneh’s research is focused on healthcare systems engineering, operations management, and data science. He serves as the director for the Watson Institute for Systems Excellence (WISE), an institute for advanced studies that generates $2.5-3 million in research funds annually. In addition, he is the founding director of the Healthcare Systems Engineering Center, an organized research center (ORC) at ÌìÃÀ´«Ã½.

Since 2003, Khasawneh has been leading a wide spectrum of projects with U.S. hospital systems that focus on applied research in the area of healthcare systems engineering. More specifically, his research is focused on the novel application of systems engineering to transform healthcare systems into high-performance environments that produce better patient outcomes at lower costs. His work is applied in ways that lead to optimal healthcare, including more efficient use of hospital resources; better outpatient scheduling; streamlined patient flow; improved patient satisfaction; reduced hospital-acquired conditions (such as infections and patient falls) through predictive analytics; and improved clinical, operational and financial performance using advanced data science methods.His health systems engineering center generates over $1 million annually in sponsored research from various healthcare and hospital systems.

Building on a successful research program and partnerships with health systems around the country and an academic concentration/minor, at the graduate/undergraduate levels, Khasawneh developed a 12-month Executive Master of Science with a Health Systems Concentration, which has been offered in Manhattan since 2013. He has also been instrumental in developing a new MS degree program in Healthcare Systems Engineering.

Over the years, Khasawneh presented his research at various national and international conferences, including China, India, Mexico, Jordan, Korea, Thailand, Japan, Turkey, Indonesia, and Canada. His research activities thus far have led to 60-plus refereed journal articles, 120-plus conference articles, one patent and three new invention disclosures. In addition, his sponsored research efforts thus far have resulted in over $15 million in external funding and over $39 million in software/equipment grants.

In 2006, Khasawneh received a U.S. Air Force Summer Faculty Fellowship to evaluate the use of multi-sensory cues to improve the landing of unmanned aerial vehicles. In 2009, he received another fellowship from the U.S. Air Force Office of Scientific Research to design ergonomic computer workstations for very large displays. He received the State University of New York (SUNY) Chancellor’s Award for Excellence in Teaching in 2011, the University Award for Outstanding Graduate Director in 2015, the University Award for Excellence in International Education in 2016, and the SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2021. He is a member of the
Alpha Pi Mu and Alpha Epsilon Lambda honor societies.

Khasawneh also holds Diplomate status with the Society for Health Systems (SHS), a professional society within the Institute of Industrial and Systems Engineers (IISE) that supports the industrial engineering profession and individuals involved with improving quality and productivity within healthcare. More recently, Khasawneh was also recognized as an IISE Fellow, the highest classification of the IISE membership. He also holds an honorary visiting professor position with the Industrial Engineering Department at Hebei University of Technology in Tianjin, China, and Vellore Institute of Technology in Vellore, India.

In 2022, Khasawneh was named a SUNY distinguished professor, the highest faculty rank that SUNY awards. It is reserved for those who have achieved national or international prominence and an exemplary reputation within their discipline.

Education

• BS, MS, Jordan University of Science and Technology
• PhD, Clemson University

Research Interests

• Healthcare systems engineering
• Operations management
• Advanced analytics

Awards

• University Award for Excellence in International Education, State University of New York (2015-2016)
• University Award for Outstanding Graduate Director, State University of New York (2014-2015)
• Chancellor’s Award for Excellence in Teaching, State University of New York (2010-2011)
   

Teaching Assistant

Rahaf Matahen

• Master’s student in Industrial and Systems Engineering
• BS, Industrial Engineering, Applied Science University

Fees & Deadlines

Group pricing may be available upon request. 

We reserve the right to cancel our sessions.  

If canceled, fees will be refunded in full.

Please note the registration deadlines.

	Green Belt Course Only  (Twelve 90-minute Lectures)	Statistics Refresher+Green Belt Course  (Sixteen 90-minute Lectures)
Standard/Industry	$850	$950
Government/ÌìÃÀ´«Ã½ Alumni, staff & faculty ÌìÃÀ´«Ã½ Alumni	$650	$750
Current ÌìÃÀ´«Ã½ students	$350	$450
Non-ÌìÃÀ´«Ã½ students Please email proof that you are a matriculated student at a University (a screenshot of class schedule/ transcript will work) Email to: wtsnindy@binghamton.edu	$450	$550

Registration

Registration confirmations will be sent by email. Detailed instructions about accessing the course are included in the email.

If you have not received confirmation within seven days of registering, please contact the Office of Industrial Outreach to make sure we received your registration.

Contact information:

Office of Industrial Outreach: 

Kodylynn Perkins SPIR Staff Assistant), wtsnindy@binghamton.edu, (607) 777-6251

Mike Testani (Director of Industrial Outreach), wtsnindy@binghamton.edu, (607) 777-6243

 

Cancellation and Refund Policy

• All cancellations must be received in writing (email, fax, or letter).
• No refunds for cancellations or non-attendance after the day before class starts at 5 pm.
• Refunds are not issued after a day before class starts at 5 pm. Substitutions may be made anytime before the beginning of the course by informing the Office of Industrial Outreach.
• If the course is canceled, enrollees will be advised and receive a full refund.

Deadline for refund	The day before class starts
Administrative Fee	$20