Lean Six Sigma Green Belt courses via Distance Learning
May 20, 2019 - June 30, 2019 via Distance Learning
(Open for all engineers, professionals and students. Special pricing for BU and other university students)
December 16, 2019 - January 19, 2020 via Distance Learning
Spring course: REGISTER HERE
Fall course: REGISTER HERE (registration opens soon)
This 4-week course is offered by the Systems Science and Industrial Engineering (SSIE) Department at Binghamton University. Pre-requisite statistics instruction also offered. This course includes:
- Twelve 90-minute pre-recorded lectures and includes soft copies of presentation slides. (A prerequisite statistics day of instruction which is comprised of four addition 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 a month of the start of the course.
- Support via access to virtual office hours and email contacts to the professor and teaching assistant
- Links to the pre-recorded sessions and slides will be sent out at the start of the course.
- An open-book take-home exam will be distributed by the end of the first week of the course.
- Allot 30-40 hours for completing the exam.
- Exam will be due by June 30, 2019 11:59 p.m.
- A Lean Six Sigma Green Belt certificate will be provided to those who successfully complete the course
- Current Binghamton and non-Binghamton students
- Binghamton alumni
- Members of industry, non-profit and government agencies
- Knowledge/experience/education in probability & statistics is required. Students without this background are strongly encouraged to consider the statistics refresher.
- Binghamton students: Some examples of courses that fulfill this prerequisite include ISE 261, ISE 362, SSIE 505 or CSQ 112.
- Computer with a high-speed internet connection and audio. Microsoft Excel is also required.
- A trial version (free for 30 days) of the Minitab software (for statistical analysis) at the start of the training program. Minitab is available for free for all Binghamton students
- Updated version of JAVA
- Headset with microphone may be useful but not necessary.
- Instruction via pre-recorded lectures
- Soft copies of slides.
- Support via virtual office hours and email with the instructor and teaching assistant.
- Grading of take-home exam
- Lean Six Sigma Green Belt Certificate of Completion
Prerequisite Statistics: Four 90-minute lectures
For those who do not have prerequisite coursework in statistics
Session 1 - Course Introduction, Course Outline, Fundamental 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, Variables 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 its 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 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 to provide effectively successful projects.
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, Bottle Necks, 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
Dr. Mohammad T. Khasawneh
- Professor and Chair in Systems Science & Industrial Engineering Department
- Associate Director, Watson Institute for Systems Excellence
- Director, Healthcare Systems Engineering Center
- Director, Human Factors and Ergonomics Laboratory
- Graduate Program Director, Executive Master of Science in Health Systems
- SUNY Chancellor's Award for Excellence in Teaching
- PhD, Industrial Engineering, Clemson University
Teaching Assistant - TBD
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.
Regular registration ends one week before course start at which a 50 $ registration fee will be added to the regular course fee.
Registration closes on Date TBD at noon Eastern Time.
Green Belt Course Only
|Prerequisite Statistics+Green Belt Course
(Sixteen 90-minute Lectures)
Government/Binghamton Alumni, staff & faculty
Binghamton Alumni (graduated in May 2018 or prior)
Current Binghamton studentsand Binghamton Alumni (Graduating in May 2019 or graduated in December 2018)
Arrangements for payment must be made before the start of the program.
Link to payment site sent after registration
Notation on statement will read: PAYPAL*RFBU WATSON
Payable to: Research Foundation "LSSGB 83545"
Memo: registrant's name and "LSSGB 83545"
Required: Course name, Registrant's name, Complete billing, Contact name and phone number
Registration confirmations will be sent by email. Detailed instructions about accessing the course is 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: Astrid Stromhaug (Sr. Staff Assistant), email@example.com, (607) 777-6241 Mike Testani (Director of Industrial Outreach) firstname.lastname@example.org, (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 May 20, 2019.
- Refunds are not issued after the course has begun. 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.
May 20, 2019, 9 a.m. (4 week course)