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The Chinese and Spanish versions are unofficial translations provided for convenience; the English projects page governs.
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Project requirements: the deliverable ladder
The project is worth 30% of your grade in the 4-credit version of the course, and completing it is what earns the six sigma black belt certification. You build it across the term in ten steps. Each rung is graded on its own and feeds the next; together they become the final report and the poster. Every step is submitted on Canvas — see there for the current due dates.
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Team & topic
Form a team of 3–5 and commit to one process you can actually observe or get data about. Pick from the catalog above or bring your own — the constraint is access, not ambition.
Your project team and your homework team are separate Canvas groups. Both were seeded from your Starter Team, so they line up right now, but they do not have to stay the same — talk to the teaching team if your project team ends up different from your homework team.
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Your dataset
Deliver the actual data — experimental, observed, simulated, or found — with a codebook that says what every column means, what its units are, and where each number came from. A plan to find a dataset later does not count.
Details What to submit by project type (Experimental · Simulation · Monitoring)
Submission Requirements by Project Type
Depending on whether your project is an experimental study, a simulation project, or a monitoring project, the structure of your dataset submission is different. In all cases, submit up to 10 variables (or parameters) along with a complete codebook.
In All Cases: Provide a Codebook Table
A codebook is a clear reference table describing every variable in your data. Each variable must have its own row in the table, with three columns:
Variable / Column Name Unit of Measurement Definition & Role cure_timeminutes Predictor: duration the adhesive bond is cured at 65°C. shear_strengthMPa Outcome: peak mechanical shear stress measured before joint failure. operator_idcategory (A, B, C) Covariate / factor: technician who prepared and assembled the test coupon. EXPERIMENT1 · Experimental Study (Designed Test / Intervention)
State what variables you will collect: You have not run your experiments yet, so you have not collected the data — you are reporting what you will collect (planned variables, maximum 10 variables). Use “we will collect” phrasing.
- Outcomes vs. Predictors: Clearly distinguish what outcomes you are measuring and what predictors you are testing. Predictors are the input factors, treatments, or operating conditions you think will affect the outcomes.
- Measurement Scales & Units: There are many different valid measurement scales you can use:
- Real physical quantities: grams, meters, seconds, degrees Celsius, beats per minute, etc.
- Likert scales: 1 to 5 (or 1 to 7) ratings for satisfaction, perceived difficulty, or product preference. Likert scales are a completely valid measurement option!
- Counts: discrete integers like 0, 1, 2, 3, 4, 5, 6 (e.g. defect counts, event occurrences).
- Percentages: 0% to 100% (e.g. process yield, inspection accuracy, defect rate).
- Design & Recording: Specify your planned sample size per condition (number of observations per treatment cell), who will record the observations, on what instrument/sheet, and in what order.
SIMULATION2 · Simulation Project (Published Parameters & Models)
Your dataset becomes parameters: In a simulation study, your data deliverable consists of the statistical parameters that drive your model. You must supply at least 10 parameters or components.
- Acceptable Parameter Examples: Relevant reliability and process statistics for each component or sub-process, such as:
- Failure rate (λ)
- Mean time to failure (MTTF or MTBF)
- Probability of failure at a specific time t for a specific sample size n (e.g. P(Fail ≤ t) or reliability R(t))
- Having any of those three metrics for your components works great. Additional parameters include standard errors and distribution shapes (e.g. Weibull β and η, Exponential, Normal).
- Full Bibliographic Citations: Every single parameter must come from published literature, manufacturer datasheets, or certified reliability registries with a full citation. No invented numbers or synthetic placeholders.
MONITORING3 · Monitoring Project (Process Observation Over Time)
Describe your data source: Clearly identify where the data comes from and state whether it is live or already collected / historical.
- Source Examples: A live stream/API (e.g. real-time traffic feed, live server telemetry, live weather station) versus historical records (e.g. historical air quality records from the EPA AQS API, municipal open data portals).
- Outcome Variables (Max 10): Identify what outcome variable(s) you are studying. For a high-quality monitoring project, you only need 4 or 5 variables in total to run an outstanding analysis.
- Observation Over Time: Quality monitoring projects track behavior dynamically. Define both:
- Unit of observation: The specific entity or location being measured (e.g. intersection, sensor station, production machine).
- Unit of time: The sampling cadence or time interval (e.g. minute-by-minute, hourly, daily, per shift).
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Project charter
One page stating the problem, the scope, the metric you will move, and the target. If your team disagrees about the project, the charter is where it shows up.
Charter templateInstructions
Project Charter
Estimated time: about 1 hour.
The Council for Six Sigma Certification requires Six Sigma Black Belt projects to include a Project Charter: a single-page document that summarizes your project.
A project charter is the living document for an improvement team that outlines the problem, target, and boundaries of a process improvement effort. It is good practice as a systems engineer and great for communicating and clarifying the exact expectations and deliverables for your client.
- Fill out the Project Charter form (template here) to clarify your project.
- For the timeline, show real dates from this term ahead.
- Goals and deliverables must be realistic for your project.
- Be specific.
Target audience: think of this document as what you might submit to a top manager (e.g., a Chief Technical Officer) or a funding agency to obtain support for your project. What information would a CTO or funding agency want to see?
Note: roles are made by you, e.g. Project Lead, Project Manager, Visualization, Modeler. "Sponsor" only applies if your team has an actual sponsoring organization giving you data.
To submit
Upload a .docx file containing your completed template.
- Fill out the Project Charter form (template here) to clarify your project.
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VOC tree & SIPOC diagram
Two halves of the same framing move, now one submission. The voice-of-customer tree turns what the customer says into a measurable requirement; the SIPOC bounds the process around it — suppliers, inputs, process, outputs, customers — so you know what is inside your project and what is somebody else's problem.
VOC tree template SIPOC diagram templateInstructions
Voice of the Customer tree
Turn what the customer says into measurable requirements. Start from the VOC tree template and customize it to your project; keep an editable version for your final report, where it is integrated with a step-by-step description in the main text.
SIPOC Diagram
Estimated time: about 1 hour.
A SIPOC diagram is a visual tool used in process improvement projects to map out the key elements of a process. It stands for Suppliers, Inputs, Process, Outputs, and Customers. It provides a high-level overview of the process and helps stakeholders understand its scope and boundaries. It includes:
- Suppliers: who or what provides the inputs to the process?
- Inputs: what materials, information, or resources are needed for the process?
- Process: the series of steps or activities that transform inputs into outputs.
- Outputs: what is produced or delivered by the process?
- Customers: who receives the outputs or benefits from the process?
Fill out this SIPOC diagram template, customizing it to match your research project. You may use Google Drawings, PowerPoint, Draw.io, Mermaid, or any other valid tool for visualizing the diagram.
Your SIPOC diagram should:
- Feature the process being studied (e.g. making coffee, making semiconductors, delivering medical care), not the process of studying it. "Make regression model" or "write report" should not be in your SIPOC diagram.
- Be visually appealing, with clear and effective use of color.
- Be readable; it should not require your reader to zoom in 1000%.
Target audience: think of this document as what you might submit to a top manager (e.g., a Chief Technical Officer) or a funding agency to obtain support for your project. What information would a CTO or funding agency want to see?
To submit
Upload a .docx file containing your diagram(s).
Note: keep an editable version of the chart for yourself. You will need it for your final report.
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Process map & literature review
Open the middle of the SIPOC into the real sequence of steps, decisions, and handoffs — including the rework loops nobody puts on the official diagram — and, in the same submission, establish what is already known about this failure mode or process. Cite what you use; the point is to avoid re-deriving a published result.
Process map templateInstructions
Process Map
Open the middle of the SIPOC into the real sequence of steps, decisions, and handoffs. Start from the process map template and customize it to your project; keep an editable version for your final report, where it is integrated with a step-by-step description in the main text.
Literature Review
Estimated time: 1–3 hours.
Background / literature review / definition of project.
- Task: summarize relevant prior scholarly research and professional practice on your topic, drawing on journal articles and reports from individual organizations. We do not expect a full literature review, but be sure to include at least 20 sources.
- Focus on recent work (which often includes synopses of early work). Your review will give you good ideas for your methods, model design, and analysis. Cite all sources appropriately in the reference list.
- E.g. for market or industry topics, describe the essential features of that market or industry. For policy questions, describe the legislation and its background.
- References: at least 20 APA-formatted references.
- In-text citations: every reference must be cited in the text at least once.
- Types of sources: a mix of scholarly journal articles, reports, and, where necessary, news articles or other sources.
- Goal: must directly connect to your research question. Helpful ways to write a literature review:
- What are the alternative explanations for your question?
- What is the background on the product, what are its known problems, and what is the treatment under study?
- Compile a table of parameters from past studies, summarizing their findings. What are the common findings? What was not well assessed or measured, or could not be, and how does your study fix that?
- Use your SIPOC diagram and/or process map, and explain each portion of it using background literature. What is not yet well understood?
Target audience: think of this document as what you might submit to a top manager (e.g., a Chief Technical Officer) or a funding agency to obtain support for your project. What information would a CTO or funding agency want to see?
To submit
Upload a .docx file containing your literature review.
Note: you may be assigned to peer review two other teams' literature reviews. Take it as a chance to see what other teams are doing. Compliment-sandwich feedback works well: three comments, two good and one area for improvement.
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Research design
Say exactly how you will get evidence: what you measure, how often, under what conditions, and what result would change your mind about the cause. Any experiment you intend to run on people gets vetted by an instructor before you run it.
Research design templateInstructions
Research Design
Estimated time: about 1 hour.
Fill out the research design form (template here) to design your project. Key components:
- Question: one concrete, testable research question.
- Measures: 2–3 specific measures you will use.
- Data sources: where you will source these data from; simulated data is fine as long as it comes from real parameters from a previous study.
- Methods: the specific methods and techniques from the course you will use to answer your research question.
Target audience: think of this document as what you might submit to a top manager (e.g., a Chief Technical Officer) or a funding agency to obtain support for your project. What information would a CTO or funding agency want to see?
Formatting requirements
- Use in-text citations.
- Include a references section listing any works cited. Use a proper and consistent referencing style; if unsure, ask the instructors or consult the referencing styles used in journals.
- Avoid footnotes; they are only for supplemental material not strictly needed in the main text.
Other notes
- Your analysis could lead to empirical work, hypothesis testing, experiment design, or systems optimization/control. It is fine to mention developing these implications as an end product of your work.
- For DL students and students with work experience, this project is a great opportunity to talk with your colleagues or managers about how the course project could improve a product or service your company provides. Previous DL students (and their managers) responded very positively to initiating a project closely related to their work. The instructors are ready to discuss project ideas to get you on the right track and form teams early.
To submit
Upload a .docx file containing your completed template.
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Preliminary results & financial impacts
First pass at the analysis — the charts, the fitted models, the intervals — reported together with what the improvement is worth in money: cost of poor quality now, cost after, and what the fix would take. Early enough that a broken measurement system is still fixable.
Financial analysis exampleInstructions
Preliminary Results
Estimated time: 3+ hours.
Summarize your preliminary methods and results. Your response should include:
- Measures [100–150 words]: what are you measuring?
- Assumptions [~100 words]: adequately describe your assumptions. Your topic is undoubtedly complex, so you will probably simplify it using assumptions, taking some things as given; explain and justify the choices you make.
- Model / approach [100–300 words, minimum]: a large portion adequately describing your model, approach, or techniques, and exactly how and why they help you answer your research question. (Detailed, not "we made a regression model. Next!")
- Results [300 words, minimum]: summarize the numbers generated by your statistical analyses.
- Key numbers in text: report the important numbers from your analysis in the text, with attention to statistical significance and confidence intervals.
- Table / figure of results: report your results transparently in a table and/or figure. If you made a regression model, report the regression table, and so on.
- Must show results: your response must actually show results, not a plan to do something in the future. That was the Research Design; this is the Results. No credit is given without results.
- Budget your time: this takes real time and effort. Reach out early to the instructor and/or TA with questions.
To submit: upload a .docx file containing your response. Keep an editable version for yourself; you will need it for your final report.
Financial Impacts
Estimated time: about 1 hour.
The Council for Six Sigma Certification expects all Six Sigma Black Belt applicants to be able to meaningfully estimate the financial value produced by their engineering work for a client. Demonstrate the potential financial impacts based on your project analysis.
Draft a 300–500 word "Financial Impacts" section of your Six Sigma report's Discussion, clarifying the specific financial impacts based on your results. It should include:
- Reasonable cost estimates based on specific documents or online resources. (Google it!)
- In-text citations and bibliographic references for any cost estimates or external data referenced.
- A cost comparison built from your results: how much X would cost before vs. after implementing a specific change proposed from your results.
- Realistic estimated benefits from your proposed change.
- Confidence intervals for your financial impact estimate(s). Have a plan for calculating them. Often there is no existing formula for a standard error in your case; you may need bootstrapping or simulation to create confidence intervals or uncertainty estimates. These estimates must be real and valid.
Your response should be evidence-based. Do not make wild claims.
See this example using the Hot Springs data: ZZ_financial_analysis.R.
Target audience: think of this document as what you might submit to a top manager (e.g., a Chief Technical Officer) or a funding agency to obtain support for your project. What information would a CTO or funding agency want to see?
To submit: upload a .docx file containing your response. One student can submit on behalf of the whole group.
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Rough draft
The whole report assembled end to end while there is still time to fix it. Complete enough to get real feedback on — gaps named as gaps, not quietly left out.
Instructions
Rough Draft
Estimated time: 3+ hours.
Create a rough draft of your Six Sigma report, synthesizing the diagrams and analyses you created previously. Submit:
- A single-spaced report-in-progress with 1800–2100 words of new content (about 5–6 pages).
- The report updates the status of your project: what you have done, what remains and the timeline, any major problems and strategies to attack them, and references used.
- Word limits are a minimum, not a maximum. If you need more words, good, go for it.
You can follow this general structure:
- Introduction
- Project Charter (template; already done, not in the word count)
- Background and project motivation [100–150 words]
- Project objectives and expected contributions [100–150 words]
- Literature review
- Methodology
- Research Design (template; already done, not in the word count)
- Data [100–150 words]
- Measures [100–150 words]
- Model / approach [100–300 words]
- Preliminary results [300+ words]
- Discussion [300 words]
- Summary of findings [~100 words]
- Financial impacts [200 words]
- References cited (not in the word count)
Target audience: think of this document as what you might submit to a top manager (e.g., a Chief Technical Officer) or a funding agency to obtain support for your project. What information would a CTO or funding agency want to see?
To submit
Upload a .docx file containing your draft.
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Final report
The whole arc in one document — define, measure, analyze, improve, control — ending in a recommendation somebody could actually act on. This is the deliverable the black belt certification is judged against, so it is expected to be thorough and complete.
Implementation plan template Control plan templateInstructions
Final Project Report
Estimated time: 5–10 hours.
Create a final report, with all code included in the appendix. The report should meet each of the following requirements (R1–R5), weighted as indicated.
R1 · Introduction (10%)
- Present your topic (any current quality or reliability issue with a product, service, or integrated system) and argue for its importance to a meaningful group of people or clientele (e.g. top business managers, a quality team leader, policy makers). You can introduce a broad topic, but your project must focus on one narrow aspect that can be analyzed in depth in one semester.
- Integrate previous diagrams here: Project Charter (template; already done).
R2 · Background / literature review / definition of project (15%)
- Summarize relevant prior scholarly research and professional practice on your topic, drawing on journal articles and reports from individual organizations. Not a full literature review, but at least 20 sources. Focus on recent work. Cite all sources appropriately in the reference list.
- E.g. for market or industry topics, describe the essential features of that market or industry; for policy questions, describe the legislation and its background.
- Integrate previous diagrams here, each with a detailed, step-by-step description in the main text: SIPOC Diagram (template), Process Map (template), Voice of the Customer Tree (template).
- Reference list and in-text citations (at the end of the paper).
R3 · Measure and design (40%)
- Summarize your methods, describing your data and variables, assumptions, specific models and techniques, and exactly how and why they help you answer your research question. Includes: Research Design form (template; already done), Data, Measures, Assumptions (your topic is complex, so you will simplify it using assumptions; explain and justify the choices you make), Model / approach / techniques.
R4 · Analysis, recommendations, and conclusions (25%)
- Report your results clearly and transparently, in accurate but easily understandable terms. Explain your analysis and modeling, key findings, any limitations, and the next steps needed to solve the problem or answer the question. Includes: Results (including the financial impacts analysis), Discussion, Recommendations and conclusions.
- Implementation Plan Table (template), with a detailed, step-by-step description in the main text.
- Control Plan Diagram (template), with a detailed, step-by-step description in the main text.
R5 · Format and writing (10%)
- Basic formatting (5%): 18–40 double-spaced pages, excluding pages of graphs, tables, reference list, etc. Include your code in an appendix.
- Quality of writing (5%): good grammar, spelling, and sentence construction, but also clear structure and organization, flow, and fair presentation (strengths and weaknesses). Your target audience is a practitioner with at least graduate-level training, not necessarily a hotshot theorist. Assume a firm advisor trained in systems engineering will read your report and disseminate your results to end users.
Extra credit (up to 8%, where relevant)
- Quality of team effort.
Reminders
When finishing your final report, lightly check it for the following; these are standard expectations in a formal Six Sigma report, but easy to forget in a busy end of term.
- Your Project Charter fits on one page (it does not have to be double spaced).
- Cite your tables, figures, and equations in the main text: "As shown in Table 1..." or "Equation 1 shows...".
- Discuss your tables and figures in the text. Walk the reader through what the figure shows: each step of your monitoring plan; which bar was highest vs. lowest. You need not describe every part, but it is your job to interpret the plot for the reader.
- Reuse your Project Charter's content in the introduction, if helpful. Many charters hold excellent stats and crisp summaries; technically the charter is a table, a stand-alone item, so it does not count as main text and a reader may skip it. You are welcome to reuse its wording and stats in your introduction.
- Good reports = good writing. Use transitions and summary statements at the start of new sections, headers and subheadings, and bolding or italics if helpful. Example transition: "Next, we introduce our approach for statistical analysis..." Example summary statement: "This included (1) modeling the lifespan distribution, (2) estimating the lifespan of a cookie, (3) modeling the cost of a cookie, and (4) projecting the cost over time. First, to model the lifespan distribution, we..."
- Appendices go at the end of the report, after the bibliography (particularly code; your reader will fall asleep reading code, so it goes at the very end). Label them Figure A1, A2, Table A1, Equation A1, Figure B1, and so on.
- Include your commented code. Transparency is key to showing the client that you did in fact do the analysis.
To submit
Upload a .docx or PDF file containing your report. It must include an appendix with your complete code.
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Poster presentation
The report compressed to what a stranger can absorb standing up: the problem, the evidence, the fix, and the number that proves it worked. Presented live to the class.
Poster templateInstructions
Poster Presentation
Estimated time: 3+ hours.
Create and present an academic poster of your Six Sigma project at the public poster session (see Canvas for the time and place).
Why a poster? This format helps you (1) explain your project concisely to future employers and (2) produce a concise, beautiful poster for your professional portfolio.
Requirements
- Contents: (1) research question, (2) data, (3) method, (4) results, and (5) discussion/implications.
- Brief bullet points succinctly explaining each section; avoid walls of text.
- Two ggplot visuals describing your findings: informative, visually appealing, and well labeled. E.g. a fault tree visual, coefficient plot, statistical simulations, process overview, or any technique from the course.
- Clearly stated quantities of interest (single numbers that answer your research question).
- Confidence intervals for any quantities of interest, e.g. simulations, probabilities, beta coefficients, predictions.
- How do I make a poster? Review Cornell Library's guide to making posters.
- Templates: you can make your poster in PowerPoint / Google Slides, LaTeX, among other methods.
- Dimensions: aim for 42 inches wide by 36 inches tall.
- Deliverables: (1) bring your PDF and (2) submit your poster PDF on Canvas.
Poster session details
- Rotation: each team's poster is mounted on a board and you succinctly summarize your work for attendees. For the first half of the session, half of the teams present while the other half look at posters; for the second half, the teams swap.
- Informal presentations: be prepared to describe your work in clear, everyday language as attendees view your poster. A good chance to work on your elevator-speech skills. Team members should take turns.
- Attendance: mandatory for Black Belt students, but everyone is welcome. Come support your peers.
- DL students: if your entire team is DL, pre-record a short 2-minute video of your poster presentation (a Zoom recording is fine) and submit it as a comment attachment on the assignment; DL posters are shared and displayed at the session. If some team members are not DL, they present the poster, so contribute to the poster design instead.
Past posters and examples
- Past posters from this class (on Canvas, for enrolled students — see the card below)
- Academic poster examples: Cookstove Emissions, Stroke Prediction, Reddit Post Popularity Prediction, other health sciences examples. Further example posters from past students are posted on Canvas for enrolled students.
- Shiny user showcase: shiny.posit.co/r/gallery
To submit
Upload a .pdf file containing your poster. If DL, also include a working share link to a short video presentation.
Bonus: if you make a Shiny app dashboard, you are eligible for a bonus on this assignment.
- Contents: (1) research question, (2) data, (3) method, (4) results, and (5) discussion/implications.
Read this before you choose
Two readings: what the project actually asks for, and how to design one that will work. Both are long — expand what you need.
📄 Read: The Project Prompt what the project asks for
For your final project, you will collaborate with 3–5 other students, applying the analytical tools learned in class to real world problems. Completion of the project is required for six sigma black belt certification through this course.
Goals
- A relevant question: identify a six sigma black belt design or research project related to systems reliability and/or quality.
- Background research: conduct background research and a literature review to define and justify your project.
- Design methods: construct an original approach or solution, and apply the reliability and/or six sigma quality tools appropriate to the topic.
- Evaluate results: provide a cogent write-up of your work.
Tools and approaches
Use at least one of the six sigma quality control and/or systems reliability models covered in class (below), or get prior approval from an instructor. Define and narrow your topic early so that it can (1) feasibly produce meaningful results in one semester, and (2) name a concrete concept, product, service, or system. Some topics are not covered until later in the term, so talk a topic through with an instructor rather than waiting.
- The six sigma approach (define, measure, analyze, improve, control)
- Failure modes and effects analysis (also a six sigma tool)
- Fault trees and event trees under uncertainty (also a six sigma tool)
- Component and system reliability
- Physical acceleration models
- Statistical process control charts, to detect when performance is deteriorating and to identify root causes
- Optimization of system design for quality and reliability (e.g. design of experiments; response surface methods)
- Analysis of variance (ANOVA)
- Regression analysis
Deliverables
- The project activities — the ten graded steps in the deliverable ladder above.
- The poster presentation.
- The final report.
All of them are submitted on Canvas.
Resources
Example abstracts, example papers, and complete reports and posters from past teams are posted on Canvas for enrolled students — see the resources below. To meet the black belt certification requirement, we expect a thorough and complete study.
🎓 Read: Getting Your Six Sigma Certification what the belt is and how to claim it
Estimated time: 5 minutes.
At the end of term, your instructor will present the slides below, on how to get (1) your Cornell Six Sigma certification and (2) your Council for Six Sigma Certification, if interested. For now, look over the frequently asked questions.
See the certification slides →
Cornell University Six Sigma Certification
- What is it? The "Cornell University Six Sigma Certification".
- How do I qualify?
- 3-credit students with a B+ or higher → Green Belt
- 4-credit students with a B+ or higher → Green Belt
- 4-credit students with a successful project and a B+ or higher → Black Belt
- Where do I find out? On Canvas, posted the same day as final grades.
- Do I need to do anything? Nope.
- When do I receive it? A few weeks after final grades are processed.
- How much does it cost? Free. (If you want it mailed far away, you might be asked to pay for shipping.)
- Who handles it? Systems Engineering Program staff. Your professor sends the program a list of students who qualified after final grades are processed.
- When should I reach out if I have not heard anything? About a month after final grades are posted.
Council for Six Sigma Certification (CSSC)
- What is it? Six Sigma certification from the Council for Six Sigma Certification (CSSC), a professional organization.
- Am I eligible? If you received a Cornell certification, you are eligible to request Green Belt or Black Belt certification from the CSSC, for a fee (see the slides). You will need to arrange payment, get the exam requirement waived or your project reviewed, and download your certification.
- Should I get it now or later? If you plan to get it at any point, do it now while you have your documentation on hand (Cornell certification, final project report, etc.).
Why should I get a Six Sigma certification?
- Which career tracks is it helpful for? Process control engineers, quality control engineers, management, but not limited to these.
- Benefits of the Six Sigma Black Belt: "Six Sigma Black Belts save companies approximately $230,000 per project" and "trained and certified Six Sigma Black Belts can earn up to $38,000 more than their uncertified counterparts". Read more here.
- Salary ranges? "In 2022, the average salary for a Six Sigma Green Belt in the United States is $110,500, with a range generally including $89,700 to $116,800." "In 2022, the average salary for a Six Sigma Black Belt in the United States is $126,900, with a range spanning from $108,900 to $136,700." More details here.
🧭 Advice on designing your project the four project types
Feeling the aaah of designing your project and looking for some advice? Good news.
Your team & topic is the starting point. The more defined it is, the less of this material you will have to redo later.
Successful projects generally fall into four types. The first two are particularly recommended.
- Experimental data in the field
- Observed data in the field
- Simulated data using parameters from past studies
- Observed data from other sources
1 · Experimental data in the field
Summary
You directly randomly assign treatments and record the resulting outcomes. This option can be genuinely fun.
You want easily manipulable treatments that can be tested many times. Think about what products you have access to, what reliability issues you might want to measure, and what treatments you could apply. Then use statistical models, t-tests, and so on to test your hypotheses.
When should I use this?
Experimental data is what you want when you are estimating the value added by a particular choice. Should I use topping A or B on my donut? Which one improves flavor, or willingness to pay, by more?
Baking, queuing, sensor position, short behavioral surveys, willingness-to-pay surveys, shampoo — all easily implemented and cheap. Even "simple" product interventions make very good six sigma projects, because the technique spans many sectors of the economy. You do not have to build a spaceship. What matters is demonstrating the method on a commercially viable product or process.
What analysis does this support?
Experimental data is the gold standard for causal inference in business planning, engineering, healthcare, and science generally. Used with t-tests, ANOVA, factorial design, regression, and response surface methodology — though it works with nearly any tool, so bring other ideas.
Examples
Building your first experiment takes some creativity. Here are starter ideas that a team could implement quickly. (Small bonus points are available as an incentive if you need classmates to participate in your research.)
- Product problem rating — clothing: how badly does clothing brand X bleed when washed, compared with brand Y?
- Willingness to pay for product X: your group has a consumer product of interest and wants to know which potential intervention consumers would pay more for.
- Write a short survey that randomly shows the respondent product version 1 or version 2, then asks how much they would pay for it.
- Randomly assign respondents to a version and compare results. Ideally, try many combinations.
- Well suited to consumer-relevant products that are otherwise technically difficult to build real-time treatments for.
- Process design — food science: six sigma is extremely well suited to making better, cheaper, tastier food.
- Take a simple recipe and make multiple batches, varying ingredient amounts — bread with a little more flour, or a little less water.
- Well suited to factorial experiments or response surface methodology, both excellent design-optimization techniques.
- The same approach applies to chemical experiments. (Instructors volunteer as test subjects.)
- Product/process outcome — the "no-poo" method: a trend of not shampooing one's hair, which for some people apparently yields an aesthetic benefit. If your loved ones love you very much, recruit a few participants, randomly assign them to a shampoo or no-shampoo regimen over several days, and have them report daily satisfaction. The same philosophy applies to any other commercial product.
- Product problem rating — ringtones: how annoying is that ringtone, really? A quick behavioral experiment, easily run at scale.
- Pick several ringtones; randomly assign people to a ringtone and a volume level, and ask them to rate annoyingness.
- Same philosophy applies to any other commercial product.
- Factorial experiment — toothbrushes 🪥: buy 12 toothbrushes (3 each of 4 brands) and 12 tubes of toothpaste (3 each of 4 types). Randomly assign a toothbrush and a toothpaste to each group mate, then measure attributes of the brushes and of the outcome after a fixed number of uses.
Example data format
It often helps to collect data with an online form, so the underlying sheet is shared with your collaborators and fills up in real time.
| Unit | Treated? | Outcome | Trait A |
|---|---|---|---|
| A | 1 | 30 bpm | 0 |
| A | 0 | 40 bpm | 1 |
| B | 0 | 50 bpm | 1 |
| B | 1 | 40 bpm | 0 |
| more rows here | add more columns as needed |
2 · Observed data in the field
Summary
You collect data in the field, tracking one or more outcomes of interest plus one or more potential treatments or covariates. Unlike experimental collection, the treatment or independent variable is not randomly assigned. That makes statistical control variables essential when you model the data.
When should I use this?
Use it when you cannot randomly assign the treatment — policy, urban planning, observed consumer behavior without intervention. Past teams have, for example, gone into the field and tracked metrics on local transit buses.
Considerations
You might choose this kind of collection if…
- You are interested in an outcome you can observe but cannot easily or ethically intervene on. For example: which shops see more foot traffic?
- You want to control the conditions under which data is collected — a random sample of buses, on a random sample of days, at a random sample of locations.
- You want to study a population with no good existing data that you believe you can reach easily.
Field study ideas
- Parking: available parking downtown, at the farmers market, or wherever you like is often hard to find. What predicts availability? Randomly select sets of parking spots within an area of interest, randomly select times to check them, and record whether the spot is free, what kind of car is in it, and — if you can stay an hour or more — how long that car stays. If that is too granular, count total usage per street across several days and evaluate the traits of those days.
- Transit service: the county bus service has visible, trackable metrics. How many people are denied entry because the bus is full? How many are aboard at a given stop? Minutes delayed? Randomly select routes at random times, ride for an hour, and discreetly record non-invasive counts.
- Water fountains: many campus water fountains are automated and display the total bottles or ounces filled since installation — essentially their lifespan of use to date. Randomly select fountains and stress-test them: how many seconds under the sensor before it detects the bottle? How many tries to fill a bottle to a consistent level?
3 · Simulated data using parameters from past studies
Summary
You use reliability functions, block diagrams, fault trees, and similar tools to approximate a system's reliability over time.
When should I use this?
Simulation is helpful for procedures that are difficult to observe or to test experimentally — expensive products like spacecraft, or sensitive processes like surgeries and medical devices. Family I in the catalog is the starting list.
What analysis does this support?
You simulate how much the system's reliability, or chance of failure, changes if the mean time to failure of component A shifts by some amount versus component B shifting by another. Which component should we spend money on to fix and improve?
What data must I collect?
Estimates of key parameters for each component in your system — usually the mean time to failure of components A, B, C, and so on.
How do I collect it?
Normally you search past studies and statistics about your device of interest, and find or derive the parameters you need.
Search with Google Scholar. Look for highly cited works published in the last five to ten years, and use the filter to narrow to review articles — a type of article that reviews dozens of prior papers. Some review articles are meta-analyses; they often report dozens of statistics from different studies in a single table and compare them, which is a fast way to assemble your own table of parameters. Here is an example of one such review — its parameters of interest are the sensitivity and specificity of continuous glucose monitors, and it lays out a great deal of study metadata in one table. Many of your projects will want failure rates instead, but the shape of the table is the same.
Tips for data collection
- Collect any parameter or statistic you can find.
- The parameters can be of different types — grab any you find. We can often derive one value from another later. For example, any of: mean time to failure; probability of failure at a specific time t; failure rate in failures per hour or per million hours.
- Always record units — per hour, per thousand hours, per day, number of failed units, probability. Without units the number means nothing.
- Collect sample size and timespan wherever possible.
- If a source reports a mean time to failure, that mean came from n observed failures over t total hours. Capture that if you can.
- If a source reports a probability of failure, you will eventually convert it to a mean time to failure or a failure rate — which needs a sample size n and a timespan t.
- Collect standard errors and/or standard deviations with sample sizes wherever possible; they are what make an uncertainty analysis possible later. Sometimes they are simply not reported.
- Not every component will have been researched before. In those cases we either (a) assume it never fails, (b) assume it always fails, or (c) derive a reasonable assumed value and justify the choice. That can be handled later.
- Always download a copy of the document and its citation information.
- Always cite where the estimate came from.
- If you have access to raw data you could estimate the value yourself, but often you will not.
Your dataset will look roughly like…
| Component / failure mode | Parameter | Estimate | Standard error | Sample size (N) | Source |
|---|---|---|---|---|---|
| Selfie stick arm | Mean time to failure | 3000 hrs | 150 hrs | 200 | Source 1 |
| Camera imaging | Failure rate | 5 per million | 1 per million | 50 | Source 2 |
| Clasp mechanism | Probability of failure at 1000 hrs | 5% | 1.2% | 1200 | Source 3 |
| etc. — expect at least 5 rows, ideally more |
4 · Observed data from other sources
Summary
Statistical analysis of pre-collected data, tracking metrics for multiple products over time or space under varying conditions. Any features, treatments, or interventions were observed out in the world rather than randomly assigned — which means it is not appropriate to draw causal conclusions directly from observed data.
When should I use this?
Observed data is useful when a topic has been studied extensively in the public domain, when there is little or no individual privacy issue or commercial risk in sharing it, and when collection systems are already well developed. Statistical models such as regression are used to study it. Good for initial exploratory analysis, but experimental designs are preferred where possible. Usually needs at least 100 observations, and more will sharpen your estimates.
A statistical model of this data can still identify which features improve quality in a specific context. It generally requires quality metrics observed over time for multiple groups — EV battery lifetimes over time by vehicle, say, or hot spring performance over time.
What data must I collect?
The entire dataset, meeting these requirements:
- It must be completely clear what a row means and what each column or variable means.
- Units must be clear for every variable used — otherwise the analysis is uninterpretable.
- There must be variation in the observed traits, or there is no variation to analyze.
- Sample size must be large enough. At least 100 rows is a good benchmark; higher is better.
- The source and quality of the data must be good. If it is not clear who collected it, how, and when, it is not good enough to present to a client — and that is what this project is about.
Tips for data collection
- Using pre-collected data may look like the easiest option. It often is not.
- Data quality varies enormously, and the goal is not merely to run a statistical model — it is to produce an empirically valid, useful analysis of a technology for the improvement of quality.
- Only take this route if you have a high-quality dataset that is meaningfully connected to your project and has a clear, valid, well documented source.
Your dataset will look roughly like…
| Unit ID | Timestep | Outcome | Trait A | More traits here |
|---|---|---|---|---|
| A | 1 | 32 bpm | 0 | |
| A | 2 | 35 bpm | 0 | |
| B | 1 | 67 bpm | 1 | |
| B | 2 | 68 bpm | 1 | |
| more rows here |
Examples from past projects
Both of these are past students' own work, so they live on the Past Projects page on Canvas rather than on the public site. Log in with your Cornell credentials to open it.
📝 Example project abstracts
Short abstracts from previous teams, showing how a good project states its question, its method, and its result in one paragraph. On the Past Projects page for enrolled students.
Open Past Projects on Canvas →🏅 Past black belt projects & posters
Complete reports and posters from previous teams — the standard a certification-level study is held to. On the Past Projects page for enrolled students.
Open Past Projects on Canvas →