Context
This use case is built from a peer-reviewed case study published in Heliyon (and mirrored on PMC) describing a rejection-rate problem at an Indian manufacturing company that produces rubber weather strips, the rubber seals fitted around automotive doors, windows and trunks. The published study runs the full Six Sigma DMAIC cycle at a scale well beyond what a single Yellow Belt would own. What is useful for a Yellow Belt trainee is the front half of that project: the part where the team simply defined the problem and collected data. That slice maps almost exactly onto what a Yellow Belt is trained to do on their own line, with no statistics beyond counting and a Pareto chart.
The problem
The plant was seeing a high rejection rate on its weather-strip line and needed to know where to aim its improvement effort. On the define phase, the database from January to April was reviewed and the biggest defect was found to be veneer edge peel off, and using value stream mapping, the defect could have been detected earlier at the coating machine output and appearance check station on the packing line, but detection came late and caused loss and waiting time. In the weather-strip line itself, the underlying issue was the same shape: a lot of scrapped product, and no shared, data-based view of which defect types were actually driving that scrap.
How a Yellow Belt would scope it
A Yellow Belt working alongside their normal job would not take on the whole DMAIC project shown in the paper. They would take one narrow slice of it: one line, one shift, one short data window, and one question, which defect types are showing up and how often. The scoping decisions a Yellow Belt makes here are what separate this from a Green or Black Belt project:
- Pick a single process step or line, not the whole plant.
- Define the problem in one sentence a supervisor can approve without a business case: “rejection rate on line X is too high, we do not know which defect is the biggest contributor.”
- Collect counts, not measurements. No control limits, no capability indices, no design of experiments.
- Hand the ranked list to a Green Belt or engineer once the vital few are visible, rather than running the root cause investigation personally.
Tools applied, phase by phase
Define
The Yellow Belt version of Define is a short problem statement and a rough map of where in the process the defects appear, not a formal charter. In the source case, this step surfaced that the data collected was analyzed using Pareto Analysis Charts and Cause and Effect Diagrams to identify major defects and their causes, and twelve defects were identified as mainly responsible for the high rejection level. A Yellow Belt would stop well short of the cause-and-effect work here and simply confirm which defect categories exist and where they are seen.
Measure: the check sheet
This is the core Yellow Belt tool. Instead of guessing which defects matter most, the team builds a simple tally sheet with the defect categories down one side and a mark for every occurrence observed during the shift or the data window. In the published case, defective data was collected for a first month and again for a second month, giving the team two independent samples to compare rather than relying on a single snapshot. A Yellow Belt project would compress this to days or a couple of weeks, but the mechanics are identical: walk the line, mark the sheet, do not stop to analyze causes yet.
Analyze, scaled down to a Pareto chart
Once the check sheet has enough tallies, the counts are turned into a Pareto chart, which is the one piece of “analysis” a Yellow Belt is expected to do. Pareto charts were plotted from the check sheet data, and it was analyzed that the percentage of defects due to the first four defect types, joint crack, underfill, press mark, and overflow, was considerably high at about 74% of the total number of defects. That single chart is the entire “insight” a Yellow Belt needs to deliver. The ASQ guidance on building a Pareto chart is the same regardless of scale: subtotal the measurements for each category and determine the appropriate scale for the measurements collected, with the maximum value set by the largest subtotal. No hypothesis tests, no regression, just sorted counts and a cumulative line.
Where the real project goes past Yellow Belt scope
The published study continues into territory a Yellow Belt would hand off rather than own. The project team decided to address the causes responsible for generating the five significant defects, using a cause-and-effect diagram to identify possible root causes so that corrective action could be taken through a structured approach. Building and interrogating a fishbone diagram against process variables, then designing corrective actions and a control plan, is Green Belt and Black Belt work. A Yellow Belt’s job ends at the ranked Pareto chart and a short write-up of what it shows, plus maybe a standard work suggestion for the top defect if the fix is obvious and local, for example a checklist step at the point identified in the process map where inspection is currently catching problems too late.
What the result was, at Yellow Belt scope
At Yellow Belt scope, the “result” is not a scrap rate reduction, it is a clear, data-backed answer to the question the project started with: which handful of defect types account for most of the rejections, and at which step in the process are they showing up. In this documented case that answer was four defect types driving roughly three-quarters of the scrap, a result that on its own tells a plant exactly where to send its next, deeper improvement effort. A Yellow Belt closing out a project this size would present that Pareto chart, note the process step where the defects are surfacing, and recommend either a quick standard work fix if one is obvious, or escalation to a Green Belt if root cause work is needed.
What a trainee should take from it
Two things stand out for anyone approaching their first Yellow Belt project in manufacturing. First, a check sheet and a Pareto chart, run over a short and narrow scope, can produce a genuinely useful, decision-ready result without any statistics beyond counting and sorting, this real case shows that method working on live production data. Second, knowing where the Yellow Belt’s job ends matters as much as knowing the tools. The moment a project needs a fishbone diagram interrogated against process variables, or a corrective action plan tied to a control chart, it has moved into Green Belt territory, and the right move for a Yellow Belt is to hand over a clean, well-documented Pareto chart rather than try to carry the investigation further alone.
