Last year I was assisting a client in identifying key dashboard monitoring elements for his laboratory operation. He ask to include a First-Pass Yield metric that he had use at a prior company. He asked me if I was familiar with it and I told him I was not, but I would research it and develop a model for his company. First I checked my quality reference books—Quality Tool Box by Nancy R. Tague and The Six Sigma Handbook by Thomas Pyzdek—First-Pass Yield was not found in either indexes. Next I went on line, checking ASQ first—bingo; see the ASQ definition below:
“First pass yield (FPY): Also referred to as the quality rate, the percentage of units that completes a process and meets quality guidelines without being scrapped, rerun, retested, returned or diverted into an offline repair area. FPY is calculated by dividing the units entering the process minus the defective units by the total number of units entering the process.” (ASQ Quality Glossary—simply use the search tool.)
Next I decided to Google the long-tailed key words: “First pass yield model.” The Velaction Continuous Improvement Company caught my eye (http://www.velaction.com/first-pass-yield/).
Here is what their definition provided me:
“First pass yield (FPY) is a metric that indicates the percentage of items moving through a process without any problems. One such problem, of course, is scrap, which makes the output of items from a process lower than the input. But, because many processes have built in rework, simply measuring at the end of a process doesn’t give a true picture of quality. Instead, first pass yield is calculated from the individual yields of each process.
First Pass Yield = Process 1 Yield * Process 2 Yield *…*Process ‘n’ Yield
As you can see, it doesn’t take long for defect rates to stack up. For example, four processes with a 95% yield only produce good products without any rework 81% of the time. One of the challenges in understanding first pass yield is the lack of visibility. Because most frontline workers want to do a good job, they fix problems on the spot, or help out their upstream coworkers. As a result defects are not recorded, inflating the first pass yield rate.”
This information was exactly what I need to develop a model for my client so he could monitor and improve his operations over time. Here is an example of what the model should look like using a typical clinical lab value stream as an example:
Note that each process step experiences defects every day; process Step 5 might be where you want to focus first. You should use Lean tools like root cause problem solving and/or an A3 approach to eliminate these defects over time. The most important idea to keep in mind is that moving a First-pass Yield from 65.922% to 100% is a long term goal and it may take you a year or two to get there—think small incremental steps achieving short term targeted conditions, continuously.
Defects are an “outcome” generated by a process and should be considered the problems you are attempting to solve using a variety of quality tools including root cause problem solving and/or an A3 approach.
Errors and Mistakes that occur during a process are causes of the defects and should be the focus of the analysis and subsequent error-proofing process improvement.
Zero Quality Control (ZQC): Is a quality control approach for achieving zero defects. ZQC assumes that defects are prevented by controlling the performance of a process so that it cannot produce a defect—even when a mistake is made by a machine or a person.
First Pass Yield—First pass yield is a measure to evaluate the initial efficiency of a multistep production process and can be used to monitor continuous process improvement over time.
As always I look forward to your comments and questions.