PPAP File Package Complete, but the Production Line Can't Keep Up? —— Five Steps for Capacity Verification (Run@Rate)
A certain automotive parts company submitted a PPAP, and the file package passed on the first attempt: 18 items were complete, the process capability was 1.42, and the PFMEA matched the control plan. Three months later, during mass production, the customer increased the daily order from 800 to 1,200 units, but the production line could only produce around 950 units, leading to a shortfall in deliveries. Upon reviewing the documents, the capacity verification study stated "theoretical cycle time 42 seconds, daily capacity 1,700 units" —— these numbers were derived from the equipment capability table and a two-hour full-speed trial run.
The common issue here is that the capacity is "calculated" rather than "verified." The client's requirement for a capacity verification in the PPAP is to have the supplier run a continuous batch under mass production conditions, exposing all bottlenecks, downtimes, inspection, and packaging times. However, in practice, it often degenerates into a theoretical capacity calculation table.
1. Capacity Verification Validates "What Cost to Stably Produce"
First, let's distinguish three concepts.
Theoretical cycle time is the reciprocal of the single-piece processing time under ideal conditions for equipment or processes. It can be calculated, but it does not constitute verification.
Trial production output proves "it can be produced." However, it often involves a small sample size, optimal conditions, and the best teams, and does not cover shift changes or model changes, so it also does not constitute verification.
Capacity verification (Run@Rate) involves running a continuous batch under mass production conditions, using real people, machines, materials, methods, and measurements to account for the production of conforming products, downtimes, rework, inspection, and packaging times. It answers the question: how many conforming products can this production line deliver in a day?
The key lies in the phrase "conforming product output." Output figures that ignore yield, omit inspection and packaging times, or are achieved with the best teams are not reliable.
2. Three Things to Determine Before Running
First, the target cycle time and batch size. Base the target on the customer's demand cycle time, multiplied by a buffer factor (commonly 1.1 to 1.15) to set the verification goal. The batch size should be large enough, typically requiring continuous operation to cover a full shift or four to eight hours, including at least one model change or shift change.
Second, the statistical criteria. Output should be counted in terms of the number of conforming products, and working hours should be counted based on actual effective operating time. Inspection, packaging, and rework times should be included, and the method for amortizing model change times should be clearly stated. Without a unified criteria, the numbers will be subject to post-run debates.
Third, the judgment rules. Write clear, actionable clauses: the average achievement rate over several days should not be less than a certain percentage of the target, process capability and first-pass yield should meet standards simultaneously, and there should be no significant unplanned downtimes. Unclear rules will result in "we'll discuss it after the run."
3. Execute in Five Steps
Step 1: Reset Conditions. Clear the line and replace all tooling, gauges, materials, packaging materials, labels, work instructions, and inspection standards with mass production versions. Personnel should be configured and scheduled according to mass production requirements, without temporarily assigning experienced workers.
Step 2: Rehearsal and First Article. Follow the mass production process for startup, inspection, and first article confirmation, aligning the start point for cycle time, timing method, and record forms to avoid post-run disputes over "where to start counting."
Step 3: Continuous Operation. Run the agreed batch size at the mass production cycle time and shift schedule, without special accommodations: normal shift changes, normal breaks, normal material changes, and normal inspections. All downtimes, waits, and anomalies should be recorded in real-time, with no post-run additions.
Step 4: Record Losses. Document the actual cycle time, downtime reasons and durations, nonconforming and rework quantities, and the location of work-in-progress accumulation by process. This step is more valuable than the total output figure —— it identifies bottlenecks and the proportion of losses.
Step 5: Judgment and Output. Provide one of three conclusions: achieved, conditionally achieved (with conditions and deadlines), or not achieved (with bottleneck identification and improvement plans). Simultaneously, record the actual cycle time, bottleneck process, and effective output as the basis for the ramp-up plan and delivery commitment.
4. Classify and Address Non-Conformities
The reasons for insufficient capacity generally fall into three categories, each requiring a different approach.
The bottleneck process is too slow. A certain process has a cycle time higher than the required cycle time, causing blockages regardless of scheduling. The direction for improvement is process optimization, tooling replication, or process decomposition.
Variability eats into capacity. Long model change times, incoming material variability, and rework and repair occupying machine time can make the average cycle time appear sufficient but result in insufficient output. The direction for improvement is to shorten model change times, stabilize incoming materials, and isolate rework areas.
Supporting systems lag behind. The pace of inspection, packaging, labeling, and material delivery cannot keep up with the main production line. The direction for improvement is to parallelize inspections, adjust packaging cycle times, and modify delivery frequencies.
Short-term solutions like overtime, increased shift frequency, and temporary inventory can help, but they must be combined with long-term process improvements. Relying solely on short-term measures for delivery will result in the same scenario when the customer increases the order again.
5. An Example
A certain automotive parts company conducted a capacity verification during the PPAP preparation for a new project: the customer's daily demand was 1,200 units, with a required cycle time of 48 seconds, and the verification target was set at 1,380 units/day.
The first continuous two-hour run showed a theoretical cycle time of 42 seconds, seemingly meeting the target. However, when calculated based on actual effective output, it only produced 950 units/day. Breaking down the losses revealed: the first article confirmation and inspections, conducted every two hours, occupied 12 minutes; the actual cycle time for the packaging station was 55 seconds, becoming a new bottleneck; and there were average downtimes of 8 minutes each.
The improvement actions were straightforward: parallelize the first article confirmation with the main production line, change inspections to end-of-line sampling with online monitoring; add a parallel packaging station and move label printing forward; and include the top two downtime causes in the startup inspection items. After two weeks, a re-run produced 1,180 units/day, which was judged as conditionally achieved —— the delivery commitment was signed based on this number and included in the ramp-up plan for follow-up.
Capacity verification is not just an attachment in the PPAP file but the basis for calculating delivery commitments. A two-hour run is more valuable than ten pages of theoretical capacity.
Capacity verification validates the output of conforming products, not the theoretical cycle time —— including inspection, packaging, model change, and downtime times makes the numbers reliable for signing delivery commitments.
Knowledge code: 8.3.3
Version: v20260921
Author: QTank QTank is dedicated to providing systematic professional knowledge, methodologies, and practical tools for quality management practitioners, helping companies continuously improve their quality capabilities.