{"id":1431,"date":"2026-08-29T00:30:51","date_gmt":"2026-08-29T00:30:51","guid":{"rendered":"https:\/\/mestric.com\/fpy-v-proizvodnji\/"},"modified":"2026-08-29T00:30:51","modified_gmt":"2026-08-29T00:30:51","slug":"fpy-v-proizvodnji","status":"publish","type":"post","link":"https:\/\/mestric.com\/sl\/fpy-v-proizvodnji\/","title":{"rendered":"Raise FPY in 12 Weeks on the Shop Floor: Measure, Pilot, Sustain"},"content":{"rendered":"<\/p>\n<p>First pass yield is the share of units that clear a production step correctly the first time, with no rework and no scrap. Track it at the step level, and you find capacity you already paid for but never see on a standard scrap report. The immediate action for any manufacturing or quality manager: measure FPY at each critical step, then multiply those figures into rolled throughput yield to see what the line actually delivers end to end.<\/p>\n<hr>\n<blockquote>\n<p><strong>TL;DR:<\/strong><\/p>\n<ul>\n<li>Most plants underestimate their losses because they do not consistently log rework, which inflates FPY and hides true process inefficiencies.<\/li>\n<li>Calculating FPY involves excluding reworked units, meaning a second-pass unit counts as a failure on the first attempt, reducing the true yield.<\/li>\n<li>FPY at individual steps can be high, but multiplying multiple steps into RTY often reveals a much larger capacity loss end-to-end.<\/li>\n<li>Automated measurement systems and real-time dashboards significantly improve FPY tracking accuracy and help identify specific process drifts early.<\/li>\n<li>Targeting over 99% FPY per step is ideal, but understanding process complexity and maintaining strict acceptance criteria are key to realistic, sustainable improvements.<\/li>\n<\/ul>\n<\/blockquote>\n<hr>\n<h2 id=\"table-of-contents\">Table of Contents<\/h2>\n<ul>\n<li><a href=\"#what-is-fpy-in-production-and-how-do-you-calculate-it\">What is FPY in production and how do you calculate it?<\/a><\/li>\n<li><a href=\"#fpy-versus-rty-and-the-link-to-oee\">FPY versus RTY and the link to OEE<\/a><\/li>\n<li><a href=\"#how-do-you-measure-fpy-correctly-on-the-shop-floor\">How do you measure FPY correctly on the shop floor?<\/a><\/li>\n<li><a href=\"#common-causes-of-low-fpy-and-how-to-diagnose-them\">Common causes of low FPY and how to diagnose them<\/a><\/li>\n<li><a href=\"#proven-strategies-to-improve-fpy-process-tooling-and-people\">Proven strategies to improve FPY: process, tooling and people<\/a><\/li>\n<li><a href=\"#the-role-of-mes-and-real-time-dashboards-in-sustaining-fpy-gains\">The role of MES and real-time dashboards in sustaining FPY gains<\/a><\/li>\n<li><a href=\"#what-is-a-good-fpy-benchmark-and-how-do-you-interpret-it\">What is a good FPY benchmark and how do you interpret it?<\/a><\/li>\n<li><a href=\"#quick-start-checklist-to-raise-fpy-this-quarter\">Quick-start checklist to raise FPY this quarter<\/a><\/li>\n<li><a href=\"#author-perspective-why-fpy-deserves-more-attention-than-it-gets\">Author perspective: why FPY deserves more attention than it gets<\/a><\/li>\n<li><a href=\"#how-mestric-helps-you-capture-and-improve-fpy\">How Mestric helps you capture and improve FPY<\/a><\/li>\n<li><a href=\"#sources\">Sources<\/a><\/li>\n<\/ul>\n<h2 id=\"what-is-fpy-in-production-and-how-do-you-calculate-it\">What is FPY in production and how do you calculate it?<\/h2>\n<p>FPY strictly excludes anything that needed a second attempt. The formula is straightforward:<\/p>\n<p><strong>FPY = (units entering the step, minus scrap, minus rework) \u00f7 units entering the step<\/strong><\/p>\n<p>A unit that gets fixed and passes on the second try still counts as a first-pass failure, because it never completed the step correctly the first time. This is the detail most teams get wrong, and it inflates their numbers without anyone noticing. The <a href=\"https:\/\/en.wikipedia.org\/wiki\/First-pass_yield\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Wikipedia entry on first-pass yield<\/a> sets out the counting rule clearly: rework is excluded from the numerator, full stop.<\/p>\n<p>Here\u2019s a worked example from a solder paste inspection step:<\/p>\n<ol>\n<li><strong>Units entering the step:<\/strong> 500 boards<\/li>\n<li><strong>Units passing first time:<\/strong> 460 boards<\/li>\n<li><strong>Units requiring rework:<\/strong> 30 boards<\/li>\n<li><strong>Units scrapped outright:<\/strong> 10 boards<\/li>\n<li><strong>FPY calculation:<\/strong> 460 \u00f7 500 = 92%<\/li>\n<\/ol>\n<p>Those 30 reworked boards still ship, and they still count as revenue. But they consumed labour, machine time, and inspection capacity twice. That\u2019s the cost FPY makes visible and a scrap-rate report hides.<\/p>\n<p>Before you calculate anything, agree what \u201cpass\u201d means. A cosmetic blemish that fails one inspector\u2019s eye but passes another\u2019s will corrupt your data long before any process problem does.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/csuxjmfbwmkxiegfpljm.supabase.co\/storage\/v1\/object\/public\/blog-images\/organization-16618\/1787915527944_Inspector-measuring-machined-part-manually.jpeg\" alt=\"Inspector measuring machined part manually\"><\/p>\n<h2 id=\"fpy-versus-rty-and-the-link-to-oee\">FPY versus RTY and the link to OEE<\/h2>\n<p>FPY tells you about one step. Rolled throughput yield (RTY) tells you what happens when you chain several steps together, and the difference between the two numbers is usually where the real story sits.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/csuxjmfbwmkxiegfpljm.supabase.co\/storage\/v1\/object\/public\/blog-images\/organization-16618\/1787915545398_Diagram-comparing-FPY-RTY-and-OEE.jpeg\" alt=\"Diagram comparing FPY, RTY and OEE\"><\/p>\n<p>RTY multiplies the FPY of every step in sequence: RTY = FPY\u2081 \u00d7 FPY\u2082 \u00d7 FPY\u2083 \u00d7 \u2026 \u00d7 FPY\u2099. A line with five steps, each running a respectable <a href=\"https:\/\/www.sixsigmaonline.org\/calculating-rty-in-six-sigma\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">95%<\/a> FPY, delivers an RTY of just 77%. That gap between \u201c95% looks fine\u201d and \u201c77% is what customers actually get first time\u201d is what practitioners call the <a href=\"https:\/\/resources.rework.com\/libraries\/process-management\/first-pass-yield\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">hidden factory<\/a>: capacity spent entirely on redoing work nobody budgeted for.<\/p>\n<p>A few points worth holding onto when you compare the two metrics:<\/p>\n<ul>\n<li>FPY diagnoses a single station or process step; RTY diagnoses the whole line or value stream.<\/li>\n<li>RTY compounds losses multiplicatively, so small per-step drops become large end-to-end problems fast.<\/li>\n<li>Overall Equipment Effectiveness (OEE) uses a quality factor that is essentially a step-level FPY figure, multiplied against availability and performance to give the full picture.<\/li>\n<li>A plant can hit strong OEE quality scores on individual machines while its RTY across the full line tells a much worse story.<\/li>\n<\/ul>\n<p>If you only ever report FPY per station, you will miss where rework actually accumulates. Calculate both, and report them side by side.<\/p>\n<h2 id=\"how-do-you-measure-fpy-correctly-on-the-shop-floor\">How do you measure FPY correctly on the shop floor?<\/h2>\n<p>Measurement fidelity is where most FPY programmes quietly fail. The formula is simple; the data collection behind it rarely is.<\/p>\n<ol>\n<li><strong>Define the process boundary first.<\/strong> Decide exactly where the step starts and ends, and get quality, production, and engineering to sign off on the same acceptance criteria before you take a single measurement.<\/li>\n<li><strong>Log four things at minimum:<\/strong> units entering the step, units passing first time, rework counts, and scrap counts, all timestamped so you can later correlate a dip with a shift change or a material batch.<\/li>\n<li><strong>Choose your capture method deliberately.<\/strong> A manual paper tally works for a pilot, but it invites gaps whenever a line is busy. Automated capture through test equipment or an MES removes that human judgement call entirely.<\/li>\n<li><strong>Validate against final shipped yield.<\/strong> If step-level FPY says 92% but your finished-goods yield tells a different story, someone is logging rework outside the system, and that gap needs closing before you trust any of the numbers.<\/li>\n<li><strong>Plot the results on a control chart<\/strong>, not just a monthly average, so a slow drift in solder paste volume or reflow temperature shows up in weeks rather than quarters.<\/li>\n<\/ol>\n<p><strong>Pro Tip:<\/strong> *Run a one-week parallel check where a supervisor manually counts units alongside your automated system.<\/p>\n<h2 id=\"common-causes-of-low-fpy-and-how-to-diagnose-them\">Common causes of low FPY and how to diagnose them<\/h2>\n<p>Low FPY rarely has one cause. It usually has three or four, and the trick is working out which one is costing you the most units, not which one looks the most dramatic on a chart.<\/p>\n<ul>\n<li><strong>Process parameter drift<\/strong> \u2014 solder paste volume, reflow temperature profiles, and torque settings all shift gradually as equipment wears, and small drifts compound into defect clusters before anyone notices.<\/li>\n<li><strong>Operator variation<\/strong> \u2014 unclear or outdated work instructions mean two operators on the same station get different results, especially across shift handovers.<\/li>\n<li><strong>Material and supplier variability<\/strong> \u2014 a component batch with slightly different lead coplanarity or a new paste lot can tank FPY on a station that was stable for months.<\/li>\n<li><strong>Fixture and tooling wear<\/strong> \u2014 misaligned stencils or worn nozzles produce defects that look random until you plot them against tool age.<\/li>\n<\/ul>\n<p>Diagnose by absolute rework volume, not just percentage drop. Fix the first one first, even though its percentage looks healthier.<\/p>\n<h2 id=\"proven-strategies-to-improve-fpy-process-tooling-and-people\">Proven strategies to improve FPY: process, tooling and people<\/h2>\n<p>The strongest FPY gains come from combining technical controls with disciplined people management. Neither works well alone.<\/p>\n<ul>\n<li><strong>Standardise work and tighten acceptance criteria.<\/strong> Ambiguous pass\/fail definitions are often the single biggest source of inconsistent FPY reporting between shifts.<\/li>\n<li><strong>Introduce poka-yoke wherever a mistake is physically possible.<\/strong> A fixture that only accepts the correct component orientation removes an entire defect category permanently.<\/li>\n<li><strong>Close the loop between printer and SPI.<\/strong> Documented SMT case studies show closed-loop integration between the solder paste printer and Solder Paste Inspection, combined with line-level inspection, lifting FPY from around <a href=\"https:\/\/pcbandassembly.com\/sl\/blog\/Kako-izbolj%C5%A1ati-donos-SMT-brez-zmanj%C5%A1anja-hitrosti-proizvodnje\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">85% to 98% or higher<\/a>, with rework falling sharply alongside it.<\/li>\n<li><strong>Run preventive maintenance on a schedule, not a failure basis.<\/strong> Worn nozzles and misaligned stencils rarely fail catastrophically; they degrade FPY quietly for weeks first.<\/li>\n<li><strong>Monitor with SPC and CPk<\/strong>, so parameter drift shows up as a trend line before it shows up as a defect spike.<\/li>\n<li><strong>Train to the failure mode, not the general process.<\/strong> A ten-minute targeted session on the specific defect a station is producing beats a generic annual refresher every time.<\/li>\n<li><strong>Build a daily quality huddle<\/strong> into shift handover, where the outgoing shift reports FPY and any anomalies directly to the incoming one.<\/li>\n<\/ul>\n<p><strong>Pro Tip:<\/strong> <em>When you pilot an intervention, run it on one high-volume station for two to four weeks and compare FPY and RTY before and after, rather than rolling changes across the whole line at once. You isolate the effect, and you avoid disrupting stations that were already performing well.<\/em><\/p>\n<h2 id=\"the-role-of-mes-and-real-time-dashboards-in-sustaining-fpy-gains\">The role of MES and real-time dashboards in sustaining FPY gains<\/h2>\n<p>Manual tallies work for a pilot. They fall apart the moment you try to sustain gains across multiple lines and shifts, because someone eventually stops logging the rework bench, and your numbers quietly drift from reality.<\/p>\n<ul>\n<li><strong>Automated data capture<\/strong> removes the human decision of whether to log a reworked unit, closing the exact gap that inflates FPY figures.<\/li>\n<li><strong>Real-time dashboards and alarms<\/strong> flag a drop the moment it happens, not at the end of a shift when the affected batch has already moved three stations further down the line.<\/li>\n<li><strong>Event correlation<\/strong> connects a dip in FPY to a specific timestamp, operator, or material batch automatically, cutting root-cause investigation from days to hours.<\/li>\n<li><strong>Instrument the highest-value points first<\/strong>: SPI and test stations on your highest-volume line, before you try to cover every station in the factory.<\/li>\n<\/ul>\n<p>A platform like <a href=\"https:\/\/mestric.com\/sl\/production-quality-monitoring-manufacturing\/\" target=\"_blank\" rel=\"noopener\">Mestric<\/a> captures units in and out at each step automatically, flags rework the moment it happens, and puts FPY on a live dashboard rather than a weekly spreadsheet.<\/p>\n<h2 id=\"what-is-a-good-fpy-benchmark-and-how-do-you-interpret-it\">What is a good FPY benchmark and how do you interpret it?<\/h2>\n<p>World-class per-step FPY generally sits <a href=\"https:\/\/www.deltek.com\/resources\/articles\/first-pass-yield-fpy\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">at or above 99%<\/a>, though the realistic target for your line depends heavily on process complexity. A multi-step SMT process with dozens of components has more places for a defect to enter, so the honest target for each individual step matters more than any single blended number.<\/p>\n<p>Watch for metric gaming: a step \u201cimproves\u201d when its boundary quietly shifts to exclude a rework loop, or when acceptance criteria loosen. Neither is a real gain, and both will show up as a mismatch against final shipped yield.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/csuxjmfbwmkxiegfpljm.supabase.co\/storage\/v1\/object\/public\/blog-images\/organization-16618\/1787915534737_Hands-performing-manual-product-rework.jpeg\" alt=\"Hands performing manual product rework\"><\/p>\n<p>Every point of FPY recovered on a high-volume step returns capacity without new capital. That\u2019s the business case, stated plainly, and it\u2019s why the metric deserves more attention than a monthly scrap report gives it.<\/p>\n<h2 id=\"quick-start-checklist-to-raise-fpy-this-quarter\">Quick-start checklist to raise FPY this quarter<\/h2>\n<ol>\n<li><strong>Week 1:<\/strong> define process boundaries and acceptance criteria, then run a manual tally to establish your baseline FPY and RTY.<\/li>\n<li><strong>Weeks 2 to 4:<\/strong> pilot one intervention on your highest-volume step, such as closed-loop printer-SPI integration, targeted operator retraining, or preventive maintenance on worn tooling.<\/li>\n<li><strong>Weeks 5 to 8:<\/strong> measure the result against your baseline, put the gain onto a control chart, and document the revised work instruction before you scale it.<\/li>\n<li><strong>Weeks 9 to 12:<\/strong> roll the validated change to a second line, and repeat the cycle on your next-highest rework volume.<\/li>\n<\/ol>\n<h2 id=\"author-perspective-why-fpy-deserves-more-attention-than-it-gets\">Author perspective: why FPY deserves more attention than it gets<\/h2>\n<p>Most plants I\u2019ve reviewed find their real losses one or two stations deeper than where they were looking. The hidden factory is rarely hidden by much, just by whoever stopped logging the rework bench. Real-time monitoring closes that gap fast. Pilot one station, measure FPY and RTY before and after, and the case usually makes itself.<\/p>\n<blockquote>\n<p><em>\u2014 Andra\u017e<\/em><\/p>\n<\/blockquote>\n<h2 id=\"how-mestric-helps-you-capture-and-improve-fpy\">How Mestric helps you capture and improve FPY<\/h2>\n<p>Chasing FPY with spreadsheets and shift-end tallies means you\u2019re always reacting to last week\u2019s problem. Mestric connects directly to your equipment and captures units in, units passing first time, and rework flags automatically, so nothing gets lost in a handover.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/csuxjmfbwmkxiegfpljm.supabase.co\/storage\/v1\/object\/public\/blog-images\/organization-16618\/1771068359718_mestric.jpg\" alt=\"Mestric\"><\/p>\n<p>Start with a pilot on one high-volume step, the same approach that drove the SMT FPY gains covered earlier, and compare your before-and-after numbers on a live dashboard rather than a monthly report. You\u2019ll see where rework is actually accumulating within days, not weeks. If you\u2019re ready to see how automated FPY capture works on your own line, request a Mestric demonstration and bring your current scrap and rework figures to the call.<\/p>\n<h2 id=\"sources\">Sources<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.deltek.com\/resources\/articles\/first-pass-yield-fpy\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">First pass yield (FPY) \u2014 Deltek<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/First-pass_yield\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">First-pass yield \u2014 Wikipedia<\/a><\/li>\n<li><a href=\"https:\/\/pcbandassembly.com\/sl\/blog\/Kako-izbolj%C5%A1ati-donos-SMT-brez-zmanj%C5%A1anja-hitrosti-proizvodnje\/\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">Kako izbolj\u0161ati izkoristek SMT brez zmanj\u0161anja hitrosti proizvodnje \u2014 PCBandAssembly<\/a><\/li>\n<li><a href=\"https:\/\/resources.rework.com\/libraries\/process-management\/first-pass-yield\" rel=\"nofollow noopener noreferrer\" target=\"_blank\">First Pass Yield (FPY) and Rolled Throughput Yield \u2014 Rework resources<\/a><\/li>\n<\/ul>\n<h2 id=\"recommended\">Recommended<\/h2>\n<ul>\n<li><a href=\"https:\/\/mestric.com\/sl\/how-to-optimise-shop-floor-efficiency-measurable\/\" target=\"_blank\" rel=\"noopener\">How to optimise shop floor efficiency for measurable results<\/a><\/li>\n<li><a href=\"https:\/\/mestric.com\/sl\/examples-of-yield-improvement-strategies\/\" target=\"_blank\" rel=\"noopener\">Yield improvement strategies: practical examples for plant managers<\/a><\/li>\n<li><a href=\"https:\/\/mestric.com\/sl\/factory-performance-tracking-workflow-2026-guide\/\" target=\"_blank\" rel=\"noopener\">Factory performance tracking workflow: 2026 guide<\/a><\/li>\n<li><a href=\"https:\/\/mestric.com\/sl\/real-time-performance-tracking-for-manufacturing-efficiency\/\" target=\"_blank\" rel=\"noopener\">Real-time performance tracking for manufacturing efficiency<\/a><\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>Practical shop floor plan to measure FPY, pilot fixes, and sustain gains with MES dashboards. Use a 12 week checklist to reclaim lost production capacity.<\/p>","protected":false},"author":1,"featured_media":1433,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1431","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-learn"],"acf":[],"_links":{"self":[{"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/posts\/1431","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/comments?post=1431"}],"version-history":[{"count":1,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/posts\/1431\/revisions"}],"predecessor-version":[{"id":1432,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/posts\/1431\/revisions\/1432"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/media\/1433"}],"wp:attachment":[{"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/media?parent=1431"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/categories?post=1431"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mestric.com\/sl\/wp-json\/wp\/v2\/tags?post=1431"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}