{"id":104566,"date":"2026-10-20T19:34:39","date_gmt":"2026-10-20T19:34:39","guid":{"rendered":"https:\/\/skyplanner.ai\/?p=104566"},"modified":"2026-10-20T19:35:17","modified_gmt":"2026-10-20T19:35:17","slug":"first-pass-yield","status":"publish","type":"post","link":"https:\/\/skyplanner.ai\/et\/ressursid\/first-pass-yield\/","title":{"rendered":"First Pass Yield: valem, n\u00e4ide ja varjatud \u00fcmbert\u00f6\u00f6tlus"},"content":{"rendered":"
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First Pass Yield: valem, n\u00e4ide ja varjatud \u00fcmbert\u00f6\u00f6tlus<\/h1>\n\n
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Avaleht<\/a> » Ressursid<\/a> » First Pass Yield: valem, n\u00e4ide ja varjatud \u00fcmbert\u00f6\u00f6tlus<\/p>\n\n\n

<\/path><\/svg><\/span><\/span>Hinnanguline lugemisaeg: <\/span>9<\/span> minutit<\/span><\/p>\n\n<\/div>\n

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First Pass Yield (FPY) ehk esimesel katsel n\u00f5uetele vastanud toodangu osakaal n\u00e4itab, mitu protsenti detailidest vastab m\u00e4\u00e4ratletud protsessis kokkulepitud kvaliteedin\u00f5uetele kohe esimesel katsel, ilma parandamise, \u00fcmbert\u00f6\u00f6tluse v\u00f5i eba\u00f5nnestunud katsele j\u00e4rgneva kordustestita. See n\u00e4itab, kui suur osa toodangust oli kohe n\u00f5uetekohane, mitte seda, kui suur osa l\u00f5puks vastuv\u00f5etavaks sai.<\/p>\n\n\n

See erinevus on oluline, kui valmistoodangu kogus paistab korralik, kuid tehasel on raske tootmistempoga sammu pidada. Parandatud detailid saab k\u00fcll v\u00e4lja saata, ent nende valmimiseks on kulunud aega, mida vajasid teised tellimused. FPY j\u00e4lgimine koos parandust\u00f6\u00f6de mahuga toob n\u00e4htavale selle, mis n\u00f5uetekohase toodangu koguarvu taha peitu j\u00e4\u00e4b. Kui siduda need t\u00f6\u00f6d SkyPlanneri tootmisandmete ja ajakava koostamisega, on lihtsam n\u00e4ha nende m\u00f5ju tootmisv\u00f5imsusele ja tarnetele.<\/p>\n\n\n

Kuidas arvutada FPY-d?<\/h2>\n\n\n

Kasuta j\u00e4rgmist valemit:<\/p>\n\n\n

FPY (%) = Esimesel katsel vastuv\u00f5etud detailide arv \u00f7 M\u00e4\u00e4ratletud protsessi sisenenud detailide arv \u00d7 100<\/strong><\/p>\n\n\n

Loenda iga detaili ainult \u00fcks kord, mitte kontrollimisi ega \u00fcksikuid defekte. Detail, mis esialgu n\u00f5uetele ei vasta, kuid p\u00e4rast parandamist kontrolli l\u00e4bib, kuulub n\u00f5uetekohase l\u00f5pptoodangu hulka, ent ei ole esimesel katsel \u00f5nnestunud detail. Ka mitme defektiga detail l\u00e4heb selles arvutuses kirja \u00fche n\u00f5uetele mittevastava detailina.<\/p>\n\n\n

Enne andmete kogumist m\u00e4\u00e4ra protsessi piirid. M\u00f5\u00f5ta v\u00f5ib n\u00e4iteks \u00fcht mehaanilise t\u00f6\u00f6tlemise operatsiooni, testimiskohta v\u00f5i kogu tootmisteekonda. Lugeja ja nimetaja peavad kirjeldama sama detailir\u00fchma sama protsessipiiri ulatuses.<\/p>\n\n\n

N\u00e4ide: FPY on 90%, l\u00f5plik n\u00f5uetekohase toodangu osakaal 96%<\/h3>\n\n\n

Oletame, et meil on 500 mehaaniliselt t\u00f6\u00f6deldud detailiga partii ning k\u00f5igi detailide esimese kontrolli tulemused on teada:<\/p>\n\n\n

Esimese katse tulemus<\/th>Detailide arv<\/th>Mis saab edasi?<\/th><\/tr><\/thead>
Vastu v\u00f5etud ilma parandusteta<\/td>450<\/td>Valmis j\u00e4rgmiseks operatsiooniks<\/td><\/tr>
Saadetud \u00fcmbert\u00f6\u00f6tlusse<\/td>40<\/td>30 vastavad hiljem n\u00f5uetele; 10 l\u00e4hevad l\u00f5puks praaki<\/td><\/tr>
Kohe praagiks tunnistatud<\/td>10<\/td>Ei saa j\u00e4rgmisse etappi liikuda<\/td><\/tr>
Operatsiooni sisenenud detaile kokku<\/td>500<\/td>Igal detailil on \u00fcks esimese katse tulemus<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

FPY on:<\/p>\n\n\n

450 \u00f7 500 \u00d7 100 = 90%<\/strong><\/p>\n\n\n

P\u00e4rast \u00fcmbert\u00f6\u00f6tlust on vastuv\u00f5etud detaile 480: algsed 450 ja 30 parandatud detaili. Seega on selle partii l\u00f5plik n\u00f5uetekohase toodangu osakaal:<\/p>\n\n\n

480 \u00f7 500 \u00d7 100 = 96%<\/strong><\/p>\n\n\n

M\u00f5lemad n\u00e4itajad on kasulikud. FPY kirjeldab esimese katse tulemust. L\u00f5plik n\u00f5uetekohase toodangu osakaal n\u00e4itab l\u00f5puks vastuv\u00f5etud detailide osakaalu, sealhulgas edukalt \u00fcmbert\u00f6\u00f6deldud detaile. Kui esitada ainult 96%, j\u00e4\u00e4b varju t\u00f6\u00f6, mida oli vaja nende 30 detaili parandamiseks.<\/p>\n\n\n

Ka FPY arvutusest \u00fcle j\u00e4\u00e4v 10% ei ole praagim\u00e4\u00e4r. Selles n\u00e4ites l\u00e4heb l\u00f5puks praaki 20 detaili, seega on l\u00f5plik praagim\u00e4\u00e4r 4%. \u00dclej\u00e4\u00e4nud 6% algsest partiist muudeti lisat\u00f6\u00f6ga n\u00f5uetekohaseks. Ainult praagi mahaarvamine annaks l\u00f5pliku n\u00f5uetekohase toodangu osakaalu, mitte FPY.<\/p>\n\n\n

Enne protsendi usaldamist pane loendusreeglid paika<\/h2>\n\n\n

Arvutamine on lihtne. Rohkem t\u00e4helepanu n\u00f5uab see, et \u201eesimene katse\u201c t\u00e4hendaks andmetes alati sama, eriti kui tootmis- ja kontrolliandmed p\u00e4rinevad eri s\u00fcsteemidest.<\/p>\n\n\n

1. Vali \u00fcks protsess ja v\u00f5rreldav tooter\u00fchm.<\/strong> Mehaanilise t\u00f6\u00f6tlemise esimese katse tulemus ei t\u00e4henda kogu koostu l\u00f5plikku vastuv\u00f5tmist. M\u00e4\u00e4ra tootmisteekonnad ja vastuv\u00f5tukriteeriumid selgelt, et tootevaliku muutust ei peetaks ekslikult kvaliteedi paranemiseks.<\/p>\n\n\n

2. Seo protsessi sisenenud detailir\u00fchm selle enda tulemustega.<\/strong> Alusta partiist v\u00f5i muust j\u00e4lgitavast r\u00fchmast, mille esimese katse tulemused on teada. Kui jagada t\u00e4na valminud ja vastuv\u00f5etud detailide arv t\u00e4na alustatud detailide arvuga, v\u00f5ivad arvutusse sattuda eri detailid, eriti kui t\u00f6\u00f6d kestavad \u00fcle vahetuste piiride.<\/p>\n\n\n

3. S\u00e4ilita algne tulemus.<\/strong> Hoia esimesel katsel vastuv\u00f5tmine lahus hilisemast vastuv\u00f5tmisest p\u00e4rast parandamist v\u00f5i kordustesti. Kui parandatud detaili eduka kontrolli j\u00e4rel algne eba\u00f5nnestunud tulemus \u00fcle kirjutada, paistab ajalugu parem, kuigi tegelikult toimunu ei muutu.<\/p>\n\n\n

4. Loenda iga detaili valitud protsessipiiril ainult \u00fcks kord.<\/strong> Mitu defekti, korduvad testid ja korduvad ajakanded ei suurenda algsete detailide arvu. Kasuta detailide tunnuseid v\u00f5i kontrollitud ja vastavusse viidud partiiarvestust, milles s\u00e4ilivad kogused ja \u00fcmbert\u00f6\u00f6tluse ajalugu.<\/p>\n\n\n

5. N\u00e4ita ka veel selgumata tulemusi.<\/strong> Kontrolli ootavad detailid ei ole automaatselt vastuv\u00f5etud ega tagasi l\u00fckatud. Too nende kogus eraldi v\u00e4lja ja kinnita r\u00fchma l\u00f5plik FPY siis, kui k\u00f5igi detailide esimese katse tulemused on teada, selle asemel et probleemsed detailid vaikselt arvestusest v\u00e4lja j\u00e4tta.<\/p>\n\n\n

Operatsiooni tavap\u00e4rane t\u00f6\u00f6j\u00e4rjestus v\u00f5ib sisaldada mitut plaanilist kontrolli. Selle kokkulepitud j\u00e4rjestuse korrektne l\u00e4bimine esimesel katsel ei ole sama mis eba\u00f5nnestunud testi kordamine v\u00f5i defekti parandamine. Kirjelda seda erinevust m\u00f5\u00f5diku m\u00e4\u00e4ratluses ja j\u00e4rgi seda j\u00e4rjepidevalt.<\/p>\n\n\n

Kui aruandes on ainult valminud ja defektsete detailide kogused<\/a>, kontrolli enne nende p\u00f5hjal FPY arvutamist, mida need v\u00e4ljad t\u00e4hendavad. Pead teadma, kas vastuv\u00f5etud kogus sisaldab parandatud detaile ja kas korduvat t\u00f6\u00f6tlemist on loendatud mitu korda.<\/p>\n\n\n

FPY ja kogu tootmisteekonna esmakordne \u00f5nnestumine ehk RTY<\/h2>\n\n\n

\u00dcksiku operatsiooni tulemus v\u00f5ib paista hea, kuigi kogu tootmisteekond n\u00f5uab palju parandust\u00f6\u00f6d. RTY ehk kogu tootmisteekonna esmakordse \u00f5nnestumise m\u00e4\u00e4r n\u00e4itab j\u00e4rjestikuste protsessietappide koondtulemust esimesel katsel.<\/p>\n\n\n

\u00dchtse t\u00f6\u00f6j\u00e4rjestuse puhul korruta etappide FPY-v\u00e4\u00e4rtused k\u00fcmnendmurdudena:<\/p>\n\n\n

RTY (%) = 1. etapi FPY \u00d7 2. etapi FPY \u00d7 \u2026 \u00d7 100<\/strong><\/p>\n\n\n

Oletame, et j\u00e4lgime 1 000 detaili l\u00e4bimist kolmest operatsioonist. Selles n\u00e4ites j\u00e4tkavad j\u00e4lgitavas r\u00fchmas ainult detailid, mis on iga etapi l\u00e4binud esimesel katsel edukalt; parandatud detaile r\u00fchma tagasi ei lisata.<\/p>\n\n\n

Operatsioon<\/th>Etappi sisenevaid detaile j\u00e4lgitavas r\u00fchmas<\/th>Esimesel katsel n\u00f5uetele vastanud detaile<\/th>Etapi FPY<\/th><\/tr><\/thead>
Mehaaniline t\u00f6\u00f6tlemine<\/td>1 000<\/td>900<\/td>90%<\/td><\/tr>
Viimistlus<\/td>900<\/td>810<\/td>90%<\/td><\/tr>
Testimine<\/td>810<\/td>729<\/td>90%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

0,90 \u00d7 0,90 \u00d7 0,90 \u00d7 100 = 72,9% RTY<\/strong><\/p>\n\n\n

Iga operatsiooni FPY on 90%, kuid algsest 1 000 detailist l\u00e4bib k\u00f5ik kolm etappi \u00fchegi eba\u00f5nnestumiseta ainult 729. Kolme protsendi keskmine oleks 90% ja j\u00e4taks kumulatiivse m\u00f5ju varju.<\/p>\n\n\n

Tegelikus tootmises v\u00f5ivad parandatud detailid tootmisvoogu naasta ja tooted l\u00e4bida erinevaid tootmisteekondi. Kasuta sama tootmisteekonna etappide FPY-v\u00e4\u00e4rtusi, mis p\u00f5hinevad \u00fchtsetel loendusreeglitel; \u00e4ra korruta omavahel seostamata osakondade keskmisi ega nimeta tulemust tegelikult m\u00f5\u00f5detud kogu tootmisteekonna \u00f5nnestumise m\u00e4\u00e4raks. Iga detaili esimese katse ajaloo s\u00e4ilitamine v\u00f5imaldab kontrollida, mitu algset detaili l\u00e4bis tegelikult kogu teekonna ilma parandusteta.<\/p>\n\n\n

Milline FPY on hea?<\/h2>\n\n\n

Kasulik sihttase arvestab toodet, protsessi, kvaliteedin\u00f5udeid ja eba\u00f5nnestumise tagaj\u00e4rgi. \u00dcldisest v\u00f5rdlusn\u00e4itajast ei piisa, et otsustada, kas konkreetse operatsiooni tulemused on head.<\/p>\n\n\n

Alusta v\u00f5rreldavate t\u00f6\u00f6de tulemuste muutumisest ajas. Vaata protsendi k\u00f5rval ka m\u00f5\u00f5detud detailide arvu, eba\u00f5nnestumiste p\u00f5hjuseid ja parandust\u00f6\u00f6de mahtu. K\u00f5rgem FPY on sisuline n\u00e4itaja siis, kui kehtivad samad vastuv\u00f5tureeglid. Paranemisega ei ole tegu, kui meeskond on vaid leevendanud kontrollikriteeriume v\u00f5i j\u00e4tnud probleemsed detailid nimetajast v\u00e4lja.<\/p>\n\n\n

Parandamiseks otsi korduvat probleemi, mida saad m\u00f5jutada. R\u00fchmita esimesel katsel eba\u00f5nnestunud tulemused operatsiooni ja kinnitatud p\u00f5hjuse j\u00e4rgi. \u00dche toote korduv m\u00f5\u00f5duviga v\u00f5ib suunata t\u00e4helepanu t\u00f6\u00f6riistale, rakisele, materjalile v\u00f5i t\u00f6\u00f6meetodile, kuid protsent \u00fcksi ei n\u00e4ita, milline neist on p\u00f5hjus. Vaata t\u00f5endid koos t\u00f6\u00f6 tegijatega \u00fcle, k\u00f5rvalda p\u00f5hjus ja m\u00f5\u00f5da v\u00f5rreldavat toodangut uuesti.<\/p>\n\n\n

Arvesta ka parandust\u00f6\u00f6de koormusega. Kahel sama FPY-ga operatsioonil v\u00f5ivad olla v\u00e4ga erinevad tagaj\u00e4rjed, kui \u00fche puhul piisab kiirest parandusest, teine aga h\u00f5ivab mitme t\u00f6\u00f6 jaoks vajaliku masina tundideks. Siin muutub kvaliteeditulemus planeerimisotsuseks.<\/p>\n\n\n

Arvuta FPY taga peituva \u00fcmbert\u00f6\u00f6tluse tegelik tootmisv\u00f5imsuse vajadus<\/h2>\n\n\n

Tuleme tagasi 500 detailiga partii juurde. Oletame, et selle partii k\u00f5ik 40 \u00fcmbert\u00f6\u00f6tluskatset v\u00f5tavad iga\u00fcks samas t\u00f6\u00f6tluskeskuses veel 12 minutit, olenemata sellest, kas detail l\u00f5puks vastab n\u00f5uetele v\u00f5i l\u00e4heb praaki.<\/p>\n\n\n

40 katset \u00d7 12 minutit = 480 minutit = 8 masinatundi<\/strong><\/p>\n\n\n

L\u00f5pliku saagise aruandes on kirjas 480 vastuv\u00f5etud detaili. Tootmisplaanis tuleb aga arvestada ka nende kaheksa lisatunniga. T\u00e4iendav seadistamine, kontroll v\u00f5i teisaldamine v\u00f5ib n\u00f5uda lisaaega ka mujal.<\/p>\n\n\n

\u00c4rge eeldage, et kui 10% detailidest esimesel katsel n\u00f5uetele ei vasta, kaob t\u00e4pselt 10% tootmisv\u00f5imsusest. Parandust\u00f6\u00f6de operatsioonijadad ja kestused on erinevad. Arvutage v\u00e4lja tegelik t\u00f6\u00f6maht nende ressursside kaupa, millel t\u00f6\u00f6 tuleb teha. Samuti tuleb hoida masinatunnid ja operaatori t\u00f6\u00f6tunnid lahus, kui need v\u00e4ljendavad erinevaid piiranguid.<\/p>\n\n\n

Kui see t\u00f6\u00f6tlemiskeskus on olulise saadetise v\u00e4ljasaatmiseni juba t\u00f6\u00f6dega h\u00f5ivatud, ei teki tootmisv\u00f5imsust juurde sellest, et surute \u00fcmbert\u00f6\u00f6tluse eraldi j\u00e4rjekorda. See muudab vaid seda, milline tellimus saab esimesena valmis. Kui n\u00e4ete vastuolu varakult, j\u00e4\u00e4b teil aega m\u00e4\u00e4rata prioriteet, kasutada teist sobivat ressurssi v\u00f5i vaadata lubatud tarneaeg \u00fcle enne, kui olukord muutub viimase hetke \u00fcllatuseks.<\/p>\n\n\n

V\u00f5tke registreeritud \u00fcmbert\u00f6\u00f6tlus SkyPlanneri abil j\u00e4rgmises tootmisplaanis arvesse<\/h2>\n\n\n

SkyPlanner \u00fchendab tootmise ajakulu registreerimise, \u00fcksikasjalikud aruanded ja tehisintellekti abil tootmise ajakava koostamise. Tehases, kus \u00fcmbert\u00f6\u00f6tlus pidevalt tarneid h\u00e4irib, on see praktiline j\u00e4rgmine samm: teha parandust\u00f6\u00f6dele kuluv aeg n\u00e4htavaks ning anda kinnitatud parandust\u00f6\u00f6dele klienditellimuste k\u00f5rval ajakavas realistlik koht.<\/p>\n\n\n

SkyPlanneri ajam\u00f5\u00f5dik Timer registreerib t\u00f6\u00f6aja ning toodetud v\u00f5i praakdetailide kogused. Aruanne All Timelogs Report seob ajakirjed t\u00f6\u00f6, protsessietapi, t\u00f6\u00f6keskuse, t\u00f6\u00f6taja, algus- ja l\u00f5puaja, valminud ja praakdetailide, registreeritud ajakulu ning m\u00e4rkustega. Seal saab kirjeid \u00fcle vaadata, muuta ja kinnitada.<\/p>\n\n\n

Kui parandust\u00f6\u00f6d on t\u00f6\u00f6- ja ajakirjetes eristatavad, saate uurida, millele registreeritud aeg kulus, selle asemel et see kaoks p\u00e4eva \u00fcldise kogusumma sisse. FPY arvutamiseks s\u00e4ilitage kvaliteedi- v\u00f5i MES-s\u00fcsteemi kirjetes ajalugu selle kohta, millised detailid v\u00f5eti vastu esimesel katsel ja millised l\u00e4bisid \u00fcmbert\u00f6\u00f6tluse. Need kirjed selgitavad kvaliteeditulemust; nendega seotud ajakirjed n\u00e4itavad, kui palju aega kulus detailide n\u00f5uetega vastavusse viimiseks.<\/p>\n\n\n

Seej\u00e4rel lisage ajakavasse kinnitatud parandusoperatsioonid<\/a>, nende kestused ja sobivad ressursid. SkyPlanneri tehisintellektil p\u00f5hinev ajastamismootor Arcturus paigutab t\u00f6\u00f6d ajakavasse, arvestades seadistatud tootmisv\u00f5imsust, operatsioonidevahelisi s\u00f5ltuvusi ja prioriteete. Gantt-ajateljel on n\u00e4ha tulemuseks saadud t\u00f6\u00f6de j\u00e4rjekord ja valmimiskuup\u00e4evad. Teie m\u00e4\u00e4rate prioriteedid ja otsused, mis peavad j\u00e4\u00e4ma muutmata; Arcturus arvutab nende p\u00f5hjal omavahel seotud t\u00f6\u00f6de ajakava.<\/p>\n\n\n

Selle n\u00e4itliku partii puhul muutub k\u00fcsimus konkreetseks: kuhu mahutada veel kaheksa masint\u00f6\u00f6tlustundi ja mis saab tellimustest, mis seda ressurssi juba kasutavad? \u00dchise plaani<\/a> \u00fcmberarvutamine toob valikute m\u00f5ju n\u00e4htavale, ilma et peaksite iga m\u00f5jutatud t\u00f6\u00f6keskuse j\u00e4rjekorda eraldi k\u00e4sitsi parandama.<\/p>\n\n\n

SkyPlanneri saab avatud REST API kaudu integreerida mis tahes ERP- v\u00f5i MES-s\u00fcsteemiga. Saate alles j\u00e4tta s\u00fcsteemid, milles hoiate tellimusi ja kvaliteediajalugu, ning siduda neist tulenevad t\u00f6\u00f6d tootmise ajakava koostamisega piiratud tootmisv\u00f5imsuse tingimustes.<\/p>\n\n\n

Vaadake, kuidas esimesel katsel tekkinud kvaliteedikaod m\u00f5jutavad tarneid<\/h2>\n\n\n

Valige \u00fcks operatsioon, mille puhul l\u00f5plik vastuv\u00f5etud kogus varjab sageli lisat\u00f6\u00f6d. V\u00f5tke SkyPlanneri esitlusele kaasa selle operatsiooni esimese katse tulemused, t\u00fc\u00fcpiline kinnitatud parandust\u00f6\u00f6de operatsioonijada ja \u00fcks m\u00f5jutatud klienditellimus.<\/p>\n\n\n

Nii on teil konkreetne l\u00e4htekoht, et vaadata, kuidas registreeritud tootmisandmed ja \u00fchine ajakava saavad aidata teie meeskonnal reageerida: hinnata parandust\u00f6\u00f6de mahtu, vaadata \u00fcle vastuolu tootmisv\u00f5imsuse vajaduse ja saadavuse vahel ning teha j\u00e4rgmine tarneotsus plaani alusel, mis arvestab ka veel tegemata t\u00f6\u00f6d. Broneerige SkyPlanneri esitlus<\/a>, et uurida oma protsessi v\u00f5imalusi, v\u00f5i proovige esmalt SkyPlannerit n\u00e4idisandmetega<\/a>.<\/p>\n\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

First Pass Yield: valem, n\u00e4ide ja varjatud \u00fcmbert\u00f6\u00f6tlus Avaleht » Ressursid » First Pass Yield: valem, n\u00e4ide ja varjatud \u00fcmbert\u00f6\u00f6tlus First Pass Yield (FPY) ehk esimesel katsel n\u00f5uetele vastanud toodangu osakaal n\u00e4itab, mitu protsenti detailidest vastab m\u00e4\u00e4ratletud protsessis kokkulepitud kvaliteedin\u00f5uetele kohe esimesel katsel, ilma parandamise, \u00fcmbert\u00f6\u00f6tluse v\u00f5i eba\u00f5nnestunud katsele j\u00e4rgneva kordustestita. See n\u00e4itab, kui suur … Read more<\/a><\/p>\n","protected":false},"author":3,"featured_media":104594,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"_uag_custom_page_level_css":"","footnotes":""},"categories":[313,2549],"tags":[],"class_list":["post-104566","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ressursid","category-tootmise-tohusus","generate-columns","tablet-grid-50","mobile-grid-100","grid-parent","grid-33"],"acf":[],"yoast_head":"\nFirst Pass Yield: valem, n\u00e4ide ja varjatud \u00fcmbert\u00f6\u00f6tlus<\/title>\n<meta name=\"description\" content=\"Arvuta FPY, j\u00e4ttes parandatud detailid esimesel katsel vastuv\u00f5etute hulgast v\u00e4lja. 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See n\u00e4itab, kui suur…","_links":{"self":[{"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/posts\/104566","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/comments?post=104566"}],"version-history":[{"count":1,"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/posts\/104566\/revisions"}],"predecessor-version":[{"id":104628,"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/posts\/104566\/revisions\/104628"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/media\/104594"}],"wp:attachment":[{"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/media?parent=104566"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/categories?post=104566"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/skyplanner.ai\/et\/wp-json\/wp\/v2\/tags?post=104566"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}