rq massmedic capa on capa webinar v2€¦ · § indications that your capa system may be feeling a...
TRANSCRIPT
C A P A O N C A P A
“We s h o u l d w o r k o n o u r p r o c e s s , n o t t h e o u t c om e o f o u r p r o c e s s e s ”
A B O U T R & Q
RegulatoryandQualitySolutions(R&Q)providesindustry-leadingregulatoryandqualityengineeringservicesthroughouttheentireproductlifecycle.
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A B O U T T O D A Y ’ S S P E A K E R
§ 25+yearsofdomesticandinternationalexperience
§ Previous:Covidien,BostonScientific,BARD
§ VPQuality- Maintained20+manufacturingplantsworldwide
PaulRobinson,SeniorDirectorofRegionalOperations,R&Q
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A G E N D A
§ Why?§ Realworld
§ Readyforanaudit?WhatdoestheFDAlookfor?§ CAPAregulations/requirements
§ 820.100
§ CAPAsystem§ Feeders§ Datatrending/evaluation/alerts§ CAPAprocess
§ Riskmanagement§ Systemmanagement
§ TheCAPA§ Rootcauseanalysis§ Statisticaltools§ ELCD– Engineering,Logic,CommonSense,andDiscipline!
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W H Y ?
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W H Y ?
§ Realworld§ FDAaudit§ 80/20rule– everheardofthat?
§ Remember:CAPAisnot anassignmentorproject.Itisasystem.§ CAPAhasbeencalledthe”immunesystem”oftheQualityManagementSystem.Itmustworkforyourorganizationtorepairorimproveitselfproperly.
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TimeforaCheckup?
W H Y ?
§ IndicationsthatyourCAPAsystemmaybefeelingalittleundertheweather:§ CAPAmetricsareinadequateformanagementunderstandingandcontrol;toomany,toofew,tooconfusing
§ CAPAprojectsarenotprogressing§ Thereisaveryweak“CAPAculture”§ Yourorganizationseemstohavetoomany CAPAprojectsortheyarelanguishing
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We’lldiscusswhattodoina
fewslides
W H Y ?
§ Realworld?§ Managedcorrectly,it’sanopportunityforimprovement
§ Reportingproblems§ Proactivelyaddressingproblems§ Closedloopsystem
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Don’tbeafraid!
W H Y E L S E ?
§ Regulationsrequireit!§ 21CodeofFederalRegulations(CFR)820.100“Eachmanufacturershall establishandmaintainproceduresforimplementingcorrectiveandpreventiveaction.”
§ ISO13485:2003,8.5.2,8.5.3“Theorganizationshall identifyandimplementanychangesnecessarytoensureandmaintainthecontinuedsuitabilityandeffectivenessofthequalitymanagementsystemthroughtheuseofthequalitypolicy,qualityobjectives,auditresults,analysisofdata,correctiveandpreventiveactionsandmanagementreview.”
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Evenifnotrequired,itisstillsmarttodo.Ishouldknow,I’m
anowl!We’rewickedsmaht!
C A P A S Y S T E M
F e e d e r s
D a t a T r e n d i n g / E v a l u a t i o n /A l e r t s
C A PA P r o c e s s
S y s t e m M a n a g em e n t
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Onemoretime.Itisnotanassignment
oraproject.ItisaSYSTEM!
T H E F E E D E R S
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CAPASystem!
ManagementReview
Audits
Complaints/MDRs
Non-ConformingMaterial
EnvironmentalMonitoring
SupplierQuality
Scrap
Re-work
Allfeedersareequal,somemoreequalthanothers!
D A T A T R E N D I N G / E V A L U A T I O N / A L E R T S
§ How?§ Tables§ Runcharts§ SPC§ Paretoanalysis§ Audits§ Complaints§ Etc.
§ What’simportanttomonitorwithinyourfeedersystem?§ Howyouaregoingtoevaluate;andwhatareyouralert/actionlimits?
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Feeder Owner Metric ReportingFrequency
Method Alert Limit
Scrap Operations % Scrap Monthly SPC SPC Rules
Audits Quality % Schedule Monthly Limit >95%
Howyoudoin?
C A P A P R O C E S S ( R I S K - B A S E D A P P R O A C H )
InitiateCorrectiveActionRequest
1.NoAction 2.Correction 3.CAPA
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C A P A P R O C E S S
1. ProblemStatement
2. Feeder/Source/Alert3. ProductsAffected
4. ProcessesAffected
5. Departments/FunctionsAffected
6. EventDescription/Summary
7. InitialRiskAssessment(withRationaleandDocumentation)8. InitialFrequencyAssessmentwithrationaleanddocumentation
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InitiateCorrectiveActionRequest
Seerisk-baseddecisionmatrixexamplenextslide
P R O C E D U R E
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RISK BASED ESCALATION
FREQUENCYProbability is so
remote Unlikely probability Occasional
probability Moderate probability
High probability
1 2 3 4 5
PRO
DUCT
AND
/OR
BUSI
NESS
RIS
K
Negligible Patient Risk
Business Performance Risk 1 NO ACTION REQUIRED
NO ACTION REQUIRED CORRECTION CORRECTION CORRECTION
Minor Patient Risk
Internal Compliance Risk 2 NO ACTION REQUIRED CORRECTION CORRECTION CAPA CAPA
Moderate Patient Risk
Documentation Compliance Risk 3 CORRECTION CORRECTION CAPA CAPA CAPA
Severe Patient Risk
Quality System Compliance Risk 4 CAPA CAPA CAPA CAPA CAPA
Catastrophic Patient Risk
Regulatory Approval/Warning Risk 5 CAPA CAPA CAPA CAPA CAPA
C A P A P R O C E S S
1. OK,butprovidesolidrationale…
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NoActionRequired
C A P A P R O C E S S
1. Contain
2. Correctanddispose
3. Verify
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Correction
Timetolean?TimetoClean!
C A P A P R O C E S S
1. Correction1. Contain2. Dispose3. Verify
2. Investigation
3. Solutionverification/validation
4. Implementation
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CAPA
S Y S T E M M A N A G E M E N T
§ Managementreview
§ CAPAboard
§ Qualityreports
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ItwouldnotbeincorrecttoassumethattheCAPAsystemisoneof,ifnot,THEmostimportantcomponentsofaQualitySystem,SOMONITORitshealthandeffectivenessandTAKEACTIONasneeded!- PAULROBINSON
T H E C A P A
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R O O T C A U S E A N A L Y S I S & S T A T I S T I C A L T O O L S
1. Correction1. Contain2. Dispose3. Verify
2. Investigation
3. Solutionverification/validation
4. Implementation
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Thereisno
MagicWand!
E.L.CS.D!
Ok,I’mdone…
R O O T C A U S E A N A L Y S I S & S T A T I S T I C A L T O O L S
§ Avoiding…
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ORPROBLEMPARETO
Isolvedthisone,I’mdone!
R O O T C A U S E A N A L Y S I S & S T A T I S T I C A L T O O L S
§ Investigation
1. Problemdescription1. ”What’syourproblem?”
2. Backgroundinformation1. “HowdidIgethere?”
3. Sourceinvestigation1. “Iknowit’sinheresomewhere.”
4. Miningtools1. Dig,dig,dig
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§ Implementation
1. Verification/validation!
2. Verification/validation!
R O O T C A U S E A N A L Y S I S & S T A T I S T I C A L T O O L S
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DeterminingProbabilities Theprobabilityofaneventisthenumberofoutcomesthattheeventoccursdividedbythenumberofpossibleoutcomes.Forexampletheprobabilityofgettinga“heads”ontheflipofacoinis½or.5(heads/heads+tails).Thistoolcanbeusedindeterminingcomplaintrates,defectlevels,non-conformances,etc.
R O O T C A U S E A N A L Y S I S & S T A T I S T I C A L T O O L S
§ MeasurementSystemAnalysis§ Overallvariationcomingfromaproductorprocessismadeupofthevariationinherent
intheproductorprocessitselfandanaddedcomponentofvariationcomingfromthemeasurementsystem.
§ Measurementsystemvariabilitycanbebrokendownintotwocomponents,repeatabilityandreproducibility.Repeatabilityisthebuiltinvariationofameasurementsystemandiscalculatedusingrepeatedmeasurementsmadeofthesamevariableunderidenticalconditions.Reproducibilityresultswhendifferentconditionsareimposedonmeasurements(differentoperators,setupmethods,environment,etc.)
§ ThemetricsoftencalculatedaretheGaugeR&R(repeatabilityandreproducibility)andtheP/Tratio.TheR&Rrepresentstheproportionoftheoverallvariationcomingfromyourmeasurementsystem.TheP/Tratioistheamountofyourspecificationtakenupbymeasurementsystemvariationalone.
§ Beforedoinganyanalysis,itiscrucialtohavecapablemeasurementsystems.ValuesforR&Rshouldbelessthan30%.ValuesforP/Tratioshouldbelessthan15%.Note-therequiredvaluescanbechangedbasedonrisk.
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R O O T C A U S E A N A L Y S I S & S T A T I S T I C A L T O O L S
§ Processanalysis§ Shape§ Spread§ Centraltendency§ Temporalelement§ Capability
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Processanalysiscanbedonebylookingatvariouselementsofdatadistribution.
Shape- Reviewingtheshapeofadistributioncanhelpidentifycertainfactsaboutaprocess.Isitskewedtoonesizeoranother?Doyouhaveabi-modeldistribution(potentialoftwosamplepopulations?)Isitnormal?Flat?
Spread- Howvariableisyourprocess?Oftendefinedbythestandarddeviation(squarerootofvariance)orinsomecasestherange(differenceofthehighpointversesthelowpoint- sensitivetooutliers)
CentralTendency- Thecenterofthedistribution.
TemporalElement:Howdoestheprocesschangeovertime?Typicallyrunchartsareeffectivewaystomeasurehowaprocesschangesovertime.
Capability:Severaltermsareusedtodefinecapability.
Cp istheshortterm/limiteddatacapabilityofyourproductorprocessinmeetingtherangeofyourspecification.Itistheratioofyourshorttermvariationdividedbythewidthofyourspecificationrange.Ifbarelycapable,thatratiowillbe1.0.Iflessthan1.0,yourprocessisnotcapable.Itdoesnottakeintoaccountadistributiondeviatingfromcenterspecification.
Cpk istheshort/termlimiteddatacapabilityofyourproductorprocess.Itdoestakeintoconsiderationanydeviationfromcenter.Cpk’s barelycapablehaveavalueof1.0.Ifaprocessorproductislessthan1.0,itisconsiderednotcapable.
Pp isthelongtermprocessperformanceindicator.Itistheratioofyourlongtermprocessvariationdividedbythewidthofyourspecification.Itdoesnotconsiderdeviationfromthecenterofthespecification
Ppk isthelongtermcapabilityofyourproductorprocess.Itdoestakeintoconsiderationanydeviationfromcenter.Ppks barelycapablehaveavalueof1.0.Ifaprocessorproductislessthan1.0,itisconsiderednotcapable.
R O O T C A U S E A N A L Y S I S & S T A T I S T I C A L T O O L S
§ Relationships§ Scatterplots§ Boxplots§ Paretocharts§ Piecharts
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ScatterPlots- ThedataisdisplayedasacollectionofpointsonanXYchart.Eachhasthevalueofonevariabledeterminingthepositiononthehorizontalaxisandthevalueoftheothervariabledeterminingthepositionontheverticalaxis.ThedisplayenablesanunderstandingoftherelationshipbetweentheXandYvariableinadditiontothestrengthofthatsignal.
BoxPlots- isastandardizedwayofdisplayingthedistributionofdatabasedonthefivenumbersummary:minimum,firstquartile,median,thirdquartile,andmaximum.Itisaconvenienttooltodisplaythespreadandshapeofadistribution.
ParetoCharts- atypeofchartthatcontainsbothbarsandalinegraph,whereindividualvaluesarerepresentedindescendingorderbybars,andthecumulativetotalisrepresentedbytheline.Thischartisveryeffectiveinidentifyingthetopcontributorstoaconditions.RemembertheParetoprinciple:“80percentoftheproblemscomefrom20percentofthesources”
PieCharts- aconvenientchartwhichclearlydemonstratestheproportionofsourcesrelativetoasignal.
R O O T C A U S E A N A L Y S I S & S T A T I S T I C A L T O O L S
§ Relationships§ XYdiagram§ Correlation§ Regression§ Hypothesistesting§ ANOVA
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XYdiagram- Thisisaveryusefuldiagramdemonstratingtherelationshipofaninput(theXaxis)toanoutput(theYaxis)
Correlation- isameasureofthedegreeofassociationbetweentwoquantitativevariables.Thevaluerangesfrom1(averystrongpositiverelationship,0(norelationship),to-1(averystrongnegativecorrelation)Thisisveryusefulindeterminingifchangeswithparticularvariablepotentiallyinfluences,causeschanges(oristheacorrelation?)withanothervariable.
Regression allowsyoutoquantifytherelationshipbetweenoneormany“input”variablesagainstanother“output”variable.ThestrengthofthatrelationshipismeasuredbyatermknownasR-squared.ThevalueofR-square(describedasafractionorapercentage)istheestimateoftherelationshipthatcanbeexplainedbythemathematicalmodel/regression.ForexampleifaregressioniscalculatedandcomesupwithanR-squaredvalueof.89or89%,thenyouwouldsaythatthe89%ofthechangesinoutputvariableYcanbeexplainedbytheassociatedinputvariablesinthemodel.Regressionequationsareveryhelpfulinmaking“output”estimatesusingdifferentvaluesofpotential“input”settings.
Hypothesistesting- isapowerfultoolusedininferentialstatisticsordecisionmaking.Itisusedtoprojectaninferencewithacertainprobability,therelationshipoftwosamplesorpopulations.Forexample,afinalstatementwouldbe“with95%confidence,wecanstatethatthereappearstobenosignificantstatisticaldifferencebetweenthetwosamples”Note:Itdoesnotsaythattheyarethesame,itjustsaysthatthereisarelativeconfidenceinsayingwedon’tseeasignal.
ANOVA- isananalysissimilartoaHypothesistesthoweverwithanANOVA,youaremakinganinferencecomparingmultiplesamplesorpopulation.Ultimately,youwillbeabletoinfer:“with95%confidence,wecannotstatethatallofthesesamplesisstatisticallyequaltotheothers”(oneormoreislikelydifferent)
Q U E S T I O N S
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T H A N K Y O U !
F o r m o r e i n f o r m a t i o n a n d t o …
D O W N L O A D W H I T E P A P E R S A N D C A S E S T U D I E S
S U B S C R I B E T O R & Q ’ S B L O G
B E G I N A C O N V E R S A T I O N
v i s i t
f a c e b o o k . c o m / r a n d q
t w i t t e r . c o m / R Q E x p e r t s
l n k e d . i n / r q t e a m
i n s t a g r a m . c o m / r q c a r e e r s