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Daily Boardwalks took 3 hours ofmanualpreparation time

Lack of common metrics and olddata causeddisagreements Lack of visibility of key metricsresulted in lack ofunderstanding of the factory statusand slowedwork ow

Manufacturing challenges

CampainchangeBreaking down barriers to change


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Production System Reports and MetricsManufacturing Process & System Integration(MPSI)Business teamedto produce shop oor reports to meet the high levelmanagement

requirements using BI tools !ear real time data pulled from multiple sources Common metrics for all levels of management to run the "#"factory $ncreased visibility of constraints enabled reduced shop cycletime

%he &'erformance Boards( were implemented in the "#"program in)verett *inal +ssembly, )verett Modi-cation Center.)MC/, )verettDelivery Center.)DC/, Boeing 0outh Carolina.B0C/ and 0an+ntonioCustomer $ntro and 1efurb.C$21/ to meet management

reuirements 'erformance Boards allow "#" manufacturing to retrieve the


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!at the Right "ime! 'erformance Boards o@er near realtime on4demand sourcing anddelivery of data to the customer $ssue +lerts o@er a Aust4$n4%imereport delivery on a subscription

basis!in a Meaningful #isual $isplay 'erformance Boards o@er a colorfulgraphical display of the eyMetrics 'erformance Board metrics aredesigned to be understood uicklyfrom a distance


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$ntegrated 0ervice Management is

key)nable =ptimi>ed $% workloads 5computing

#ISIBI%I" '"R%*+"M*"I


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*ssem,ly 'onstraints)isting B$ toolset and model used toprovide awareness of allconstraining issuesConstraint %ypes

'art 0hortages 1evision olds 2 olds !onconformance

Constraint 1esolution Day to Day =perationsAob $ssue %racking Eniue )mergent !eeds


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Fourney toward of operationalecellence areed operationsH

+utomated tasks, alerts, noti-cationsfor proactive resolutionH*ast access to relevant data frommultiple sourcesH

Compliant =perations


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1educed cost of uality .reFects,reworks, etc/H

1educed cost of complianceHDelivering high4uality products andservices with integrated ualitystandardsH$ntegrated uality management withoperations, ensuring regulatory, legal

and environmental standardsH

H $ndustry Leadership$mprovedcustomer service by delivering onpromisesH0ustained superior performance by

ensuring continuous improvement ofprocesses and systemsH)asy adoption of initiates such asHLean, 0i 0igma, etcH+bility to innovate industry bestpracticesH


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Gisibility drives performance


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perations $ata 'hallengesCostsOrdersMaterialsResourcesCostsOrdersMaterials

ResourcesMost operations data is currently onlyavailable at the 'lant levelData is spread out among severalapplications2componentsData is not contetuali>ed

0ometimes diIcult to access#isuali-ation. Reporting and*nalysis are di/cult and timeconsuming%he answers to improve plantperformance resides within this data


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"urning $ata into IntelligenceInformationCostsOrders

MaterialsResourcesCostsOrdersMaterialsResources

OrdersMaterialsCostsOrders'roduction data &as4is( does not helpanalysis and decision making

$t needs to be turned into informationthat isation1eal4time screens and operationalreports are made available to keyplantpersonnel, to allow root causes to beuickly identi-ed and production kept

incontrol##iissuuaallii--aattiioonn**ccqquuiissiittiioonn..**ggggrreeggaattiioonn &&''oonnttee00ttuuaallii--aattiioon

nSIM*"I' I"(Production Suite and R&$ Suite)%arget ;roupontally oriented manufacturing intelligence to


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Jhat is Business$ntelligenceK

Business $ntelligence enables thebusiness to make intelligent, fact4

based decisions

+ggregateData

Database, Data Mart,Data Jarehouse, )%L

%ools, $ntegration %ools

'resentData

)nrichData

$nform aDecision

1eporting %ools,Dashboards, 0tatic

1eports, Mobile1eporting, =L+' Cubes

+dd Contet to Create$nformation, Descriptive

0tatistics, Benchmarks,Gariance to 'lan or L

Decisions are *act4based and Data4driven


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Jhy is Business $ntelligence 0o $mportantK

"ime

4ith Business Intelligence. 1e can get data to you in a timelymanner

Ma5ing Business$ecisions is a Balance

$ata pinion

(a5a BestProfessional

6udgment)

In the a,sence of data. ,usiness decisions are often made ,y

the 7iPP


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MaFor B$ %rends Mobile

Cloud

0ocial Media

+dvanced +nalytics


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4hat BI technologies 1ill ,e the mostimportant to your organi-ation in thene0t 8 years9

9H 'redictive +nalytics8H Gisuali>ation2Dashboards

3H Master Data Management

H %he Cloud

:H +nalytic Databases

H Mobile B$

"H =pen 0ource

#H %et +nalytics

%DJ$ )ecutive 0ummit N +ugust8797


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B$ %oday vs %omorrow

&B$ today is like reading thenewspaper(

B$ reporting tool on top of a datawarehouse that loads nightly andproduces historical reporting

B$ tomorrow will focus more on real4time events and predicting

tomorrowOs headlines


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8

Customer0at 0urveyComments

+nstructured "e0t Processing

*acebook'age

Blogs

CompetitorsO*acebook

'ages'ublic Jeb 0ites,

Discussion

Boards, 'roduct1eviews +lerts,

1eal4time+ction

%witter'age

Services

:uality

'ost

;riendliness

)mail

+dhoc*eedback

CallCenter!otes,Goice

# l


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#isi,ility 'hallenges and Improvement Priorities

Data acuisition and

consolidation Contetuali>ation,

Collaboration and 0ecurity

!eed for uick analysis,insight 5 actionable metrics

!ear real time visibility toproduction performance in

single2multiple sites +ccess to information

through mobile devices

+utomated correlation ofmanufacturing data toprocess speci-cations andtesting data

+sset performance andreliability centeredmaintenance

Large #olumeof Data

igh #elocityof Data ow

uge #arietyof sources

#eracityofdata

*ccess

*ny1here

*ctiona,leCD

+LL)!;

)0

'1

$=1

$%$)

0


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%ransformative $nsight Jith

%argeted +pps

Mobility

1eal %ime Data Discovery

1eal %ime 0hopoor

$ntelligence

8#

"urn factory 1or5ers into 5no1ledge 1or5ers.providing access to information & insights

M


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System Automation &

Control

Deliver Real-Time Shop Floor Visibility to Multiple Layers oMana!ement

Re"u#e" TC$ % Compleity via 'nte!rate" M! M!mt &(ro"u#tion )e#ution

MES

ERPERP

Corp

M!mt

Shop Floor

Control

(ro"u#tion M!mt

Process Engineer Mana!e re#ipes & a"van#e"

pro#ess instru#tions

Production Supervisor Create & mana!e bat#h

status

Lo! non-#onorman#es

Dispensing / Prod Operator Tou#h S#reen *ser 'ntera#e

(re-+ei!h "esi!nate"in!re"ients

(ro#ess bat#h operations

$perator Certii#ation

Operator ,or rom "ispat#h list

Re#or" transa#tions. /ualityino. +or #ompletion. an"

time Vie+ +or instru#tions an"

#omponent ino

Report pro"u#tion e#eptions

Sills #ertii#ationSupervisor Revie+ & resolve

e#eptions Monitor shop loor

pro!ress

MES for Discrete Mfg MES for Process Mfg

=ptimi>e Manufacturing =perations

racle Manufacturing


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Mobile Discrete 'roduction

0upervisor

Easily viewwork orders

andoperation

progress

Search workordersor barcodescan

Collaboratein contextto resolveexception

Quickly acton the

work order

3

e1


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=racle Manufacturing =perations Center .M=C/

PIs

Plant2Speci3c >PIs

=)) bydepartment,

'roduction'erformance

Metrics byeuipment

earlyimprovementgoals andtrends

Bydepartment,plant

Role2Based

$ash,oards

'onte0tuali-ation


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37PGiew of )uipment

'erformance

Ma0imi-e Performance of our Manufacturing*ssets

et a !"# degree viewof the e$uip%entperfor%ance

- ProductionSupervisor

- %ine Manager

- Maintenance

Supervisor

&or

)uipment downtime Mean time to failure

)@ective run4time

'ressure %emperature umidity

?uantity produced By hour, shift, week

?uantity reFected ?uantity scrapped Defects by reason code

Process Parameters

*vaila,ility

Production :uality

Production utput

)vents for eceptions


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'roduction 0upervisorDashboard

+lert for igh0crap

)uipment 0tatus

)uipment'roduction'erformance

)uipment 0tatus+nalysis

)uipment 0tatus%rend

)uipment =utput%rend

al "ime Shop ;loor Monitoring #ie1


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Manufacturing +nalyticsPrepackaged Manufacturing Analytics

Saple Reports(lan to

(ro"u#e (ro"u#tion

Attainment (ast Due

,or $r"ers (lanne"

(ro"u#tion

(ro"u#tion

Costin! Cost Metri#s

0(' Manua#turin!

Cost Summary Manua#turin!

Cost Varian#e

Summary Cost Tren"

Manua#turin!

1uality 1uality

Summary 1uality

Results 2on

Conorman#e

(ro#ess

Manua#turin! Formulation

3iel"s A"4uste"

Formulation

Varian#e

S#ale"Formulation

Varian#e

Plan to

Produce

Manufacturing E!ecution Process Manufacturing

Manufacturing

"uality

Production Costing #nventory

Manua#turin!

)e#ution ,or $r"er Detail $pen ,or $r"ers ,'( $peration

Analysis S#rap Summary Re+or an" First

(ass 3iel"

'nventory 'nventory

)pirations )#ess an"

$bsolete'nventory

'nventory A!in!


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Manufacturing +nalyticsPrepackaged Manufacturing Analytics

Saple Reports(lan to

(ro"u#e (ro"u#tion

Attainment (ast Due

,or $r"ers (lanne"

(ro"u#tion

(ro"u#tion

Costin! Cost Metri#s

0(' Manua#turin!

Cost Summary Manua#turin!

Cost Varian#e

Summary Cost Tren"

Manua#turin!

1uality 1uality

Summary 1uality

Results 2on

Conorman#e

(ro#ess

Manua#turin! Formulation

3iel"s A"4uste"

Formulation

Varian#e

S#ale"Formulation

Varian#e

Plan to

Produce

Manufacturing E!ecution Process Manufacturing

Manufacturing

"uality

Production Costing #nventory

Manua#turin!

)e#ution ,or $r"er Detail $pen ,or $r"ers ,'( $peration

Analysis S#rap Summary Re+or an" First

(ass 3iel"

'nventory 'nventory

)pirations )#ess an"

$bsolete'nventory

'nventory A!in!


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MES 'orkstation

36


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+nalytics Driven Manufacturing?aining #isi,ility 1ith *dvanced Intelligence

B


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;irst Step

Ese data to make better decisions in our organi>ation

Collect appropriate data and analy>e it in a meaningful fashion

$mprove uality of our services

;et the data into the hands of the people who need it

$ncrease eIciency and e@ectiveness

4hat does it mean for us to ,e @$ata2drivenA9


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Second Step

1egardless of how good we are, we will striveto be better

Je are not asking you to do more with lessRwe want you to do things di@erent

Doing good is not good enough

$evelop a description of our ,elief system

$ t l ti


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$ata evolution 2Stages

Data877:

$nformation8797

nowledge879

Jisdom879:

ad a wealth ofdata across theagency

Data silos

Lack ofconsistency

+dded value todata andinformation

Central location

Minimi>edinconsistencies

Ese internal datato select, facilitate,and monitoruality and impactin real timethrough all ourprograms

0trategic planningand decision4making is based toa large etent oninternalinformation,analysis andrelevant eternal

informationavailable in themarket


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Big Data


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+ -nal thought from J )dwards

Dening

(In od wetrust)

)*ll other%ust bringdata+,

&What decisions could we make if we had all the information we need?A


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;irst. companies must ,e a,le toidentify. com,ine. and managemultiple sources of data Second.they need the capa,ility to ,uild

advanced2analytics models forpredicting and optimi-ingoutcomes "hird. and mostcritical. management mustpossess the muscle to transformthe organi-ation so that the data

and models actually yield ,etterdecisions

opportunities to e0pand insights,y com,ining data are

acceleratingH

"he a,ility to see 1hat 1aspreviously invisi,le improvesoperations. customere0periences. and strategypotential value of the daily or

hourly factory and customer2service data they possess

Social mediagenerates terabytes ofnontraditional, unstructured data inthe form of conversations. photos.and videoH +dd to that the streamsof data owing in from sensors.monitored processes. and

e0ternal sources ranging fromlocal demo ra hics to 1eather

The goal: to give frontline managers intuitive tools and interfaces that help them with their jobs.


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performance improvements andcompetitive advantage arise fromanalytics models that allo1managers to predict and optimi-e

outcomesmismatch ,et1een anorgani-ations e0isting cultureand capa,ilities and emergingtactics to e0ploit analyticssuccessfullyclear ,lueprint for reali-ing ,usiness goals

vie1 it as central to solvingpro,lems and identifyingopportunities


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staff $ehavior and custoer interactions can $ecoe a copanys $iggest copetitive advantage%

recommendations. inferring 1hat1ill ,ring the greatest value toyour customers ,ased on theirhistory and characteristics

$ata infrastructure is theunderlying technologicalplum,ing that collects. transmits.stores. and delivers data to ,eleveraged for monitoring the,usiness and understandingopportunities

Data infrastructure investents &ont provide value unless the data collected is accessi$le% 'he ore people &ho canaccess and use data to easure perforance( evaluate iproveents( and learn a$out the $usiness and custoerspatterns( the $etter%

philosophy of innovation and e!perientation( &here eployees are constantly seeking opportunities for ne&

$reakthrough products or features%


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g p

3ou #an oster a data)driven culture $y al&ays asking for and consulting the data &hen aking decisions 5

data are everyones ,usiness

clear lin5 ,et1een 3nancial performance and

use of dataho1 to translate the data intoactiona,le insightH

use of data in real time to ma5epredictions and ta5e actions

assuring gro1th and marginsmaterial impact on theproductivity. pro3ta,ility ore/ciency

Social Media Monitoring to identify inuencers and applysentiment analysis of various products and services.Predictive Maintenance in the process industry. Pattern

Mining in


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igher Collective $? 1esults froma More Gibrant nowledge )cosystem

6oostin! your !roup7s%or!ani8ation7s #olle#tive intelli!en#e.its #apa#ity to evolve. re/uires to feed and be fed by

its no+le"!e e#osystem5

A 9no+le"!e e#osystem: is a value-#reatin!. sel-or!ani8in! system.

"eine" as a triple net+or #omprise" o;

a Peoplenet+or o #o-#reative #onversations that

#reates

a *no&ledgenet+or o share" insi!hts. inspirations.

su##essul pra#ti#es an" rame+ors

both o +hi#h is supporte" by a 'echnologynet+or

o tools. an" virtual environments5

$ata and algorithms have a


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$ata and algorithms have atendency tooutperform human intuitionin a1ide variety of circumstances

1. What was the source of your data?2. How well do the sample data represent the population?

3. Does your data distribution include outliers? How did they aect the results?4. What assumptions are behind your analysis? Miht certain conditions render your assumptions and your modelin!alid?". Why did you decide on that particular analytical approach? What alternati!es did you consider?#. How li$ely is it that the independent !ariables are actually causin the chanes in the dependent !ariable?Miht other analyses establish causality more clearly?

"he ,est training is almostal1ays going to ,e hands ontraining,(
https://hbr.org/2013/12/big-datas-biggest-challenge-convincing-people-not-to-trust-their-judgment/https://hbr.org/2014/01/when-human-judgment-works-well-and-when-it-doesnt/https://hbr.org/2014/01/when-human-judgment-works-well-and-when-it-doesnt/https://hbr.org/2013/12/big-datas-biggest-challenge-convincing-people-not-to-trust-their-judgment/


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L t W d


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Last Words

Than you or your

listenin! an" /uestions

that inspire" +hat ' ha"

to share +ith you

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