T HI NGWORX PL AT F ORM T E CHNI CAL OVE RVI E W June - PowerPoint PPT Presentation

T HI NGWORX PL AT F ORM T E CHNI CAL OVE RVI E W June 2018

T E AR OF F S L I DE T his pre se ntatio n pro vide s a te c hnic al o ve rvie w o f the T hing Wo rx platfo rm I t is g e ne rally to be use d by te c hnic al pre -sale s and S E te ams T he inte nde d audie nc e are te c hnic al buye rs, g e ne rally mid-to -hig h le ve l me mbe rs o f the I T func tio n o r me mbe rs o f o pe rating func tio ns with te c hnic al ac ume n T his pre se ntatio n is no t spe c ific ally inte nde d fo r de ve lo pe rs 2

SAF E HARBOR ST AT E ME NT Today’s presentation and Q&A session will include forward -lo o king statements regarding PTC’s future financial performance, strategic o utlo o k a nd e xpe c ta tio ns, a ntic ipa te d future o pe ra tio ns, a nd pro duc ts a nd ma rke ts. Be c a use suc h sta te me nts de a l with future e ve nts, a c tua l re sults ma y diffe r ma te ria lly fro m tho se pro je c te d in the fo rwa rd -lo o king sta te me nts. I nfo rma tio n c o nc e rning fa c to rs tha t c o uld c a use a c tua l re sults to diffe r ma te ria lly fro m tho se in the fo rwa rd -lo o king sta te me nts can be found in PTC’s Annual Report on Form 10 -K , F o rm 10-Q a nd o the r filing s with the U.S. Se c uritie s a nd E xc ha ng e Co mmissio n. During the pre se nta tio n PT C will disc uss no n-GAAP fina nc ia l me a sure s. T he se no n-GAAP me a sure s a re no t pre pa re d in a c c o rda nc e with g e ne ra lly a c c e pte d a c c o unting princ iple s. A re c o nc ilia tio n o f the no n- GAAP fina nc ia l me a sure s to the mo st dire c tly c o mpa ra b le GAAP me a sure s c a n b e fo und o n o ur we b site . 3

WHY T HI NGWORX? 4

• I I o T is a b o ut applying dig ital te c hnology to the physic al wor ld and c r e ating value – I I o T is a b o ut muc h mo re tha n a c q uiring da ta fro m ma c hine s – T his physic a l dig ita l c o nve rg e nc e re q uire s T HE E SSE NCE o rc he stra tio n o f pro duc t de sig n c o nte nt, o pe ra tio na l da ta , suppo rting b usine ss syste ms, a nd OF I NDUST RI AL the pe o ple who o pe ra tio na lize pro c e sse s I NT E RNE T OF • While the re a re c o mmo n, re c urring so lutio n T HI NGS pa tte rns, all e nte rprise solutions r e quir e c ustomization to me e t c omple x use c ase s SOL UT I ONS • So lutio ns de ma nd a g ility – this me a ns e na b ling do ma in e xpe rts, pa rtne rs, a nd c usto me rs the mse lve s to pa rtic ipa te in the so lutio n de sig n, c re a tio n, a nd e vo lutio n 5

• A syste m o f inte g ra l c a pa b ilitie s fo r c re a ting so lutio ns with physic a l a nd dig ita l T HI NGWORX c o nve rg e nc e S … I • An e c o syste m o f pa rtne rs a nd de ve lo pe rs 6

ADDRESSING THE NEEDS FOR “LONG TAIL” APPLICATIONS L e ve l o f Usa g e • Hig h-Vo lume (Billio ns) • Me dium c o mple xity a st – Ra pid a pplic a tio n de ve lo pme nt • F • Se tting -pe rfe c t L ong T ail of B2B Apps Numb e r o f Apps App # 1 App # N (T ho usa nds) 7

I OT PL AT F ORM VAL UE DRI VE RS Ag ile & Wra p & E xte nd E xpa nsive Purpo se -Built fo r Quic kly Cre a te F le xib le E xisting T e c hno lo g y E c o syste m I ndustria l a nd De plo y I ndustria l Apps & I nno va tio n AR E xpe rie nc e s 8

Industrial Digital Innovation is complex … ▪ Highly dispersed device environments ▪ Constantly evolving platform architectures ▪ “Plumbing” battles with disparate technology frameworks ▪ Repurposed legacy technology stacks ▪ Volume, velocity and variety of data making data analysis challenging ▪ Project requirements outweigh current resources and development tools ▪ Complex Value Chain that needs to engage

• De mo c ra tize de ve lo pme nt o f I ndustria l So lutio ns • Po we rful T o o ls fo r the rig ht te a m me mb e rs T HE • Ce nte r to o ls a ro und a T hing Mo de l T HI NGWORX • Ope n, E xte nsib le Arc hite c ture WAY • Rig ht Ca pa b ilitie s in the Rig ht Pla c e • Po we re d b y the T hing Wo rx “ThingModel” Engine 10

I NDUST RI AL I NNOVAT I ON PL AT F ORM Mic ro so ft Azure AWS SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE Mo de l Dig itize via sc a n, pho to , o r vide o 11

I NDUST RI AL I NNOVAT I ON PL AT F ORM Mic ro so ft Azure AWS SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE SOURCE Mo de l Dig itize via sc a n, pho to , o r vide o 12

T HI NGWORX CONNE CT I VI T Y T O DAT A SOURCE S De vic e Co nne c tio n Se rve r Clo uds o T Azure I o T Hub Build-Yo ur-Own AWS I ThingWorx Edge SDK’s T hingWor x E dg e T hingWor x RE ST API Mic r oSe r ve r • • Build ro b ust, se c ure , full- Bring the po we r o f the fe a ture d e dg e inte g ra tio ns a nd • T hing Wo rx pla tfo rm to e ve n the Pre -b uilt I o T Ga te wa y fo r e a sily g a te wa ys fo r a ny pla tfo rm. sma lle st o f de vic e s. c o nne c ting yo ur Windo ws, L inux, o r L inux ARM de vic e s a nd de vic e s o n lo c a l ne two rks. L ua Sc r ipt Re sour c e • Ra pidly inte g ra te da ta so urc e s via simple L ua sc ripts. 14

I NDUST RI AL I NNOVAT I ON PL AT F ORM Mic ro so ft Azure AWS SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE CONT E XT UAL IZE Mo de l Dig itize via sc a n, pho to , o r vide o 15

T HE T HI NG MODE L • T he T hing Mode l is a c o lle c tio n o f e ntitie s tha t re pre se nt yo ur pro c e ss, so lutio n o r a pplic a tio n. • T hings pro vide c o nte xt into yo ur I o T da ta a nd a re the b uilding b lo c ks fo r a pplic a tio n de ve lo pe rs • T hings ha ve struc ture a nd re la tio nships tha t re pre se nt yo ur re a l wo rld o b je c ts 16

T HI NGS AS APPL I CAT I ON BUI L DI NG BL OCK S Pro pe rtie s • Running ho urs • Ave ra g e te mp T hing T e mpla te T hings • Wa rra nty • L o a d Size 3c 90056 Pro pe rtie s Se rvic e s • Se rvic e s Che c k Wa sh • Upda te firmwa re 56a 897c • E ve nts Re po rt F a ilure Sub sc riptio ns 4jklzp0 Applia nc e E ve nts • Wa sh Co mple te • Wa sh Sta rte d • Ma lfunc tio n E nte rprise Syste ms Sub sc riptio ns Se rvic e • Clo the s re a dy • De te rg e nt Ma nufa c turing a va ila b le Ope ra tio ns F ina nc e E ng ine e ring Sa le s 17 17

COMPOSE R T HI NG MODE L I NG T OOL Mode l Ke pSe r ve r T ags into the T hingMode l 18

I NDUST RI AL I NNOVAT I ON PL AT F ORM Mic ro so ft Azure AWS SOURCE CONT E XT UAL IZE SYNT HE SIZE ORCHE ST RAT E E NGAGE SYNT HE SIZE Mo de l Dig itize via sc a n, pho to , o r vide o 19

AUT OMAT E D PRE DI CT I VE MODE L I NG WI T H T HI NGWORX ANAL YT I CS 20

RE AL -T I ME I NT E L L I GE NT ANOMAL Y DE T E CT I ON • F inds a no ma lie s in re a l-time • Auto ma tic a lly o b se rve s a nd le a rns the no rma l sta te pa tte rn • No ne e d fo r se tting rule s o r a pplying pre -c a lc ula tio ns • Mo nito rs fo r a no ma lie s a nd de live rs re a l-time 21

MANAGE E XT E RNAL APPL I CAT I ONS • F unc tio na lity to a llo w de plo yme nt a nd e xe c utio n o f c o mputa tio na l mo de ls fro m e xte rna l a pplic a tio ns • L e ve ra g e pro duc t-b a se d a na lysis mo de ls de ve lo pe d using PT C a nd third-pa rty to o ls • Pro vide s a fra me wo rk fo r the e xe c utio n o f c o mputa tio ns in e xte rna l a pplic a tio ns b a se d o n e ve nts a nd da ta 22

T HI NGWORX ANAL YT I CS 8.1 – SI ARCHI T E CT URE SL I DE NGL E SE RVE R, NAT I VE OS ThingWorx Analytics Server (single server) Analytic s Se r ve r T hing • Mic ro se rvic e T E dge hing s Route r • RE ST ful API suppo rt (via pla tfo rm) Age nt • Na tive sc ript suppo rt (in pla tfo rm) Native Anomaly Ale r ts Data Mic r ose r vic e Pr e sc r iptive Sc or ing Signals Mic r ose r vic e Na tive Ano ma ly De te c tio n Da ta ma na g e me nt, filte rs Mutua l Info Ca lc ula tio ns Mic r ose r vic e Re a l-T ime ThingWorx Analytics Extensions T r aining Mic r ose r vic e Pr e dic tive Sc or ing Pr ofile s Mic r ose r vic e Mo d e l c re a tio n Cha ra c te rize to p & Mic r ose r vic e Analytic s Builde r b o tto m pe rfo rme rs Ba tc h & Re a l-T ime UX fo r T ra ining & De sc riptive Cluste r ing Mode l Validation Re sults Mic r ose r vic e Analytic s Manage r Mo d e ls, c o mputa tio ns Mic r ose r vic e De plo y/ e xe c ute mo d e ls Mic r ose r vic e Cluste r c a lc ula tio ns Va lid a te mo d e l a c c ura c y T hingWatc he r Svc s T hingPr e dic tor T ra ining , Mo d e l Mg t Pre dic tive Sc o ring Ana lytic s T ools / SDKs 23 23