Big Visibility Wp 2014.1 (1)
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WhitE papEr Big VisiBility
turnn “B Daa” ino
“B Vb”leadn compane are bennn o everae her
daa o an vauabe neence and auomae
procee acro her end-o-end upp chan
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Mos ms ve ledy nvesed n busness nellgence,
suly cn mngemen (SCM), nd modelng ools
clm o mke ossble o dll deee no e suly cnd n sec of svngs. tese ools e oen mkeed w
vgue omses ey wll ness e ognzon’s “bg
d” nd/o ovde “end-o-end” (e2e) vsbly.
Ye dese e fc ey ve undegone numbe of
comlex nd exensve ecnology mlemenons, mos
C-level nd suly cn execuves dm ey sll ve
lle de of w s enng ougou e exended
suly cn unl long e evens ve ken lce. i s
nely mossble fo e ognzon o sense n ssue nd
modfy o omze s esonse n mely mnne.
as esul, ody’s execuves e fused—ey know
e comnes e sng on exemely vluble nfomon
sses nd ye ey e unble o levege fo e bene
of e ognzon. Wle ey wok d evey dy unnng
oeons o yng o gue ou ow o bes lloce e
lmed cl, e oug s lwys n e bck of e
mnds ee s o be bee wy. te oblem
oweve s s vey cllengng o know w ools o
nves n nd ow o me nvesmen.
ts we e exloes ow comnes cn successfully
levege e bg d o gn unecedened levels of
vsbly nd conol coss e suly cn. i wlldemonse w e g ecnology oc,
comnes e mkng sgncn ss n e use of d
o nclude dvnced nlycs nsfom socl
nd el me d cued n e own SCM nd Erp
sysems (nd ose of e dng nes) no edcve
nd escve nsgs. tese comnes e usng
dvnced nlycs o ovde el me vsbly coss e
suly cn nd move foecsng, demnd lnnng,
elensmen, soucng, elensmen, oducon,
nsoon nd logscs, nd dsbuon ocesses.
Noe wle no ll ognzons e edy o ness
e bg d nd mlemen dvnced nlycs, mny
dvnges cn sll be gned fom nlycs wou e
lcon becomng ovely sosced o comlex. Fo
some, smly gnng moe ccue wndow no w
s enng coss e end-o-end suly cn s
wowle nvesmen. in e end, ee cn be lle doub
wee o no you ledy ve d wng o be
used, nvesng n new nlycl ools wll lkely be n you
ognzon’s fuue.
advnced nlycs cn genee dee nd exnsve vlue by ovdng el me vsbly cosse suly cn nd movng foecsng, demnd lnnng, soucng, elensmen, oducon,
nsoon nd logscs, nd dsbuon ocesses.
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supp chan’ be buzzword: “B Daa” and“end-o-end vb”
Evey we e, webn, nd cle bou suly cns
ese dys seems o nclude some menon of bg d, end-
o-end (“e2e”) vsbly, o dvnced nlycs. as w mos
buzzwods, e denons e vgue nd s uncle ow, f
ll, ey e eled o ec oe.
Wen lkng bou bg d, mos souces emsze e
see scle of e d ses now exs, w nyng ove
1 ebye usully geng e bg d lbel.1 a ebye of
1. “Bg d: e we mkng bg mske?”, Fnncl tmes, 2014.
d s 1024 ebyes, nd ebye of d s 1024 ggbyes.
to ovde some sense of scle (nd ow f we’ve come),
20 ebyes s e ol moun of d dsk dve scemnufcued n 1995. tody s e moun Google
ocesses on dly bss. Fo 15 ecen of mnufcues n
ecen suvey, 20 ebyes lso eesens e cuen sze
of e Erp dbses.2
Fndng consensus fo w suly cn vsbly mens s
muc de, bu by ny denon ’s vey ssve execse.
Essenlly, comnes e execung nscons, song e
2. “5 Ses Suly Cn Execuves Cn tke o hness Bg D”,Suly Cn insgs, 2013.
20 petabytes is the total amount of hard disk drive spacemanufactured in 1995. Today it is the amount that Google
processes on a daily basis.
Ke Fac
• a ecen suvey of 400 execuves (moe n lf n C-sue oles) found 51% of
esondens beleved edcve nlycs wll ovde moe ecse sk ssessmen
of sules, bu only 31% e cuenly usng edcve nlycs n s mnne
(te Economs).
• 58% of comnes n e suly cn ndusy ve nvesed o ln o nves n bg
d ecnology dung nex wo yes (Gne resec 2013).
• a ecen suvey of 127 comnes n suly cn nduses, mjoy ndced
e bgges cllenge w bg d ws o undesnd ow o exc vlue
(Gne resec 2013).
• By 2017, 30% of comnes wll do sm mcnes fo no-umn decson-
mkng n one o moe suly cn ocesses (Gne pedcs 2014).
• By 2017, e convegence of sucued nd unsucued d wll fundmenlly
cnge e wy Cp suly cns delve vlue (Gne pedcs 2014).
30%
Using predictiveanalytics, 51% sayuseful but notusing
31%
will adopt smartmachines by2017
58%Invested or planto invest in bigdata in next 2 yrs
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esuls n d weouse, usng e d o ols nd/
o busness nellgence ools, unnng nlycs on w s
ened, nd jus yng o do bee nex me. and wlee “end-o-end” lbel s oen used, e u s mos
ecnology sysems sll oe smlsc fom of vsbly no
one of e suly cn e exense of e oe. a
bes (nd s s e), s mens vsbly coss e nenl
demens of e ognzon w nonl sles nd
ucsng vsbly no s mmede dng nes.
Gven ese lmons, s no wonde Gne resec
ecenly eveled vully no comnes e ble o o
wll be ble o ovde end-o-end suly cn vsbly n
e ne fuue; n fc, by 2016, ey esme less n 20%of comnes wll be ble o ovde end-o-end suly cn
vsbly.3 te u s mos comnes e essenlly
yng blnd.
Wy s end-o-end vsbly so d o obn? te my
eson (nd one s been ovelooked by mos oe
commenos), s suly cn vsbly s mly bg
d oblem.
3. Gne “pedcs 2013: Collboon, Cloud nd EvolvngSeges wll Dve Globl Logscs”, vlble (ged):://www. gne.com
the ouourced upp chan’ b daa probem
Smly u, o obn end-o-end vsbly you need o solve
numbe of bg d oblems. te eson wy ee s been
so lle ogess on e vsbly fon s ody’s suly
cns e oo swlng, oo ousouced, nd oo comlex
fo donlly ceced sysems o ndle. Comnes
ve bndoned vecl negon, nsed ousoucng
lge mjoy of e suly cn funcons ey once
mnged n-ouse. te esul s suly cns ve
become ncedbly comlex globl webs of dng nes
sceed ll ove e wold, ec focusng on now slce
of e fulllmen o mnufcung ocess. tey e lled
w 1000s of sules, SKUs, nd comonens.
Execuves undesnd e donl enese
mngemen sysems e no desgned o mnge cvy
beyond e fou wlls of e enese, nd e cenly
no equed o del w e bg d oblem. as suly
cns become moe ngled, w gee numbe of f
ung sules, cusomes, nd logscs ovdes, mnges
e fced w sks cn co u n dozens of counes.
No wonde ecen Deloe suvey of 600 execuves
mnufcung nd el comnes found 63% wee
gly concened bou sks wn e exended suly
cn comsng vendos nd cusomes, nkng mong
e o-wo concens.4
4. Deloe Consulng, “te rle Eec”, 2013, vlble : ://www.deloe.com/vew/en_US/us/Sevces/consulng/Segy-Oeons/09e44390e17c310VgnVCM1000003256f70rCrD.m
Gartner Research recently revealed that virtually no companiesare able to or will be able to provide end-to-end supply chain
visibility in the near future.
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Wh B Vb you Can...
• Model suly cn d w muc moe ecson.
• Cue nd neconnec d n mul-ne, mul-
ecelon envonmen o cee nellgence.
• ale decsons n el me (wn nsconl
ocess wokow).
• Ulze edcve nd escve nlycs o solve
oblems befoe ey occu nd ulze omzon
o move oucomes n evenue, nvenoy, nd
nsoon exenses.
in esonse, comnes ve long used comlex d
ses o ln mnufcung o mee cusome demnd e
now lookng o combne d fom exenl souces o beeedc fuue sks. te oblem s, f sngle mnufcue
lone cn ouse 20 ebyes of d (s we ledy lened),
ow muc moe d mus e es of e suly cn
conn?
inroducn “b vb” (upp chan’advanced nh)
Fowd-nkng comnes undesnd elnce on
nlycs esens e only sclble oc o nlyzng
nd gnng nsgs fom deluge of bg d. Muc lke
gndmse n cess, ey mus become exe n lookng
deen ens wn e suly cn. a cess
gndmse emloys se of conble seges o wn
mces, wc djus el-me deendng on e moves
seleced by e oonens. in sml fson comnes
mus esbls se of oocol seges wc cn be
eecvely deloyed on el-me bss s e eces on ou
bod cnge on dly nd weekly bss s esul of suly
condons, consume decsons, vlble deos, o some
elevn combnon of fcos.
te gndmse does no mke d oc decsons n e
momen. re e o se deloys move wn muc
lge conex. Smlly, s comnes seek o undesnd
ow e oucomes e eled o ll e cons nd
decsons of e dng nes nd consumes, ey mus
consde oocol seges ele o e ene end-o-
end connuum. One Newok s cllng s cbly “bg
vsbly”.
Combining human insights with stascal/mathemacalapproaches yields beer predicons than either is capable of
producing on their own.
an mon on o emembe s elyng on umn
nuon o mnul nlyss cnno suo e knd of
fc-bsed, fs, oble decson mkng s equedby bg vsbly. advnced nsgs of s ye e dven
by combnon of bo umn nd mcne necon.
Combnng umn nsgs w sscl/memcl
oces yelds bee edcons n ee s cble of
oducng on e own. a good exmle of s s wee
foecsng wee e moun of d nd comue owe
boug o be on e oblem s uge, ye e umn
elemen sll dds vlue. 5
5 ts exmle ken fom Ne Slve’s “te sgnl nd e nose”,2013.
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inroducn he b vb road map
recng e oly gl of bg vsbly wll no be esy. i
eques wde nge of d fom coss e nenl suly
cn, e dng ne newok, nd fom mcoecomomccondons. Cuen suly cn nlycs ools e nowee
close o delveng ese knds of dvnced nlycs.6 tey
suggle w cung, ousng, nd nlyzng d, muc
less ecognzng demnd nd suly ens.
Even moe dscougng, Gne edced mny of
e nlycs-bsed suly cn decson suo ools wll
lkely become obsolee due o e nbly o del w bg
d, conduc nlyss wn e equed me cycle fo e
decson, nd uome decson mkng.7
So ow cn comnes ceve bg vsbly? W follows
s 5 ae maur mode ognzons cn use
s odm. Ec sge of muy oes s own
6. Gne resec ecenly eoed e sclbly, dgovennce, nd ovell soluon muy e no s dvnced sn moe esblsed ecnology ools. Gne resec, “MkeGude fo Suly Cn anlycs tecnology”, 2014
7. Gne resec, “pedcs”, 2014.
unque cllenges (nd benes f ceved). te ve
sges (llused bove) e eesenon, ccessbly,
nellgence, decson mngemen, nd oucome-bsed
efomnce. to fue lluse ec sge, One Newok
s ovded el exmles fom cul One Newok
cusomes.
1. Repreenaon (Daa)
te s se owd bg vsbly s meely eesenng
e suly cn’s d. W e dven of bg d,
eesenon s cnged foeve. i s no longe necessy
o lm eesenve d n e suly cn bsed on
donl sysem o ecnology consns. advnces
suc s hdoo bsed cecue nd ozonl gd
comung enble ognzons o eesen d w nunecedened moun of exbly nd sclbly.
Cae sud: Wlm, fo exmle, mkes on of sle
(pOS) d vlble evey 15 mnues. ts d s vluble
sse nd sould be used o move evenue nd mgn
e sque foo e soe self. W cuen ecnology
cecues would ve been mossble (o nfesble) o
BIG VISIBILITY ROADMAP
www.onenetwork.com
2. ACCESSIBILITY (PROCESS& PERMISSION)
Is my data useful at
the process and decision-layers?
Has my data has beenconverted into actionable
information through
interactive processdesigns?
(e.g. improving a processmid transaction)
1. REPRESENTATION (DATA)
Does my technology
support precision datamodeling?
(e.g. millions ofitem/location
combinations)
3. INTELLIGENCE(PREDICTIVE ANALYTICS)
Am I meeting expectations
(both current and future)?
Do I have full visibility to all
performance gaps across myvalue chain?
4. DECISION MANAGEMENT
Is my system capable of
taking action, both auto-mated and interactive,
against all performance
gaps and exceptions?
(e.g. prescriptive analyt-
ics, trade-off analysis)
5. OUTCOME-BASEDMETRICS PERFORMANCE
How can big visibility and
real time collaborationimprove my organization
and underlying processes?
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oely model, ln nd execue s level of del. One
Newok s oven o be ble o eesen mllons of em/
self/soe combnons, ude d evey 15 mnues,
un nscons gns d, nd solve fo exceons
on connuous bss wle ovdng vsbly coss mulle
dng nes nd suly cn ecelons.
2. Acceb (Proce and Permon)
te second sge of bg vsbly s ccessbly, nd ee el
me ocess uomon nd mngemen ve cnged e
gme. in donl cecues, ccessbly s somew
synonymous w negon, unvesl objec denons,
nd longevy. howeve new cecues ovde el me
busness ocess mngemen lye wc n un ovdes
full conol nd necon wle execung nscon
– oug se cnge nd ckng even lnkges. and
no only s e d ccessble, bu oug dvnced
ocess cbles ncludng olcy nd emsson conol,
ccessbly o bg d now genees uge vlue e
ocess nd decson-mkng lyes.
Cae sud: a e 1 uomove mnufcue (nd cuen
One Newok cusome) d mde good movemen n
nvenoy ove e s ye, bu d eced on of
One Network has proven to be able to represent millions of item/ shelf/store combinaons, update that data every 15 minutes,
run transacons against that data, and solve for excepons on a
connuous basis while providing visibility across mulple trading
partners and supply chain echelons.
lack of acceb w pca reu n hefoown commenar…
• DC Manaer - “i kes sue use o gue ou ow
o ck nd ce.”
• Buer – “i cn ck Wip by pO#, bu os fcoy ive o use conne #’s, BOL #’s, cse #’s ec.”
• Accounan – “Smens fom 2012 ve cul
feg coss, bu 2013 only sow ssumed pO feg
fco – cn’ come.”
lack of repreenaon w pca reu nhe foown commenar…
• DC Manaer – “We cn’ see nbound delvees
coss mos of ou md sze o smlle sules.”
• Buer – “My pO’s don’ sow s ‘n-ns’ unl
week e ey s.”
• Accounan – “Ec que close s ngme
becuse we don’ know bou goods delveed FOB
mon end.”
• sore – “Smens ve n esmed Eta, bu sow
u 2 o 3 dys le.”
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dmnsng euns. te consn ws n ode o
connue dvng down nvenoy, ey needed o mnge
new sule olces wn nscon, e n eo meod of yng o move e olcy os nscon
(wen ws usully oo le). in s cse, sules wee
sng ely, bsed on n dvnced sng noce (aSN),
nd nng e mnufcue’s nvenoy osons. Sml
o e Wlm exmle, e only wy o solve s oblem
ws o ncese eesenon o nclude evey numbe
evey locon wn e ocess ws execung
beween e mnufcue nd s sules, ncludng 3d
y nsoon ovdes nd dsbuon nes.
Ye gong beyond eesenon, ccessbly needed o beovded oug new nsconl ocess llowed
e mnufcue o goven e new nvenoy olcy wle
lso conollng nsconl se eled o e aSN’s.
Fnlly, e new ocess needed o nege o e Erp
sysems n ode o comlee e nscon nd “ove
o y”. Ely esuls sow sgncn decese n nvenoy
usng e new ccessbly/ocess/olcy/nscon.
3. ineence (Predcve Anac)
Becuse cevng bg vsbly’s eesenon nd
ccessbly sges ve swned new nd moe esonsve
se of ocesses nd olces, comnes now ve e
oouny o genee ge level of nellgence by
comng e ncomng ocess d o e execons of
bo e ocess’ efomnce self s well s e ocess’
oucomes. inellgence s ll bou knowng wee comny
s vesus wee wns o be. pedcve nlycs ly key
ole s sge.
Cae sud: One Newok need w to 3 globl oy
mnufcue ws losng sles dung mjo oldy
sesons becuse dd no ve e eesenon o
ccessbly n ode o cee e g nellgence eled o
eles’ evenue ges, n soe/n sock levels, o dys of
suly. W One Newok’s dvnced eesenon ws
ble o model s mos mon ele’s pOS d nd un
foecs n connuous mode o undesnd wee ey
wee meeng execons bo cuen nd fuue.
lack of neence w pca reu nfoown commenar…
• DC Manaer – “inbound volume foecss e only
sen once e mon. te sysem sould genee
new foecs nfomon weneve ee s
sgncn cnge o even.”
• Buer – “i nk lnded cos s sng, bu i ve o
us lge d ses o excel o clcule ollng
wndow mecs.”
• Accounan – “Lnded cos s sown on weekly
execuve dsbod, bu e clculon gnoes DC
byss smens.”
• sore – “i ve o un fou see eos o
clcule wen ou of sock ems wll be
vlble gn.”
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4. Decon Manaemen (Precrpve Anac)
Ulmely, bg vsbly nellgence s useless unless
comny cn neu busness decson on n
ocess nd cee mesubly deen oucome. i’s
mzng ow mny mlemenons so e nellgence
sge nd jus us d no d weouse o
busness nellgence ol. te eson of couse s e
cecue sn’ desgned fo e bg vsbly ccessbly
o ocess uomon ws descbed ele. W s
decson mngemen cbly, comnes cn ke e
newly dscoveed nellgence nd neu nscons nd/
o ocesses ee se nsons o by ggng ckng
evens. Once e nellgence sows n exceon o e
execed efomnce exss, ey cn en nvoke numbe
of meods o mnge s exceon, bo w uomed
ounes nd umn nevenon nd/o collboon. tese
meods nclude escve nlycs, omzon, nd
de-o nlyss.
Cae sud: anoe One Newok cusome ( mjo CpG
mnufcue) d moved gely on s foecs eo
usng bg vsbly nellgence, bu of couse (gven no
foecs wll eve be 100% ccue) sll exeenced some
eo n ls mnue sles e self o o elensmen
smens fom e DC. One Newok gve e bly o un
ls mnue llocon omzon edsbued e
mx bsed on el me vew of soe/n sock fo evey em
on evey self, wc ws ugely benecl.
5. Oucome-Baed Merc and Performance
acevng eesenon, ccessbly, nellgence, nd
decson mngemen enbles e ges fom of bg
vsbly—oucome-bsed mecs nd efomnce. a
necessy oucome of ou evolvng suly cn newoks wll
be o mke sue we lce e g sses n e g lce
e g me n e g mouns, ll bound by el me
collboon nd bg d vsbly. included n s evoluon
wll be smulneous eos focused on ognzonl
cnge, ocess eengneeng, nd len/sx sgm ogm
mngemen.
lack of decon manaemen w pca
reu n he foown commenar…• DC Manaer – “i eceve n uomed eo wen
soe eceves con s dmged n e nvenoy
sysem. W m i suosed o do w s le?”
• Buer – “i would ke lo of me nd eo o
lwys consde cgebck ends wen ssung new
pO’s, so i only evew em befoe sesonl pO’s.”
• Accounan – “i un eo on ll cgebcks
e endng, nd en ve o eml ec sule/
buye ndvdully. i would be bee f e
les wee sysem geneed decly o e eced
es.”
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as Mcel poe oved n s wok on mke comeon,
e coce fo ny comny o emn on e fence n ems
of djusng segy bsed on foeseeble mke ss wllesul n ulme busness flue.8 tnk bou e ycl
suly cn nfsucue. Sucued negon nd
communcon vecles lke EDi en’ gong wy, bu e
bly fo e donl suly cn desgn o ec nd
esond, gven ll e nfomon nd led me delys bo
usem nd downsem, s smly no comeve. te
fequency nd dely of vlble d coss ou demnd/
suly ecosysem s gowng geomec e. ts d s
n ncedble sse nd One Newok s seen w s clens
wen s d s leveged gns n dvnced suly
cn newok deloymen s e oenl o dve u o 4% ncese n sles, 10% educon n oeng exense,
nd 30% educon n nvenoy.
Demng oned ou long go no only s von e
enemy of ocess movemen, bu you mus lso be ble o
eecvely mesue ocess o move .9 a ycl mul-
ecelon, mul-ne globl suly cn deloymen s fe
w von n cce, mesuemen nd decson mkng.
Descve nlycs s well s moe dvnced edcve nd
escve nlycs cn el o denfy oo cuse eled
o ocess von. a oely desgned wokbenc wllllow uses vous levels wn n ognzon o gn
vsbly o bo ocess efomnce nd ocess oucomes,
8. Mcel poe, “Comeve Segy: tecnques fo anlyznginduses nd Comeos”, Fee pess.
9 W. Edwds Demng, “Quly poducvy nd Comeveposon”, Mit.
s well s execue cons o move bo e ocess desgn
self nd e oucomes genees.
Von s mos lkely e bgges cuse fo oo
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When data is leveraged against an advanced supply chainnetwork deployment it has the potenal to drive up to a 4%
increase in sales, a 10% reducon in operang expense, and a
30% reducon in inventory.
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