Primed for transactional efficiency, these legacy architec - tures based on relational databases drive order-to-cash and procure-to-pay efficiencies. However, they are inef - fective in analyzing flow, understanding relationship pat - terns, using disparate data, or driving multi-tier insights. Amsutl ht iep lwe ot irel dr sboefc soumpepsl i me ros raencdo mo upt lseoxu–rdcei np ge nr de leant it oonns h i p s – legacy approaches using relational database architecture are insufficient. Using NoSQL and rethinking supply chain pf ol ramn nsi no fg eanrgc hi ni teescat uv ar ei lsa bi sl ea tphrreoruegqhu iAsIi .t e t o u s i n g n e w Imn i al i ar re coernvtesrtyu fdaym, riol iua gr hwl yi t 1h /t3h eo fc roenscpeopnt sd eonf tt sh ewGe rr ea pf ah- At rpa pd ri toi ao cnha.l Tohpet i fmo ci zuast ioofnc uo rnr reenlta st ipoennadl idnagt ai sboa ns eds e. p l o y i n g Preamble SL ui npupsl yc lci nh ga ii nn gl et ao dhei rss bl loavnek teht e, si ru rpopwl ys cahnadi nc ot el ua mm ns sm. La ikkee most of their decisions on Excel spreadsheets. Relational database structures are a barrier to modeling relationships and flow. Isn’t it ironic that a relational da - tsahbi pa ss ?e Oi sr pt oh oe rr ef oi sr nmoi nt ri nuge dr ealtaat iaobnosuhti pmmu lat in- at igeermr eelnatt iionnt-h e structured data models in Supplier Relationship Man- a( Cg Re mM e) .nOt r( SaRuMn )i f oi erdCduas tt ao mmeordRe le al actri oo snss hs oi pu rMc ea ,nma gaekme , eanntd deliver for planning? (TMS and DRP have little in com - mon, and revenue management operates isolated from demand planning.) It nu rceo bn at rs ae sdt ,oan gvrearpt hi c ed sa taanbda seed gi se as . nTohne- lvi en retai cr edsaat ar es at rl suoc -
167 Semantics The movement to flow and relationship sensing also requires a greater focus on semantics. Why are semantics important? Let’s start with a basic definition. Semantics ar or el eds aatna dc hu as er accat seer si s. tSi ce smtahnatti ce nr ea cbol en cuisl iaagtei oanc rgoi vs es smdua lttai p l e ms eet sa na il nl ogw. Ts rdaantsal at ot i obne ot rf atnhsel as tcehde ma nadainmdpsreomv easn ut iscasgoef. d a t a Examples are product attributes, units of measure, selling uonniatsn,danodn.regional channel characteristics. The list can go Wo nhmi l ea nt hy el ecvoenl cs e, tpht es roef ids aat as isme mp l ae ngtoi casl . aSreemhai gnht ilcy raebcsot nr ac ci lt- iation allows multiple parties to interact with data based on an embedded set of clear, well-defined rules everyone ca anna luong do ue rssttoa nmdaapnpdi nfgo lgl or awm. Cmr ea ar tai nn gd ds tayt lae sreuml easnitni ccsoins t e n t writing style guides (Think Chicago Style Guide 10). Simi - lsaerml ya, nt ht iec Dr ee cwoenyc iDl ieact ii mo na ls ys ys tset emm. Si enmt ha en tl ii cbsr aarnydi ss cahme ma na us a l go hand-in-hand to enable effective discovery. Ontologies tt ewromneoddneos di ne st;hteh eg readpghe. sMaor er el ifnoer smoarl layr, ca sGcroanpnheicst icnogma-n y posed of a set of vertices( V ) and a set of edges( E ). Tt hhee sghriaf tpsha rt et lhaet ensosdt oe sr eadn dd aetda gteos u. Tn hd ee rgsrtaapnhd raenl ad t ai onnasl yhzi pe smt rauncyt uc ar es eesn, ar eb tl re ise sv teodr ewdi tdha ot an et oobpee rl iant ki oend. dGirraepc ht l yd aa tnadb, ai ns - es hold the relationships between data as a priority. As a rmees ua nl ti,ndga?t aB yq udeerf yi niist ifoans ,t .i nA Ng roaSpQhL i, sS aQ Lf ocramn noof tNboeS Qu sLe. dT ha es aopqtuimeriyzalatinognutaegceh.nGorlaopghieasr. chitectures also use different
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