and then drive improvements. An Outside-in Model Beats an Inside-Out Model Every Time. I laugh when I watch the current APS dmeamn au nf adc tpul ar innngi ns igt et e, ec ha cnho ltor gy ii sntgs tsoc rparpo vi te ot uh te ya thaa v e ao pbtei mt t ei zr eorpftriommi zoenr. eI nt eecahcnho cl oags ye , ttoh ae ncohtahnegrehiandt hl i et t l e impact on the inside-out model. The most signifi - ca anndt tihme pt rroa vnes mf o er mn taht iaopnpfernoemd twi mi t eh- op uh tassiedde -di na tda actoan - sumption to flow management. Data Everywhere, Insights Are Few. In one of the c1a7s0e0s et umdpi el os y, ae ems awniut hf atcht ue rt ee rr mr e p“ doar tt ae ”d i tnh taht et ihr etyi t hl ea, d but they lacked insights. This gap happens despite the deployment of ERP, descriptive analytics, APS, Sd Ra tMa , l aa nk eds t. eI an me as cohf tdeastta, ss uc ipepnl tyi scths awi no tr ekai nmgs own et rhee i r amazed to find data they did not know existed. The db ua tt ai nocwr ende idb lbyy vtahl eu ac bo lme .pIaennyc wo uarsang oe taol lntloy euxnpul soerde the data available and how to drive insights. (Set up as atliems ea cf oc ro udni gt i tt ea al mm sa,rakne dt i ns ug pt epal ymcsh, adiant ag rsocui epnst itsot s , brainstorm using various data sources to improve tahned me voednetl si n. Es ixgphl tosr, ec hr aa tni nn ge /l rdeavt iae, wf i edl da t iam, wa geeast ,haenr d unstructured text mining by brokers/customers/ distributors.) So What? Who Cares? Push the FVA and Bullwhip fma cotdoer ls t po rue nadnedr sptoa sntd- t tehs et i ni mg pi natcot ao nn ec tuws toormk edre ss ei grn- vt iioc ne , i inn vneent wt oorrykpdoel isci yg ,na mn do dmeal sr gt ion e. Ud us ec at thee t ve iasmu as l oi zna -
187 What Is An Outside-in Model? Ad rni voeurtss it do ep- ri ne dmi cot ddeel mu saensdm. Tarrakdei tt idorni va el rdse ma nadn dd epml aannndi n g iosr bd ae sr ei ds ao np opoart tperronx rye fcoorg dn ei tmi oann odf dour ed et or st/hseh ibpuml l we nhtisp. T h e ee fnf ae bc tl easntdh ed evmi s auna dl i zl aa tt ieonnc yo. f Bcuhial dn innegl od ua tt sa i dwei -t ihn mmi on di me las l latency and distortion. The use of the graph allows for the vt hi seurael di zeafti inoi nt i oonf fol of ww. oFrokr. Ut hsei nagd nv ee nwt uf or or mu ss, oi tf aalns oa l ey nt iac bs ,l e s the planners become orchestrators, and the planning data becomes self-service by business leaders. (Reduces polit - ical bias by aligning to balance sheet metrics. And enables wt ohbyet “hbeeys st hpor ua cl dt i cc ae r” ei n. Cc or enavseen ct ioosnt aalnmd oi ndveel sn bt oerl iye v e d wt e ha mi l es dui ns tdoerrt si nt agncdu. sUt os emtehre rne el i tawb oi l ri tky dme soirgent mh aond me l os s t to assess the market potential. Change Your Relationship With Demand Plan- ning. Focus less on the error and more on im- proving your company’s potential . Measure FVA, tchhea nbguel l wt hhei pd ies fcfuescst i, oannsd tdoedmr iavnedi ml apt ernocvye ma nedn tt.rWy thoi l e you should continue to measure error and bias, fo- ceutcs.)m. ore on MPE and fire the APEs (MAPE, WMAPE, Make a Date with Your Supply Chain Planning Technology to Test and Learn with Outside-in Models. A decrease of 10% FVA is a gift. Measure as hn idp mu nednet rs sttoa nf odr tehc ea si tmdpeamc ta on fd uasni nd gbouni lldy oo ur dt seirdse a- innd minog dtheles FtoVAdeacnrdeabsuelltwhhe ispigenffaelclta. tency while improv-
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