planning implementations. • Mm ee anst su roef md ee mn t a. nI nd tphlea nc lnaisnsg, ehiagdh ta onuetgoaft itveen Fdoerpelcoays-t Value Added (FVA) measurement. Only three out oclfa7s2s.cFoomr mpaonsites were measuring FVA before the , the measurement of FVA was a np leawn . cWo nhcye wp to. uNl od cwoemsppaennyd kmn ei l wl i oinf st ht eo yi mh apdl eamgeonotd at hsey es tf ef emc t wi v ei tnhehs us no df raepdrsooc fe ps sl aannnde ri ns ctrheaats de es ct rheea s e s lTaht ee nrcoyo tt oi sms uaek ei s aa dl ae cc ki s ioof nc?l aI rki tnyoown. It th de edf ei ef si nl iot gi oi cn. of supply chain excellence and governance. (Who should decide, and what does good look like?) • Inventory. Advanced planning models, often tsearf emt ye ds teoncdk -. tIon- et hn ed epvl ao nl untiino gn, of of ct uh se og nl oobpa tl i smu ipzpi nl yg cfahsatienr, tchyacnlessatfoectky,satnodckin-transit inventories grew ; as a result, in most global souf pt hpel yi nc vheani nt os ,r oy.pTt ihme iaz ne rs swoenr ?l yUospet ni me ti wz eo1r k5 -d2e0s%i g n ti ne vc he nntool or yg iaens dtor iogphtti-ms iizzeebtuhfefefor sr.m a n d f u n c t i o n o f • Model Adaptability. Only one company had back- ce ansgtiende .t oA ltlebs lti nt hdel ydda et ap lmo yoedde li taenmd- tbhaes ef odrme coads et l s without testing the validity of the demand plan- nseinvegnmfolodwelss. (Global supply chains have five to , each needing testing and model align- ment.) Today’s discussion is m ainly on engines, not models or testing. Most don’t know the difference. (l iBt tel set f po ircmk mo deetlhdoedfoi nl oi tgi ioens. )s eTlhe ec taonpstwi me ri z? eTrhs i annkdhdo o - listically. In Table 4. 2, I share a simple framework to spark your thinking.
Table 4.1. Comparison of Overtime with Supply Chain Planning Implementations
% of Inventory as Safety Stock
Forecastablity
Leadtime
My Experience with Rolling Out Supply Chain Planning in the 1990s
85-90% of items were forecastable with a COV of less than .3 40-50% of items with COV greater than .7
Low Variability
50-55%
174 High Variability The first row reflects my brief implementation experi - ence as a consultant with Manugistics (now a part of Blue Yonder) in 1992-1994 and my work with Debra Hofman at AMR Research as we worked with benchmark dthaatta.itHiesrleaties.)my assessment and answer for Pete. (Sorry The traditional focus is on reducing error, assuming that ictleams ss, 9a 2t %a l oocf asttiuodnelne tvse hl (aSdKpUr)o adruec tf os rwe ci tahs taa Cb ol ee. fIfni c ti ehne t o f Vf oarrei ca absi ltiat by l ge raeta at enr i tt he ma n l .e7v. eTl .hYi se tl ,etvheel ior fcvoamr ipaabni liiet sy wi s enroe t tsrhyi ipnmg et on tf/oor redc ea rs tduast ianign ipt ue mt s -ul es vi ne gl fao rsehci pa s- ftri no gmbma soedde ol . nN o cl ionme pd ae nmya inndt, haen dc l adsesmma en ads suhr ae pd i mn ga/rrkeevtepnoutee nmt ai anl aogre bmaes ne -t os tpuedreant et ds hi na rae sdetphaartatthe emr eo wd eelrfer oo mv e rd e1m7 0a 0n di npdl iavni dn ui nagl s. O n e wb ui tt ht ihna ht itsh oe rogragnainz iaztai ot ino nwliot hn gt hs ef ot re r“ mi n s“idgahttas”.” i n t h e i r t i t l e Observations We need to question the basics of today’s supply chain Current State 15-20%
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