Integrating Dynamic Models for CMM-Based Software Process Improvement

In eg a ing Dynamic Models o CMM-Based So wa e P ocess Imp o emen Me cedes Ruiz1, Isabel Ramos2, and Miguel To o2 1 Depa men o Compu e Languages and Sys ems Escuela Supe io de Ingenie ía. Uni e si y o Cádiz C/ Chile, nº1. 11003 – Cádiz, Spain [email p o ec ed] 2 Depa men o Compu e Languages and Sys ems Escuela Técnica Supe io de Ingenie ía In o má ica. Uni e si y o Se ille A da. Reina Me cedes, s/n. 41012 – Se ille. Spain {isabel. amos,m o o}@lsi.us.es Abs ac . Du ing he las decade so wa e p ocess simula ion has been used o add ess a wide di e si y o managemen p oblems. Some o hese p oblems a e ela ed o s a egic managemen , echnology adop ion, unde s anding, aining and lea ning, and isk managemen , among o he s. In his wo k a dynamic in eg a ed amewo k o so wa e p ocess imp o emen is p esen ed. This amewo k combines adi ional es ima ion s a ic models wi h an in ensi e u iliza ion o dynamic simula ion models o he so wa e p ocess. The aim o his amewo k is o suppo a quali a i e and quan i a i e assessmen o so wa e p ocess imp o emen and decision making o achie e a highe so wa e de elopmen p ocess capabili y acco ding o he Capabili y Ma u i y Model. The pape desc ibes he concep s unde lying his amewo k, i s implemen a ion, he dynamic app oach ollowed o sys ema ically de elop he dynamic modules, and an example o i s po en ial use and bene i s. 1 In oduc ion Wo ld-wide he demand o high complex so wa e has signi ican ly inc eased in such a way ha so wa e has eplaced ha dwa e as ha ing he p incipal esponsibili y o much o he unc ionali y p o ided by cu en sys ems. The apid pace in which his so wa e is equi ed, he p oblems ela ed o cos and schedule o e uns, and he cus ome pe cep ion o low p oduc quali y ha e changed he ocus o a en ion owa ds he ma u i y o so wa e de elopmen p ac ices. O e he las ew decades, he so wa e indus y has ecei ed a signi ican help by means o CASE ools, new p og amming languages and app oaches, and mo e ad anced and complex machines. Howe e , i is widely accep ed ha he po en ial bene i o a be e echnology canno be ansla ed in o a mo e success ul p ojec i he p ocesses used o execu e i a e no well de ined, es ablished, and execu ed. P ope p ocesses a e essen ial o an o ganiza ion o consis en ly deli e high quali y p oduc s wi h a high p oduc i i y. Cu en ly, many amewo ks a e a ailable o so wa e p ocesses, being CMM [1] and ISO 9001[2] he mos in luen ial and widely used. Al hough ISO 9001 is a s anda d, and has been in e p e ed o a so wa e o ganiza ion in ISO 9000-3 [3], i has been w i en om he cus ome and ex e nal audi o ’s pe spec i e. On he o he hand, CMM is no a bina y ce i ica ion p ocess, bu a amewo k ha ca ego izes so wa e p ocess in i e le els o ma u i y and p o ides oadmaps o e alua e he so wa e p ocess o an o ganiza ion as well as planning so wa e p ocess imp o emen s. Dynamic modeling and simula ion as p ocess imp o emen ools ha e been in ensi ely used in he manu ac u ing a ea. Cu en ly, so wa e p ocess modeling and simula ion a e gaining an inc easing in e es among esea che s and p ac i ione s as an app oach o analyze complex business and sol e policy ques ions. Howe e , simula ion is only e ec i e i bo h he model and he da a used o d i e i , accu a ely e lec he eal wo ld. As a consequence, i is possible o say ha he cons uc ion o a dynamic model o so wa e p ocess i sel poin s o wha me ic da a mus be equi ed and, hence, collec ed, p o iding clea guidelines on wha o collec . A e ha ing applied he sys em dynamics app oach o model and assess so wa e p ocess imp o emen in a local o ganiza ion [4], lessons lea n om his expe ience mo ed us owa ds he de elopmen o , ini ially, a amewo k and, hen, a wo king en i onmen which combines he adi ional and s a ic echniques used in p ojec managemen wi h he p ocess dynamic modeling and simula ion. The aim o his pape is o p esen his combina ion o build a amewo k o suppo a quali a i e and quan i a i e assessmen o so wa e p ocess imp o emen and decision making. The pu pose o his dynamic amewo k is o help o ganiza ions o achie e a highe so wa e de elopmen p ocess capabili y acco ding o he Capabili y Ma u i y Model [1]. The dynamic models buil inside his amewo k p o ide he capabili y o gaining insigh o e he whole li e cycle a di e en le els o abs ac ion. The le el o abs ac ion used in a ce ain o ganiza ion will depend on i s ma u i y le el. Fo ins ance, in a le el 1 o ganiza ion he simula o can es ablish a baseline acco ding o adi ional es ima ion models om an ini ial es ima e o he size o he p ojec . Wi h his baseline, he so wa e manage can analyze he esul s ob ained wi h he simula ion o di e en p ocess imp o emen s and s udy he ou comes o o e o unde es ima e o cos o schedule. Du ing he simula ion me ic da a a e sa ed. These da a con o m o he SEI co e measu es [5] ecommenda ion and a e mainly ela ed o cos , schedule and quali y. The s uc u e o he pape is as ollows. Sec ion 2 desc ibes in de ail he dynamic amewo k p oposed. I includes he mo i a ion we ound o design and de elop i , he concep ual app oach ollowed, he po en ial uses o he amewo k, i s a chi ec u e, a b ie desc ip ion o he di e en echniques which ha e been in eg a ed in he amewo k, and how i has been implemen ed o de elop a ully unc ional wo king ool. In Sec ion 3 an example o how he amewo k may be used o design and e alua e he e ec o a ce ain p ocess imp o emen is p esen ed. The example shows he use o he in eg a ed echniques oge he wi h he dynamic modules inside he amewo k. Finally, Sec ion 4 summa izes he pape and d aws he conclusions and lessons lea n . 2 Desc ip ion o he F amewo k 2.1 Mo i a ion The Dynamic In eg a ed F amewo k o So wa e P ocess Imp o emen (DIFSPI) has been designed wi h he aim o c ea ing bo h a concep ual amewo k and a wo king en i onmen o help in he achie emen o highe ma u i y le els acco ding o CMM. T adi ionally, sys em dynamics and simula ion ha e been conside ed as help ul ools o design and e alua e so wa e p ocess imp o emen s. In ac , some eal applica ions can be ound in he li e a u e ega ding he applica ion o his app oach inside so wa e de elopmen o ganiza ions [6]. All hese applica ions sha e a common cha ac e is ic: hey conclude ha o a success ul sys em dynamics applica ion, i is a equi emen o he o ganiza ion o ha e hei p ocesses well de ined and s uc u ed, as well as de ined me ic p og ams o p o ide he models wi h accu a e da a. I is absolu ely ue ha dynamic models a e only use ul i he da a used o d i e hei nume ical pa ame e s a e a ailable in he o ganiza ion, and ha he a ailabili y o hese da a is only possible i a me ic p ocess is applied o he so wa e p ocess execu ed a he o ganiza ion. As a consequence, many o he applica ions o sys em dynamics in he ield o p ocess imp o emen ha e equi ed om o ganiza ions a ce ain le el o ma u i y in hei p ocesses, ypically le el 3 o abo e acco ding o CMM. In o de o design a so wa e p ocess dynamic model is necessa y o know he ea u es o he p ocess which is going o be modeled. I becomes appa en ha he highe he ma u i y le el, he be e he p ocess is de ined and es ablished, he easie o ob ain in o ma ion o de elop he dynamic model, and, he e o e, he be e esul s a e ob ained when using he model as a ool o e alua ing so wa e p ocess imp o emen s. Bea ing his s a emen in mind, ou in e es is o help o ganiza ions wi h less ma u e p ocesses o design and execu e he p ocess imp o emen s which could help hem o achie e uppe le els o ma u i y. Thus, we p opose an app oach which is mainly ocussed on he de elopmen o dynamic models, using hem o igge a p ocess inside he o ganiza ion aimed o inc ease he le el o knowledge i has abou i s so wa e p ocess, and o design a so wa e me ics collec ion p og am which can be applied o ob ain he equi ed da a o d i e he pa ame e s o he dynamic model. This me ic collec ion is no only use ul o he dynamic models; i also se es as an in aluable oppo uni y o ob ain a eal knowledge o he s a e o he so wa e p ocesses inside an o ganiza ion. This knowledge is essen ial be o e ackling any p ocess imp o emen . The u iliza ion o dynamic models is he main echnique in he amewo k, bu i is no he only. S a ic o adi ional algo i hmic models a e in eg a ed wi h he dynamic models in o de o supply impo an in o ma ion du ing he ea ly s ages o he li e cycle simula ed. O he ecommenda ions and echniques a e also used inside he amewo k o p o ide use ul ools o analysis and e alua ion o he esul s ob ained h ough simula ion. 2.2 Concep ual App oach o he F amewo k S a ic models use empi ically ob ained o mulas o compu e some p ojec es ima es as a unc ion o some p ojec ini ial es ima ion, ypically he size. The empi ical da a ha suppo hese models come om a limi ed sample o p ojec s, and his equi es a ca e ul u iliza ion o hese models, as only i he ea u es o he p ojec unde es ima ion a e simila o hose o he p ojec s used o calib a e he s a ic models, he esul s ob ained will be eliable. Al hough s a ic me hods o so wa e p ojec managemen ha e e ealed du ing he las decades hei weaknesses, he e is common ag eemen ha hey a e s ill use ul. A leas , in lowe ma u i y o ganiza ions hey a e use ul o es ablish an ini ial baseline o he p ojec wi h which e alua e he esul s o he p ojec and he p ocess imp o emen en a i e. S a ic and dynamic models ha e simila objec i es, ye he pe spec i e unde which hey wo k is comple ely di e en . S a ic models a e no mally based on a op-down decomposi ion o he so wa e p ojec , while he dynamic models can be cha ac e ized by he agg ega ion p ocess hey a e ocussed on, acco ding o which some ea u es o a p ojec a e joined oge he unde a simula ion model. In acco dance wi h wha has been said, i can be deduced ha dynamic models a e sui able o deal wi h p oblems placed a he s a egic le el, while adi ional me hods a e use ul a he ope a ional le el o so wa e p ojec s. By combining bo h app oaches in a common amewo k, a use ul me hodology and e en a wo king ool can be de eloped o p o ide insigh in o he so wa e p ocess and o help in he achie emen o uppe ma u i y le els. Ne e heless, he achie emen o highe ma u i y le els can only be possible i he da a ha enable he e alua ion o he esul s o he p ocess imp o emen p ac ices a e a ailable. The e o e, he design o a p ope me ics collec ion p og am o he o ganiza ion is manda o y. The me ics collec ed a e use ul in many di e en aspec s: 1. The me ics collec ed mus be used o calib a e and ini ialize he dynamic models. Lowe ma u i y o ganiza ions a e cha ac e ized by he absence o me ic p og ams and his o ical da abases. In his case, i is comple ely necessa y o begin iden i ying he gene al p ocesses and he in o ma ion ha has o be collec ed abou hem. The ques ions o wha o collec , a wha equency and wi h wha accu acy ha e o be answe ed a his momen . The design p ocess o dynamic models helps o come o a solu ion o hese ques ions. When de eloping a dynamic model i is equi ed o know: a) wha is in ended o be modeled, b) he scope o he model, and c) wha beha io s need o be analyzed. Once he model is de eloped, i needs o be ini ialized wi h a se o ini ial condi ions in o de o execu e he uns and ob ain he simula ed beha io s. These ini ial condi ions cus omize he model o he p ojec and o he o ganiza ion o be simula ed, and a e e ec i ely implemen ed by a se o ini ial pa ame e s. These pa ame e s ha ule he e olu ion o he model uns answe p ecisely he o me ques ion o wha da a collec : hose da a equi ed o ini ialize and alida e he model will be he main componen s o he me ics collec ion p og am. 2. Once he componen s o he me ics collec ion p og am ha e been de ined i can be implemen ed inside he o ganiza ion. This p ocess will lead o he achie emen o a his o ical da abase. The da a ga he ed can hen be used o simula e and empi ically alida e he dynamic model. When he dynamic model has been alida ed, he esul s o i s uns can be used o gene a e a simula ed da abase; wi h his da abase i is possible o pe o m p ocess imp o emen analyses. An inc ease in he complexi y o he ac ions in ended o be analyzed will di ec ly lead o an inc ease in he complexi y o he dynamic model equi ed and, he e o e, o a new me ics collec ion p og am o he new simula ion modules. Thinking om a pe spec i e o causal loops, wha has been desc ibed is no mo e han he ansla ion o a posi i e ein o ce causal loop which is shown in Figu e 1. The bene i s o using dynamic models ha e been widely discussed in he li e a u e [7]. I can be concluded ha he use o hese models leads o be e p edic ion ac i i ies, mo e accu a e cos p edic ions, and mo e e ec i e p ocess imp o emen ini ia i es. Th ee ac o s which a e known o d i e o ganiza ions owa ds uppe ma u i y le els acco ding o CMM. 2.3 Po en ial Use o he F amewo k Dynamic models help o unde s and he in eg a ed na u e o p ojec managemen as hey desc ibe i h ough di e en p ocesses, s uc u es and main in e ela ionships. In he amewo k p oposed he e, p ojec managemen is conside ed as a se o dynamic in e ela ed p ocesses. P ojec s a e composed o p ocesses. Each p ocess is composed o a se ies o ac i i ies designed o he achie emen o an objec i e [1]. F om a gene al poin o iew, i could be said ha p ojec s a e composed o p ocesses which all in one o he ollowing ca ego ies: Managemen p ocess. This ca ego y collec s all hose p ocesses ela ed o he desc ip ion, o ganiza ion and con ol o he p ojec . Enginee ing p ocess. All hose p ocesses ela ed o he speci ica ion and de elopmen ac i i ies o he so wa e p oduc a e collec ed in his ca ego y. Bo h ca ego ies in e ac du ing he li ecycle o he p ojec . F om an ini ial plan pe o med by he p ojec managemen p ocesses, enginee ing p ocesses begin o be execu ed. Using he in o ma ion ga he ed abou he p og ess o his second g oup o p ocesses, p ojec managemen p ocesses de e mine he modi ica ions ha need o be made o he plan in o de o achie e he p ojec objec i es. The DIFSPI p oposed ollows his same classi ica ion and i is s uc u ed a ending o p ojec managemen and enginee ing p ocesses. In bo h le els, he u iliza ion o dynamic models o simula e he eal p ocesses and o de ine and de elop a his o ical da abase will be he main ea u e. Fig. 1. Causal diag am (+) Da aDynamic model P ocess complexi y + + + 2.3.1 Enginee ing P ocesses in he F amewo k On his le el he dynamic models simula e he li e cycle o he so wa e p oduc . Figu e 2 shows a schema ic ep esen a ion o how DIFSPI may be used inside an o ganiza ion. In low ma u i y o ganiza ions, he amoun o in o ma ion equi ed o begin unning simula ions is ela i ely small and mainly ocussed on he ini ial es ima ions, ha is he es ima ed size o he p ojec , and he ini ial size o he wo king eam. Depending on he pa adigm ollowed o de elop he so wa e p oduc and he ma u i y le el o he o ganiza ion, he sui able dynamic model is simula ed. The main pa adigms ha can be simula ed inside he amewo k a e he adi ional wa e all and COTS pa adigms. Depending on he pa adigm chosen, di e en dynamic modules will be joined in o de o c ea e a inal and ully ope a ional dynamic model. Once he simula ion has been un, i p o ides da a ha a e sa ed in a simula ed da abase. These ini ial da a con ain he esul s o he simula ion oge he wi h a se o ini ial es ima ions esul ing om he compu a ion o he s a ic models. These ini ial es ima ions es ablish he base line o he p ojec , and he simula ed da a ob ained ep esen he dynamic e olu ion o he p ojec a iables along he whole li e cycle. As well as he ini ial baseline and he simula ed da a, he so-called simula ed da abase con ains a hi d componen . This hi d componen con ains he esul s o applying some o he echniques du ing he simula ion o he p ojec , which a e o ien ed owa ds he gaining o insigh in o he p ocess unde simula ion. These echniques, which ha e been in eg a ed wi h he dynamic modules, a e desc ibed in Sec ion 2.5. As i was men ioned be o e, he p ocess o modeling he so wa e p ocess equi es a good knowledge o he so wa e p ocess i sel , and igge s a me ics collec ion p og am which can hen be used o ini ialize he pa ame e s o he model and inc emen he le el o isibili y he model has abou he p ocess. All ha has been simula ed so a , mus be aken in o p ac ice. A e ha ing de e mined he ini ial es ima es and un he simula ions o es ablish he ini ial base line, i is possible o un di e en scena ios in o de o ind ou he ou comes o di e en ini ial alues o he p ojec es ima es. This e lec s, o cou se, he le el o unce ain y low ma u i y o ganiza ions ha e a he ini ial s ages o a p ojec . When he eal p ojec begins, he me ics collec ion p og am may be applied o ga he eal in o ma ion abou he p og ess. These eal da a a e also sa ed in he da abase, enabling he de elopmen o a his o ical da abase. As hese da a become a ailable, i is possible o pe o m analysis and calib a e he unc ions and pa ame e s o he dynamic modules so ha hei accu acy may be imp o ed. Imp o ing he accu acy o he dynamic modules may equi e an imp o emen in he knowledge we ha e abou he so wa e p ocess and, his way, he loop is closed. The dynamic models o his le el a DIFSPI should ollow he le els o isibili y and knowledge o he enginee ing p ocesses ha o ganiza ions ha e a each ma u i y le el. I is ob ious ha he complexi y o he dynamic model used in le el 1 o ganiza ions canno be he same as ha one o he models capable o simula ing he enginee ing p ocesses o , o ins ance, le el 4 o ganiza ions. Fig. 2. Enginee ing p ocesses in DIFSPI Fig. 3. Managemen p ocesses in DIFSPI 2.3.2 P ojec Managemen P ocesses in he F amewo k Inside he amewo k, managemen p ocesses a e di ided in o wo main ca ego ies: Plan. I g oups he p ocesses de o ed o he design o he ini ial plan and he equi ed modi ica ions when he p og ess epo s indica e he appea ance o p oblems. The models o his g oup in eg a e adi ional es ima ion and planning echniques oge he wi h dynamic ones. Con ol. In his g oup all he models designed o he moni o ing and acking ac i i ies a e ga he ed. These models will also ha e he esponsibili y o de e mining he co ec i e ac ions o he p ojec plan. The e o e, he simula ion o p ocess imp o emen s will be o an eno mous impo ance. Figu e 3 shows he u iliza ion o DIFSPI a his le el. As i was men ioned be o e, he ini ial baseline o he p ojec is es ablished using he s a ic models buil inside he amewo k. The dynamic modules ha model he planning ac i i ies pe o med in he o ganiza ion ha e no only he di e en ial equa ions o model hese ac i i ies, bu he equa ions o he adi ional s a ic es ima ion models. In o de o gain a use ul in o ma ion om hese s a ic models, he same knowledge abou he so wa e p ocess which is equi ed o use hese models is necessa y a his momen . The con ol modules model and simula e all he ac i i ies which de e mine he p og ess o he p ojec , and make he co ec i e decisions which a e equi ed o mee he p ojec objec i es. These modules ha e a g ea impo ance in he design o he p ocess imp o emen s. 2.4 Dynamic Modules A chi ec u e The app oach ollowed in he cons uc ion o he dynamic models is based on wo undamen al p inciples: 1. The p inciple o ex ensibili y o dynamic models. Acco ding o his p inciple, di e en dynamic modules a e joined o an ini ial and basic dynamic model. This ini ial model models he undamen al beha io o a so wa e p ojec . Each one o he dynamic modules models each one o he key p ocess a eas which con o m he s ep o e ol e o he nex le el o ma u i y. These modules can be ei he „enabled“ o „disabled“ acco ding o he objec i es o he p ojec manage o he membe s o he SEIG. 2. The p inciple o agg ega ion/decomposi ion o asks acco ding o he le el o abs ac ion equi ed o he model. Two le els o agg ega ion/decomposi ion a e used: Ho izon al agg ega ion/decomposi ion acco ding o which di e en sequen ial asks a e agg ega ed in o a unique ask wi h a unique schedule. Ve ical agg ega ion/decomposi ion acco ding o which di e en and indi idual, bu in e ela ed and pa allel asks a e conside ed as a unique ask wi h a unique scheduled oo. The de ini ion o he igh le el o agg ega ion and/o decomposi ion o he asks mainly a ec s he modeling o he enginee ing ac i i ies and p incipally depends on he ma u i y le el o he p ocess in ended o be simula ed. To de ine he ini ial dynamic model he common eedback loops among he so wa e p ojec s we e aken in o accoun . The objec i e o his app oach was o achie e a gene ic model and a oided modeling speci ic beha io s o conc e e o ganiza ions which migh limi he lexibili y o DIFSPI. To ini ialize he unc ions and pa ame e s o he ini ial model, da a o igina ing o his o ical da abases collec ed in he a ailable li e a u e we e used [8]. Figu e 4 shows he main s uc u e o he ini ial model. Fou dynamic modules a e joined oge he o de elop an ope a ional model ha p o ides he se o inal di e en ial equa ions o gene ically simula e he so wa e p ocess in low ma u i y o ganiza ions. Fig. 4. Submodules a chi ec u e o he ini ial model Remaining ime Requi ed de elopmen a e Accomplished p ojec ac ion Pending asks P ojec inished Quali y P oduc i i y Pe sonnel Con ol Module Human Resou ce Module De elopmen Module Requi ed de elopmen a e Plan Module Accomplished asks Requi ed pe sonnel By eplica ing some o he equa ions o he ini ial model i is possible o model he p og ess o highe ma u i y le els. The ini ial model can be used o simula e so wa e p ojec s de eloped in o ganiza ions p og essing o le el 2. Gene ally speaking, he so wa e p oduc de elopmen p ocess can be conside ed as ollows. The numbe o asks o be de eloped is de e mined om an ini ial es ima e o he size o he p ojec . These pending asks become accomplished asks acco ding o he de elopmen a e. Du ing his p ocess, e o s can be commi ed. Thus, in acco dance o he desi ed quali y objec i e o he p ojec , he quali y a e and he e ision a e a e de e mined. These wo a es go e n he numbe o asks ha a e e ised. To model he p og ess o le el 3, he model will make use o a ho izon al decomposi ion, c ea ing as many subs uc u es as phases o ac i i ies a e p esen in he ask b eakdown s uc u e o he p ojec (analysis, design, code and es , in he wa e all pa adigm). Acco ding o his app oach, each ime a comple e model o some pa o i is eplica ed, i will be necessa y o de ine he new ixing mechanisms (dynamic modules) o he new s uc u es. These mechanisms e ec i ely implemen he p inciple o agg ega ion/decomposi ion p e iously men ioned. The eplica ion o s uc u es also p o ides he possibili y o eplica ing he modules ela ed o he p ojec managemen p ocesses. This eplica ion is especially use ul o high ma u i y le el o ganiza ions, which will be able o es ablish p ocess imp o emen p ac ices o each ce ain ac i i y o he li e cycle. 2.5 In eg a ed Techniques As i was men ioned be o e, ou aim is o de elop a wo king en i onmen whe e he simula ion o di e en scena ios can be used o gene a e he simula ed da abase whe e manage s can expe imen di e en p ocess imp o emen s and ac i i ies ocussed on he implemen a ion o me ics p og ams and alue analysis. The ollowing echniques and me hods a e cu en ly success ully implemen ed in DIFSPI: T adi ional Es ima ion Techniques. T adi ional algo i hmic es ima ion models ha e been implemen ed inside his amewo k wi h he aim o p o iding an ini ial baseline o so wa e p ojec s ca ied ou in low ma u i y le el o ganiza ions [9] and [10]. SEI Co e Measu es. Recen s udies and expe iences highligh he bene i s o he applica ion o hese ou co e measu es o he so wa e li e cycle. The main aspec s o he p oduc and p ocess (quali y, ime, size and cos ) a e moni o ed and acked o acili a e p ojec success and highe ma u i y achie emen . Inside his amewo k heses ou measu es cons i u e he basics o bo h, he dynamic models and he g aphical ep esen a ion o he p ocess pe o mance [5]. Me el Rules. Gi en he dynamical na u e o he DIFSPI p oposed, we conside i could be use ul o in eg a e a axonomy o so wa e me ics which is de i ed om he needs o use s, de elope s, and managemen . Among all he ad an ages ha can be ob ained wi h he use o his sys em o me ics, we would like o poin ou he dynamic pe o mance o hese me ics, ha is, how hei accu acy, p ecision, and u ili y changes o e he du a ion o a p ojec , he li e o a p oduc o he s a egic plan o an o ganiza ion. In DIFPSPI Me el ules ha e been used as an e icien me hod o