Advanced decision making tools in the production company

ADVANCED DECISION MAKING TOOLS IN THE PRODUCTION COMPANY =GHND9LGHFNi 1 1 Ing. Zdenka Videcka, Ph.D. Facul y o Business and Managemen . B no Uni e si y o Technology. Technicka 2, 616 69 B no, Czech Republic. E-mail: idecka@ bm. u b .cz ADVANCED DECISION MAKING TOOLS IN THE PRODUCTION COMPANY =GHND9LGHFNi Abs ac : Today’s companies collec a la ge amoun o ope a ional da a ela ing o all kinds o ac i i ies. This da a holds and hides he expe ience o company’s his o y. The impac o “ lood o da a” pe o m unsa e si ua ion. I managemen use hese da a and don’ disco e mu ual ela ionship o hese da a i can lead o w ong decision. I igh decision ools doesn’ exis in he ame o companies e y o en is decision based on he pas expe ience o manage s. I can lead o w ong decision i si ua ion on he decision a ea is changed. P ope ly analyse o da a can ha e signi ican e ec on a company’s pe o mance and p o i abili y. This pape desc ibe possibili y o using ad anced decision ools in day- o-day decision manage s. Key wo ds: Simula ion, da a mining, ERP, BPR ADVANCED DECISION MAKING TOOLS IN THE PRODUCTION COMPANY =GHND9LGHFNi 1. In odu ion In o ma ion sys ems o p oduc ion planning and con ol (PPC) we e implemen ed al eady long ago in almos all indus ial companies. They a e using in day- o-day p ac ise. P oduc ion Planning and Con ol Sys ems a e ocused o p o ide decision on ope a ional and ac ical le el. The economic e olu ion o indus y is a ec ed h ough he implemen a ion o inno a i e in o ma ion echnology. In o ma ion sys ems o p oduc ion planning and con ol al eady long ago implemen ed in almos all indus ial companies a e in hei unc ionali y limi ed. ERP sys ems o e a la ge amoun o asks ha can help o manage s making decision. Un o una ely he e is no ools o disco e mu ual ela ionship da a – knowledge inside ERP sys ems. The e is as de elopmen o da a mining ools in he las i e yea s. They a e used oge he wi h ERP sys ems. This is oge he , wi h many o he s ad an ages, he eason why is inc easing he usage o simula ions ools, which could do nea ly unlimi ed numbe o es s and expe imen s in wide scope. Wi h all his so wa e ools doesn’ need o be only specialized bu also hey can be wide uni e sal applicable and hei usage need no only special knowledge and high educa ion. The main aim o his connec ion is disco e knowledge in a la ge amoun o da a. Decision suppo sys em based on knowledge sys ems is called business in elligen sys ems. 2. P edic i e Tools Decision Suppo Sys ems (DSS) use di e en kinds o echniques and models which help o igh decision. Gene al de ini ion o decision suppo sys em doesn’ exis . “Decision Suppo Sys ems a e in e ac i e compu e sys ems ha use da a and models o solu ion nons uc u ed p oblems o help o decision subjec s” Sco Mo on (1971). E ec i e ool is “wha -i ” analysis. One o Decision Suppo Sys ems is disc e e e en simula ion. The p inciple o simula ion is expe imen wi h a compu e model – an exac image o s ochas ic disc e e sys em. The e is allowed dynamic ep esen a ion o ma e ial o cus ome low, simula ion expe imen s a e, p ocesses, machine using, e c. A use can moni o dynamic p ocess. I means ha he has necessa y in o ma ion o decision and sys em e alua ion commensu a e wi h cosen c i e ias. He ecei e answe o ques ion: “Wha is he ma e i I change (se up, in es , …) …?”. The sys em beha iou is moni o ed h ough epo s and s a is ics. P edic ion o sys em beha iou is e icien ool in p ojec phases, eenginee ing o p ocesses and on-line simula ion nowadays. The e is possible ind an op imal se up o pa ame e s o he bes solu ion (minimum o maximum o objec i e unc ion). On one hand i is possible o ind op imal solu ion h ough simula ion on he o he hand he e is no possible ind ou ela ionship be ween di e en pa ame e s o a scope o he bes solu ion o decision suppo . The e is no possible o mine knowledge o models sa ed in da a ob ained du ing expe imen s wi h compu e model. The powe ul ool o a la ge amoun da a analysis is da a mining which help o disco e ela ionship o da a and hidden ules. The e is no uni ied de ini ion o da a mining. I is possible o say ha da a mining pe mi disco e hiden ules and ela ionship be ween da a in wide da abases. The e is pa adox: i we ha e mo e da a he e is di icul and ime consuming analyse hem wi h adi ional me hods. The e is possible as and ully isola e aluable and use ul in o ma ion h ough analy ical echnologies o da a mining. I is s a e when models and expe ience can be ecie ed om da a. Simula ion ools His o ically, simula ions a e de eloped o -line using cus om so wa e packages/languages wi h limi ed di ec connec ions o he ac ual da a gene a ed by he p oduc ion sys em (D ake and Smi h 1996) [4] This adi ional simula ion gene ally examines long- e m sys em pe o mance, mos ly o planning and design pu poses. These models a e usually “one-sho models” because hey a e seldom used a e he p ojec is inalized (i - hen mos ly only o checking he sys em abili y unde new p ojec condi ions). P ima y easons o his in lexibili y a e ha he inpu da a o he simula ion a e collec ed and analyzed ou side he simula ion model and simula ion en i onmen and ha he simula ion sys em canno communica e au oma ically wi h he in o ma ion sys em - En ep ise Resou ce Sys em (ERP) ha is esponsible o collec ing, adminis a ion and dis ibu ion o s a us in o ma ion. On- line simula ion in eg a es he in o ma ion sys em wi h he simula ion model. On-line simula ion o p ocess scheduling, eal- ime “in elligen ” con ol, pe o mance o ecas ing, p ocess capabili y es ima ion, eal- ime con ol sys ems emula ion, eal- ime displays o sys em s a us, and sho e m decision making is nowadays ac i e a ea o so wa e de elopmen . By using he mos cu en in o ma ion sys em, accu a e p edic ions abou he sys em and u u e con ol al e na i es canno be de eloped. The eason o using simula ion is ha simula ion can be e cap u e and desc ibe he complex in e ac ions whe e analy ical me hods ail. Cu en simula ion so wa e ools a e al eady able o communica e wi h da abases – bu he ull in eg a ion is mo e o less s ill u u e o see p obably only in au omo i e indus y by using he concep o digi al ac o y. In his way s ays he combina ion o simula ion, in o ma ion sys em and eal- ime con ol e y p omising amewo k o op imiza ion o dynamic cha ac e is ic in lexible p oduc ion sys ems. Gene ally he simula ion models can be buil by gene al p og amming language as C++. Bu his way needs “special” p og amming knowledge. People able o build simula ion model in his way a e specialized and ha en’ un o una ely on he o he hand o en p ac ise expe iences wi h he eal sys em. This ac leads o building o bigge eams dealing wi h simula ion o o buying simula ion so wa e ool. Cu en ly he e a e se e al so wa e ools a ailable. Wi h he i s al eady men ioned g oup hese ools can be di ided in o ee basic classes: − Gene al pu pose simula ion language – weakness o his g oup is ha he use has o be p o essional p og amme and compe en simula ionis in one. − Simula ion on -end – essen ially p og ams be ween use and simula ion language. − Simula o s – so wa e packages u ilize cons uc and e minology common o he businesses communi y (manu ac u ing, se ices…) and o e g aphical p esen a ion and anima ion. Las g oup, in case o disc e e simula ions, is e y well usable o simula ion o p oduc ion sys ems. Building he model is ela i ely easy and needs ne e y speci ic knowledge o p og amming. Simula ion expe s can educe he ime needed o building he simula ion model. Cu en ly is possible o buy many o hese packages wi h ollowing common cha ac e is ic (needed o be success ul on ma ke ): − Rela i ely good execu ion speed. − In e aces o da a inpu /ou pu ( om ex e nal da abases). − Easy syn ax. − Random a iable gene a o s o o en-used dis ibu ions. − G aphical en i ies (elemen s) ep esen ing unc ion o eal sys em objec :  P oduc ion machines.  Assembly machines.  Mul iple machines.  Con eye s.  S o ages.  Pa s.  e c. − In e ace wi h gene al used language – possibili y o add own p og ammes − Possibili y o impo g aphical objec s ( o layou s o elemen s design) − On-line anima ion and s a us indica ion − Op imisa ion − Good building help and documen a ion. These a e he main ea u es cha ac e ising p esen so wa e packages. Bu no each o p esen so wa e ools has all o hese. You can ind eally good ools bu speci ied on only one ype o p oduc ion as o example on low shop p oduc ion. One o his specialised so wa e is Simp o. This is used in au omo i e indus y and i s elemen s could be eally good used o his kind o p oduc ion. So i is easy o simula e special kinds o con eye s. Disad an age is bu he di icul y o in o ma ion low by models ha should simula e o he kinds o p oduc ion as o example mass p oduc ion. In his case you ha e o use mo e ic i e elemen s and so ha e p oblems wi h s a is ic documen a ion by in e p e a ion. 3. The ela ionship o simula ion and da a mining Da a mining me hods a e used on he ame o eal da a s o ed in da abases o En e p ise Resou ce Planning (ERP) sys ems, CRM sys ems and wa enhouses. The e a e implemen ed analy ical me hods om he classical s a is ical me hods, neu al ne wo ks, decision ee, e c. Excep hese sys ems he special so wa e p oduc s o da a mining a e used o da a mining p ojec s. App op ia e solu ion o using hidden knowledge om expe imen al modelling da a is connec ion o disc e e e en simula ion and da a mining. The ou pu o simula ion (see Fig.1) a e pa ame e s’ se up o he op imized sys em and consequen ial mining o knowledge hidden in expe imen al da a. I allows de ine app op ia e manage ial sys em model . Figu e1: Rela ionship o disc e e e en simula ion and da a mining 4. Case s udy o da a mining using in p ac ise Case s udy is ocused o using o disc e e e en simula ion and model op imising o manu ac u ing p ocess. The esul o op imalisa ion is ile o expe imen al da a. The da a a e used in da a mining p ojec . The aim is knowledge ha a e neccesa y o inc easing p oduc i i y o manu ac u ing p ocess. Scena y is simula ed h ough compu e simula ion. The p ocess s a wi h cus ome equi emen s, manu ac u ing include building and es ing o compu e s. Compu e Simula ion Sys em Op imisa ion Da a Mining Se up o Sys em Pa ame e s Using o Knowledge o a Sys em Simula ion model The inpu o model a e cus ome s’ equi emen s. The speci ica ion is cla i ied h ough phone communica ion. The compu e s a e assembled, es ed and dis ibu ed (see ig.2). They a e wo skills labo s (A and B). Figu e 2: The model o manu ac u ing sys em The op imalisa ion is aimed o maximize o p o i . The pa ame e s o expe imen a e numbe o A skill labo s, B skills labo s, numbe o assembly and es ing wo kplace. The bes esul s we e eached using Adap i e The mos a is ical SA me hod. The op imal esul is 11 758 money uni s. I was ound a e 65 expe imen al uns. Expe imen al da a we e expo ed o Mic oso Excel. File o expe imen al da a is sou ce ile o da a mining p ojec (see ig. 3). Figu e3: S uc u e o p ojec in Wi ness Mine The esul o da a mining p ojec (see ig.4) is decision suppo o maximum o p o i and ecommenda ion o pa ame e s’ se up. I means numbe o enginee s highe hen 2, numbe o es ing wo kplaces highe han 4 and numbe s o B skilled labo s highe han 4. This solu ion co espond o aim o case s udy – elimina ion o labo cos s and he o he sou ces o maximize p o i o compu e building. Figu e 5: Knowledge disco e y and da a mining esul