RISK MANAGEMENT OF UNCERTAIN INNOVATION PROJECT BASED ON BAYESIAN RISK DECISION

Transcription

1 Sept 201. Vol., o EAAS & ARF. All rghts reserve ISS RISK MAAGEMET OF UCERTAI IOVATIO PROJECT BASED O BAYESIA RISK DECISIO Yngchn Go College of Mathematcs & Compter Seence, Hebe Unversty, Baong , Chna E-mal goyc@hb.cn Ths paper s a part of the proect Research on Innovaton Rs Management Mechansm of Instral Chan of Hebe Soft Scence Program ( D-2). Abstract Innovaton s the nexhastble motve force for the prosperty of one naton, an also the lfe sorce of enterprse. However, the hgh rss of nnovaton actvtes reqre managers to mplement the scentfc an effectve nnovaton rs management. On the bass of a general revew of the rs management of ncertan nnovaton proect, by combnng the case of nnovaton proect, ths paper scsse the qanttatve rs management of nnovaton proect base on the Theory of Bayesan Rs Decson, n the hope of provng scentfc references for managers mang nnovaton rs management ecsons. Keywors Uncertan Innovaton, Innovaton rs, Rs Management, Bayesan Rs Decson 1. Introcton Innovaton s the sol of a naton s progress, an nexhastble force for the prosperty of a naton, an the lfe sorce of enterprses (Swe Cheng, 2009, p1-1). Wthot nnovatons, enterprses wol not be able to pgrae the procton strctre. Wth weaenng compettveness, enterprses wll e. However, nnovaton s a oble-ege swor, wth characterstcs of hgh potentals, hgh npts, hgh retrns, an hgh rss. Partclarly, hgh rss from technologes, maret, an management frstrate or even ll many nnovaton actvtes, whch may even threaten the healthy evelopment of hman socety. Therefore, to manage the nnovaton rss s sgnfcant. At present, the classfcaton of nnovaton proect s manly base on the nnovaton contents, whch s ve nto three types,.e. technologcal nnovaton, proct nnovaton, an management nnovaton. In perspectve of rs management, ths paper ves nnovaton proect nto proper nnovaton, mproper nnovaton, an ncertan nnovaton. The proper nnovaton refers to the nnovaton that s benefcal to hman healthy evelopment an the harmonos evelopment of hman socety, sch as varos new technologes an procts that help to mprove people s lfe qalty an socal satsfacton. All socal management organzatons shol create the envronment an contons to actvely encorage an avocate ths type of nnovaton. The mproper nnovaton refers to the nnovaton that s harmfl to hman healthy evelopment or the harmonos evelopment of hman socety. For 1 example, companes may eceve the qalty nspecton epartment by replacng qalfe procts wth fae ones or sng fewer raw materals for procton n orer to obtan more economc benefts. All socal management organzatons shol strctly montor an control ths n of behavors, forbng ths type of nnovaton completely. The ncertan nnovaton refers to the nnovaton that proces nclear mpacts on hman healthy evelopment an ncertan conseqences on the harmonos evelopment of hman socety, sch as the genetcally mofe botechnologes, an the se of a varety of foo atves, etc. All socal management organzatons shol strengthen the management of ths type of nnovaton, perform the whole-process an comprehensve montorng, establsh the rs-early-warnng an fast-response mechansm, an rece the potental harms to the mnmm level. Ths paper wll focs on the rs management of ncertan nnovaton proect. Becase of the ncertan of nnovaton proect, t mght brng abot amages or ba nflences on enterprses, nstral chan, or even the entre socety,.e. the rs. At present, most scssons on nnovaton focs on the approaches an patterns for promotng enterprses nepenent nnovaton. Only few researches are abot the nnovaton rs. In aton, the exstng lteratres sally st scss the economc rss of nnovaton proect, mostly from the pont of the nnovaton proect falng to reach the expecte retrn. Ths paper wll not only conser the economc rss, bt also thn abot the socal rss of nnovaton proect, nclng the envronmental

2 Sept 201. Vol., o EAAS & ARF. All rghts reserve ISS rs, hman health rs, etc., from the long rn. As an nnovaton proect, ts mpacts are stll nclear n the ftre. Therefore, managers shol aopt a set of scentfc an effectve management approaches to entfy problems an revse ecsons mmeately, n orer to acheve the prpose of comprehensve rs control. Rs ecsons rn throgh the entre process of rs management. The rs ecson-mang s to mae ecson accorng to ncomplete nformaton. Accorng to the obectve of rs management, wth bass of rs entfcaton an rs evalaton, mae reasonable choce an combnaton of fferent rs management methos, an offer a specfc program for rs management. Face hgh rss from technologes, maret, an management, enterprse managers shol master the scentfc an feasble rs ecson-mang metho, managng nnovaton rss effectvely. Ths paper s to explore the effectve qanttatve rs ecson-mang metho, n orer to help enterprse managers to acheve effectve nnovaton rs management. Bayesan approach s a powerfl tool for rs ecson-mang (Rchar Braley, 2007; aomo Jang & Sanaran Mahaevan, 2007). Becase of ts smple an convenent attrbtes, ths approach s wely apple to many fels. De to ts convenence an easness, ths approach s applyng n many fels. Jacobs P. Venter an Cornels C. V. Waveren (2009) se the Bayesan Decson technology to spport the new proct evelopment management. Ramar Venatesan, V. Kmar, an Tmothy Bohlng (2007) apple the Bayesan Rs Decson-Mang Theory to the choce of cstomers n cstomer relatonshp management. Kwa-Sang Chn, Da-we Tang, an Jan-bo Yang (2009) apple the Bayesan networ metho to the rs evalaton n new proct R & D. Pal L. Reynols an Geoff Lancaster (2007) propose a Bayesan solton for enterprses prectng the strategc maretng management ecson. Mn Chen, Ysen a, an nle Wang (2010) blt a Bayesan moel to acheve ynamc nowlege pate, n orer to eal wth the spply ncertantes an rss. However, few lteratres are abot applyng the Bayesan Rs Decson approach to the rs management of nnovaton proect. Ths paper wll scss how to se the Bayesan Rs Decson approach to perform rs management of ncertan nnovaton proect. 2. The process of rs management of ncertan nnovaton proect The process of rs management of ncertan nnovaton proect ncles fve stages,.e. rs entfcaton, rs pre-assessment, rs ecson-mang, rs montorng, an emergency-response to rs. See to Fgre 1. Rs entfcaton refers to entfy the rs sorces of nnovaton proect, sch as economc rs, nstral chan rs, envronmental rs, hman health rs, etc. Rs pre-assessment means before the applcaton of nnovaton proect, nvte some professonals to form an expert team, as them to mae a comprehensve assessment of the rs, prect the conseqences, an mae a feasblty analyss of the proect n perspectve of the potental rs. Rs ecson-mang means on the bass of comprehensve assessment of all potental rss, mae relevant ecsons abot proect npts an operatons. At ths stage, we col not completely control the conseqences of ecson. So, t s calle rs ecson-mang. The rs ecson-mang s the most ey stage n the process of rs management. Rs montorng means rng one ecson-mang cycle for the tral operaton of nnovaton proect, nvestgate an follow the progress of the proect, collect relevant ata an nformaton feebac, an montor the postve or negatve effects of the proect on all socal fels. Accorng to the analyss of new nformaton, entfy problems an mae rs pre-warnng, ast relevant rs ecsons of nnovaton proect n tme, an acheve the prpose of rs control. Repeat the two rs management stages many tmes,.e. from rs ecson-mang to rs montorng or from rs montorng to rs ecson-mang, we can acheve the ynamc rs management. Emergency-response to rs means f nexpecte rs event happens rng the rnnng cycle of proect, we mst perform the emergency management, accorng to the pre-establshe plans for emergences. For example, call an emergency stop to the nnovaton proect, or mae mmeate proct recalls. By ths way, we can rece the rs conseqence to the mnmm level.. The Introcton of Bayesan Rs Decson Rs ecson-mang ecson rns throgh the whole rs management process. By analyzng rss an losses scentfcally, t can help to choose the reasonable rs management technqes an methos an fnally get the most satsfyng solton from several optons. Every rs ecson-mang ncles three elements the state grop consste of fferent natral stats, the acton grop consste of a set of 2

3 Sept 201. Vol., o EAAS & ARF. All rghts reserve ISS actons taen by ecson maers, an the escrpton of tlty or losses from fferent combnatons of states an actons. From the three elements, we can get fferent rs contons. Once the ecson maer maes a ecson wth ncertan reslt, t means certan rs. The rs ecson-mang nees to get changeable maret nformaton by ncreasng npts. Base on masterng varos natral contons n tme, se the collecte nformaton reasonably, an select the ecson scentfcally, recng rss, an mprovng economc an socal benefts. In rs ecson-mang, the accracy of estmaton of natral contons can rectly affect the expecte retrns. In orer to mae better ecson, t nees to pate the nformaton n tme. After gettng new nformaton, we can revse the orgnal estmate probablty of emergence of certan natral conton, an se the revse probablty strbton to mae new ecson. Becase the probablty correcton s base on the Bayesan Theorem n probablty theory, ths ecson s calle Bayesan Decson.. The case sty of nnovaton rs management.1 Three elements for nnovaton rs management ecson (1) The grop of natral states. The comprehensve evalaton on nnovaton proect s 1, 2,, m. For nstance, 1 stans for best, 2 stans for better,, an m stans for worst. Experts gve the precton posteror probablty of each state, 1,..., P m (2) The grop of actons. The acton towar nnovaton proect s D 1, 2,, n.. Here 1 stans for hgh nvestment, sch as more nvestment n R & D, new procton eqpment, an new proct. 2 stans for mem nvestment,. stans for low nvestment, stans for no nvestment n nnovaton. () The grop of escrptons of tlty or losses U. Here, -100,100 s the mn economc tlty that can be evalate by money, or the tlty fncton evalate by non-monetary factors. Here, we sggest the secon meanng, becase nnovaton actvtes can not only generate economc benefts, bt also socal benefts, so as to brng ntangble assets an long-term nterests for enterprses. Here, the tlty fncton can be measre by the satsfacton egree, sch as enterprses satsfacton egree, cstomers satsfacton egree, expert scorng, an other comprehensve scores (see to Table 1)..2 Descrpton of proct nnovaton rs Sppose there are fve states of comprehensve evalatons on economc tlty an socal benefts of a new nnovaton proect 1, 2,,,. Here, 1 stans for best, 2 stans for better, stans for mem, stans for worse, an stans for worst. Accorng to the ata analyss of the maret srvey an the expert precton, the probablty strbton of each state s P, P, 1 P =0.1, P 2 =0., P =0.0 The manager has for optons D,,, stans for hgh nvestment, 2 stans for mem nvestment, stans for low nvestment, an stans for no nvestment. The tlty of for optons ner fferent states s n Table 2. Data escrpton the expecte tlty eclnes along wth the mnshng prospect of maret state. For nstance 11 ner the conton of hgh nvestment an best maret, the economc tlty an socal benefts reach the hghest. The expecte tlty 11 =100;. 21 ner the conton of hgh nvestment an better maret, the economc tlty an socal benefts are hgh. The expecte tlty 21 =70; 1 ner the conton of hgh nvestment an ornary maret, the economc

4 Sept 201. Vol., o EAAS & ARF. All rghts reserve ISS tlty an socal benefts are mem. The expecte tlty s 1 =0; 1 ner the conton of hgh nvestment an worse maret, the economc tlty an socal benefts are worse. The expecte tlty s 1 = ner the conton of hgh nvestment an worst maret, the enterprse sffers from seros losses. The expecte tlty s 1 =-100; Here, focs on the last lne. If the enterprse taes the no nvestment strategy, the expecte tlty wll be negatve. For nstance 1 the enterprse oes not nvest, thogh the maret contons are goo. It wll mae the enterprse lose potental economc tlty an socal benefts. The expecte tlty 1 =-80; the enterprse oes not mae nnovaton nvestment an the maret contons are ba. Then, there s no economc beneft or socal beneft. The expecte tlty. The Bayesan Rs Decson-Mang process..1 Pror analyss =0. Accorng to the probablty of natral state an the expecte tlty (see to Table 2), by followng the law of expectaton, calclate the expecte tlty of each program. E P, 1,..., 1 Accorngly, the optmal expectaton for the optmal program s nstance, max E E EMU.. For E 1 = 0.2*100+0.*70+0.2*0+0.1*(-20)+0.0 optmal expecte tlty s EMU = E =8.. It means that the manager can tae the low-nvestment strategy f only wth the pror nformaton...2 Precton posteror analyss In precton posteror analyss, estmate the vale of complete nformaton frstly. As the precton of complete nformaton s n the state, t becomes the ecson-mang ner certanty. Apparently, the optmal program s max. Then, wth complete nformaton, the maxmm expecte tlty from ecson-mang s EUPI P max *80+0.1*0+0.0*0=72.. *100+0.*8 Therefore, the vale of complete nformaton EVPI EUPI EMU =72.-8.=1. It means the vale of complete nformaton s eqal to 1 nts of tlty... Posteror analyss (1) Spplement new nformaton Accorng to the maret contons, nvestgate, explore, an conslt the fve states 2 (better), (mem), 1 (excellent), (worse), an (worst), an prect whch one wll appear. Meanwhle, get the contonal probablty P prectng the emergence of, whch s the probablty of when the natral *(-100)=0; smlarly, E 2 =., E =8., state actally appears. (See Table ) E =-1. Then, the optmal ecson an the (2) Revse the probablty Base on the pror probablty P ( =1, 2,, )

5 Sept 201. Vol., o EAAS & ARF. All rghts reserve ISS an the contonal probablty 2,, ; strbton of P ( ==1, =1, 2,, ), calclate the probablty P P P 1 For nstance, 1 P 0.2*0.+0.* * * *0.01. Smlarly,. P , 1 1 E 0.762* * * *(-20) *(-100)=77.1. Smlarly, there s 2 1 E , E E 1 =6., These reslts mean f the maret conton s better, the manager can tae the ecson maxmm expecte tlty s 1 1, for the E P 0.27, P 0.1, an Smlarly, f the maret conton s 2, calclate P Use the Bayesan formla to an compare the expecte tlty of each. The calclate the revse probablty of, namely the manager can tae the ecson 2, for the maxmm posteror probablty (see to Table ) P =1, 2,, ). () Posteror ecson P P P, ( ==1, 2,, ; Sppose the spplement nformaton prects the appearance of state probablty strbton. Use the posteror revse P ( =1, 2,, ) to calclate the expecte tlty of each program. By followng the law of expectaton, mae the ecson. Then, E P 2,,, =1, 2,, ). 1, (=1, For nstance, f the maret srvey shows that the maret conton s of 1 (see to Table )., calclate the expecte tlty expecte tlty s 2 2 E If the maret conton s, calclate an compare the expecte tlty of each. The manager can tae the ecson,for the maxmm expecte tlty s, E 6.6. If the maret conton s calclate an compare the expecte tlty of each. The manager can tae the ecson,for the maxmm expecte tlty s E.9. If the maret conton s, calclate an compare the expecte tlty of each tae the ecson tlty s E The manager can,for the maxmm expecte

6 Sept 201. Vol., o EAAS & ARF. All rghts reserve ISS () Dscsson If the expecte tlty of rs ecson s more than 0, that means the proect s o more goo than harm, then we can contne to promote ths proect; If the expecte tlty of rs ecson s less than 0, that means the pros an cons of the proect almost the same, then we can mantan the orgnal scale an see how t behaves n the ftre; If the expecte tlty of rs ecson s negatve, that means the proect s o more harm than goo, then we shol stop the proect.. Conclson The rs management of nnovaton proect s vtal to the srvval an the evelopment of socety, enterprse, an procton chan. It s beleve that the rs management of ncertan nnovaton proect s an sse of bg system. The scentfc nnovaton rs management nvolves many socal aspects not only the economc rs, bt also socal rs; not only the short-term rs, bt also the long-term rs. Some proect sponsors are eager for qc sccess. They st care abot the short-term economc nterests, bt not conser the long-term conseqences an socal rss, whch mght case seros amages to the nstral chan, socal envronment, or even hman srvval. In orer to avo sch n of tragees, all managers at fferent socal levels shol tae responsbltes an perform scentfc rs management of nnovaton proect. By combnng the case of ncertan nnovaton proect, ths paper scsse the rs management of nnovaton proect base on the Bayesan Rs Decson approach from the angle of bg system. The rs management of nnovaton proect nees to motvate all socal powers to be nvolve, nclng proect sponsors, relate experts, government reglators, most cstomers, an so on. Here we shol frther emphasze one pont,.e. the rs management of ncertan nnovaton proect nees to repeat the applcaton of the rs ecson process n cycle, ast the rs ecson n tme, an carry ot the ynamc rs management of nnovaton proect, n orer to aapt to the constantly-changng socal envronment an help managers to acheve the scentfc management of nnovaton rs. Reference Cheng, Swe. (2009). On the constrcton of an nnovatve contry. Chna Soft Scence. o.12. p1-1. Jacobs P. VETER, Cornels C.V.Waveren. (2009). ew proct evelopment wth ynamc ecson spport. Internatonal Jornal of Innovaton an Technology Management. o.6(2). p Kwa-Sang Chn, Da-We Tang, Jan-Bo Yang, etc. (2009). Assessng new proct evelopment proect rs by Bayesan networ wth a systematc probablty generaton methoology. Expert Systems wth Applcatons. o.6(6). p Mn Chen, Ysen a, nle Wang. (2010). Managng spply ncertantes throgh Bayesan nformaton pate. IEEE Transactons on Atomaton Scence an Engneerng. o.1. p2-6. Pal L. Reynols, Geoff Lancaster. (2007). Prectve strategc maretng management ecsons n small frms A possble Bayesan solton. Management Decson. o.(6). p Ramar Venatesan, V. Kmar, Tmothy Bohlng. (2007). Optmal cstomer relatonshp management sng Bayesan Decson Theory an applcaton for cstomer selecton. Jornal of Maretng Research. o.liv(). p Rchar Braley. (2007). A nfe Bayesan Decson Theory. Theory an Decson. o.6(). p2-26. aomo Jang, Sanaran Mahaevan. (2007). bayesan rs ecson-mang metho for moel valaton ner ncertanty. Relablty Engneerng & System Safety. o.92(6). p

7 Sept 201. Vol., o EAAS & ARF. All rghts reserve ISS Fgre 1.The process of rs management of ncertan nnovaton proect Rs entfcaton Rs pre-assessment Rs ecson-mang nformaton feebac Rs montorng rs event Emergency-response Table 1. Utlty State&probablty 1 Utlty 1 Program P n 12 1n 2 P n m P m m1 2 m mn Table 2. The expecte tlty of nvestment. Program Utlty State&probablty 1 2 P 1 P 2 = = =70 1 =60 1 = =70 22 =80 2 =70 2 =-60 P 1 =0 2 =60 =80 =-0 P =0.1 1 = =10 =0 =-20 P =0.0 1 = =-80 =-0 =0 7

8 Sept 201. Vol., o EAAS & ARF. All rghts reserve ISS Table. The lelhoo rato. Lelhoo rato P P 1 P 2 =0. P P =0.1 P = Table. The posteror probablty. Posteror probablty P Table. The posteror expecte tlty E 1 Posteror expecte tlty