Challenges in Business Analytics An industrial research perspective
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- Elwin Hicks
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1 Zurich Rsarch Lab Challngs in Businss Analytics An industrial rsarch prspctiv 30 Octobr 2008 Elni Pratsini IBM Zurich Rsarch Laboratory Alxis Tsoukiàs LAMSADE-CNRS Univrsité Paris Dauphin Frd Robrts DIMACS Rutgrs Univrsity Copyright IBM Corporation 2005
2 60 Yars of IBM Rsarch 1952 San Jos California Almadn Establishd: 1986 Employs: 500 Th Sun Nvr Sts at IBM Rsarch st IBM Rsarch Lab in NY (Columbia Watson U) Establishd: 1961 Employs: 1750 Zürich Establishd: 1956 Employs: 300 Bijing Establishd: 1995 Employs: 90 Austin Establishd: 1995 Employs: 40 Haifa Establishd: 1972 Employs: 500 Dlhi Establishd: 1998 Employs: 60 Tokyo Establishd: 1982 Employs: Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
3 Evolution of Rol Work on clint-spcific tchnology businss problms 1970's 1980's 1990's 2000's Corporat fundd rsarch agnda Tchnology transfr Cntrally fundd Collaborativ tam Shard agnda Effctivnss Joint programs Work on clint problms Crat businss advantag for clints Industry-focusd rsarch On Dmand Businss Rsarch Rsarch in th marktplac 3 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
4 Ovrviw Businss Environmnt nd for analytics in a world of incrasing complxity Tchnical Challngs: Exampl from th Pharma Industry - Solution architctur - Data availability a plasur and a plagu quality uncrtainty missing - Prscriptiv modls dpnd on prdictiv modls Optimization undr uncrtainty Businss Challngs - Clint xtrnal projcts - Clint intrnal projcts - Acadmic projcts 4 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
5 Businss Environmnt W liv in a highly constraind rsourc world Rapidly raching capacity for many rsourcs - natural infrastructur human Study: What do you s as th primary businss challngs currntly affcting your organization? - Improving oprational ffctivnss Analytics ar ssntial to addrss th constraind rsourcs challng. Th historical barrirs to th us of analytics ar rapidly disapparing nabling us to tackl complx high-valu problms. 5 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
6 Analytics Landscap Stochastic Optimization Optimization How can w achiv th bst outcom including th ffcts of variability? How can w achiv th bst outcom? Prdictiv modling What will happn nxt? Analytics Comptitiv Advantag Simulation Forcasting Alrts Qury/drill down Ad hoc rporting What will happn if? What if ths trnds continu? What actions ar ndd? What xactly is th problm? How many how oftn whr? Rporting Standard Rporting What happnd? Dgr of Complxity Basd on: Compting on Analytics Davnport and Harris Challngs in Businss Analytics; An industrial rsarch prspctiv
7 Analytics Landscap Stochastic Optimization Optimization How can w achiv th bst outcom including th ffcts of variability? How can w achiv th bst outcom? Prscriptiv Prdictiv modling What will happn nxt? Comptitiv Advantag Simulation Forcasting Alrts Qury/drill down Ad hoc rporting What will happn if? What if ths trnds continu? What actions ar ndd? What xactly is th problm? How many how oftn whr? Prdictiv Dscriptiv Standard Rporting What happnd? Dgr of Complxity Basd on: Compting on Analytics Davnport and Harris Challngs in Businss Analytics; An industrial rsarch prspctiv
8 Challngs in a world of incrasing complxity Advancs in computr powr bring capabilitis (and xpctations) for solving largr problm sizs - Problm dcomposition not always obvious - Squntial optimization - Exact vs approximat solution or mixd - Hybrid mthods Information Explosion - Thr is a lot of data (too much!) but how about data quality? Not nough of th right data Missing valus Outdatd information - Data in multipl formats Can our optimization modls handl th information? 8 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
9 9 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
10 Whr do w start? 10 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
11 Ovrviw Businss Environmnt nd for analytics in a world of incrasing complxity Tchnical Challngs: Exampl from th Pharma Industry - Solution architctur - Data availability a plasur and a plagu quality uncrtainty missing - Prscriptiv modls dpnd on prdictiv modls Optimization undr uncrtainty Businss Challngs - Clint xtrnal projcts - Clint intrnal projcts - Acadmic projcts 11 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
12 Exampl: Risk Basd Approach to Manufacturing FDA s GMPs for th 21st Cntury A Risk-Basd Approach Post-Markting Advrs Evnt Rports Numbr *Horowitz D.J. Risk-basd approachs for GMP programs PQRI Risk managmnt Workshop Fbruary Calndar yar - Manag risk to patint safty Critical to Quality - Apply scinc in dvlopmnt and manufacturing - Adopt systms thinking build quality in 12 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
13 j j j j j j j Risk Managmnt and Optimization industry data clint Rv p X p a p Risk pj pj pj C pj X pj CT X pj 1 p Y jk 1 p j k Y jk = X pj j k X pj CAPj p j = Haz pjk Y jk X pj p j k pj T pj Rv p Risk pj k CR jk p j X pj T pj X pj X ipj 1 + T pj p j T pj 1 p T pj 1 X pj 1 p j Y jk Y jk 1 + R jk j k Y jk Y jk 1 Y jk R jk j k j k R jk clint xprts industry / xprts Risk 0 X Y T R = 0 /1 Rvnu at Risk as a prcntag of Rvnu CAPE VIAL 3 11% CAPE VIAL % CAPE VIAL 1 5.3% CAPE BOT 3 1% CAPE BOT 2 1% CAPE BOT 1 1.3% CAPE PTCH 1 6.4% CAPE PCH 1 9.7% CAPE CRD 1 7% CAPE BLST 4 1.6% Phas 1: Idntify Risk Sourcs Phas 2: Quantify Risk Valus Phas 3: Optimal Transformation Using company s knowldg industry xprtis and industry guidlins & rgulations w idntify th sourcs of risk that nd to b considrd in th analysis. Basd on historical data industry xprtis and othr sourcs of causal rlationships w st th causal link strngths (conditional probabilitis) and dvlop a modl for risk quantification Givn th risk valus financial and physical considrations w dtrmin th bst squnc of corrctiv actions for minimizing xposur to risk and optimizing a givn prformanc mtric. 13 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr
14 Dfinition of Risk Fitnss for Us - Quality: product conforms to spcifications - Efficacy: nsurs product has a positiv ffct (drugs trats a patint) - Safty: nd product is not harmful (patint dos not suffr from fatal sid ffcts) Complianc: failur to comply with rgulations (quality fficacy and safty not fully undr control).g. lack of documntation 14 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
15 Risk: Thr sourcs of hazards Products: Traditional focus; Basd on portfolio analysis - product is unstabl or contaminatd Tchnologis: som tchnologis may not b appropriat givn risk thy crat. - a nw blndr dos not work wll and th mixing is not don proprly Systms: basis for rgulatory inspction. Crat transfr risks btwn product profil classs. Systms capabilitis & tchnologis. - a biotch product is dvlopd and thr is no control of th spcification of its componnts 15 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
16 Ovrviw of Approach Data Collction Envisioning supportd by R-factoring modl Implmntation Stratgy Establish Risk Profil Products Systms Tchnology Rviw Cost bas of Sits Collct ky data Assss radinss for chang Input 4 Input Dfin Scnarios Product/Asst Configuration Input Rfactoring Modl Rout map Calculat Rvnu profil Output 5 Adapt scnarios Optimis rout-map for implmntation basd on Impact on: Risk profil Invstmnt Stratgy Impact of Chang 6 Tst scnarios: Prioritis on impact on Rvnu Risk and Profitability Rviw rout maps slct final scnario options Slct scnario Dfin implmntation stratgy 16 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr
17 Ovrviw of Approach Data Collction Establish Risk Profil Products Systms Tchnology Rviw Cost bas of Sits Collct ky data Assss radinss for chang Input 4 Input Dfin Scnarios Product/Asst Configuration Input Rfactoring Modl Rout map Calculat Rvnu profil optimization modl Output 5 Adapt modl 6 Tst solution: Prioritis on impact on Rvnu Risk and Profitability Implmntation Stratgy Rviw rout maps slct final solution options Dfin implmntation stratgy 17 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr
18 Effct of uncontrolld data collction A pharmacutical firm is assssing th risk of two of thir sits: Country A and Country X. Enginrs at both sits ar askd to collct non-compliant and dfctiv batchs in th last two yars. Analysis of th data indicats that sit A has 50% mor dfctiv / non-compliant batchs than sit X. Thy dduc that thy should mov thir manufacturing procsss to X or drastically chang thir oprations at A. Just bfor launching an audit of thir sit A thy rciv a lttr from th FDA mntioning many rcalls of batchs all coming from sit X. Furthr invstigation indicats that sit X dos not rport th many dfcts thy notic vry wk and thy ar indd mor risky. Lack of obsrvations dos not man no vnts; it mans no rporting of vnts Extract knowldg from non statistical sourcs.g. xprts 18 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
19 Tchnology product and systm failur ar all linkd via common root causs. Th risk modl starts from building a dpndncy graph of all ths root causs. Caus Effct Analysis industry Exprtis Clint Constraints Past Exprincs Diffrnt sub-modls cratd: Dosag form variability Employ xcution Procss control Tchnology lvl 19 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
20 Employ Ability to Excut Sub-modl Dirctly linkd to GMPs. Exampl vnts: rporting not don proprly quality control protocol not implmntd tc. 20 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
21 Elicitation of Exprt Opinion - Challngs Ntwork paramtrs can b stimatd from data and manual ntry of probabilitis. Using xprt knowldg in Baysian ntworks can b an intractabl task du to th larg numbr of probabilitis that nd to b licitd W nd an licitation mthod that is fast and givs not only a probability but also a confidnc in th probability an aggrgation mthod for combining th licitatd probabilitis taking into account th xprts confidnc in thir assssmnts A probability stimation mthod to rduc th numbr of probabilitis to licit in a noisy MAX nod 21 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
22 Elicitation / Aggrgation / Estimation 22 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
23 Basd on location tchnologis or any prviously idntifid componnts involvd in th drug production procss th ntwork modl computs an stimat of th quality risk. Changing th way a drug is producd will (a priori) induc a chang in th quality risk stimat. Sit A Quality failur: 2.4% Sit B Optimization modl Quality failur: 1.9% 23 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr
24 Risk Exposur Prformanc masur for xposur to risk: (Insuranc prmium) p = Rvnup Risk pj j Risk valu: Risk pj = k Haz pjk Y jk X pj 24 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
25 Formulation MAX ST p p a Risk Rv pj pj j k k X p j X pj j C pj X pj X 1 p pj Y 1 p j Y jk pj k CAP = pjk j jk X pj p jk pj j = Haz Y X p j Capacity Risk Stat Lvl j j CT pj T pj Rv p j pj T pj Risk pj j k CR jk X p j X X 1 + T p j pj T j pj ipj pj T 1 p jk pj 1 X pj 1 jk jk p j Y Y 1 + R j k Y jk Y jk 1 Transfr Y jk R jk Rmdiation j k j k R jk Risk 0 X Y T R = 0 /1 25 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
26 Problm charactristics 17 product familis 3 systms plus on for subcontracting 14 tchnology platforms most availabl in all systms Planning horizon: 5 yars split into 10 priods of 6 months Rstriction on rat of chang Statistical analysis gav risk valus basd on historical data Rvnu projctions availabl Aggrgatd costs pr scnario latr disaggrgatd Simulation of givn scnarios (nding rsult); optimization 26 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
27 Exampl Analysis Optimal squnc of actions for minimizing risk xposur Work cntr Currnt Scnario X Tch 1 Tch 2 Tch 3 Tch 4 Sit A Sit B Sit B Tch 5 Tch 6 Sit C Sit A Tch 7 Tch 8 Sit A / Sit Sit B Tch 9 C Tch 10 Tch 11 Tch 12 Sit A Sit C Tch 13 Sit C Tch 14 Sit D Scnario Comparison Rvnu@risk distributions Valus in 10^ Calculat Businss Exposur Man= Valus in Thousands 5% 90% 5% E(xposur) = rv i pi i 2 2 Var( xposur ) = rv i var (p i ) = rv i p i( 1 i i p i ) Dnsity Scnario 1 Scnario 2 As Is Fully Opt Rvnu@risk 27 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
28 28 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
29 Data Variability Th considration of th frquncy distribution of ach solution is ssntial whn thr is uncrtainty in th input paramtrs λ = 4300 Which Solution / Plan is bttr? ara = 5% frquncy distributi λ = ara = 20% Plan A Plan B Rv@Risk Robust Optimization tchniqus to dtrmin th solution that is optimal undr most ralizations of th uncrtain paramtrs 29 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
30 Possibl Approachs Stochastic Programming - Avrag Valu crtainty quivalnt - Chanc constraints (known CDF continuous) Robust Optimization - Soystr (1973) - Brtsimas & Sim (2004) CVaR - Rockafllr & Uryasv (2000) 30 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
31 Modling with th approach of Brtsimas and Sim Formulation adjustd to incorporat a constraint that could b violatd with a crtain probability: max s.t. x X E x ( ScurdPro fit(x) ) Rv@Risk(x t s x p t s p t s 1 {01} p p t s Uncrtain paramtrs hav unknown but symmtric distributions Uncrtain paramtrs tak valus in boundd intrvals: ) critical valu Haz ts [ Hˆ - H ; Hˆ + ] t s ts ts H ts Uncrtain paramtrs ar indpndnt 31 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
32 Brtsimas & Sim cont d Providd guarant: Pr ( Rv@Risk( x ) > critical valu) γ γ = 1 2 n ( 1 μ) n + ν n l= ν +1 n l n = total numbr of uncrtain paramtrs ν = Γ+n μ = ν ν 2 Exampl Valus of Γ: 32 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
33 33 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008 Application to pharma xampl Robust Formulation: ( ) {01} 1 ˆ max + Γ Z s t Y s t V s t p X p X s t Y X Y s t Y V Z V Z X s t s t s t p s t s t p s t p s t p s t s t s t ts pts H Rv valu critical Rv s.t. ts p ts p ts p s t + + H fit ScurdPro ) ( E x X x
34 Rsults! Actual Probability of Constraint Violation Idal Prformd Thortical Bound 34 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
35 Obsrvations Provids solutions that ar much mor consrvativ than xpctd Pr( x ) > critical valu) << γ Plans satisfying risk rquirmnt ar discardd by th optimization - suboptimal solutions Adjustmnts to calculation of paramtrs to improv rsults - Extrm distribution - Considr th numbr of xpctd basic variabls and not th total numbr of variabls affctd by uncrtain cofficints.g. production of 100 products 3 possibl sits uncrtainty in capacity absorption cofficint: possibl variabls (valu of n) - ach product can only b producd on on sit i.. only 100 variabls > 0 (us this valu for th calculation of Γ) 35 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
36 Brtsimas & Sim approach Production Distribution Pharma Rgulatory Risk Actual Probability of Constraint Violation continuous variabls Idal Original sttings: production not rstrictd to a singl sit Production on a singl sit: Γ computd with th numbr of activ uncrtain paramtrs Production on a singl sit: Γ computd with th total numbr of uncrtain paramtrs Actual Probability of Constraint Violation binary variabls Idal Γ computd with th numbr of activ uncrtain paramtrs Γ computd with th total numbr of uncrtain paramtrs Thortical Bound Thortical Bound 36 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
37 Rlatd work Robust optimization Modls for Ntwork-basd Rsourc Allocation Problms Prof. Cynthia Barnhart MIT - Robust Aircraft Maintnanc Routing (RAMR) minimizing xpctd propagatd dlay to gt schdul back on track (takn from Ian Clark and Barnhart 2005) Among multipl optimal solutions no incntiv to pick th solution with highst slack Rlationship of Γ (pr constraint) to ovrall solution robustnss? - Comparison with Chanc constraind Programming Explicitly diffrntiats solutions with diffrnt lvls of slack Extndd Chanc Constraind Programming - Introduc a budgt constraint stting an uppr bound on accptabl dlay - Improvd rsults 37 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
38 Valu at Risk & Conditional Valu at Risk VaR Maximum loss with a spcifid confidnc lvl - Non sub-additiv Dnsity of f (x) β 1-β - Non-convx β-quantil CVaR β-quantil α β (x) (β=95%) E [ f (x) f (x) > α β (x)] Dnsity of f (x) CVaR(x) Advantags - Sub-additiv α β (x) - Convx 38 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
39 CVaR in Math Programming: Linar Program with Random Cofficints Considr a LP whr cofficint vctor ξ is random: Min T c x T ξ x b Dx = d x 0 CVaR can b usd to nsur 'robustnss' in th stochastic constraint: Min T c x T CVaR α ( ξ x) b Dx = d x 0 39 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
40 Rformulation of Rockafllar & Uryasv (2000) Rformulation Each CVaR xprssion say a constraint: ξ CVaR ( ξ x) is rformulatd as: i th α 1 T + t + ( ξi x-t ) b N(1 α) i= 1 : i sampl of random vctor ξ (out of N total sampls) T b N Linar Rformulation 1 t + zi b N(1 α) z z i i 0 T i ξ x-t N i= 1 Good nws: Fully linar rformulation so prsntabl to any LP solvr Bad nws: Nw variabls and Nw constraints = O (Numbr of Sampls) - Rformulatd LP has grown in both dimnsions! - Larg numbr of CVaR constraints & larg numbr of sampls => Vry Larg LP 40 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
41 Obsrvations Numbr of scnarios ncssary for a good stimation of CVaR(α x) and thus maningful rsults? >20000 scnarios ncssary for stabl rsults (Kunzi Bay A. and J. Mayr. Computational aspcts of minimizing conditional valu at risk NCCR FINRISK Working Papr No ). Can handl up to 300 scnarios computational complxity 41 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
42 Rcnt advancs in CVaR Kunzi-Bay & Mayr (2006) prsntd a 2-stag intrprtation if CVaR appars only in th Objctiv Function & thir algorithm CVaRMin ld to an ordr of magnitud spd-up P. Huang and D. Subramanian. Itrativ Estimation Maximization for Stochastic Programs with Conditional-Valu-at-Risk Constraints. IBM Tchnical Rport RC Thy xploit a diffrnt linar rformulation motivatd by Ordr Statistics and this lads to a nw and fficint algorithm. -Th rsulting algorithm addrsss CVaR in both th Objctiv Function as wll as Constraints Intrsting work on Robust Optimization tchniqus & CVaR David B. Brown Duk Univrsity: - Risk and Robust Optimization (prsntation) - Constructing uncrtainty sts for robust linar optimization (with Brtsimas) - Thory and Applications of Robust Optimization (with Brtsimas and Caramanis) 42 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
43 Ovrviw Businss Environmnt nd for analytics in a world of incrasing complxity Tchnical Challngs: Exampl from th Pharma Industry - Solution architctur - Data availability a plasur and a plagu quality uncrtainty missing - Prscriptiv modls dpnd on prdictiv modls Optimization undr uncrtainty Businss Challngs - Clint xtrnal projcts - Clint intrnal projcts - Acadmic projcts 43 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
44 Clint xtrnal projcts Vry intrsting and challnging problms W might spak English / Grman / Frnch / but w do not spak thir languag! - Hav you usd simulation to prov your LP givs th optimal solution? Important to hav industry knowldg Oftn rly on consultants to sll our work - Ovrsll / undrsll Tim horizon - Thy want th optimal solution now Prfr maturd tchnology - Thy do not want to b th first to try a nw tchniqu 44 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
45 All thy nd is a simpl solution Th fastst travling salsman solution * 45 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
46 Thy don t want to collct th data You gt what you pay for th amount and typ of information usd in th analysis affcts th output* * 46 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
47 Clint intrnal projcts Intrsting ral lif application and clint agr to us advancd analytics oftn dal with vry tchnical businss partnrs so w undrstand ach othr Limitd in scop - oftn considr a small aspct of th whol problm Mdium tim horizon - Usually 1 yar but oftn continus for 2 or mor yars Intrnal politics! 47 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
48 Acadmic projcts Lif is good!! Acadmic collaborations: - Oftn killd by th lawyrs bcaus of IP (dis)agrmnt It dos not pay th bills!! 48 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
49 49 Challngs in Businss Analytics; An industrial rsarch prspctiv 30 Octobr 2008
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