Patent SG-184658-A1 (SG)
Title: Mvt optimization of business process modeling and management
Inventor: WILLIAMS MURRAY TODD
Filed: 2012-03-13
Abstract: 10001] Serv c Oriented Architecture (SOA) refers to a set of design principles that,when applied to a comp er architecture, creates a system that is built out of smaller pieces (services) that can be easil reconfigured and reused.In the context of a corporation's IT infrastructure, SOA can refer to e various systems, such as web sites, personnel databases, product databases, inventory sys ms, accounting systems, etc., that are built as interchangeable elements. In this SOA context, business process modeling or management (BPM) is the "wiring" of individual servi s into a composite system. BPM software can allow users (e.g. business analysts) to mot different possible systems, and system developers to implement the models into large, ausmatcd systems that act as new software applications. These models can include actual individu s that perform predetermined tasks. [0002] Currently BPM process optimization is perfonne using an iterative process inwhich the existing process is modeled, for example,by creating mat matical predictions as to the costs and benefits of each sub-process and, using these predic s an examination is made of whether a new BPM methodology would theoretically improve tt. system. That is, the same mathematical assumptions would be applied logically to dec. whether an improvement would be realized, and thus BPM process optimization is reduced ti a thought ex eriment.Once the model is implemented, it can be validated by confirming if the actual impro ments match the predicted model. An example of a BPM process includes automation of a hom mortgage application review and approval process, which generally involves computer syst msand human agents for facilitating the processing of mortgage applications. In such ahome mr.tgage application review and approval process, current techniques of BPM process optimization nay include re-modeling of the process by including, removing or modifying one or more rocesses based on, for example, customer feedback or industry research.100031 Current BPM process timization however does not use methods associatedwith statistical experiments. The model approach does not provide for the use of liveexperimentation, hypothesis testing and statist]. ally significant results to be used for gain/loss predictions (e.g. coming up with predictive estimites as to what the actual possible loss or gain is of an alternative business process), or confide ce intervals. Moreover, the modeling approach also does not provide for a scientific approach fo detcnnining optimum settings. 100041 A multivariate business process modeling or managemen (BPM) optimizationsystem for optimizing a BPM system may include a logic layer to contro operation of the BPM system, a utility layer including software executed by a computer systemt implement a randomized experimental treatment based on a hypothesis, and a persistence layer o collect dat from application of the randomized experimental treatment to the BPM system. The multiva•ate BPM optimization system may optimize the BPM system by computerized testing of th hypothesis and generation of results to add, remove or modify a step or modify a point in a fib of the BPM system based on the data collected from application of the randomized expeni ental treatment. [0005] For the multi ate BPM optimization systemdescribed above, the logic layermay includcan experiment trans itter serviceto modify or replace an existing service, and thus integrate the BPM optimization stem with the BPM system or a BPM sub-system. The logic layer may also include a deleg tor servicetomap a specific BPM application to anappropriate account and configuration in je BPMoptimization system, and thus associate the BPM system or the BPM sub-system th an experimental treatmentin the BPM optimization system. The logic layer may urther include a segment manager servicetomanage all incoming segmentation variables tht identify test subject qualifications, and thus define experimental segments based on the data llected from application of the randomized experimental treatment.[0006] For the multivariate BPM optimization system describe above, the utilitylayer may include a treatment provider serviceto assignthe randomiz t experimental treatment based on a particular set of attribute name/value pairs, and thu create the randomized experimental treatment based on a particular attribute. The utility layer ay also inc de anidentity provider service tomanage the unique identification of experimental test subject and thus manage reference keys used by an instance cache service, a history archive xtcrnal BPM data systems to uniquely identify experimental test subjects. The utility layer me further include an outcome recorder servicetostore individual experimental test results, and th manage delivery and storage of experimental outcomes. 100071 For the nn tivariatc BPM optimization system described above, thepersistence layer may include a og serviceto record events associated with the BPM system. The persistence layer may also in udean instance cache serviceto store treatment, identity and segmentation data for accessibilit The persistence layer may further include a history serviceto store outcome data, and thus ore the data collected from application of the randomized experimental treatment.10008] Forthe multivariate BPM optimization system described above, the logic,utility and persistence layers may enable customization d adaptation of the multivariate BPM optimization system to the BPM system. The randomiri experimental treatment may include an assignment of levels of attributes in a statistical expert ental design.An example of levels of attributes may refer to specific settings of a parame in a BPM system. Different levels of attributes may also refer to entirely different variations i f sub-process in a BPM system. Thus, the randomized experimental treatment may includea variation in a process in the BPM system. The multivariate BPM optimization systemmay fur er include a adaptive process toindicateif therandomized experimental treatment is suboptimal prior to corn etion of a statistical experiment. The foregoing results may be automatically generated statistica significant results for gain and loss predictions based on modification of the I3PM system. Almatively, the results may be automatically generated statistically significant results for dete i ination of significance of contribution of a particular attribute of the randomized experim ntal treatment.Yet further, the results may be automatically generated statistically significant r ults for determination of an interaction between different processes of an optimized BPM syste or the BPM system. 100091 According to an embs4iment, a method performs optimization of a businessprocess modeling or management (BPM system. The method may include identifying a location in theBPM systemto test, and definin a randomized experimental treatment based on a hypothesis for the identified location, and appl ing the randomized experimental treatment to the BPM system. The method may further ii Jude optimizing the BPM system by computerized testing of the hypothesis and generatirit results, andadding, removing or modifying a step or modifying a point in a flow of the BP system based on data collected from application of the randomized experimental treatment.[0010] For the method described above, the method may fur includedefiningattributes of the randomized experimental treatment and assigning levels of th attributes in a statistical experimental design.Attributes may also be referred to as independent ariables in flow a process in the BPM system for the randomized experimental treatment.The method may als include automaticallyindicating if a randomized experimental treatment is suboptimal p or to completion of a statistical experiment. For the foregoing method, the results may be utomatically generated statistically significant results for determining significance of contri'ution of a particular attribute of the randomized experimental treatment. Alternatively, the result: may be automatically generated statistically significant results for determining an interaction tween different processes of an optimized RPM system or the BPM system.According to an embodi ent, a non-transitory computer readable medium having stored thereon a computer execut le program to optimize a business process modeling or management (RPM) system is pr. ided. The computer executable program when executed may cause a computer system to idenify a location in the BPM systemto test, and define a randomized experimental treatment basel on a hypothesis for the identified location, and apply the randomized experimental treatment ti the BPM system. The computer executable program when executed may further causethe co suter system tooptimize the BPM system by testing the hypothesis and generating results, an dd, remove or modify a step or modify a point in a flow of the BPM system based on data colF ted from application of the randomized experimental treatment.6 012] For the computer readable medium described above, the computer executablepro&am when executed may further causethe computer system todefine attributes of the random]. ed experimental treatment and assign levels of the attributes in a statistical experimenta design. The computer executable program when executed may further causethe computer systei to vary a process or relative flow of a process in the BPM system for the randomized experi ntal treatment. BRIEF DESCRIPTION OF I WINGS 100131 Theembodiments o the invention will be described in detail in the following description with reference to the folio Cng figures. [0014] Figure 1 illustrates asyste diagram for a multivariate BPM optimizationsystem, according to an embodiment; 100151 Figure2 illustrates an additionals tem diagram for amultivariate BPMoptimization system, according to an embodiment; 100161 Figure3illustrates a methodfor perfonnin ultivariate BPM o