a conceptual framework for business process...

A Conceptual Frameworkfor Business Process Redesign

George Koliadis Aditya K. Ghose

Decision Systems Lab (DSL)School of Computer Science and Software Engineering

University of Wollongong, Australia

10th Workshop on Business ProcessModeling, Development and Support, 2009

Koliadis, Ghose (Decision Systems Lab) Framework for Business Process Redesign BPMDS 2009 1 / 44

Outline

1 MotivationExampleSome Relevant MilestonesCurrent State-of-the-ArtRelated WorkResearch Questions

2 ContributionsDrivers and ConsiderationsRedesign GuidanceModel Extensions

3 SummarySummary


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Example

Package Screening Process:

Current Context:I Route a package if held, otherwise only update status.I Routing prior to handling screen requests improves routing time.


Example


Revised Goals:I G u Performed(SO,Route,Package) ∧

Knows(SO,Package,Status,Clear) ∧Performed(SO,Update,PackageStatus)


Example


Revised Business Rules:I R u G(Knows(RA,Package,Status,Held)⇒ ¬Performed(SO,Route,Package)W Knows(RA,Package,Status,Clear))


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Some Relevant Milestones

Operational task decomposition and optimization.I The Principles of Scientific Management, 1911

(Cross) Organizational process decomposition and optimization.I Competitive Advantage: Creating and Sustaining Superior

Performance, 1985Enterprise architecture - multi-dimensional modeling.

I A Framework for Information Systems Architecture, 1987Enterprise governance, planning and execution reference.

I Value Chain Operational Reference (VCOR), 2005Zero Code Business Process Management System.

I Intalio|BPMS 4.2, 2006Challenge: Systematic Support for Governing and EngineeringDynamic Processes.

I Service-Oriented Computing: a Research Roadmap, 2008


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Preliminary Evaluation

Business Process Redesign is an architectural activity.I Method and ProductI Effective / Repeatable ConstructionI Holistic / Context Sensitive (Milestones)

Enterprise Business Process Architecture [Koliadis et al., 2008]I Reference Models, Reference Frameworks (Structural, Functional)

F Value Chain-Based, BMM, HOBE, i*, e3 Value, TOVE

MethodI Literature Review: Proposal of a Functional Competence TheoryI Evaluate Frameworks with respect to Functional Competence

Preliminary ResultsI Highly Variable CoverageI Competencies: Actors, Goals, ProcessesI Deficiencies: Inter-relation, Strategy, Risk


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Related Work

FrameworksI Planning and Configuration [Hendler et al., 1990]

[van der Aalst et al., 2005]I Dynamic Change [Rinderle et al., 2004]

Framework AspectsI Compliance Management [Ghose and Koliadis, 2007]

[Governatori et al., 2008]I Validation [Weber et al., 2008]I Variation Generation and Management [Lu and Sadiq, 2006]

[Ponnalagu and Narendra, 2008]I Change Impact Scope Analysis [Soffer, 2005]


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Research Questions

1 What are the main drivers and business process redesign?2 How do we systematically react to these drivers?3 What should we consider when reacting to these drivers?4 How do we extend existing frameworks to support redesign?


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Drivers of Redesign

Business Processes “Execute Strategy”[Smith and Fingar, 2003]Strategic / Regulatory View of Process Change[Regev and Wegmann, 2002] [Regev and Wegmann, 2004]

I Processes regulated by interrelationships with other systems (e.g.strategic management - competitive, technological e.t.c.).

I Process change occurs to maintain constancy (or “value” / utility) inthe context of these interrelationships.

Strategic vs Operational Change [Munive-Hernandez et al., 2004]I Operational influence (“emergent”) is also critical.

Regulation can occur by communicating change requests,representations - system products, or through observation.


Considerations for Redesign

Identity (Intentionality: P |= G)I Satisfaction of the intent / operational goals of the process

[van Lamsweerde, 2001].I Permit innovation / flexibility [Wing, 1990].

Conformance (Realizability: R |= P and P |= C)I Consistent, complete, and minimal satisfaction w.r.t. resource,

operational and compliance constraints [van Lamsweerde, 2001].I Satisfy stakeholder requirements with available resources.

Direction (Optimality: P ′′ <O P ′)I Continual improvement [Harmon, 2003].I Balance quantitative (e.g. cost, time) and qualitative (e.g. quality,

learning and growth) [Kaplan and Norton, 1992] criteria.Degree (Minimality: P ′ <P P ′′)

I “Evolution” vs “revolution” [Smith and Fingar, 2003]I Minimize the degree of disruption and leverage existing assets.


Process Context

Definition (Process Context)A process context is given by a tuple 〈P,R,C,G,O〉 where:

P is a process model;R is a resource model (or description);C is a set of constraints or business rules;G is a set of goals (or, without loss of generality, a singleconjunctive goal assertion);O is a set of objectives (or, objective functions),

such that P, R, C, G are mutually consistent.

Process contexts change in response to change requests andimprovement requests.


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Process Context DynamicsChange Requests

Process Constraint (e.g. add/remove/re-order task)I Implemented if P |= P ′ or P ′ v P (inclusion - graph morphism

[Ehrig et al., 2006]).Resource Constraint (e.g. add/remove resource)

I Implemented if R |= R′ or R′ ⊆ R (sentential inclusion).Compliance Constraint (e.g. add/remove business rule)

I Implemented if C |= C′ or C′ ⊆ C (sentential inclusion).Goal Constraint (e.g. add/remove goal)

I Implemented if G |= G′ or G′ ⊆ G (sentential inclusion).Objective Constraint (e.g. add/remove a preference)

I Implemented if O |= O′ (O’ asserts [non]membership).


Process Context DynamicsChange Requests

A revised process context 〈P ′,R′,C′,G′,O〉 is a minimalimplementation of a change request iff:

I 〈P ′,R′,C′,G′,O′〉 implements the change request in question;I there exists no P ′′ such that:

F P′′ ≺P P′ (minimal);F P′′ �P P′ and P′ <O P′′ (optimal); andF 〈P′′, R′, C′, G′, O〉 is a process context implementing the request;

I there exists no R′′ such that R′ ⊂ R′′ ⊆ R and 〈P ′,R′′,C′,G′,O〉 isa process context implementing the request;

I there exists no C′′ such that C′ ⊂ C′′ ⊆ C and 〈P ′,R′,C′′,G′,O〉 isa process context implementing the request;

I there exists no G′′ such that G′ ⊂ G′′ ⊆ G and 〈P ′,R′,C′,G′′,O〉 isa process context implementing the request.


Process Context DynamicsImprovement Requests

Proximity Threshold dPe (e.g. cost or other comparison value)I Improvement if P ′ �P dPe and P <O P ′ (w.r.t. the objectives).

An improved process context 〈P ′,R′,C,G,O〉 is an optimalimprovement of an improvement request iff:

I 〈P ′,R′,C,G,O〉 is an improved process context;I there exists no P ′′ such that:

F P′′ �P dPe;F P′ <O P′′; andF 〈P′′, R′, C, G, O〉 is an improved process context;

I there exists no R′′ such that R′ ⊂ R′′ ⊆ R and 〈P ′,R′′,C,G,O〉 isan improved context.


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Model Extensions

Minimal analyst involvement [Jackson and Wing, 1996].Functional Effect Annotations

I Improves limited expression of functionality in high-level processmodels (including un-observable, compliance-related, side-effects).

I Can use free-form (NLP) or structured (CNL) natural languages.I Annotations in some service description languages.I Immediate effects are accumulated to produce a more complete

description of functionality achieved by the process (with referenceto some background knowledge).

Non-Functional AnnotationsI Based on an abstract preference structure [Bistarelli, 2001].I Generic scheme for representing non-functional objectives.I Recently generalized into bi-polar scheme [Pini et al., 2007].I Annotations available in service description languages.I Immediate descriptions are accumulated as per the definition of the

combination operators in the associated preference structure.


Functional Annotations


Effect:I Performed(SO,Handle,Package)

causes Knows(RA,Package,Status,Clear)

Background:I ∀a,∀o,∀p,∀v1, v2Knows(a,o,p, v1)∧¬equal(v1, v2)⇒ ¬Knows(a,o,p, v2)


Non-Functional Annotations

Definition (Constraint-Semiring [Bistarelli et al., 1999])A Constraint-Semiring is a 5-tuple S =〈A,⊕,⊗,0,1〉:

A is a set of abstract preference values and 0,1 ∈ A;⊕ and ⊗ are closed, commutative, associative binary operators.⊕ is a comparison operator;

I 0 is unit. 1 is absorbing. Idempotent.⊗ is a combination operator;

I 1 is unit. 0 is absorbing.

⊗ distributes over ⊕.The comparison operator induces a partial ordering ≤S such that foralla,b ∈ A a ≤S b iff a⊕ b = b. Both ⊕ and ⊗ are monotone on suchordering (negative preference structure).


Non-Functional Annotations

Table: Package Screening Process

Activity OR OS OCTScan Package 0.98 128 20

Assess Package 0.96 128 30Route Package 0.94 64 600Handle Package 0.88 128 50Update Status 0.98 128 10

Success Rate: OR = 〈[0,1],max , ·, ·,0,1〉 (independence)Security: OS = 〈N+,max ,min,min,0,+∞〉Cycle Time: OCT = 〈R+,min,+,max ,+∞,0〉


Process Proximity

Structural Proximity (V or E)I Cardinality: P1 ≤P[V ]

P2 iff |V ∆V1| ≤ |V ∆V2|I Inclusion: P1 ≤P[V ]

P2 iff V ∆V1 ⊆ V ∆V2

Semantic ProximityI Final or Intermediate Scenarios (Final Preferred)I ∆E (P ′,P) = {e∆e′| |e∆e′| = min({|e1∆e′1|, . . . , |en∆e′m|})}I Cardinality: P1 ≤P[E ]

P2 iff Σ|δ1 ∈ ∆E (P1,P)| ≤ Σ|δ2 ∈ ∆E (P2,P)|I Inclusion: P1 ≤P[E ]

P2 iff ∀δ1 ∈ ∆E (P1,P) ∃δ2 ∈ ∆E (P2,P) δ1 ⊆ δ2

Proximity Metric (Combined)I d(P1,P2,P) = wV dV (P1,P2,P)/DV (P1,P2) + wE . . .+ wS . . .


Resolved Package Screening Process 1

Figure: Resolved Package Screening Process (R1)


Resolved Package Screening Process 2

Figure: Resolved Package Screening Process (R2)


Functional and Non-Functional Analysis Analysis

First Resolution:I Node difference: 0.I Edge difference: 6 (not included).I End effects: identical.I Performance:

F 1 : OR = 0.76, OS = 64, OCT = 710F 2 : OR = 0.96, OS = 128, OCT = 30

Second Resolution:I Node difference: 0.I Edge difference: 4 (not included).I End effects: {{Performs(SO,Route,Package)}}.I Performance:

F 1 : OR = 0.76, OS = 64, OCT = 710F 2 : OR = 0.90, OS = 64, OCT = 630


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3 SummarySummary


Summary

ContributionsI Conceptual Framework for RedesignI Systematic [Non]Functional Analysis to Support RedesignI “Lightweight” Notational Extensions to Support Redesign

Future WorkI Role of other knowledge-related operators (e.g. postdiction).I Efficient implementation.I Experimental evaluation.


Thankyou for Your Attention

Questions and Comments


For Further Reading I

Bistarelli, S. (2001).Soft Constraint Solving and Programming: a General Framework.PhD thesis, Computer Science Department, University of Pisa.

Bistarelli, S., Montanari, U., Rossi, F., Schiex, T., Verfaillie, G., andFargier, H. (1999).Semiring-based csps and valued csps: Frameworks, properties,and comparison.Constraints, 4(3):199–240.

Ehrig, H., Ehrig, K., Prange, U., and Taentzer, G. (2006).Fundamentals of Algebraic Graph Transformation (Monographs inTheoretical Computer Science. An EATCS Series).Springer.


For Further Reading II

Ghose, A. K. and Koliadis, G. (2007).Auditing business process compliance.In Proc. of the International Conference on Service-OrientedComputing (ICSOC’07).

Governatori, G., Hoffmann, J., Sadiq, S., and Weber, I. (2008).Detecting regulatory compliance of business process modelsthrough semantic annotations.In Proceedings of the 4th International Workshop on BusinessProcess Design.

Harmon, P. (2003).Business Process Change.Morgan Kaufmann.


For Further Reading III

Hendler, J., Tate, A., and Drummond, M. (1990).Ai planning: Systems and techniques.AI Magazine, 11(2):61–77.

Jackson, D. and Wing, J. (1996).Lightweight formal methods.IEEE Computer, pages 21–22.

Kaplan, R. S. and Norton, D. P. (1992).The balanced scorecard - measures that drive performance.Harvard Business Review.

Koliadis, G., Ghose, A. K., and Padmanabhuni, S. (2008).Towards an enterprise business process architecture standard.In Proceeeding of the 2008 IEEE International Conference onServices Computing (SCC’08).


For Further Reading IV

Lu, R. and Sadiq, S. (2006).Managing process variants as and information resource.In Proceedings of the Fourth International Conference onBusiness Process Management (BPM’06).

Munive-Hernandez, E. J., Dewhurst, F. W., Pritchard, M., andBarber, K. (2004).Modelling the strategy management process - an initial bpmapproach.Business Process Management Journal, 10(6):691–711.

Pini, M. S., Rossi, F., Venable, K. B., and Bistarelli, S. (2007).Bipolar preference problems: Framework, properties and solvingtechniques.In Proceedings of the 2006 ERCIM Workshop on Constraints,Springer LNAI 4561.


For Further Reading V

Ponnalagu, K. and Narendra, N. C. (2008).Deriving service variants from business process specifications.In Proceedings of the 1st Bangalore annual Compute conference(COMPUTE’08), pages 1–9, New York, NY, USA. ACM.

Regev, G. and Wegmann, A. (2002).Regulation based linking of strategic goals and businessprocesses.In Proceedings of the 3rd BPMDS Workshop on Goal-OrientedBusiness Process Modeling (GBPM’02).

Regev, G. and Wegmann, A. (2004).Remaining fit: On the creation and maintenance of fit.In Proceedings of the 5th BPMDS Workshop on Creating andMaintaining the Fit between Business Processes and SupportSystems (BPMDS’04).


For Further Reading VI

Rinderle, S., Reichert, M., and Dadam, P. (2004).Correctness criteria for dynamic changes in workflow systems - asurvey.Data and Knowledge Engineering, 50:9–34.

Smith, H. and Fingar, P. (2003).Business Process Management: The Third Wave.Meghan-Kiffer Press, Tampa, FL.

Soffer, P. (2005).Scope analysis: identifying the impact of changes in businessprocess models.Software Process: Improvement and Practice, 10(4):393–402.


For Further Reading VII

van der Aalst, W., Dreiling, A., Rosemann, M., and Jansen-Vullers,M. H. (2005).Configurable process models as a basis for reference modelling.In Proceedings of the Workshop on Business Process ReferenceModels (BPRM’05).

van Lamsweerde, A. (2001).Goal-oriented requirements engineering: A guided tour.In Proceedings of the International Joint Conference onRequirements Engineering, pages 249–263. Toronto, IEEE.

Weber, I., Hoffman, J., and Mendling, J. (2008).Semantic business process validation.In Proceedings of the 3rd International Workshop on SemanticBusiness Process Management.


For Further Reading VIII

Wing, J. M. (1990).A specifier’s introduction to formal methods.IEEE Computer, 23(9):8–24.


Similarities with Soft Constraint Problems

Solution uncertainty.Non-traditional valuation structures (“soft” constraints).

I FuzzyI ProbabilisticI Weighted

Table: C1

X def (x)

〈0〉 0.25〈1〉 1

Table: C2

X def (x)

〈0〉 0.5〈1〉 0.4

Table: C1 ⊗ C2

X def (x)

〈0〉 0.25〈1〉 0.4

An algebraic generalization of constraint valuation.I The constraint-semiring structure [Bistarelli et al., 1999].


Examples of Constraint-Semirings

Constraints LiteratureI Classical = 〈{0,1},∨,∧,0,1〉I Fuzzy = 〈[0 . . . 1],max ,min,0,1〉I Probabilistic = 〈[0 . . . 1],max ,×,0,1〉I Weighted = 〈R−,max ,+,−∞,0〉

Process-RelatedI Cost = 〈R+,min,+,+∞,0〉I Quality = 〈{1,2,3,4,5},max ,min,1,5〉I Security1 = 〈N0,max ,min,0,+∞〉I Security2 = 〈N0,min,+,+∞,0〉 (+ is problematic)I Security3 = 〈2U,⊂,∪,U, ∅〉I Security4 = 〈{L,M,H}, cpS4 , cbS4 ,L,H〉I Risk = 〈R+,min,+,+∞,0〉I Vulnerability = 〈2A,⊂,∪,A, ∅〉

Constraint-Semirings can be combined.


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