Business Process Simulation - How to get value out of it (bpm portugal 2013)


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Presentation to the BPM Portugal 2013 Conference

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Business Process Simulation - How to get value out of it (bpm portugal 2013)

  1. 1. Business Process Simulation:How to get value out of itDenis Gagné,www.BusinessProcessIncubator.comChair BPMN MIWG at OMGBPMN 2.0 FTF Member at OMGBPMN 2.1 RTF Member at OMGCMMN Submission at OMGChair BPSWG at WfMCXPDL Co-Editor at WfMC
  2. 2. Marketing Poster of BPM• “Doing things right” Do more with lessBusiness Efficiency• Quickly adapt to changing Business ConditionsBusiness Agility• Current status, outcome, complianceBusiness Insight
  3. 3. Improvement vs. ManagementProcess ImprovementIs project basedIs discontinuous improvementAddresses particular process problemsProcess ManagementIs not project based but a management philosophyIs a continuous improvement cultureIs about process-based managementWill require some culture change
  4. 4. Poor Performing ProcessesMay lead to:DelaysBack logRefund ClaimsAngry customersLost of goodwill (Mission Critical)Lost of lives (Life Critical)Gain Insight: Thoroughly analyse businessprocess in a safe isolated environment prior toDeploying
  5. 5. BPMN Process
  6. 6. Simulation for Process AnalysisProvides a priori InsightCan be Effective Process Analysis tool for:Alternative EvaluationDecision SupportPerformance PredictionOptimization
  7. 7. Benefits of SimulationAdvantages of simulation over testing on thereal world include:Lower relative cost of businesstransformation explorationsSpeed of validation of potential scenariosNo disturbance to current operations
  8. 8. Types of Process Analysisusing SimulationStructural AnalysisThe structural aspects (configuration) of a process modelUsually Statistical Analysis (using static methods)Capacity AnalysisThe capacity aspects of a process modelUsually Dynamic Analysis (using discreet simulationmethods)
  9. 9. When is Numeric Simulationmost AppropriateCapacity analysis of processes that potentially areHighly VariableVariability makes outcomes difficult if not impossible to predictInterdependentChanges in one process affect other processesComplexComplex structure or complex behaviorCapacity ConstraintsHard resources constraints (as independent variables)
  10. 10. Process Improvement Projectusing SimulationGet the Goal RightClearly define the goal or problem to be investigated usingsimulationClearly state the objectives of the simulation investigationMatch Expertise to Desired ExperimentationDifferent levels of Investigation ComplexityGet the Model RightModel GranularityModel Parameterization
  11. 11. Clearly Define the GoalIntentions ExamplesReduce headcounts or expensesImprove process predictability or reliabilityIncrease throughputIncrease outputEnsure SLADesign the Experiment AccordinglyIndependent vs dependent variablesSame process model under different parameterisationsDifferent process models under same parameterizationNumber of distinct model settings to be runThe experiment should provide insightThe experiment should help inform a decisionThe experiment should be in response to clearly defined objectivesthat are relevant to a decision
  12. 12. Expertise vs ExperimentationVerify ProcessStructure and logicOptimizationLearning viaExperimentationsQuantitativeAnalysisNoviceExpertExpertNoviceProcess ModelingSimulation
  13. 13. Model GranularityPick the right level of process model abstractione.g. What is an atomic taskFor example a certain level of details may suitable tocompare relative throughput of alternative process designswhile not be detailed enough to provide reliable predictionof actual throughput
  14. 14. Model Input ParameterizationSetting Input parameters for process model elements to reflect externalstimulatione.g. Arrival PatternsWhen randomness is introduced replications should be usedReplication = same scenario but with different sequences of randomvariablese.g. repeated coin tossWarm up periods may be requiredReflect the notion of work in progress (WIP)Time during which results are either not collected, or which can beseparated off from the main results collection periode.g. A bank (opens empty and idle each day) model does not require warm-up (and indeed should not have warm-up). Common examples of situations requiring warm-up are manufacturing in general, hospital emergency rooms,24-hour telephone exchanges, etc
  15. 15. DemoRandomness and likelihood
  16. 16. When Examining ResultsUnexpected result are not necessarily a problemPrimary reason for your simulation experimentationNeed to find an explanationWill provide enlightenment of actual process behaviorvs assumed process behaviorUnexplainable results are a problemSimulation is often a process of discovery
  17. 17. BPSim ScopeComplements existing process modeling standards“Not Reinvent the Wheel”
  18. 18. Why BPSimEncourage wider adoption of simulation within BPMcommunity through a standards led approachProcess simulation is a valuable technique to support processdesign, reduce risk of change and improve efficiency in theorganisationProvide a framework for the specification of simulationscenario data and results as a firm foundation forimplementationOpen interchange of simulation scenario data betweenmodeling tool, simulator, results analysis/presentation tool
  19. 19. BPSim Element ParametersEach element parameter of a scenario references a specific element ofa process within the business process modelEach element of the business process model may be parameterizedwith zero or multiple element parametersPPerspectives TimeParameters ControlParameters ResourceParameters CostParameters InstanceParameters PriorityParameters
  20. 20. Demo
  21. 21. Business Process SimulationBest PracticesThe Right Model for the Right GoalAlign Modeling Objectives with Simulation ObjectivesAbstractionFidelityValidity (soundness and completeness)The Right Answer to the Right QuestionMake sure to instrument your business process model with parameters thatare actual indicators (influencers) of what you wish to exploreThe Right Expert for the Right TaskAlthough conceptually simple to grasp, successfully (meaningfully) usingnumerical simulation for business modeling still requires some expertise(Advanced Mathematical Skills)
  22. 22. Discussions &