Analytics for Process Excellence
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This document discusses business process analysis, simulation, and optimization. It provides an overview of structural and statistical analysis, capacity analysis using dynamic simulation methods, visualization and numeric simulation techniques in business process simulation. Optimization techniques are discussed for selecting optimal scenarios. The benefits of simulation over real-world testing are speed and low cost. Process simulation best practices and caveats are also covered, such as ensuring the right model, parameters, and expertise for the intended goals.
Analytics for Process Excellence
- 1. Analytics for Process Excellence Robert Shapiro & Denis Gagné
- 2. Business Process Analysis, Simulation and Optimization Denis Gagné, www.BusinessProcessIncubator.com Chair BPSWG at WfMC XPDL Co-Editor at WfMC BPMN 2.0 FTF Member at OMG BPMN 2.1 RTF Member at OMG CMMN Submission at OMG
- 3. Analysis Structural Analysis The structural aspects (configuration) of a process model Usually Statistical Analysis (using static methods) Capacity Analysis The capacity aspects of a process model Usually Dynamic Analysis (using discreet simulation methods)
- 4. Simulation Visual Depiction (Visualization & Animation) User is presented with a (sometime interactive) animated depiction of the business process model Numeric Simulation (Discreet Events) User asked to provide values for input and decision parameters of a simulated business model Role Play (Serious Gaming) User asked to take actions and make decisions within a simulated business environment
- 5. Optimization selection of a best scenario (with regard to some criteria) from some set of available alternatives Almost impossible without tool support Sub optimization caveat Optimizing the outcome for a subsystem will in general not optimize the outcome for the system as a whole.
- 6. Why Simulation Training & Learning Although very popular in support of operations, limited use in other Business Modeling disciplines Persuasion & Selling Simulating results of a proposed solution Cause and Effect simulation Analysis of a Business Situation Simulation complements static analysis techniques with a dynamic perspective Verification & Validation Validation: Are we doing the right “thing”? Verification: Are we doing “it” right?
- 7. Benefits of Simulation Advantages of simulation over testing on the real world include: speed low cost no disturbance of current operations
- 8. Business Process Simulation Working Group (BPSWG) Goal Define a Specification for the Parameterization and Interchange of Process Analysis Data allowing Structural and Capacity Analysis of a process model providing for Pre-execution and Post-execution optimization. We are interested in the Data («in/out») (commonly the «what») and its Interchange not its interpretation or its use (the «how») or tool smart.
- 9. BPSim Conceptual Model BPSim BPMN or XPDL Scenario (Input)Process Model Reference Reference instance instance Scenario (Output) Reference instance Reference
- 10. Scenario Used to provide complementary information to a BPMN or XPDL business process model in the context of process analysis, simulation and optimization (ASO). A collection of element Parameters augmenting the Business Process Model Scenario can capture: input parameter specification for ASO; or results from ASO; or historical data from past real world execution of the business process model.
- 11. Element Parameters Resource Parameters Cost Parameters Time Parameters Control Parameters Property Parameters Process Priority Parameters
- 12. Business Process Simulation Best Practices The Right Model for the Right Goal Align Modeling Objectives with Simulation Objectives Abstraction Fidelity Validity (soundness and completeness) The Right Answer to the Right Question Make sure to instrument your business model with parameters that are actual indicators (influencers) of what you wish to explore The Right Expert for the Right Task Although conceptually simple to grasp, successfully (meaningfully) using numerical simulation for business modeling still requires some expertise (Advanced Mathematical Skills)
- 13. Business Process Simulation Caveats Unrealistic User Expectations Simple Press-Button Simulation Deterministic behavior assumptions A Business Model is a Simulation Model (not necessarily) Their goals (purposes) may be misaligned Be Mindful of Misleading Results (Garbage in Garbage Out) A simulation model that is fidel &valid with uncharacteristic data can lead to Incorrect conclusions or predictions, Negative Training, … Sub-Optimization Partial or sub-model optimization can lead you astray
- 14. Modeling and Analytics for Process Excellence Robert Shapiro SVP Research: OpenText
- 16. Integrated set of visual toolkits for modeling, simulating, analyzing and optimizing business processes.
- 17. Visual representations within and across the toolkits are synchronized and provide the user with multiple perspectives exposing the behavior of a complex process and the root causes of that behavior.
- 18. Initial resource allocation uses a two phase approach. First the process is simulated with unlimited resources in order to get the demand for each performer (the workload drivers). Then a resource is defined for each performer with a capacity determined by the workloads, and the process is simulated a second time. The results of the second run are then loaded into the other toolkits, ANALYZE and OPTIMIZE.
- 19. To validate a model we use Gantt Charts displaying information about the path of a work item arrival through the process. They are synchronized with the Process Preview, a compressed view of all the pages in the model which in turn is synchronized with the MODEL toolkit. The Gantt charts are synchronized with MODEL and with other chart groups and tables within ANALYZE.
- 20. For a simple process like our example there is not much to validate; for more complex, ‘real-life’ models, however, the combination of Gantt chart, process preview and time slicer turns out to be quite powerful. And if the travel of a work item through the process comes to an untimely end, due to inconsistent XOR branching, for example, the ANALYZE toolkit will mark this by moving the time slicer and scrolling the preview.
- 21. When we focus on the workflow process as depicted in the MODEL toolkit and want to know whether and where a particular node appears in a Gantt chart, we do a right-click on that flownode and select Synchronize. The Gantt time slicer is moved to point where the work item arrives the first time at that node, if at all. Figure 5 depicts the case for the subprocess process premium and work item arrival 11.
- 22. We see that cycle times are mostly distributed between 30 and 135 minutes, and then at the right edge there a few outliers. We select them for a closer look. When we browse through the selected Gantt charts by means of the spinner we see that all contain the process premium branch, some end approved and others rejected, and wait times in front of the tasks receive application and KYpremiumC differ but their sums remain the same.
- 23. Path patterns classify the work item arrivals by counting how often the work item passes through the different flow nodes. The third ANALYZE pane shows the path patterns. Pattern 3, the one with the smallest size, characterizes those work items that pass through process premium and get rejected. If we click on the Arrival List cell of a pattern, the subset is selected in the History chart and we can scan all the histories with the same path pattern.
- 24. The resource idleness analysis for the whole period of time and – in a copy – the same data for a single day. It seems that a maximum cycle time of about 2.5 hours may be unnecessarily low for the given application.
- 25. From the OPTIMIZE toolkit we can start our Smart Allocation tool for any of the scenarios available in ANALYZE or for a fresh simulation. Once started, the Smart Allocation tool allows in fast iterations the changing of the number of copies (‘clones’) of each resource, simulation of the given set of work item arrivals and comparing of the results in terms of resource and cycle times.
- 26. We have a simple idleness reduction algorithm that removes clones from a resource as long as its remaining average idleness does not fall below 20%. Here it comes up with a much leaner allocation, with two immediate consequences. The average per-arrival cost goes down from $14.45 to $9.40. The cycle time goes up to about 5 hours, with two groups of outliers, one around 19 hours and one around 64 hours.
- 27. obese fat meager idle-red’d lean receive app. 7 6 2 3 2 premium 10 4 0 2 1 regular 13 6 1 2 2
- 32. Thank you. Contact Information: rshapiro@opentext.com Acknowledgements Dr. Hartmann Genrich: For the charts and tables in the presentation. Shelley Sweet (www.i4process.com): The Hiring Model. Rudiger Molle (itab@molleruediger.de): The Breast Cancer Model.