Emerging Technologies in BPM Keynote: Emerging BPM Techniques & Technology Summit Building Business Capability 2012 Sandy Kemsley www.column2.com @skemsley
Emerging BPM Techniques & Technologies Summit The Hurricane Sandy edition Thinking on the Job: Adaptive Case Management in Practice [cancelled] Modeling and Analytics for Process Excellence [speaker replaced] Process Mining: BPM Upside-Down [speaker arriving from Europe 9pm tonight] Copyright Kemsley Design Ltd., 2012 2
Technology: Social BPM How Social Changes Everything Copyright Kemsley Design Ltd., 2012 3
Consumer Tools Set Expectations Consumption Participation Creation User experience Access anywhere Copyright Kemsley Design Ltd., 2012 4
Social BPM Business Benefits Weak ties/tacit knowledge exploitation Knowledge sharing Social feedback Transparency Participation Activity and decision distribution (crowdsourcing) Copyright Kemsley Design Ltd., 2012 5 Source: Brambilla et al, A Notation for Social BPM
Collaborative Process Modeling Multiple people participate in process discovery, modeling and documentation Internal and external participants Technical and non-technical participants Preserves institutional memory Facilitates cross-silo collaboration and innovation Copyright Kemsley Design Ltd., 2012 6
Process Event Streams Timeline of activity for social monitoring Process models during creation Process instances during execution Publish/subscribe model to watch certain processes or event types Direct link to underlying process model or instance for unsolicited participation Usually mobile-enabled Copyright Kemsley Design Ltd., 2012 7
Technology: Dynamic/Adaptive Case Management The Changing Nature of Work Copyright Kemsley Design Ltd., 2012 8
The Extremes Of Work Routine Work Knowledge Work Copyright Kemsley Design Ltd., 2012 9
Goals Of Work Types Routine Work Efficiency Accuracy Process improvement Automation Knowledge Work Flexibility Assist human knowledge work Collect artifacts Classic BPM Adaptive Case Management (ACM) / Production CM / Dynamic CM Copyright Kemsley Design Ltd., 2012 10
Characterizing The Extremes Routine Work A priori process model Controlled participation Automatable, especially with service integration, rules and events Knowledge Work No a priori model Collaboration on demand Little automation, but guided by rules and events Copyright Kemsley Design Ltd., 2012 11
The Structured/Unstructured Debate If you can t model it up front, you just don t understand the process Exceptions are the new normal: every process is different Copyright Kemsley Design Ltd., 2011 12
But It s Not That Simple Structured Work Some process are that repeatable, especially automated processes Ad hoc process exceptions already exist, they re just off the grid Unstructured Work Some processes have sufficient variability that modelling is inefficient Instrumentation of unstructured processes provides value Copyright Kemsley Design Ltd., 2011 13
Structure Spectrum Structured e.g., automated regulatory process Structured with ad hoc exceptions Unstructured with pre-defined fragments Unstructured e.g., investigations e.g., financial backoffice transactions e.g., insurance claims Copyright Kemsley Design Ltd., 2012 14
Dynamic Process Runtime User can add participants from own network or recommended expert Non-participant can opt-in to process Audit trail captured within BPMS Eliminates uncontrolled email processes Captures patterns for process improvement Copyright Kemsley Design Ltd., 2012 15
Technology: Process Mining Discovering Hidden Process Gems Copyright Kemsley Design Ltd., 2012 16
Process Mining Sources Copyright Kemsley Design Ltd., 2012 17
BPMS Event Log Format Trans. ID Activity Start Time End Time Resource 8287 Enter customer data 08:34:15 08:37:44 User jsmith 8287 Check credit 08:37:52 08:38:05 Equifax service call 1399 Enter customer data 08:37:59 08:44:40 User sjones 8287 Enter order 08:38:09 08:38:39 ERP system call 1399 Check credit 08:44:58 08:45:06 Equifax service call 4283 Enter order 08:45:01 08:45:35 ERP system call 1399 Enter order 08:45:18 08:45:38 ERP system call Copyright Kemsley Design Ltd., 2012 18
Combining All Event Logs Trans. ID Activity 8287 Enter customer data 8287 Create customer record 8287 Create address record Start Time End Time Resource 08:34:15 08:37:44 User jsmith 08:34:25 08:35:55 User jsmith 08:36:12 08:37:39 User jsmith 8287 Check credit 08:37:52 08:38:05 Equifax service call 8287 Enter order 08:38:09 08:38:39 ERP system call 8287 Check PO 08:38:10 08:38:15 System 8287 Create order 08:38:18 08:38:31 System Copyright Kemsley Design Ltd., 2012 19
Generating A Process Model Copyright Kemsley Design Ltd., 2012 20
Generated Model Data Source: Fluxicon 21
Working With Process Mining Results Actual flows, not idealized models Frequency and duration of each path Optimization: Detect main flows and common variations Detect loopbacks and other inefficiencies Detect wait times Analyze variations over time Copyright Kemsley Design Ltd., 2012 22
More On Process Mining Process Mining: BPM Upside-Down Thursday, 11:30am, Diplomat 5 Anne Rozinat Fluxicon Copyright Kemsley Design Ltd., 2012 23
Technology: Process Simulation Charting A Course In Uncertain Conditions Copyright Kemsley Design Ltd., 2012 24
Model-Simulate-Analyze-Optimize Copyright Kemsley Design Ltd., 2012 25
Simulation Goals Test and validate process models Establish path patterns Estimate end-to-end times Optimize resource utilization and SLA performance across peak/slack periods During runtime, predict performance based on realtime analytics Copyright Kemsley Design Ltd., 2012 26
Simulation in the BPM Lifecycle Source: Lanner 27
More On Analytics And Simulation Modeling and Analytics for Process Excellence Thursday, 10:10am, Diplomat 5 Denis Gagné Workflow Management Coalition (replacing Robert Shapiro) Copyright Kemsley Design Ltd., 2012 28
Technology: Predictive Analytics/Process Intelligence Smarter Processes for Smarter Outcomes Copyright Kemsley Design Ltd., 2012 29
Why Predictive Processes? Predictive analytics is not just about forecasting what s coming down the pike. It s also about keeping the bad alternative futures from happening. James Kobielus, Forrester Copyright Kemsley Design Ltd., 2012 30
Process + Analytics + Decisions = Intelligent Processes Business Process Business Rules Business Intelligence Copyright Kemsley Design Ltd., 2012 31
Process Analytics in a BPMS Executing process Realtime process dashboard Copyright Kemsley Design Ltd., 2012 32
What You Can Do With Process Analytics Information to support manual decisions E.g., display queue sizes to help manager to reallocate work Data to trigger automated actions E.g., spawn fraud detection process when series of events occur for same customer Predict missed SLAs E.g., compare history of activity timeline to estimate overall time to completion Copyright Kemsley Design Ltd., 2012 33
Focus On The Goal, Not The Task Compare: Current to baseline model Current to historical Analyze: Process dependencies and critical path Simulate to identify future problems Act: Self-adjust through feedback to decisioning In-process user guidance Copyright Kemsley Design Ltd., 2012 34
Slides at www.slideshare.net/skemsley Sandy Kemsley Kemsley Design Ltd. email: sandy@kemsleydesign.com blog: www.column2.com twitter: @skemsley Copyright Kemsley Design Ltd., 2012 35