When Artificial Intelligence meets Process-Based Applications. Nicolas Chabanoles, R&D Lead Nathalie Cotté, Product Owner

Transcription

1 When Artificial Intelligence meets Process-Based Applications Nicolas Chabanoles, R&D Lead Nathalie Cotté, Product Owner

2 Business Digital Platform Living applications Multi-layer architecture of Bonita Living Apps Focus on the User experience Custom UIs Business Data Processes Supports Agile development and continuous delivery Live update of artifacts Forms Organization,...

3 BPM benefits Formalization Standardization Automation Digitization Traceability Continuous improvement by humans, for humans

4 Business Activity Monitoring vs Artificial Intelligence => BAM is Descriptive Statistics Traceability: Process execution generates logs that create data combined by software Analysis of data from the past

5 Improvements based on BAM: the long loop Measure Archived data Impact of actions Combine Act. LONG-term: - Process redesign - UI redesign - More internal research Deduct improvements Decision takes time and is based in human deductions

6 Business Activity Monitoring vs Artificial intelligence => AI is Power Deductions Machine learning/predictive analytics: Algorithms Detect patterns in archived data and apply what is learnt on on-going instances Anticipation of data from the future

7 Improvements based on AI: the short loop Measure Living data Impact of actions Get Predictions/ Recommendations Act. SHORT-term: - Reallocate tasks - Fix a form - Replace a connector Decide on AI data reliability Decision is fast, based on man-machine collaboration

8 Continuous improvement with AI: use-cases Automation Triggering a new process Routing processes in motion Decision support Recommendation of the next best action Prediction on instance duration

9 Prediction on instance duration: delays Causes of delays Limited resources or workload peak: bottlenecks External dependencies Process design flaws (e.g., loops) How can we help on the short loop?

10 AI in BPM: the approach 4. Tune the model Test on real use cases 3. Choose a model Process mining 2. Prepare the data Polling in ICI 1. Know the data BPM archives 0. Goal Make production comply with SLA Smoothing Test & validate Configure model Choose a model Create variables Descriptive stats Tweak data Record data

11 0. Goal Complete each case within a given SLA Predict chances for each open case to finish within target If chances are small, short-term action is needed After action, give feedback of impact

12 1. Know the data BPM archives All generic BPM events in Bonita database case start task ready task done gateway executed new assignee on the task case end,...

13 2. Prepare the data Polling Extraction from Bonita database Transformation in a single document Injection into Elasticsearch

14 3. Choose a model Two objectives Allow predictions (duration, path, data, ) Give recommendations of improvements Models considered: Linear regression Neural networks Process mining

15 3. Choose a model Traditional Process mining Process discovery Conformance checking Process mining Extension Any kind of process No need to know the data Easy to interpret results Both prediction and recommendation Potential for further use cases

16 3. Choose a model Turn process execution into a transition system Use Events timestamps Path analysis Compute Chances (%) to finish on time Prediction on case duration * Source: W.M.P. van der Aalst, M.H. Schonenberg, and M. Song - Time prediction based on process mining

17 4. Tune the model Test on a real use-case Personas Omar (Operations Manager) Dave (IT Developer, not Data Scientist) Patty (Platform administrator)

18 Feeding the long loop: Business Process Intelligence Detect flaws in the process design Detect outliers, loops, latencies, and bottlenecks At the level of processes, paths, and tasks Using durations and occurrences

19 What s next? Enhance our algorithm for better accuracy Include context and business data Include human resources data Other predictions End of the flow for each case (quality of service) Technical errors or outages (technical quality) Recommendations Improve the overall User experience

20 DEMO SLA compliance in Loan Management