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Estimating the Impact of Incidents on Process Delay - ICPM 2019

  1. ESTIMATING THE IMPACT OF INCIDENTS ON PROCESS DELAY Felix Mannhardt, Petter Arnesen, Andreas D. Landmark
  2. 2
  3. 3 Image sources:
  4. 4 Case Activity Railway Traffic Control Logs
  5. 5 Railway Traffic Logs – Example Process Model
  6. What is process delay? 6 Definition A Expected/Scheduled vs. Actual Performance Definition B Normal performance vs. Actual performance
  7. What is known about incidents? 7 Image sources:
  8. What is known about incidents? 8 Incident Log Issue registered Mover/motor turnout km 453 Work order created Repair Process Work Order DB Manual registration Manual registration Registered? Work started? Contractor notified? When was it fixed?
  9. The Problem – Linking Incidents to Delay 9
  10. • Internal performance factors • Alignments to project performance information • Identification of slow variants / combination of attributes • Identification of slow resources • Prediction of performance • Remaining time to completion • Some work considering inter-case parameters • Visualisation of performance • Dotted chart • Others: Process Profiler, Performance Spectrum etc. 10 Existing work None is addressing the linking/estimation challenge!
  11. Proposed Approach – Assumptions #1 11
  12. Proposed Approach – Assumptions #2 12 Resource required for trains to pass station Støren! Image sources:
  13. Proposed Approach – Impact Estimation #1 13 Step 1: Collect performance information from event log Case 5262 took about 460s for activity LMO-STØ (single track) Approx. time for incident on turnout XYZ 𝑇𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑡
  14. Proposed Approach – Impact Estimation #2 14 Step 2: Determine normal process performance
  15. Proposed Approach – Impact Estimation #3 15 Step 3: Classify activity instances into three classes
  16. Proposed Approach – Impact Estimation #4 16 Step 4: Determine likely start/end of impact using MCMC 𝑇𝑠𝑡𝑎𝑟𝑡 ? 𝑇𝑒𝑛𝑑 ? Metropolis-Hastings algorithms 20000 iterations Priors for standard delay e.g., 𝑝0 = (0.94, 0.055 0.005) and incident-affected delay e.g., 𝑝1 = (0.93, 0.06, 0.01) hand tuned on small dataset.
  17. Proposed Approach – Impact Estimation #4 17 Step 4: Determine likely start/end of impact using MCMC 𝑇𝑠𝑡𝑎𝑟𝑡 𝑇𝑒𝑛𝑑 Times at least 50% of the samples between 𝑇𝑠𝑡𝑎𝑟𝑡 and 𝑇𝑒𝑛𝑑
  18. Proposed Approach – Impact Estimation #5 18 Step 5: Accumulate delay 𝑇𝑠𝑡𝑎𝑟𝑡 𝑇𝑒𝑛𝑑Count fully Discount with prob.Discount with prob.
  19. Evaluation – Case Study in Norway 19 TIOS BaneData Save result back Traffic Control System Maintenance Management PRESENS-Algorithm Data Warehouse Calculated the impact on delay for each major incident since 2011 Work orders Driving time Delay tagging Validation
  20. Evaluation – Delay Dashboard 20
  21. Evaluation – Predictive Maintenance 21 • Prediction of "avoided" delay due to smart maintenance • Smart monitoring of turnouts • Justification of investments • Using the delay effect base on historical data as proxy • Not perfect, often rather small data basis for prediction • Better than management by `rule of thumb`
  22. • Explore application on non-infrastructure focussed processes • Activity-incident relation is less obvious? • Estimation of `normal` process performance challenging? • Address the issue of multiple co-occurring incidents • MCMC would have trouble with multi-modal distributions • Address non-local knock-on effects on process delay • Initial solution addresses the problem for single-track railway networks, but difficult to generalise! • Investigate effects of queues etc. in non-physical processes • Address the strong dependency on the chosen parameters in the prior distribution  possible but high computational cost 22 Future work
  23. 23 Contact @fmannhardt Thanks to BaneNOR which funded part of this research!
  24. Teknologi for et bedre samfunn

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