Business Process Optimization: Status and Perspectives
Download as PPTX, PDF1 like773 views
The workshop at bpm'2023 focused on business process optimization, emphasizing that accurate simulations require reliable process models and realistic parameters, as incorrect assumptions can lead to ineffective optimizations. It introduced concepts like tactical vs. operational process optimization, emphasizing the need for human involvement in adaptations and the evaluation of performance measures through data-driven and conversational techniques. Various methodologies for process enhancement were discussed, including expert-driven simulations and prescriptive monitoring to address unforeseen changes.
Business Process Optimization: Status and Perspectives
- 1. Workshop on Business Process Optimization @ BPM’2023, Utrecht, September 2023
- 4. 4 The process model is authoritative • No deviations, no workarounds The simulation parameters accurately reflect reality • …in reality, they are often guesstimates A resource only works on one task instance at a time / a task is performed by one resource • No multi-tasking / no multi-resource tasks (teamwork) Resources have robotic behavior (eager resources consume work items in FIFO mode) • No batching, no prioritization • No tiredness or stress effects, no interruptions, no distractions Undifferentiated resources • Every resource in a pool has the same performance as others No time-sharing outside the simulated process • Resources fully dedicated to one process
- 5. End Result Business process simulations based on incomplete models, guesstimates, and simplifying assumptions are not faithful optimization based on such models is at best perilous 5
- 6. 6 {T1 -> T2 -> T3} {T1 -> T3 -> T3} {T1 -> T2 -> T3} {T1 -> T2 -> T3} {T1 -> T2 -> T2} {T1 -> T2 -> T3} {T1 -> T2 -> T3} {T1 -> T3 -> T2} {T1 -> T2 -> T3} {T1 -> T2 -> T3} Stochastic Process Model Discovery Congestion model enhancement Vs. Generated Ground truth Accuracy assessment Tuning Hyperparameter optimizer Simulator Simulated Log https://github.com/AutomatedProcessImprovement/Simod
- 9. Given • one or more event logs recording the execution of one or more processes • one or more performance indicators that we seek to maximize/minimize • a process model, decision rules, resource allocation rules, other process knowledge • a set of allowed changes to the process model and associated rules Find • Possible sets of changes to the process to optimize the performance measures
- 12. Discover Process Model Metaheuristics Optimizer (e.g. Genetic, Hill Climbing) Candidate Changeset Evaluator Candidate Changeset Generator New Pareto front Event log Candidate Change-sets Discover Simulation Model Simulation Model As-Is Process Model Current Pareto front Business Process Simulator (Prosimos) Allowed Changes add/remove resource adjust schedule…
- 13. Conversational Process Optimization • Search-Based Process Optimization is about exploitative process redesign • Repeatedly applies a set of predefined adaptations • Does not put into question the existing process structure • Cannot handle unforeseen changes • Conversational Process Optimization • Makes search-based optimization a step in a human-in-the-loop optimization approach • Brings in general knowledge together with domain knowledge to transform human directives into search space specifications
- 15. Summary • ATAMO Process Optimization • Expert-Driven Process Optimization with Simulation-in-the-Loop • Expert-Driven Process Optimization with Data-Driven Simulation • Search-Based Process Optimization • Conversational Process Optimization
- 16. Tactical vs Operational Process Optimization • The approaches reviewed focus on tactical optimization • The goal is to go from an as-is to a to-be process • Operational process optimization is also a fertile ground for research • Prescriptive process optimization • Triggering predefined interventions at runtime to optimize case outcomes • Augmented process execution • Triggering adaptations at runtime to respond to drifts in process behavior, including previously unobserved or unforeseen changes
- 17. References Data-Driven Simulation • Camargo et al. Automated discovery of business process simulation models from event logs. Decision Support Systems 134:113284, 2020 • Chapela-Campa et al. Can I Trust My Simulation Model? Measuring the Quality of Business Process Simulation Models. BPM 2023, pp. 20-37 • De Leoni et al. Investigating the Influence of Data-Aware Process States on Activity Probabilities in Simulation Models: Does Accuracy Improve? BPM 2023: 129-145 Search-Based Process Optimization • Satyal et al. Business process improvement with the AB-BPM methodology. Inf. Syst. 84: 283-298 (2019) • López-Pintado et al. Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes. BPM (Forum) 2021: 92-108 • Peters et al. Resource Optimization in Business Processes. EDOC 2021, pp. 104-113 Conversational Process Optimization • Barón-Espitia et al. Coral: Conversational What-If Process Analysis. ICPM Doctoral Consortium / Demo 2022 • Berti et al. Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study. BPM Workshops 2023. • Berti & Sadat Qafari: Leveraging Large Language Models (LLMs) for Process Mining (Technical Report). Arxiv 2307.12701 (2023)
- 18. References Prescriptive Process Monitoring • Fahrenkrog-Petersen et al. Fire now, fire later: alarm-based systems for prescriptive process monitoring. Knowledge and Information Systems 64(2): 559-587 (2022) • Kubrak et al. Prescriptive process monitoring: Quo vadis? PeerJ Comput. Sci. 8: e1097 (2022) • Dasht Bozorgi et al. Prescriptive process monitoring based on causal effect estimation. Information Systems 116: 102198 (2023) • Padella & de Leoni: Resource Allocation in Recommender Systems for Global KPI Improvement. BPM (Forum) 2023: 249-266 • Weytjens et al. Timed Process Interventions: Causal Inference vs. Reinforcement Learning. In BPM Workshops 2023. Augmented Process Execution • Dumas et al. AI-augmented Business Process Management Systems: A Research Manifesto. ACM Transactions on Management Information Systems 14(1): 11:1-11:19 (2023) • Kurz et al. Reinforcement Learning-Supported AB Testing of Business Process Improvements: An Industry Perspective. BPMDS/EMMSAD@CAiSE 2023
Editor's Notes
- #3: https://github.com/AutomatedProcessImprovement/Simod
- #4: https://github.com/AutomatedProcessImprovement/Simod
- #7: We have developed a tool called Simod capable of generate simulation models automatically based on an event log. The tool combines an automated process discovery technique to extract a process model, with trace alignment and replay techniques to extract the simulation parameters, and a hyper-parameter optimizer to evaluate and search for the best simulation model parameters configuration. Simod has been integrated into a beta state on the Apromore platform and has been submitted to the demo track of the same BPM 2019 conference.
- #8: https://github.com/AutomatedProcessImprovement/Simod
- #9: https://www.if4it.com/core-domain-knowledge-critical-foundation-successful-design-thinking/ https://towardsdatascience.com/minimum-viable-domain-knowledge-in-data-science-5be7bc99eca9
- #15: https://github.com/AutomatedProcessImprovement/Simod