AI for business process automation
Date:
Tools: Python , docker , REST APIs , OpenShift , pm4py , networkx
Methods:Several Deeplearning and Statistical techniques such as
- Heuristic miner algorithm for process discovery from logs
- Efficiency ranking of agent using KL divergence based statistical distribution comparison
- GARCH , LSTM models for KPI forecasting
- Auto process aware feature engineering pipeline
- Recommendations for workflow decisions using log
Description: Business processes are quintessential for effective business operations in an enterprise. There exists a wide range of processes such as HR process and customer support which are executed in an enterprise. A workflow is defined for each process which comprises various activities, decisions, and resources such as actors performing the activities. Often some factors are overlooked or missed out while designing any workflow which in turn leads to inefficiencies. Workflow inefficiencies are undesired as they degrade the user experience and the organisations’ productivity.There is a need to identify and mitigate inefficiencies in the workflow thus improve the process performance.
AI Integration in business process is crucial to enable a more efficient and cost effective business process and below are the capabilities
1)Realtime hotspot analysis: offers the ability to mine process logs and unstructured documents to identify bottlenecks and measure the effects of the workforce using unsupervised pattern mining for hotspot detection, including work vs wait times and path frequencies and actor efficiency ranking to compare relative performance of team members
2)Process KPI forecasting: To predict KPIs and alert users when there is a risk of violating SLAs in the future
3)Decision recommendation: To recommend decisions to agents at decision points in a workflow based on predicted outcomes. Including capabilities such as process aware machine learning pipelines to train ML models for decisions based on historical data and give predictions with process aware explanation
Outcomes: - Inlucded in IBM cloudpak for automation product
- A-level research accomplishment with 3M USD per year revenue
- OTAA award for leading and developing the product
Related papers: - ICPM application paper: https://icpmconference.org/2020/wp-content/uploads/sites/4/2020/10/ICPM_2020_paper_152.pdf
- Blog on product features: https://www.linkedin.com/pulse/identifying-hotspots-improvement-opportunities-using-tool-bandlamudi/?trackingId=3kf9b2HOY25SDVOewTCn2w%3D%3D
Realted Patents : - Enhancing process model and computing multi dimensional KPI index
- Conversational Process conformance using historic chats