Smart Planning and Timing Under Specificity in Business Processes

Optimization of business processes in corporate environments has become a critical challenge to reduce operational costs and shorten project completion times. A new research released on ArXiv is taking an important step towards dealing with this difficulty: ** Control flow timing work processes under uncertainty**.

The basic problem of work processes is that the operating sequence required to complete an example is not fully known. The decisions taken on the basis of the data that arises during the execution, since determining which steps will be applied, this makes the planning and timing work complicated. The research explores the ways to manage this uncertainty:

• Historical data obtained from past execution logs can reveal how likely different ways in decision points
• Probability information shows which process paths are repeated more often and which scenarios are high likely to be seen
• Makespan (battery process time) can be reduced and resources can be allocated more efficiently
• AI-based planning can predict future executions and perform optimization at the beginning

This approach can be quite useful in complex workflows such as insurance request processing, credit application management or supply chain. Here you can have different control ways.

Research offers a valuable contribution in the intersection of machine learning and business process management areas. Making smarter under certainty, organizations host potential to significantly improve operational performance.