Research

In this work, we look into how to exploit business process execution data for characterizing human resource group behavior from multiple aspects relevant to workforce performance.

Empower HR Decisions with Workforce Analytics

  • 💯Organizational success depends on employees and how they are managed
  • Good HR decisions need to be reinforced by timely and evidence-based insights💡
  • 📈Workforce analytics facilitates effective HR decisions by analyzing the deployment and performance of employees and their groups
  • Workforce Analytics × Process Mining: A Perfect Match

    ✅ Business process execution data — readily available in many enterprise information systems — is a promising source for workforce analytics

    ✅ We develop process mining methods that systematically exploit process data and support organizations to better understand their employees and continually improve HR decisions alongside core business processes

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    • A recent outcome of our ongoing research enables using common process data to analyze employee groups (departments, teams, etc.) from various aspects
    • We apply interactive visualization techniques to allow analyses on multiple dimensions and across different levels

    Demo

    The following demo is based on a case study conducted using data from five municipalities working with a building permit handling process (data source: BPI Challenge 2015).

    Compare across groups and time periods
    Productivity of five municipalities based on the amount of completed cases across a four-year period: The trend is similar while unique patterns exist.
    Understand groups from multiple dimensions
    Workload of the five municipalities:
    Their focuses on different process tasks seem to align (left), but municipality #1 have its own working hours arrangements on Wednesdays (right)
    Explore and highlight patterns at different levels
    Expand the data on different time levels: the unique “batch working” behavior of municipality #4 in certain months
    Drill down to within-group analyses: individual workload distributions manifesting employees’ role differences
    Plug-in tailored measures to cater for different analyses
    Various measures can be applied according to the purposes:
    Productivity of the five municipalities is again analyzed, but measured by average case throughput time

    Further Reading