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SIGNAL

challenge

Challenge 6

brief

SIGNAL delivered SIGNAL (Schedule Intelligence and Guidance for Noticing At‑Risk Lines), a production‑ready data enhancement and alerting engine that turns raw schedule and capacity data into early‑warning signals. The solution enriches datasets with drift metrics, risk scores, behaviour archetypes and plain‑English recommendations, ready for Power BI dashboards and proactive intervention.

Please be aware that this content was generated follwing an automated review so may not be perfectly accurate; refer to the original challenge brief and team files for authoritative information

key outcomes

Enables earlier, more confident action by converting complex delivery data into prioritised risks and automated recommendations, reducing late recovery effort and improving confidence in committed work.

important files

  • 3.0 Solution/signal_enhance.py: Core data enhancement engine that calculates drift, risk scores, behaviour archetypes and outputs Power BI‑ready CSVs.
  • 3.0 Solution/signal_notify.py: Notification and escalation module integrating with Microsoft Teams and email to alert the right roles.
  • 4.0 Supporting Documentation/README.md: Overview of the SIGNAL approach, pipeline and outputs.
  • 4.0 Supporting Documentation/activity_data_ENHANCED.csv: Enhanced activity dataset with risk categories, traffic lights and recommended actions.

details

team: SIGNAL members: tbc topics: solution-centre, hack27, challenge6, python, power-bi, scikit-learn, rule-based-models, clustering, microsoft-teams, delivery-confidence, early-warning, schedule-intelligence, capacity-management, decision-support technologies: Python, Power BI, scikit-learn, rule-based-models, clustering, Microsoft Teams

About

SIGNAL delivered SIGNAL (Schedule Intelligence and Guidance for Noticing At‑Risk Lines), a production‑ready data enhancement and alerting engine that turns raw schedule and capacity data into early‑warning signals. The solution enriches datasets with drift metrics, risk scores, behaviour archetypes and plain‑English recommendations, read...

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