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challenge5

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PRISM – Planning Risk Insight & Scheduling Monitor (Team 5G) built PRISM, an end‑to‑end behavioural analytics solution that combines data science, AI, and interactive dashboards to surface risky resource and forecasting behaviours. The solution identifies overloads, underloads, forecast bias, milestone drift, and planning volatility, and p...

  • Updated Apr 10, 2026

Data Quality & Forecast Integrity (Team 5C) concentrated on strengthening data quality and analytical foundations for behavioural insights into resource planning and forecasting. They built a Python‑based data cleansing and analysis workflow to validate synthetic schedule and resource data, calculate forecast versus actual performance, and exp...

  • Updated Apr 10, 2026
  • Jupyter Notebook

Project Arches – Resource Behaviours Dashboard (Team 5B) delivered an interactive Power BI dashboard that analyses resource utilisation and forecasting behaviour to expose inefficiencies and productivity blockers. The solution cleans and normalises schedule and financial data, visualises effective utilisation, and surfaces behavioural patterns...

  • Updated Apr 10, 2026

Persona‑Driven Planning Insights (Team 5E) developed a persona‑led analytical framework for the Risky Resource Routines challenge, focusing on how different roles experience and act on poor planning behaviours. They combined behavioural metrics, data profiling, and LLM‑assisted analysis to map forecasting and resource issues to tailored in...

  • Updated Apr 10, 2026

Forecast Force (Team 5F) delivered a proactive behavioural analytics concept to identify risky resource and forecasting patterns before they impact delivery. Branded as Forecast Force, the team combined Python‑based analysis with visual storytelling to highlight overloads, underloads, forecast drift, ignored dependencies, and planning volatili...

  • Updated Apr 10, 2026
  • Jupyter Notebook

Project Health & Behaviour Monitor (Team 5A) designed a behavioural analytics approach to expose hidden resource and scheduling patterns that undermine delivery confidence. Their concept focuses on turning plan data into actionable insight by highlighting chronic overloads and underloads, forecast inaccuracy, milestone drift, ignored dependencie...

  • Updated Apr 10, 2026

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