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early-warning

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Delivery Confidence Assistant built a data‑driven Delivery Confidence Assistant that scores forecast‑credibility risk and translates complex delivery signals into clear, actionable insights. The solution combines transparent rules, lightweight modelling and Power BI‑ready outputs to show which tasks are likely to slip, why programmes and w...

  • Updated Apr 28, 2026
  • Jupyter Notebook

Assumption Drift Canvas focused on collaboratively mapping how critical assumptions emerge, drift and impact delivery confidence across projects. Using a shared visual workspace, the team structured the logic linking assumptions, confidence, external signals and portfolio‑level assurance to support earlier, clearer decision‑making.

  • Updated Apr 28, 2026

Early Slip Predictor focused on identifying early indicators of delivery slippage by analysing capacity pressure and task behaviour across work centres. Using simple machine‑learning techniques and clear capacity metrics, the team demonstrated how likely future slip can be predicted early and translated into understandable risk signals.

  • Updated Apr 28, 2026
  • Jupyter Notebook

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