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rule-based-models

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Delivery Confidence Radar built an early‑warning Delivery Confidence Radar that integrates activity and capacity data to surface instability, pressure and likely slippage before dates move. The solution emphasises explainable signals and a clear ‘what’s at risk, why, and where to intervene’ structure suitable for planners and senior lead...

  • Updated Apr 28, 2026
  • HTML

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

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...

  • Updated Apr 28, 2026
  • Python

AI Delivery Recommender combined delivery analytics with simple AI‑driven recommendations to help teams see delivery risk early and understand what actions could reduce slippage. Their solution pairs a Power BI dashboard with a lightweight recommendation model to translate delivery signals into practical guidance.

  • Updated Apr 28, 2026

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