Expertise
Digital Project Controls
Automation and data engineering that turn schedule quality into a standard.
Manual controls do not scale. Across many contractors and tens of thousands of activities, consistency is only possible when the checks themselves become code — and when data, not opinion, drives the conversation about quality.
How I approach it
I build automated, rules-based assurance. Python reads the P6 export and runs the checks: the DCMA 14-point checks provide a recognised schedule-quality reference, while a bespoke 100-point Employer Schedule Assurance Framework provides the broader, programme-specific assurance structure — scoring every schedule the same way, every cycle. That assurance output feeds Power BI, which turns the same controls data into live management intelligence, so leadership sees trend and exposure rather than raw tables. The pipeline runs P6 data → automated checks → DCMA plus the 100-point framework → assurance output → Power BI management intelligence. On a major programme this lifted contractor schedule quality from around 25% to over 83% against the 100-point framework, and held it there. Automation raises the floor; expert judgement is freed for what rules cannot catch.
Manual schedule assurance is slow and inconsistent across many contractor submissions.
P6 data → automated checks → DCMA 14-point + 100-point Employer framework → assurance output → Power BI.
Every submission scored the same way each cycle; exceptions surfaced by dimension.
Where to intervene — and what still needs professional judgement.
Consistent assurance at scale; expert judgement freed for what rules cannot catch.