In this brief dialogue, the speakers discuss how digitally capturing productivity data—especially when linked to tools like Sima or asset‑management platforms—can transform project forecasting from guesswork to data‑driven insight. By aggregating empirical productivity metrics from similar past activities, they can establish realistic run‑rates that feed into prospective assessments, long‑term learning, and more accurate forecasts for extensive programmes such as HS2. The conversation highlights how these realistic productivity figures enable more credible programme revisions and inform compensation‑event updates, ensuring that future planning reflects actual performance trends rather than speculative estimates.
Chapters
But I think actually if we digitally capture them, particularly if we link them to a tool like Sima or an asset management platform, we can start to do clever things. We can start to inform and perhaps agree with a small a rates of productivity that help us perform prospective assessments and forecasts. So in a way, we're taking the empirical contemporary adjacent activity and saying, well, that was the productivity, you know, that week where it's the same week, it's a very similar activity. Um, so it gives us that productivity bit that quite often is a source of discussion. Um, together with the, um, with, with the, the rates and percentages and contract data, we can start to get quite close to—
and this feeds into that third area of benefit and application. We've got this, these elements of long-term learning that we can use to help us assess Completely. Yeah. As we go through a long project, say HS2 is like, yeah, it could be 15 years long. And in year 6, we could be doing a lot better assessments than year 3 because we've captured all this, these productivity metrics. We've got this knowledge about what's happened. So it's much easier for us to forecast what might happen in the future in the same conditions.
And the same for the program. You know, it's got to be realistic. So if we've got realistic productivity, which can be unlocked through scrutinizing a collection of an aggregate of records rather than just looking at individual ones, then that can inform a programme revision. So it becomes more realistic because we've got realistic run rates in there. And don't forget, when you submit a compensation event, back to the question, we need to update the programme where it's got an impact on remaining activities. So it's all kind of related.
