In this discussion, the speakers explain how digitally capturing site records and linking them to tools such as Sima or asset‑management platforms enables the extraction of concrete productivity rates. These empirically derived rates can be combined with contract data to produce realistic prospective assessments and forecasts. By aggregating productivity metrics over the life of a long‑term project (e.g., HS2), organisations can learn from past performance, improve future forecasts, and make more accurate programme revisions, especially when updating schedules after compensation events. The overall argument is that moving from isolated records to an integrated, data‑driven approach transforms forecasting from reactive to predictive.
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.
