Data Date Precision

FPM’s Advanced Forensic Scheduling (AFS) software captures every driving start and all relevant paths between the data date and each milestone using proprietary algorithms. The benefits of this approach include:

  • Overcoming the limitations of float-based analysis
  • Determining concurrent and near-concurrent impacts
  • Respecting the importance of the data date
  • Clearly delineating as-built and forecast segments

Identifying how and when contractual milestones are impacted is at the basis of forensic schedule analysis. Schedules are often mitigated within the same update where impacts occur, making float-based analysis unreliable. Even for correctly maintained half-step updates, demonstrating cause and effect within CPM networks is difficult. This is due to the prospect of concurrent and near-concurrent impacts, along with multiple contractual milestones. If all root causes can be identified then each effect on associated milestones can be demonstrated through pathfinding.

It’s important to understand that root causes of schedule impacts are usually found at the data date – where driving starts of paths are either in-progress or waiting to start. Because driving starts are where concurrent delays, disruptions, and pacing exist we built flexibility into AFS that provides a clear picture of the data date.

The data date delineates actual events from forecasted events. During an update period forecasted events are either re-forecasted, modified, or actualized to the as-built side of the data date. Oftentimes activities are both created and actualized within the same window. These momentary activities can represent delay events and are easily overlooked during analysis as they are immediately recorded to the as-built schedule. The as-built schedule is fundamentally different than the forecast side of the schedule. Where forecasted durations represent work, as-built durations and lag often represent un-modeled delay events or inactivity.

AFS captures every driving start to ensure all longest paths between the data date and each milestone are processed for ranking and analysis. Further clarity around the data date is provided as AFS returns as-built segments that show progress through the duration of the analysis window. These as-built segments show preceding impacts where the driving start’s variance represents the culmination of chained root causes, resulting in an exhaustive and accurate analysis.

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