This paper discusses the role of
performing Forensic Schedule Diagnostics (FSD’s) and Criticality
Cross-Tabulations (CCT’s) for Method Implementation Protocol (MIP)
selection, and which includes determination of the likely minimum number
of variable or grouped periods necessary should one of the “windows”
style methods be selected.
The FSD and CCT methods described herein offer a fast and consistent way to assess change across many updates. The nature and degree of change between schedule updates are predictive of relative effort involved with execution of various MIP’s.
An essential part of assessing this effort is addressed by the fact that these changes are also predictive of the optimal minimum number of variable or grouped periods for efficient execution of windows based MIP’s. It identifies where best to divide updates into groups to better understand and present changes to the critical path. This can be accomplished during the first days of an FSA and therefore minimizes lost productivity due to the iterative re-grouping which is often required when such groupings are selected in a fixed, arbitrary, or a trial and error fashion.
These methods can be employed independent of specific activity knowledge. Therefore, conclusions can avoid potential bias which sometimes allegedly accompanies subjective selection of variable periods.
The FSD and CCT methods described herein offer a fast and consistent way to assess change across many updates. The nature and degree of change between schedule updates are predictive of relative effort involved with execution of various MIP’s.
An essential part of assessing this effort is addressed by the fact that these changes are also predictive of the optimal minimum number of variable or grouped periods for efficient execution of windows based MIP’s. It identifies where best to divide updates into groups to better understand and present changes to the critical path. This can be accomplished during the first days of an FSA and therefore minimizes lost productivity due to the iterative re-grouping which is often required when such groupings are selected in a fixed, arbitrary, or a trial and error fashion.
These methods can be employed independent of specific activity knowledge. Therefore, conclusions can avoid potential bias which sometimes allegedly accompanies subjective selection of variable periods.