When other facility managers reviewed the PTT results, they concluded that further modifications were needed to account for even more of the D-side work that the simple conversion thresholds neglected. Accordingly, CAASD introduced the fuzzy logic modeling process. Experts were once again consulted to assign complexity weightings to the different R-side tasks and their combinations. These weightings are intended to characterize the complexity of the D-side task load, even though none of the experts consulted was asked to identify explicitly the D-side tasks involved or to estimate the time it takes to perform each. The PTT values generated from this conversion process were again presented to facility managers for feedback. Their advice led to further adjustments to the fuzzy logic inference rules and complexity weightings until the PTT values satisfied the expectations of the managers consulted.
Both the conversion and validation processes involve repeated consultation with subject matter experts and facility managers and no evidence that data on the performance of D-side tasks were obtained and analyzed to assess their judgments. The heavy reliance on the experience and expectations of facility manager to evaluate the PTT estimation techniques and results is at odds with the purpose of PTT modeling; presumably this purpose is to provide independent quantitative estimates of staffing requirements. All of the PTT conversion methods applied, including the current method of fuzzy logic modeling, exhibit the same fundamental flaw—they imply an estimation of total task load without ever identifying the unmodeled tasks, much less measuring the time it takes to perform them. The conversions rely almost exclusively on experts to determine thresholds and to assign complexity weightings to the unidentified and unmodeled tasks. The D-side task loads implied by these thresholds and weightings are not validated, nor can they be in the absence of any empirical data on task performance.
To adjust these conversion methods further would be insufficient and would risk making the modeling process even less transparent and less convincing. Indeed, it is by no means apparent that past adjustments have led to more accurate PTT predictions—only that they have produced values closer to the expectations of facility managers. In the case of fuzzy logic modeling, this outcome has been achieved at the cost of model transparency and credibility.