## 4 Discussion  
This paper has set out to examine the feasibility and validity of extending the functional resonance analysis method for complex sociotechnical systems, by adding features to make it suitable for use in operational research.  
It has shown that, in principle, the interaction of the functions can be quantified using a combination of formal validation of the models by the application of production rules to formalise the model as a series of emergent activations of the functions and the capability to assign and update systemwide metadata for each function.  
The study then set out to demonstrate this by examining a well-characterised and familiar complex sociotechnical system with sufficient publicly available process data to model--a Formula 1 pit stop.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Assigned Function** & **WAI 231** & **WAD 231** & **Video** \\ \hline  & Seconds & Seconds & Seconds \\ Stop car & 0 & 0 & 0 \\ Lift car & 0.35 & 0.35 & 0.25–0.5 \\ Stabilise car & 0.65 & & \\ Clean front wing & 0.65 & & \\ Clean rear spoiler & 0.65 & & \\ Remove wheel nut & 0.65 & 0.3 & \\ Remove tyre & 1.15 & 0.85 & 0.7–0.85 \\ Replace tyre & 1.65 & 1.35 & 1.5–1.6 \\ Tighten wheel nut & 1.95 & 1.65 & 1.8 \\ Lower car & 2.2 & 1.65 & 1.9 \\ Get out of the way & & & \\ Check complete & 2.5 & 1.95 & 2 \\ Spot gap & & & \\ Release car & 2.7 & 2.15 & 2.12 \\ \hline \hline \end{tabular}  
* The above table shows the FRAM simulation of the ‘work as imagined’ (WAI 231) predicts a pit-stop time of some 27 s, which is perhaps a typical time recorded, but it is difficult to see where time could be saved. The second column shows the same simulation repeated for the ‘work as done’ FRAM, (WAD 231), which shows that, with those adaptations, over half a second could theoretically be saved.  
\end{table}
Table 4: Comparison of predicted and actual timings for the pit stop.  
Although the study was carried out only to a level of detail necessary to show the potential of the approach, it nevertheless provides some very interesting and relevant insights into the process.  
The analysis predicted 'as imagined' times of 2-3 s, whereas the 'as done' version could achieve a substantially better time of around 2 s.  
Actual recorded times are all around this 2 s standard, with the record currently standing at 1.88 s.  
This seems to be very hard to achieve by rigidly implementing the process as laid down by regulations (as imagined!). This is reflected in recent moves by the regulators to tighten up the rules.  
After the injury incident at the Ferrari pit stop in which a Ferrari mechanic suffered a broken leg when [PERSON] was released too early from a pit stop in Bahrain in 2018, the FIA issued a technical directive that has just come into force for the Hungarian Grand Prix on 1 August:  
\"It mandates a minimum 0.15 s delay between the wheel nuts being confirmed as tight and the mechanic operating the jack dropping the car, and 0.2 s from the jack going down to the driver receiving the signal to leave the pits.\"  
There is already a backup system of sensors and signals to indicate completion or otherwise of the critical functions (nut tightening, car raising and lowering). The outputs from these could be, (and may already be), fed to an additional auto-check function whose effect would be the same as the gap spotter, to control the release function. Presumably the extra delays could be introduced automatically.  
This would then force the release process to be more like the work as imagined (presumably by the FIA). That is, the jack must wait for the nut-tightening--a mandatory delay of 0.15 s and then the release man must impose a further 0.2 s before he can consider releasing (if the gap is there).  
How is that arrived at, going to work, be monitored, or enforced?  
The paper suggests that a FRAM model could help.