In analysing the performance of complex sociotechnical systems, of particular interest is the inevitable and inherent variability that these systems exhibit, but can normally tolerate, in successfully operating in the real world. Knowing how that variability propagates and impacts the total function mix then allows an understanding of emergent behaviours. This interdependence, however, is not readily apparent from normal linear business process flow diagrams.
An alternative approach to exploring the operability of complex systems, that addresses these limitations, is the functional resonance analysis method (FRAM). This is a way of visualising a system’s behaviour, by defining it as an array of functions, with all the interactions and interdependencies that are needed for it to work successfully. Until now this methodology has mainly been employed as a qualitative mind map.
This case study describes a new development of the FRAM visualisation software that allows the quantification of the extent and effects of this functional variability. It then sets out to demonstrate its application in a practical, familiar test case. The example given is the complex sociotechnical system involved in a Formula 1 pit stop. This has shown the potential of the application and provided some interesting insights into the observed performances.
Figure 1 – The Work as Imagined (WAI) FRAM model
Insights from the Model
The spine of the process is a very smooth, well-rehearsed, coordinated and choreographed, essentially linear series of sequential actions by the four tyre-changing teams, which operates almost autonomously; and only requires a car and fresh tyres to be available. The additional and critical functions that enable and develop the outcomes of the tyre teams are in the initial car reception phase and the final car release phases of the operation. Here it is crucial that the car stops exactly in position and that it is promptly and reliably elevated to enable the tyres to be removed.
This criticality has been recognised by the provision of two extra mechanics to ensure the car’s stabilization, and two extra jackmen to provide resilience for an essential function.
Similarly, at the rear of the car, the time taken to lower the car and move the jacks out of the way shows up as a potentially crucial delay to release. But it is clear that the last two mechanics (the “gap spotter” and the “release” controller) have the most demanding functions (with multiple aspects), which are the final and are probably crucial to determining the overall time taken.
Arrivals of other cars are completely outside of the control of the pit crews so that this variable is essentially random and needs to be accepted as a delay. The release process requires knowledge, indications, and signals that all the previous functions have been successfully achieved and that there is a clear gap available before the function can execute. Just in terms of conscious processing, this decision probably takes the most time to execute correctly and safely.
The consequences of getting it wrong add to the pressure on the decision maker. Putting a set
of notional values into the model yields a value of the time taken of around 2–3 s, which fits observed performances.
It is noticeable that in the Williams video referenced in the paper, the overall time taken is less than predicted by this study of the “as imagined’ FRAM sequence of instantiations. So, the video was examined in more detail to try and establish how exactly the teams carried out their different functions. What adaptations were made to be able to complete the tasks more quickly?
Figure 2 – The Work as Done FRAM model
The first thing that becomes apparent when the videos are examined closely is that although the officially timed start of the process is from when the car has stopped at its marks, the pit crews anticipate the stop, and the air guns are engaging, the wheel nuts and the jacks are moving into position before the car stops.
This means that none of these functions are rate-determining in adding to the time but are effectively reducing the time by anticipating the start. In the WAD instantiation, below (Figure 2) we have thus added an additional function for the car to enter the box and be active before the “official” start time. Similarly, at the rear of the car, the jacks are removed as soon as the tyres are on and the release seems to happen simultaneously with the wheel-nut-tightening completion, another corner-cutting adaptation reflected in the changing the aspect links. There does not seem to be a noticeable delay in the release of the car, after the nut is tightened, which means again, that the release function is anticipating the clearance checks. Again, this has a significant effect in further reducing the overall time taken.
When the Williams pit stop video is analysed more rigorously, we observe timings remarkably close to the WAD FRAM timings, which further supports our interpretation of the actual work as done. As it is a very competitive environment and seconds saved in pit stops can mean gaining or losing advantage, there is continuing pressure to find ways of further reducing these times.
One such initiative is rumoured to be the progressive automation of some of these critical functions like the release function, either for more speed, but more likely to be for more reliability/safety.
This is now a classic case of Rasmussen drift, where the operational safety boundaries are gradually tested and extended, to gain competitive and efficiency advantages. Unfortunately, as these boundaries can never be precisely predicted in real environments, this often results in unfortunate but totally foreseeable (in hindsight) unsafe excursions, accidents, and casualties. In Formula 1, Ferrari were fined 50,000 euros by race officials for an unsafe release at the Bahrain Grand Prix in 2018, which resulted in an injury to the front jack man who was not able to get out of the way in time. From the FRAM model, this was the result of pressuring the release mechanic to cut his decision time to such an extent that it was reflex, rather than a conscious confirmation of a safe state for release
Publication December 2021 Applied Sciences 11(24):11873 DOI: 10.3390/app112411873
(PDF) Optimising the Performance of Complex Sociotechnical Systems in High-Stress, High-Speed Environments: The Formula 1 Pit Stop Test Case. Available from: https://www.researchgate.net/publication/357045761_Optimising_the_Performance_of_Complex_Sociotechnical_Systems_in_High-Stress_High-Speed_Environments_The_Formula_1_Pit_Stop_Test_Case [accessed Aug 21 2024].