Optimising the performance of complex sociotechnical systems in high-stress, high-speed environments: the Formula 1 pit stop test case

Slater, David, Hill, Rees, Kumar, Maneesh ORCID: https://orcid.org/0000-0002-2469-1382 and Ale, Ben 2021. Optimising the performance of complex sociotechnical systems in high-stress, high-speed environments: the Formula 1 pit stop test case. Applied Sciences 11 (24) , 11873. 10.3390/app112411873

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Abstract

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 paper 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 chosen 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/
Publisher:	MDPI
ISSN:	2076-3417
Date of First Compliant Deposit:	12 January 2022
Date of Acceptance:	3 December 2021
Last Modified:	11 May 2023 17:24
URI:	https://orca.cardiff.ac.uk/id/eprint/146546

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