Human factors : a new approach for designing the truck-driver system

Abstract

The logistics sector is an often forgotten force behind modern life in the UK, and it is increasingly under pressure to become more efficient, more safety-conscious, and more environmentally sustainable. This triple bottom line necessitates deep changes to the traditional way of working. As evidenced by an expert-led technology forecast, many technological and organisational interventions are on the horizon for the next 15-30 years. This rapid pace of advancement, together with the frequent assumption that workers are ‘hyper-rational’, echoes a worrying pattern from other sectors that have since benefited from human factors & ergonomics (HF/E) expertise. This thesis aims to apply HF/E principles and methods to both current and projected future truck-driver scenarios, in order to leverage the most agile and intelligent agent in the logistics system: the human. Despite a lack of past work at this intersection, logistics and HF/E can be drawn together by their mutual use of systems complexity concepts. This thesis proposes that logistics is a large, complex adaptive socio-technical system (CASTS), and reviews HF/E methods to determine their fit to different system scales and dynamics. From this it is determined that initial work requires a bottom-up focus on the truck-driver system. A range of methods are employed to understand the existing truck driving task and what it requires of the modern driver; identify and prioritise potentially critical system ‘parts’; design new supportive technologies from scratch in a way that allows for emergent behaviour; and analytically prototype how truck-driver systems are likely to change in projected future scenarios. This work provides new practical insights for current truck-driver systems, and a map of how this may change – shedding light on potential future problems and how we might adapt to them before they occur. Not only does this thesis provide a solid empirical foundation and a ‘direction of travel’, it also contributes the methodological guidance necessary to strategise next steps beyond this thesis, into deeper logistics complexity. Taken together this demonstrates the power of human factors methods for logistics, and their potential for other unexplored ‘complex adaptive sociotechnical systems’ (CASTS).