'Journal of Humanities and Social Sciences Invention'
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)
