Human Factors is about matching ‘things’ like vehicles, technology, procedures, processes and infrastructures to ‘people’, in order to reduce error and increase efficiency. It is, in effect, a mixture of psychology and engineering. Bearing in mind all systems, including logistics, are mixtures of people and engineering, and that an increasing number of strategic challenges arise from this interface, Human Factors is becoming increasingly important. Logistics is a new and exciting domain for the field and there is a lot of potential to make progress on the social, environmental and financial triple bottom line.
A Human Factors approach offers new perspectives in optimising behaviours and decisions in road vehicle logistics. Building on a review of technology trends , this project will explore the ways technology might affect both the manager’s/despatcher’s and the driver’s tasks, situational awareness and strategies to plan journeys and respond to disruption. Possibilities from new technologies will be modelled, using Human Factors methodologies, to try and predict possible behavioural effects. In the second year case studies spanning a range of logistics operations will be combined with data from Project 2A. This will enable the supply chain system to be characterised in terms of structure, data flows, decision freedoms and the different perspectives of decision-makers in the system. Practical questions to be addressed are why humans in the system do not always behave ‘rationally’ in the face of decision support tools (such as route planning and scheduling technology) and how such issues can be overcome. In the third year, the increasingly data-rich environment provided by telemetry will be explored with recourse to best practice in other industries. An example is flight data monitoring, which has delivered significant cost and carbon reductions in aviation (e.g. ). Given that a lot of issues arise from the interface of people and systems, this project will explore how to detect meaningful Human Factors ‘leading indicators’ from telemetry data in order to provide a highly novel, and potentially very effective way of supporting eco-driving behaviours and other similar interventions.
The objective of this project is to explore how decisions and behaviours can be influenced, at multiple levels of the supply chain system, to address cost, carbon and safety issues. It will do this by using Human Factors methods to design decision support systems and steer new vehicle and data management technologies towards ‘user centred’ goals. Most importantly, this project will provide these insights early, where it is possible to intervene in the most cost effective manner.
Commercial and social impact: Enhancing system performance enhances the safety of those in the sector and the robustness of the system, with societal benefits accruing from more efficient and less carbon intensive ways of operating. The gains in productivity from sociotechnical systems research and reduced risk of product or system failure from understanding applications offer real commercial benefits for operators, manufacturers and the wider supply chain sector. Data monitoring, and resultant changes in behaviours, have had demonstrable effects in the aviation sector (e.g. 1.5% of the total fuel budget for an airline) and it is anticipated that even greater benefits will emerge from a tightly coupled system such as a logistics network – this research will pursue such outcomes. Working closely with Consortium members will ensure that the opportunities unearthed can be adopted as quickly as possible and integrated into operating practice.
Academic impact: Analytical prototyping, where we ‘stress test’ human/engineering systems before a physical prototype is built, represents the forefront of knowledge and application in the field of Human Factors. The methodological and practical outcomes will contribute significantly to the scientific literature in this area. Using Human Factors perspectives at a macro level is also cutting edge research, relating well to research grand challenges at the systems level of analysis. Linkages between data monitoring, behaviour and performance are also not well understood. This project will set a new direction by combining hypotheses drawn from different scales of analysis and testing such linkages.
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