We are working with the processing industry to help develop tools that will help us find the factors that lead to Loss of Containment (LoC) incidents.
Leading indicators
We’re working with the University of Manchester and the construction industry to provide the evidence base for using ‘leading indicators’ in preventing harm at work.
Phase one outputs from development of capacities to extract health and safety insights from free-text sources
The text mining project being delivered as part of the LRF Discovering Safety Programme is looking to build upon existing state of the art text mining and natural language processing to develop a suite of text mining and natural language processing tools and techniques for specific use on unstruc
Product safety intelligence phase one outputs
Product safety issues have the potential to affect large numbers of people at work and in the community. A single design flaw will be replicated many times and product recalls are costly and rarely recover anything more than a small proportion of defective products.
Loss of containment insights phase one outputs
This use case is aimed at developing insights to help the onshore process industries prevent and mitigate major accidents arising from Loss of Containment (LoC) events.
Leading indicators of health and safety failures phase one outputs
The Discovering Safety Programme’s leading indicator industry use case is looking to support organisations in the collection and analysis of health and safety lead indicators intelligence as part of drives to promote more proactive health and safety practice across their organisations.
Loss of containment insights workshop
We are running a workshop with representatives of the process industries and similar organisations.The purpose of this workshop is to share the project’s initial findings and secure industry feedback to direct future phases of the work.
Development of capacities to extract health and safety insights from free-text sources project
As recent developments in text mining, natural language processing and machine learning are becoming more mainstream, the task of generating data driven insights and learning from health and safety operational intelligence, particularly from
Leading indicators of health and safety failures project
Much industry guidance has been published over recent years, specific to different industry contexts, on how to better use leading indicators of health and safety performance to support assessments of the adequacy of workplace risk control m