The above chart shows how cumulative risk increases with increase in individual risk scores of barrier KPI’s. Logically, it means that more than one functional area has become risky due to complacence, or process inefficiency, and combination of inefficiencies could result in a much larger issue such as catastrophic event, or full process shutdown. Management action can focus on defusing the situation based on data collected.
3.3 Bayesian Belief Networks
Further to the above, a truly predictive risk management solution can be built based on its capability to alert management on operations or areas, where Black Swan risk levels are beyond reasonable levels.
The merit of a next generation system would need to come from the ability to precisely calculate probability of risk propagation within connected risks, using available data. For example, within process industries, enough experience is there to get data on how long and whether a pipe corrosion due to salty crude oil input would lead to a leak in that pipe. If such data is available, calculation of probability of risk of accident or an LOPC incident could be calculated.
Current Bayesian Belief models are based on expert opinion, and also provide fixed estimates of risks. If these models are connected to real data, estimates on these risks can be continuously re-evaluated, making risk management more proactive and predictive.
4.0 Insights Generated from Such Analysis
Risk management systems such as these can help HSE risk managers manage their risk portfolio, and control risks within a reasonable boundary.
4.1 It can help identify whether two indicators- logically related or not - are statistically correlated in operational areas. For e.g. In a particular process there might be very little positive correlation between preventive maintenance not done on time and corrective maintenance orders generated even though it is logically related. This can help managers to identify weak spots in the process.
4.2 It can help identify whether the effort put in managing a particular KPI is actually showing results. This can be achieved if a negative correlation is seen between a leading indicator which shows performance improvements and a lagging indicator like no. of injuries. E.g. If no. of injuries is falling with an increase in number of risk assessments then anegative correlation is seen. Ideal performance evaluation criteria would be to pursue a correlation closer to a score of -1
4.3 It can help identify whether there was unusual or extraordinary event within the business. Data will show disruptions in correlation, if there is a sudden increase or decrease -well beyond those routinely recorded- in the value of a given variable/indicator.
4.4 It can help identify accumulation of risk within a process in multiple areas, which can result in a larger catastrophic event. Measurement of cumulative value shows sudden or gradual deterioration of plant compliance to process, and gives sufficient lead time to management to act and defuse the situation.
4.5 It can provide internal or external benchmarks of safety performance to a given operation. Over a period of time, once sufficient data is gathered, the performance of a process in each area can be benchmarked with peers, and processes can be improved through internal exchange of best practices
5.0 Real-Life Use-cases for EHS Safety Monitoring
In many real life catastrophic incidents, a risk measurement system would be an ideal solution to the problem. In most incidents and accidents across process industries such as oil & gas and mining, a set of patterns are visible. The patterns are summarized here, but do not represent an exhaustive list.
Typical HAZOP/HAZID studies should capture these scenarios. So, any project that sets up leading variable risk studies can use such a repository to setup a leading risk performance system.
- Key alarm and alert system not in working condition
- Critical to operation personnel going on leave or being absent from work, resulting in operations being left to inexperienced hands
- Overriding fault conditions during operation of plant, to avoid stoppages
- Outdated equipment with lack of modern alert or fault indication systems
- More than a critical mass of personnel are either temporary staff, or new to the plant
- Shift handover done orally, and not taken seriously.
- Personnel with repeated history of poor judgment or performance at work
- External factors such as weather, visibility
In our experimental setup, an analytical model was setup to create a series of leading indicator KPIs in Asset risk, Occupational Health and Safety, Competence-led risk, and an aggregate risk scenario model was created to show aggregation of risk within a process boundary. Using such a system, it is possible to manage operational risk of process industries like refining, oil extraction, and natural resource mining industries. Some of the use cases of risk management in process industries would be
- Making sure workers absent from work do not affect process operations
- Making sure critical process equipment is in working state
- Making sure employees are not impacted due to exposure of organic chemicals, heat, dust and chemical vapors
- Making sure that data collected through all IT systems like Incident management, Sensor systems, and SCADA systems is put to good use in generating insights on a regular basis
Using the above approach enterprises can roll out an effective, future proof health and safety risk management system, and benefit from the savings in asset maintenance, workers compensation premiums reduction, better employee morale, and reduced losses due to work stoppages and productivity losses.
- International Association of Oil & Gas Producers, Safety performance Indicators 2009, 2010, 2011: Journal Report No. 439, 455, 2011s
- Workplace safety & Insurance Board and Canadian Manufacturers & Exporters, Ontario Division “Business results through Health and Safety”
- International Association of Oil & gas Producers, “Asset Integrity: The key to managing major incident risks”, Report No. 415
- International Association of Oil & gas Producers, “Cognitive issues associated with process safety and environmental indicators”, Report No. 460
- International Association of Oil & gas Producers, “Process safety: Recommended Practice on key Performance Indicators “, Report No. 456
- Martin Sedgwick & Steven Stewart “Experience with developing Process Safety KPIs within Scottish Power” 2010
- American Bureau of Shipping “Safety culture and leading indicators of safety”, 2012