Modern problems need modern solutions and data being the most sought-after commodity, implies that its use and modelling can deliver better solutions for industries today.
Let us put ‘risk’ into our crosshairs and aim to meet the target. The target of mitigation, management and even better prediction. Operational risk within EHS or safety domain has largely focussed on the systemic management – say, permit to work software with lockout-tagout riding on top. Apart from these tools, safety barrier management and safety integrity level assessments offer glimpse of resilience and longevity.
Yet, there is an inherent hole within the dykes of EHS management – they are built on top of each other, which means interconnectedness and therefore, the failure of one affects the other, so on and so forth.
Co-dependence and interdependence of systems is a fundamental element of EHS ecosystem.
Risk modelling approaches have extensively determined the success of modern fintech enterprises. The way fund management and banks work has been largely dependant on algorithms and complex formulas which integrate fundamental aspects of finances – layer them within the desirable outcomes and then multiply assets while accounting for liabilities (penny plain).
EHS software can offer plethora of insights within 12 months of its full-scale deployment and operations. The proposed idea is to look at this full-scale deployment with data capture and responsive beacons.
These intelligent ‘sinks’ of data are like logic gates which ‘sync’ with a central processing logic whenever they spot deviation from norm. These baseline norms will come pre-defined (or for more investment can be based on machine learning outcomes thus ‘intelligent’) set of actions. Such actionable intelligence can then be monitored and acted upon by the safety team.
Complex and very largescale facilities – chemical, oil and gas, pharmaceutical and automobile industries will find ready acceptability of such a logic driven intelligent EHS software. These industries already focus on the data insights not only for the purposes of data collection and compliance but to ensure thorough safety management.
Modern EHS software create plenty of safety beacons within their processes. However, such markers aren’t generally available to the average user of even the safety team, sometimes.
This trend can be suitably changed, improvised and adapted for your purposes by a bespoke EHS software provider.
Predictive analytics will prove to be the gamechanger in the next decade. But understanding such data will require knowledge engine that can function with little human support.
The proposed knowledge ‘sinks’ of data are like the catchment areas of a river. They can manage excess or erroneous data flows and help to generate better fault analysis. Similarly, under normal data flow, they can deliver and sustain a data driven EHS prediction engine, which is efficient, and cloud deployed.
Purpose has a large role to play within the set objectives of EHS software. Such a data capture and analysis module can derive glorious benefits from our current machine learning and touted AI strategies.
The key here being organization readiness and willingness which stems from the management behaviour and decision makers.
Image Courtesy – NASA/DOE/Fermi LAT Collaboration