SAP EHSM Incident Management

A Data Science Approach for effective Management of Process Safety Incidents

As mentioned in the CCPS Process Safety Guide, an essential element of any improvement program is measure of existing and future performance. The proper analysis of such measures like Leading and Lagging metrices is critical for successful process safety management. Generally, a safety pyramid consists of mix of three types of metrices like Lagging metrices – which are retrospective set of measures, Leading metrices – which are forward looking metrices and finally Near-miss which are less severe incidents, which however are very good indicators for future likelihood.

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A deep Learning approach for automatic incident detection, classification and notification system

Incident / Accident Management is requirement for many guidelines and regulations and needed to be properly managed within an organization. If an incident is not properly managed it results in loss of information, chances for incident avoidance in future and monetary challenges. Deep Learning Techniques like Tensorflow / Keras along with OpenCV and other libraries can be used to automatically manage incidents / accidents within an organization.

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Operational Risk Management using SAP Predictive Analytics and SAP EHSM Incident Management

Moving away from paper-based tracking system to automated way of managing it via ERP software is one of the major achievement for many organizations. Mere using the technology to capture the incident information is a normal business as usual practice, effectively harnessing the technology to support risk management is the greatest opportunity for most organizations. Risk Management not only enable the performance but also protect the business. It is found that majority of drivers for operational risk management are technological in nature like Industry 4.0 principles, competitive marketspace and cost-effective data automation.

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