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.
Safety Data Sheet suppliers are usually required to provide emergency telephone number in section 1 of the SDSs. Some regulations even recommend having 24/7 local emergency telephone numbers. OSHA clearly states that the telephone number should be provided for person / entity who is either knowledgeable of the hazardous material being shipped and who has comprehensive emergency response and incident mitigation information for that material. However, companies are facing issues with availability of knowledgeable persons / staff who can be available for 24/7 to carry out such tasks. There are many instances where companies rely upon 3rd party service providers, whereas EU member states use official advisory bodies. Such advisory bodies do not exist for US other countries.
Dangerous Goods classification is one of the critical activities for any organization to maintain compliance with regulatory requirements and also ensuring not to harm persons and environment. The classification of Dangerous goods is a skill which is getting scarce in organizations due to various reasons like availability of SME’s and knowledge gaps etc. Use of Machine Learning and Deep Learning for Dangerous Goods management offers a very good opportunity to compliment the resources who are already working on the DG classification. Based on DG based preliminary classifications a model can be trained over a period of time to provide you the information as accurately as possible.
It started some two years back when I started exploring Data Science (Python, R, Azure ML & Knime) to understand the new possibilities for EHS, which I slowly expanded by refreshing my IoT skills, Big Data (Hortonworks / HDInsight) & Cloud skills (AWS & Azure) and Blockchain skills. Then I started exploring various Digital Transformation books like The Digital Transformation Play book by David L Rogers and Leading Digital by HBR. After spending some two years on this topic today I am finally publishing a Not A very short video on “Digital Transformation of EHS”. There is immense possibilities but the challenge lies in how efficiently the organizations embarks into EHS Digital Journey without impacting their existing processes . A solid core that bring efficiency through standardization along quick adjustments on its structure like the ones recommended by Gartner in year 2014 – The BiModal transformation model ensuring stability and agility. Below is a video of – Not a very short presentation on Digital Transformation of EHS. Do take time to revisit at small intervals so that you don’t get overwhelmed with my talk. All most all the topics dealt in the presentation are covered as a separate blog posts – do visit the site if you are seeing for a detailed use case around those topics.
Recently a friend of mine told me that their organization is embarking on to the journey of digital transformation. I said wow, what exactly it is doing for which his response was that their implementation of portal system for logging records. Does it quality to be called a digital transformation ? is it so ? Actually, for many, digital transformation is digitization of their processes which can be a mere implementation of IT tools replacing their legacy modes of operation. Indeed digitization does not advocate replacement of legacy applications with new age tools. Rather it advocates tight integration of organization competitive tools which have evolved over years with new age technologies to stream line the user experience and operational excellence.