A recent study pointed out that on an average 3.5 clinical encounters are recorded per patient per Year in United States and number of such encounters are equally same for employees when its comes to their annual medical examinations, vaccination programmes or even adhoc medical check-ups.
SAP Environmental Compliance 3.0 was historically used to manage the emission and environmental compliance (EC) requirements. However SAP recently released Environmental Management (EM) module as part of EHS management within ECC - Component Extensions and also as part of S4HANA releases. I happened to work very closely with Environmental Compliance 2.0 / 3.0 and have previously set up proof of concept system for Environmental Management as well.
Few days back many news agencies reported J&J recalled 33,000 bottles of its baby powder lot bearing the number 22318RB after health regulators found traces of asbestos. Its not uncommon that we hear such reports from different products which undergo recall to manage the risk and lawsuits. Usually such analysis of products when they are in supply chain is a critical requirement for many companies.
Recently Greenhouse Gas emissions account to major concern owning its beings a potential cause of Global warning. There are various sources / sinks for GHG emissions be it due to use of energy consumption or practices of agriculture methods or other anthropogenic sources.
It was almost 15 years since I worked on multi-objective Air Quality Monitoring site selection using Geographic Information System. At that time use of ArcGIS / Python was in nascent stage in ESRI product. We were using python as a scripting languages with use around some minor level of automation.
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.
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.
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.
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".
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 ?
Sometime back I wrote a blog on Use of AI / ML for managing Incident Management, it has received good response. I have received many requests to know in detail on how to implement the same and details surrounding it and some even requested me to share the source code. In this current blog and series of upcoming blogs I would like to share some short course on Introduction to Data Science and how it is used for managing the EHS issues. There are many relevant issues which can be solved using AI / ML, be it Descriptive Statistics, Predictive Analysis of incidents / exceptions for air permits or equipment based pollution deviations or even customer / supplier perception of the risks around the products being sold by the organization, automatic classification of dangerous goods based on past history, prediction of product compliance based on source and many more proprietary uses.