S4/HANA 1909 EHS Implementation scenarios – A brief overview

SAP Software technologically has been changed over the years and currently it is available with version S/4 HANA 1909. Each version differs from the previous version in terms of big shift in the technology and functionality and user experience. There are majorly two different versions available for EHS. The modules like Incident Management, Risk Assessment, Environmental Management, Management of Change Products and Product Compliance modules comes under the SAP LoB or S/4 HANA Core modules. The other modules like SAP EHS Product Safety, GLM, SVT and Dangerous Goods Mgmt and Product Compliance – DI falls under the compatibility version of S/4 HANA 1909 Release. Compatibility versions are the ones which are continued to ensure business continuity and SAP will eventually may/ may not move the functionalities into core.

There are various options available when it comes to implementing the SAP EHS solution to S/4 HANA, that migrating the current ECC ERP version to S/4 HANA. Be it called side car scenario, System conversion, Landscape Transformation or even Green Field implementation. In this blog I would like to cover few basics around SAP S/4 HANA system conversion and how you can convert or implement EHS system for your client.

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Comparative assessment of S4HANA 1909 Environmental Management with SAP Environmental Compliance 3.0

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.

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Meeting Chemical Compliance using SAP S4HANA 1909 – Product Compliance – A brief System Overview

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.

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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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Conversational BOTS for Emergency Hazard Communication – SDS Section 1 Helpline!

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.

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Deep Learning / Machine Learning for Dangerous Goods Management

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.

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Digital Transformation of EHS – Not a very Short Video

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".

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