Material Safety datasheet is generally provided whenever a material is purchased. Many hazard communication regulations require provision of SDS document while shipping the materials. The use of SDS can be for different purposes like source of information for hazards, gathering safety precautions statements, worker protection and training, labelling or relabeling purposes or even information for emergency responders, poison and transportation companies. For authoring the SDS many software systems skilled resources are needed. At times organizations use sub-contracting the materials and use the supplied SDS for shipping to customers. It is also not uncommon that organizations engaging 3rd party organizations for authoring purposes. Whenever a organization receives safety datasheet for example a pharmaceutical agency it needs to check if the safety data sheet is compliant for usage. When a chemical is used for pharmaceutical purposes its dust potential and particulate size characteristics besides other data is pre-requisite for usage. As more and more stringent laws and regulations are being introduced to manage chemicals like EU SCIP, or revised EU SDS legislations or USA, Canada or even country specific chemical management and guidance laws, it needs validation of SDS.

There are many guidance documents available from different groups for carrying out safety datasheet compliance checks. Such analysis of safety datasheet compliance checks is different for each type of business based on the purpose for which a chemical is used like in earlier example particulate size and dust potential for pharmaceutical sector. However, there are some general compliance checks are available to check an SDS and organizations needs to manually analyse such information before using a chemical. There is a huge demand for IT Tools which can be used for assessment of safety datasheet for many organizations. In this blog I would like to briefly give an overview of Safety Datasheet compliance check process and different IT tools which can be used for managing such requirement.

Based on the guidance documents published a safety datasheet needs to be checked for each section to ensure it is compliant. For example, for section 1 most common noncompliance include missing emergency phone numbers, appropriate identifiers like missing CAS numbers and even labels are not matched with safety datasheet. Similarly, for section 2 we usually witness missing or incomplete hazard classification, missing symbols, disclosing both danger and warning and other like missing precautionary statements or their repetition which at times do not communicate intended meaning. Some other examples of noncompliance include unit of composition, common names, or synonyms of components besides concentration ranges etc. like that for each section we can elaborate information which is needed to be checked. The significant of missing or non-compliant data can be easily understood if you try to use this information for transportation purposes or even Exposure controls section. It is not intended to state that missing some sections data is okay for usage, rather it needs to compliant for each sections data and it depends on use of the chemical as well.

To carry out such assessment organizations usually house experts whose activity is to retrieve the safety data sheet and analyze for each section data. Different tools like manual extraction and checking of information is used for such analysis.  However, when IT tools are developed for such assessment it would streamline the activity and reduce compliance risk and save time and effort. There are various ways by which such analysis can be performed using IT tools. Some of the tools include i. Use of Automation tools like Robotic Process Automation ii. Document extraction and analysis using various 3rd party tools or iii. Converting the document to suitable format and carrying out detailed inbuilt checks iv. Use of python and data science for carrying out the analysis and v. Extending the ERP systems to automatically process SDS documents and Extract the data. The advantage of using the tools is it would streamline the activity. Once the data is extracted and use various analytics can be applied for carrying out additional data generation activities like automatic extraction and creation of labels, extraction of key information and presentation as dashboards for easy visualization. Other significant use of such tools is automatic generation of safety operation protocols or SOP documents by using various Machine Learning or Artificial Intelligence techniques.  Standard text can be easily generated for use for each safety datasheet. When ERP systems like Chemical Management scenarios extended with SDS data extraction and loading to database it can help organizations implementing various compliance checks and automatic generation of SOP based on the loaded data. Usually organizations spend huge amount of time in creating SOP documents for each language translation. When automatic extraction and authoring is done for SOP it would reduce a lot of time.   

Some of the tools are already available in commercial space and few are reported in public domains. I happened to work with such tools for GHS Hazard Calculator and Vendor SDS extractor tools using Machine Learning algorithms. A python-based tool for GHS hazard data extractor from government agencies is available which outputs data as CSV data for easy analysis. Such systems can also be used for chemical hazard assessment and toxicity prediction. To conclude details about each process and how-to carryout each activity is proprietary for each organization and start engaging your system integrators for such tools development or even Software companies, standardization of such tools using ERP systems coupled with scanning or standard pdf import will help organizations a lot. When such systems are custom developed and streamlined it would be easier for organizations to mass process the safety data sheets to create data lakes and apply validations rules for safety data sheet compliance and run machine learning algorithms to calculate toxicity or create cross section reports for internal validations and risk assessment or other product safety stewardship uses of chemical data.


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