Managing Safety Data Sheets (SDS) is a critical process for organizations across many industries. Keeping up with an ever-growing number of SDS documents can be a difficult and costly task, especially as regulations and requirements change. Fortunately, leveraging Artificial Intelligence (AI) to automate the parsing of SDS data is a viable option for many companies. This technology has the potential to save organizations time and resources, as well as reduce the risk of compliance violations. However, there are also potential challenges associated with AI-driven automation of SDS parsing, such as data accuracy, privacy concerns, and cost. This article will explore the potential benefits and challenges of leveraging AI to automate SDS parsing.

What is Safety Data Sheet (SDS) Parsing?

Safety Data Sheets (SDS) are required for all types of chemicals and many types of hazardous equipment. They contain information about the health and safety hazards associated with the chemical(s) or equipment, the recommended methods of protection, and emergency procedures. SDS documents are created with standardized formats and information fields. They can be purchased from various sources, such as chemical manufacturers, distributors, or safety supply companies. They are also available online through several resources, such as the OSHA online database. Many regulatory agencies require that SDS documents be kept on-site at the workplace. They may also be required to be kept at a central or off-site location. It is important to be aware of the regulations in your industry and region to ensure you are compliant.

Benefits of Leveraging AI to Automate SDS Parsing

Organizations can use AI to automate the process of SDS data parsing, extracting relevant information from the document to populate data fields in an SDS management system. This process can save significant time and resources. There are likely to be fewer accuracy issues and fewer human-factor issues associated with manual data entry. SDS management systems are often integrated with enterprise business systems. This integration can help organizations leverage data from various data sources to improve risk management. It can also help identify trends and potential risks and provide insights into ways to mitigate those risks.

Challenges of Leveraging AI to Automate SDS Parsing

One challenge of leveraging AI to automate the SDS parsing process is that the data fields are standardized, but the data itself is not. When the information provided in the SDS documents is inconsistent across all documents, it can be difficult for an AI system to apply consistent parsing rules. Another challenge associated with leveraging AI to automate SDS parsing is that some of the information in an SDS document is subjective. For example, an SDS document’s “FIRST AID” section might include a recommendation such as “Call a doctor or poison control center immediately.” How should a computer system deal with such a subjective recommendation? It would be difficult for the computer system to determine whether such a recommendation is urgent or can be put off until later.

What is Artificial Intelligence (AI)?

Artificial Intelligence is the technology that makes it possible for computer systems to learn and adapt. It enables computers to “understand” information like humans do, allowing them to draw insights from data that were not programmed into the system. Typically, organizations implement AI systems to handle repetitive or rule-based tasks, such as SDS data parsing. Instead of a person having to perform a task manually, an AI system can be programmed to do that task. For example, an AI system can be programmed to look at an SDS document and determine the relevant information to extract. It can then be programmed to enter that data into an SDS management system. The process can be automated.

AI-driven Automation of SDS Parsing: The Advantages

There are several advantages associated with AI-driven automation of SDS parsing. One advantage is that the process can be highly scalable. If an SDS management system is designed to accept large volumes of data, it can handle that data efficiently. It can also be programmed to be flexible so that if there are issues with data fields not populating correctly, it can be programmed to retry those fields. Another advantage of AI-driven automation of SDS parsing is that the process can be highly accurate. It can incorporate “best practices” standards for SDS data entry and be designed to minimize data entry errors.

AI-driven Automation of SDS Parsing: The Disadvantages

There are also several challenges associated with AI-driven automation of SDS parsing. One challenge is that it requires that the system be properly trained. This can add time and cost to the implementation process and require ongoing maintenance. It can also be challenging to identify and fix issues related to the system’s training. Another challenge of AI-driven automation of SDS parsing is that it may only be suitable for some organizations. Companies that work with an extremely large number of SDS documents may find that it is not feasible to enter the data from these documents manually. However, organizations that use smaller numbers of SDS documents may not be an issue.

What is Required for AI-driven Automation of SDS Parsing?

To leverage AI-driven automation of SDS parsing, it is important to have access to a source of SDS data. You can purchase SDS documents from a variety of sources, or you can source them from the SDS documents that are required on all chemical containers. Alternatively, you can use the OSHA online database to download SDS documents. It is also important to have an SDS management system that integrates with business systems. This will enable you to leverage the data from these documents for risk management and other business processes. It will also make it easier to enter the data into the SDS management system from the SDS data source.

How to Get Started with AI-driven Automation of SDS Parsing?

The first step to getting started with AI-driven automation of SDS parsing is to determine what data you want to incorporate into your SDS management system. You will then need to identify your organization’s best SDS data source(s). Once the SDS data source(s) have been identified, you will need to select an SDS management system that allows you to connect to these data sources. You will also need to select the business systems you want to integrate with your SDS management system. With this information, you can get started with AI-driven automation of SDS parsing.

Conclusion

Safety Data Sheets are required for many chemicals and hazardous equipment. Organizations can use Artificial Intelligence to automate the SDS data parsing process, extracting relevant data from the SDS document to populate data fields in an SDS management system. This process can save significant time and resources and provide insights into ways to mitigate potential risks. However, there are challenges associated with leveraging AI to automate SDS parsing, such as data consistency and accuracy and training and maintenance. The D2Platform from www.deepdataum.ai is trained to extract data from Safety Data Sheets. To know more, please email us at ask.deepdatum.ai