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by Jukka Kostiainen, Value Architect at Nortal

Five steps to revolutionize Occupational Health and Safety in your factory with AI

Prioritizing occupational health and safety (OHS) is crucial in the industrial landscape. AI presents a modern solution to revolutionize OHS practices and improve workplace safety. In this article, we present five key steps to harness AIs potential and enhance safety protocols in manufacturing. 

As the decision-maker and the responsible lead for your factory’s Occupational Health and Safety (OHS), you know your numbers: for a single tragic accidental death in the workplace, there are 30 severe injuries, 300 injuries with less serious consequences, and a whopping 3,000 near-miss situations. During your tenure, you would want to avoid anything – anything at all – from happening to your employees. The thought alone of someone getting severely injured or dying would be devastating. 

This is where the latest AI innovations come in. AI presents a transformative opportunity to revolutionize OHS practices and enhance workplace safety. Through data collection and management, deployment of modern AI tools, and the right cultural mindset, your organization can leverage technology to predict, prevent, and ultimately protect your workforce, making a life-changing difference in your factory. Next, I will introduce the five practical steps to harness AI’s potential and transform your OHS practices. 

#1. Optimize your data collection processes 

In manufacturing and forest industries, OHS organizations work hard towards continuous improvement and preventing accidents from happening in factories. In this era of data and analysis, many new ways have emerged to predict misfortunes and identify harmful patterns. However, one of the biggest challenges is the manual collection of safety-related data. It is a time-consuming and tedious process, often involving Excel sheets and forms, both digital and on paper. Even organizations that are generally advanced in digitalization and have efficient tools for collaboration and teamwork may experience collecting safety-related data, which consumes extensive amounts of time, both on the shop floor and at the headquarters.  

To address this challenge, the OHS data projects’ initial focus is to refine data collection processes. This step is critical to ensure that experts’ time is utilized efficiently and that redundant tasks are minimized. One way to do this is by leveraging Microsoft Power Platform’s low-code tools, through which organizations can swiftly develop mobile apps that streamline reporting and data submission, simplifying operations across all levels of the organization. It enables getting actionable insights on data without massive projects, steep investments, or writing custom applications whose maintenance and updates might prove problematic. If – and when – optimizing your data collection processes in your manufacturing environment was easy and fast, why wouldn’t you do it?

#2. Optimize your data management  

In addition to refining data collection methods, the efforts should often extend to a comprehensive overhaul of the entire data infrastructure and collection. This includes redefining the types of data collected and the methods employed, exploring possibilities for spatial data integration, and devising strategies for merging data from diverse sources. The intricacies of report generation should be delved into, ensuring that insights are gleaned and effectively communicated through visualizations that offer clarity and depth. 

Moreover, implementing actionable measures should be strategized based on these insights, such as targeted safety campaigns aimed at areas identified as vulnerable to security breaches or incidents. The overarching goal is to achieve a harmonized approach to data collection, formatting, and storage across the organization. By consolidating these elements under a unified management scheme, the barriers that isolate safety as a standalone silo can be braked. This integration facilitates a holistic analysis of OHS data, allowing us to uncover new perspectives on factors such as quality, production, and HR practices that influence workplace safety outcomes. 

#3. Ride the AI wave on analysis 

Once your factory’s gathering, storing, and categorizing of data are appropriately planned, the deployment of modern AI tools becomes feasible. Through such tools, we have assisted our customers in identifying trends and patterns that might otherwise remain unnoticed. For instance, we’ve uncovered near-miss situations often associated with under-resourcing, heavy overtime burdens, or exceptional factory maintenance work. This proactive approach can effectively prevent accidents and injuries before they occur. 

For instance, many of our global manufacturing customers are keen to enhance their comprehension of near-misses and risk levels within their factories. Collaborating with us, they’ve developed, for example, a mobile application for their health and safety professionals and the entire organization, operating at both international and factory levels. This application can harness AI capabilities to discern trends and patterns in the data. The insights gleaned can then inform the development of highly targeted safety campaigns. For instance, a “thumbs campaign” could be launched in regions with recorded similar thumb-related incidents. This campaign would address the specific risks associated with thumb-related injuries and offer actionable advice on prevention. 

Rather than focusing solely on investigating isolated incidents across numerous global factories, the daily work of a global OHS team should prioritize gaining a comprehensive understanding of overarching trends and patterns. While the data should still facilitate the examination of specific near-misses, the emphasis should be on leveraging analytics to assess the broader picture when necessary. This enables the team to determine whether launching preventive campaigns organization-wide is warranted, potentially safeguarding numerous individuals from harm. 

An additional valuable application of AI in analysis involves analyzing how different parts of the organization complete their near-miss forms. Relying solely on the low or mid-range of the severity scale may indicate a culture of secrecy regarding safety issues. Identifying such patterns within vast data would typically require months of manual effort. However, a well-trained AI model can swiftly detect these patterns in seconds. 

The concept of a digital twin in industrial settings holds significant promise for enhancing safety protocols, among other applications. By creating an exact digital replica of a specific factory, area, or production line, near-misses or accidents can be spatially recorded with pinpoint accuracy. This eliminates any possibility of misunderstanding and is a powerful tool for mitigating risks. Digital twins exemplify a standardized approach to data modeling, minimizing the potential for interpretation errors or human oversight. 

#4. Let generative AI speak for itself 

The latest advancement in leveraging AI for industrial OHS lies in generative AI technologies. These technologies enable humans to interact with safety and health data, among other insights, in intuitive and familiar ways, such as through common language text, speech, or visual images. 

An illustrative example applicable to many of our customers is the implementation of an OHS chatbot. This chatbot enables users to engage via text, seamlessly responding to ad hoc queries, such as comparing production data to safety abnormalities. Through our experimentation with generative AI chatbots, we have observed significant benefits, including the ability to overcome language barriers that often impede OHS collaboration and may lead to misunderstandings and risky situations in international organizations. 

#5. Mind the culture  always 

The potential of AI to revolutionize Occupational Health and Safety practices cannot be overstated. By leveraging AI to collect and analyze safety data, organizations can identify trends, patterns, and hazardous workflows that might go unnoticed. Moreover, AI enables a proactive approach to safety by predicting and preventing accidents before they occur rather than reacting after the fact. The utilization of generative AI further enhances these efforts by overcoming language barriers and accommodating varying safety habits and working methods. 

While the steps and examples presented may suggest a straightforward path to a safer working environment, it’s essential to recognize that even the most advanced AI-powered reporting cannot inherently alter behavior on the shop floor. Culture remains paramount, as it always has. Enhancing safety culture involves making safety data visible to employees on the factory floor and ensuring that reports, applications, chatbots, and insights are accessible in the right locations at the right times. However, genuine change in underlying beliefs and routines will only occur when people’s mindsets shift. 

Hence, individuals always have the ultimate responsibility for fostering a culture of health and safety. While technology serves as a powerful tool, enabling individuals to allocate their time toward impactful preventive measures rather than dwelling on past incidents, it is not the sole solution. Long-term improvements in workplace safety hinge upon nurturing a safety culture where employees authentically prioritize their well-being and that of their colleagues daily. 

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