How AI enables dynamic risk management, transforming operational efficiency

 

The heavy industry sector is leveraging the potential of artificial intelligence (AI) and using it to implement smart, sustainable, and relevant change. In a recent report, ‘Smart operators: How leading companies use machine intelligence’, McKinsey found that more and more organizations are paying attention to machine learning (ML) and AI and that those who were experiencing measurable successes were those with solid leadership, a purpose-driven approach, and who partnered with the right companies to ensure that they got their implementations right.

 

This may sound obvious, but companies often forget the value of collaborating with experts to achieve measurable results with their AI. The reality is that skilled people within this industry are in short supply – hard to find and hard to retain. Collaborating with key players within the industry has become a proven methodology that allows the industry to invest in AI that will meet their expectations over both the long and the short term, without compromise.

 

The AI in only the manufacturing market is expected to be worth $13.96 billion by 2028 for good reason. This technology can optimize operations, manage mercurial variables such as the weather, and use data to form clear pictures of conditions and performance across multiple sites. Analytics can dig deep into the reams of information provided by workers and systems to find the hidden variables and provide decision-makers with real-time reports that can fundamentally change maintenance schedules, worker safety, and operational efficiency.

 

The applications of AI

 

With real-time data collection, AI-driven predictive and prescriptive analytics will provide the organization with dynamic safety routines and digital safety solutions that take multiple variables and conditions into account. With the right system implemented across sites and facilities, organizations can use the AI to identify and monitor specific predictive and proactive metrics. This can then provide teams and management with actionable insights that they can then use to improve decision-making and undertake continuous process improvements.

 

The latter cannot be understated. When a company can meticulously track its maintenance schedules and system functionality with a smart toolkit driven by AI and ML, it can use the data to predict failure and implement maintenance cycles that effectively bypass downtime and significantly mitigate risk. It will also allow management to streamline worker allocations around faulty or defective equipment, allowing for time to repair it without losing productivity or having too many workers on site. Just this one variable, balanced correctly, has a knock-on impact across workers, timelines, and costs.

 

AI insights also allow users to dynamically assess risks in real-time, more carefully allocating work assignments to ensure ongoing safety and efficiency. This is complemented by actionable insights gleaned from real-time data analytics, historical data, and company-wide curated metrics. Gaining this level of granular insight into the data can then be used in multiple ways:

 

• The organization can enable more robust and accurate benchmarking across multiple business units, facilities, and regions. This loops back to achieving continuous process improvements and transforming operations with consistent attention to detail.
• Leverage the insights provided in detailed reports to identify pain points and bottlenecks and drive improvements in asset and safety management. This data can also adapt operations strategies to ensure that they align with actual system capabilities and worker allocations.
• Use the data to create personalized business reports that allow different roles throughout the organization to visualize and fully understand facility status and functionality so they can make the right decisions based on tangible metrics.
• Develop a clearly defined risk model that includes the data across workers, locations, time of day, weather, tools, and materials.
• Actively prevent events that threaten worker lives or that could create production failures. With AI-driven predictive and prescriptive analytics, organizations can create a personalized safety routine that effectively guides the performance of people, tasks, and assets.

 

The intelligent partner

 

As McKinsey highlighted in its report above and in its in-depth analysis of AI entitled ‘Adopting a smart data mindset in a world of big data’, the success of any AI-driven project and solution lies in partnerships. In pulling on the expertise of trusted third-party service providers to ensure that the right tools are implemented at the right time and with the right strategic imperatives in mind.

 

The GOARC Safety 4.0 platform has been developed to provide organizations within the heavy industry sector with the tools and resources they need to transform operations and benefit from AI. The platform is as unique as the companies it serves, offering you the ability to process data from IT, the Internet of Things (IoT), devices, workers, and systems in real-time, applying AI to identify risk and recommend action. GOARC has helped reduce work accidents and increase safety engagement in workforces around the world, delivering measurable savings in costs alongside impressive reductions in work incidents and permit to work processes. Find out more about how this platform can change your operational future here.