Using downtime data to reduce risks in manufacturing

In the big data and AI era, the endless streams of manufacturing data across workflow, heat, pressure, vibration, supply, tolerance and other metrics can help identify existing weaknesses and future problems. Their relation to plant, machinery, human input and the combined processes make for a powerful insight into factory and individual health. 

In health and safety terms, these insights reduce or eliminate the risk of a major failure, plus the injuries, reputational damage and delays in restarting that they presage. 

Driving health and safety improvements in the AI era

With the wider deployment of the Internet of Things and Factory/Industry 4.0 best practices, factories now measure every step of each process, refining productivity, driving operational equipment effectiveness (OEE) and limiting downtime. 

Stepping into the AI era, downtime tracking software can play a greater role in predicting health and safety issues, predictive maintenance and other issues across the factory floor. Generating insights with a higher degree of certainty and using multiple data sources to link complex factories and wider issues to root causes.

Using this data for health and safety sees engineers consider the wider ramifications of what they might think of as a technical issue. But one that could impact or injure workers if not properly addressed. It also ensures spare parts, replacements or upgrades are delivered in time before there is a major issue that could halt production. 

Teams, from managers down to shopfloor workers, can consider how unsafe, unsanctioned or out-of-order practices and patterns can create repeated stoppages or incidents. The data can also reveal previously hidden IT, technical and operational failures that lurk within the system, creating unforeseen problems or blind spots in how and what is measured. 

By understanding the data through downtime software and its analysis, manufacturers can improve productivity through reduced downtime and improve the culture of safety through transparent reporting. And companies that use digital twins to map and plan their factories and processes can see how the real-world version varies from expected norms and help identify reasons why that may occur. 

Creating a transparent safety culture

Using data to drive transparency into safety creates a system that removes the traditional blame game and focuses on outcomes. When downtime and its causes are clearly logged, they can highlight what is really happening, and those involved can demonstrate better, safer or refined approaches to deliver improvements while boosting safety. 

Additional evidence can come from smart cameras covering the factory floor to provide evidence as to what is happening, helping teach workers what they should be doing. They can also highlight health and safety failures, such as a lack of PPE or unsafe actions around robotic installations, delivery robots and conveyors. 

When workers, be they temporary, contract or FTEs, see the safety focus is based on outcomes, they are more likely to report early issues, near misses and the attitudes of others to help make the overall manufacturing system safer and stronger. Similarly, when they see how the OEE data reflects their actions, they are more likely to operate systems and perform processes in an optimal manner for productivity. 

As operator technology improves, from augmented reality and wearable devices to smart gloves and supporting cooperative robots, the live data can be shown in situ. That demonstrates how any changes can impact the production process or impact manufacturing schedules. 

Into the future of data and factory safety 

Finally, as factories become more robotic, every element of data will be shared across each system, creating a digital heartbeat that managers can track. For the remaining human workers, their biometric measurements can report mood, stress and other indicators as part of the overall system, helping notify managers why workers may be underperforming or acting in a distracted way. 

Where there is a need for human workers, temporarily replacing a tired or stressed operative can save the factory from a very unfortunate incident and the worker from potentially serious injury. And, as long as the company follows regulations on privacy of data, data geolocation, plus market, industry and national legislation, data can continue to provide a bounty of insight for leaders and operators.