Although the manufacturing process continues to improve, there is still concern among plant owners about quality control, peak production, and ensuring that factories are functioning at the best possible efficiency.
However, if not properly managed, the pressure of maintaining fast-paced production while optimizing costs can easily lead to safety problems. All it takes is a number of overlooked hazards coming together to cause an incident that results in injuries.
The US Bureau of Labor Statistics reports that this industry records more than 300 work-related fatalities every year. And out of about 12 million people employed in the industry, nearly 400,000 suffer a non-fatal injury annually.
But what if there was a reliable tool for tracking the prevailing conditions on the plant floor with the aim of predicting the possibility of injury? Well, that’s possible with IoT and predictive analytics. They allow us to improve manufacturing quality and anticipate a slew of different needs throughout the factory.
By forecasting what might happen in the future, predictive analytics offers users a remarkably reliable resource for planning organization-wide safety.
Below, is a look at few ways by which manufacturers can use predictive analytics for safety.
1) Analyze past data to predict injury-related trends
Predictive analytics are now being used to facilitate the switch from reactive to proactive safety management. It does this by giving manufacturers better transparency about their operations through smart software that controls predictive modeling functions.
By identifying the driving factors of plant floor near misses and incidents, these predictive modeling techniques empower manufacturers to sift what would otherwise have been overwhelming amounts of raw data and develop effective prevention strategies.
Important capabilities of the technology include:
- Track multiple leading indicators, also known as tracking factors, that provide insights into injury risks. These indicators are numerous but commonly include information on historical safety data, past and current employee data, costs of injuries, HR information on employee behavior, prevailing weather and environmental conditions such as heat or cold, presence of gases, etc.
- Provide a holistic view of risk for the entire organization via a user interface that provides easily understood and immediately actionable insights.
- Recommend follow-up actions for specific incidents.
- Be adaptable to specific industries and the unique risks that commonly lead to injuries in that sector. These risks are based on factors such as the work environment, type of work being done, and the hazards associated with such jobs.
The final outcome is a system that can predict and alert users to a highly reliable degree which employees are most likely to experience what types of injuries, the likely times and conditions under which these injuries will occur, the financial implications of these injuries and so on.
By acting promptly on these digital warnings, owners of manufacturing companies can create an environment that drives continuous safety improvement, improves employee morale, and reduces the psychological and financial costs of injuries.
2) Improve on traditional safety monitoring methods
The major shortcomings of traditional safety monitoring methods like root cause analyses of injuries, safety training and awareness programs, incident reports, and job safety assessments is that they are mainly human behavior-based and they recommend corrective action based off lagging indicators. In other words, they are reactive tactics that offer advice after the fact – when the injury has already occurred. This means that they cannot help pinpoint when the next incident will occur or whom it could affect.
Again, they can only answer very basic questions like what happened, when, how often has it happened, and similar. To move ahead and significantly reduce (or even eliminate) safety incidents, manufacturers need to now focus on why these incidents are happening and what could happen next.
This is where analytics improve on older safety monitoring and reporting techniques.
Predictive analytics offer manufacturers a more proactive solution by helping them identify the causal factors behind each type of incident. Instead of focusing almost entirely on the unsafe behavior of the staff in question, predictive analytics work with a broader focus on the working environment and other leading indicators we covered in the first section.
3) Improve asset reliability with Predictive Maintenance
No matter how well machines are maintained, with time components will wear. Quite often, replacing these components will require that the equipment is shut down for a while – leading to some unavoidable production losses. Usually, manufacturers work to minimize these losses through traditional maintenance strategies like preventive maintenance.
But those strategies still require that maintenance staff spend time on frequent inspections to detect faults. With predictive analytics, in the form of predictive maintenance, they can speed up the asset monitoring process with less human intervention by automating the inspection and analysis processes.
It is actually not too complicated to start a predictive maintenance program. Ideally, condition-monitoring sensors are installed on critical equipment with the aim of generating data about potential machine failure. The data is streamed directly into a CMMS to create alerts that users can make sense of. The alerts received will be for different conditions like torn belts, overheating parts, abnormal vibration, etc. Combining big data and predictive maintenance with a CMMS is actually an excellent way to ensure that machines are operating at peak condition.
Maintenance staff can then utilize this information displayed on the software’s interface to conveniently plan the necessary repairs without disrupting production.
In addition, because the machines are better maintained, the risks of machine-related injuries are significantly reduced. Both for those operating the equipment daily and those maintaining them.
Considering the multiple effects of worker’s injuries, manufacturers need to adopt the best tools available today to get themselves closer to zero safety incidents. One way they can achieve this is by exploiting the power of predictive analytics for their safety management.