5G is revolutionizing the wireless technology by providing more adjusted connectivity to concrete needs. A more efficient network, higher speed, greater capacity, and lower latency are creating unprecedented opportunities for 5G solutions in the industrial setting, enabling the development of more efficient and flexible activities with the help of solutions, such as real-time AI analytics.

Pairing real-time AI analytics with physical world provides competitive advantage that not only can reduce operational costs, but also increase efficiency and the safety of operations in industrial setting.

Let’s take a closer look at the three main ways real-time AI can be used in industrial operations.

1. Automated Maintenance Audit

Industrial activities are exposed to harsh operational environments producing large amounts of extreme heat and dust. Maintaining the performance of the machinery plays a crucial role in the operations. According to survey reports in paper and pulp mill industry conducted by ABB, 60 to 80% of all equipment malfunctions on quality control systems are caused due to incorrect maintenance or lack thereof. 

Equipment malfunctions lead to unplanned downtime, resulting in high costs; replacement of parts, repair, and restoring the plant - let alone profit losses on lost production. Every year, Fortune Global 500 manufacturing and industrial firms lose approximately 3.3 million hours of production time to machine failure, and almost $1 trillion through unplanned downtime. This amounts to an economic impact of $864 billion or 8% of their annual revenues.

Therefore, preventive maintenance strategy is the most optimal solution for avoiding unplanned downtime. How it can be done with real-time AI analytics, is to provide automatic real-time auditing of the maintenance schedule and routine inspections. The system can identify workers in certain areas as numeric objects in one-minute intervals, including location and time, providing valuable data on confirmed maintenance procedures, and enabling easier way to provide analytics on the matter.

Why You are Missing Out

Turns out, lack of preventive maintenance is a cross-industry issue: preventive maintenance is old but often overlooked procedure because of new emerging technologies introducing countless alternatives for preventive manual measures. In the field of gas and oil, fewer than 24% of operators describe their maintenance approach as a predictive one based on data and analytics, according to a study conducted by GE. However, keeping up with the preventive maintenance strategies by real-time AI monitoring, the processes can be confirmed and audited with higher precision, helping to prevent unexpected downtime.

Operators that are currently using a predictive, data-based approach experience 36% less unplanned downtime compared to those with a reactive approach.

According to the same study by GE, the operators that are currently using a predictive, data-based approach experience 36% less unplanned downtime compared to those with a reactive approach. This can result in, on average, $17 million dropping to the bottom line annually.

2. Optimizing Day-to-Day Operations

Even today, a lot of the planning and preparation is still done manually for the daily operations in industrial setting, for instance, in the pulp and paper mills. This results in low levels of efficiencies in the management of operations. Coordinating processes into larger entities takes time and resources. Using real-time AI analytics for monitoring the processes provides a more holistic view through of the site-wide operations and helps to make decisions based on predictions collected from long-term data.

On the other hand, taking advantage of the real-time monitoring can help maintaining the manufacturing processes in an optimal level; by setting up real-time alerts for large deviations we can keep a high-level utilisation rate. The system can identify workers in certain areas as numeric objects in one-minute intervals, including location and time, which is a convenient tool for monitoring certain areas and the people or vehicle flow during the processes. Additionally we can create alerts when people are entering hazardous zones,  or alarming when certain areas are occupied for longer than expected.

Why You are Missing Out

Adopting real-time AI across manufacturing processes both internally and externally is key to staying abreast with your competitors, who will be optimizing every aspect of their organization’s workflows using new technologies like these.

Using both real-time alerts and long-term data we are able to optimize the workflow in day-to-day operations and cut down costs by closing the data gaps between processes. This helps to streamline the operations across the entire production line.

3. Worker Safety

Manufacturing plants predispose workers to hazardous conditions, and lack of using personal protective equipment (PPE) can lead to severe accidents for workers. Globally, most common accidents include various trips, falls, and incidents related to manual material handling. In Finland, the cost of individual occupational accident is 6,000 euros on average, however, the cost will multiply if the accidents lead to an accident pension.

According to the Finnish Worker’s Compensation Center, a total of 13,581 occupational accidents occurred in the industrial field in 2021, with most of the related to manual material handling. However, alarmingly, around 20% of all occupational accidents in industrial setting in Finland are head related injuries.

Preventive measures at work site, including personal protective equipment, are a necessity for worker safety. With PPE compliance employers are expected to carry out protective measures and monitoring of the compliance; for instance, for head protection, the employer is required to make sure each affected employee wears a protective helmet when working in areas where there is a potential for injury to the head. Compliance breaches can lead to hefty sanctions.

Alarmingly, around 20% of all occupational accidents in industrial setting in Finland are head related injuries.

Automating the PPE compliance reduces the chance of compliance breach and helps to reduce work related accidents. With real-time AI analytics, this can be done by identifying the PPE worn by workers. This can include, for instance, the detection of hard hats, high visibility vests, and safety goggles. The detection can be done at control points, where the system automatically checks that the employee is using personal protective equipment before they enter the factory floor. If protective equipment is missing, an alert can be sent further.

Why You are Missing Out

Monitoring the safety gear compliance can be challenging due to the large size of industrial premises and the number of workers within. Often, safety managers struggle with limited resources for objective observation of large manufacturing halls and hundreds of employees or visitors that circulate within workplaces. Accidents caused by noncompliance of PPE is an industry-wide issue, and reports from the US indicate injury costs estimated to $161B annually.

Real-time AI PPE detection can assist on the process by automatically detecting personal protective equipment, reducing the required resources used on PPE monitoring.

All in all, the rules for operational efficiency and process optimization are being rewritten by new technologies, and using visual data helps to reduce the gap between operations by providing numerical insights of the processes. Additionally, using real-time alerts can help detection anomalies in the day-to-day operations and noncompliance of safety measures.