In industry, operational inefficiency can cause catastrophic problems, resulting in unexpected downtime, missed deadlines, and, ultimately, significant financial losses. These challenges are compounded by the lack of visibility and proactivity of traditional OEE (Overall Equipment Effectiveness) systems, which still predominate in many industrial environments.
One of the biggest challenges industries face is the lack of timely communication about operational issues. In traditional systems, production managers only become aware of machine failures or downtime when they're right next to the machine or at the end of the shift, when it's too late to fix and avoid delivery delays. For example, a machine might stop unexpectedly, and the manager only discovers it at the end of the day, making it impossible to make up for lost time.
Furthermore, processing machine efficiency (OEE) information using traditional systems is often manual and involves working with Excel spreadsheets to generate historical reports. This process not only consumes time but also increases the risk of human error, not to mention delays in problem detection. The lack of automation and proactivity in this process results in delayed identification of production delays and issues, with very little chance of remediation until after the problem has arisen.
Another significant problem is the lack of contextualization of OEE data. Traditional OEE systems only measure machine productivity, without considering underlying factors that can affect efficiency, such as mechanical failures or maintenance issues. This prevents a complete understanding of the causes of OEE declines, making it difficult to implement effective solutions. For example, a machine may operate at low efficiency due to persistent overheating, but because the OEE system doesn't monitor temperature, this root cause goes undetected.
A particularly problematic aspect is that OEE indicators are displayed on the machines themselves or on panels located at specific points in the factory. This requires the production manager to physically travel to these panels to check the status of the indicators, a time-consuming process that can result in delays in responding to critical issues. In other words, if a manager or operator isn't paying attention to the machine's local panel, performance deviations indicated by the traditional OEE system may go unnoticed, risking delays in corrective action. It's the equivalent of "driving with the rearview mirror in mind," reacting to problems only after they've already occurred.
How to solve the problem?
Many companies develop dashboards and solutions using third parties or their own IT departments. However, these solutions are attempts to integrate a process that requires more than just a few integrated technologies. Furthermore, most of the time, these activities are diversions from the company's core business, overloading professionals with a solution that continues to fail to meet production management's expectations.
The ideal solution should include an application capable of real-time monitoring of the variables of each machine on the production line, cross-referencing information, and alerting those responsible in a scalable, autonomous, and proactive manner. In other words, it should eliminate the need for physical presence or focus on any monitoring panel. In a simple and targeted manner, the system should send an auditable notification to users empowered to make decisions that prevent losses on the production line.
Furthermore, most equipment efficiency issues are the result of machine operational problems (a warm-up example in this same article). A system that simultaneously monitors machine operational parameters can provide process intelligence and alert the maintenance team before the issue impacts production. This means addressing the cause rather than the effect.
The big problem until now was finding a solution capable of addressing all these issues without the prohibitive development cost.
Bridgemeter Solution: Proactive and predictive monitoring of machine health and operation.
Above-Net 's Bridgemeter was an integrated monitoring solution that goes beyond simple productivity measurement. With Bridgemeter, managers have access to a comprehensive and contextualized view of production performance, including all machine operating parameters.
One of Bridgemeter's key features is its proactivity. The system sends real-time notifications directly to the production manager's cell phone, allowing them to monitor line efficiency without having to go to the information panels or equipment. This means corrective actions can be taken immediately, minimizing downtime and avoiding significant productivity losses.
Furthermore, Bridgemeter monitors not only overall machine efficiency but also critical parameters, including temperature, pressure, and electrical current. This integrated approach allows for early identification of potential problems before unexpected downtime impacts production.
Bridgemeter's flexibility and interoperability are another key differentiator. The system can be configured to adapt to each customer's specific OEE needs, seamlessly integrating with existing PLCs (Programmable Logic Controllers) or performing calculations after parameter collection. This flexibility allows Bridgemeter to be implemented in a variety of industries and production settings, providing a tailored solution for each scenario.
Additionally, Bridgemeter offers an intuitive and easy-to-use interface that provides clear and actionable visualizations of OEE data. Managers can access real-time dashboards that display alarms on machine availability, performance, and quality, as well as overall OEE indicators. This facilitates informed decision-making and prioritization of corrective actions, continuously improving operational efficiency.
Results
Implementing Bridgemeter brings a series of tangible benefits to industrial operations. First, the system's proactivity significantly reduces unplanned downtime, resulting in increased machine availability and, consequently, production capacity.
Furthermore, the contextualization of operational data provided by Bridgemeter allows for a deeper and more accurate analysis of the causes of productivity declines. This leads to improvements in production performance and quality, increasing overall OEE.
Bridgemeter's flexibility and interoperability with existing systems also facilitate its adoption, enabling managers to make more informed, data-driven decisions.
In short, Bridgemeter transforms the way industries monitor and manage the effectiveness of their equipment, providing a proactive, flexible, and integrated solution. Expected results include increased machine availability, continuous improvement in production performance and quality, and significant reductions in operating costs. By adopting Bridgemeter, industries can achieve a new level of operational efficiency and market competitiveness.
