There has been a growth in technological innovations in the mines and metal industry. Historically, the industry was very labor intensive, centering on manual labor instead of automated machines and assets. While automation is prevalent in other manufacturing industries, recent technological advancements help automate mines and metal manufacturing processes and improve data flow to benefit mines and metals producers.
Mines and metals companies continue to struggle with significant operational costs from fixing and maintaining plant equipment. 70% of operating inefficiencies are due to unplanned downtime and equipment breakdowns that prevent companies from reaching increased demands. And since urgent repairs cost significantly more than routine and predictive maintenance, as the cost of materials continues to rise, Industrials must find ways to improve productivity to maintain or grow profits.
Industry 4.0 attempts to create more interconnection between the physical and digital worlds for the mines and metal industry. Automation and digitization advances include implementing AI, IoT, and Machine Learning to streamline manual processes. Ultimately, mines and metals companies want to leverage technology to reduce hazards and equipment failures, improve predictive maintenance, and increase reliability.
Improve OEE with automation
Reliability in mines and metal manufacturing signifies that everything is on spec and without failure. Many manufacturers focus on OEE (Overall Equipment Effectiveness) to maximize their current data’s value to measure and improve reliability.
Given that unplanned downtime costs industrial manufacturers as much as $50 billion a year, it is critical for Industrials to focus on uptime. While there are several solutions for Condition Monitoring Software and OEE calculation tools, having a cost and risk reduction strategy is critical. Even so, having automated technology that can continuously collect, clean, and compute data has endless savings potential. The OEE app, available through the Braincube IIoT Platform, calculates your OEE score and explains why your manufacturing process is underperforming so that teams can adjust real-time strategies.
Gathering and cleaning data from multiple sources to calculate metrics was very challenging for one of Braincube’s CPG customers. Due to the urgency of this process, they could only report on OEE once a week which was still often delayed, inaccurate, and not accessible on the shop floor. Braincube’s app automatically calculates OEE and identifies the reasons for downtime, allowing process experts to incorporate findings immediately into production.
After applying the findings from Braincube’s OEE App, they increased their OEE by 22%. This helped managers more accurately plan production goals and increased productivity. This data was quickly shared with multiple departments, allowing operators to respond instantly to disruptions in the manufacturing process.
Additionally, they improved their data visibility and results by making the calculations accessible to different teams across the organization.
Improving reliability by monitoring and improving OEE allows your manufacturing team to increase uptimes and reduce unnecessary production stops, enabling your company to produce quality products (with less waste or rejects) more effectively without implementing new equipment. Access to accurate and quickly calculated OEE scores can reduce production costs and improve your bottom line.
Build a better predictive maintenance strategy
Over time, Industrials have moved from a reactive maintenance strategy to a more effective predictive maintenance approach. As more and more data becomes accessible across the enterprise, manufacturers can leverage automation to better track, manage, and optimize their machine fleet.
Successful predictive maintenance is focused on determining root causes to predict and, ideally, prevent issues before they occur. Mines and metal operators leverage prescriptive maintenance to optimize performance and automate data collection, cleansing, and analysis to stop machines when anomalies are detected for fewer unplanned production disruptions.
For example, predictive maintenance gives your team the data to create a need-based maintenance schedule. This reduces random maintenance events and costs as your team can source the necessary parts before an event occurs. When unpredicted maintenance events happen, technology can capture and analyze the event’s data so your team can evolve their plans for improvements in the future.
A Braincube customer implemented a Braincube Edge solution to improve quality and machine capacity. The shop floor consisted of almost 50 machines on a five-stage production line with only a handful of full-time operators. It was a massive undertaking to monitor, troubleshoot, and improve this fleet of machines.
The company was experiencing fluctuating machine performance, leading to product variability. The team knew there had to be a more efficient way to improve performance at scale. Using the Machines’ Performance Tracker App, the production team connected data from approximately 50 machines to set up multi-machine live dashboards for enhanced visibility.
Instead of viewing 50 different screens and trying to make sense of a massive amount of data, they triggered alerts for when a specific machine was drifting in performance compared to its group of machines or its usual performance in time. They also set up alerting preferences to improve reaction time. It focused teams on the machines that needed attention, saving time and resources and improving morale.
Braincube’s Predictive Maintenance package brings situational awareness to your operations team with automated controls and alerts of your equipment and assets. These apps make it easier for your organization to monitor multiple data points and machine status to address critical issues before they negatively impact production and profits.
Effective data analytics paired with predictive maintenance can be a virtual goldmine for mining operations as cost reduction and productivity gains of an estimated 10% to 20%. Understanding your company’s production equipment helps your teams launch robust predictive maintenance strategies with real-time data to enhance production capacity and profits.
However, identifying ways to reduce energy costs can be challenging because there are many parameters to monitor simultaneously. Braincube’s IIoT Platform and a suite of ready-to-use apps can monitor all parameters needed to monitor your production process accurately. In addition, the platform offers customizable dashboards that are continuously populated with real-time data to the forefront for better energy consumption to improve your bottom line.
Aubert & Duval, a metallurgical manufacturer, wanted to improve its Energy Performance Indicator for energy efficiency and reduce annual energy consumption costs by 15%.
Benoit Cugnet, an Energy Engineer for Aubert & Duval, emphasizes that Braincube helped “uncover the most significant drivers of energy usage, plus track and report on our Energy Performance Indicators. This makes it easier to dive into analysis for long-term optimizations.”
Having technology that can improve how you collect and make use of your data has a high ROI. Regardless of your energy targets, getting access to the right data should be at the forefront of your approach.
As mines and metals manufacturers struggle to remain profitable while balancing the rising costs of raw materials, tools that automate tasks, processes, and equipment can be a pivotal piece of your cost savings plan.
Tools like Braincube are designed to help your digital transformation efforts with a suite of ready-to-use tools that drive business results.
See how Braincube’s suite of ready-to-use tools can benefit your organization.
Mines and metals manufacturers face long, complex production cycles spanning both distance and time. Braincube ensures the right specs are met throughout production is vital to meet customer and quality demands.
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