The business of mining and refining metals is one of the oldest in the world, and while it has significantly benefited from the use of modern machinery and data analysis, how value gets created has remained the same for generations. The future for the metals and mining sector presents some critical challenges that need to be anticipated and managed for the wheels of this industry to keep turning profitably.
While there is growing demand, the price that metals and other minerals can achieve remains under intense pressure due to the sheer volume of product entering the market. As a result, manufacturers need to find cost efficiencies to protect the bottom line and provide some stability in an ever-fluctuating economic environment.
The ability of producers to deliver greater efficiencies, however, is challenged by the fact that major players in the market have already harvested the lowest hanging fruits of automation. The next stage in the journey towards greater efficiency requires producers to step further into the realms of Industry 4.0. While this may seem like a giant leap into the unknown, it is a relatively small, straightforward step for a sector that is already making great use of data in their production environments.
Organizations involved in the extraction and smelting of metals tend to be quite advanced when it comes to the collection of data from their production environments, and are well equipped to capitalize on the move to Industry 4.0. These companies gather significantly more data relating to the production process than most segments of the manufacturing community and are much more likely to make use of factory historians and Industrial IoT platforms to store and analyze this data.
This high degree of data maturity comes from the fact that in metals, the secret to quality isn't necessarily the machines themselves, but the processes they are involved in. Strict control over the methods and timings make the difference between producing a high quality product and having nothing of value at all. Producers monitor this process closely, and doing so collect large amounts of data to inform production and review how they perform. With the heavy lifting of extracting data from production environments done, a massive amount of additional value is now achievable.
Predictive maintenance offers a significant opportunity for organizations operating in this area of the economy to improve efficiency. Good maintenance practices are vital for keeping production online and preventing catastrophic failures.
A tremendous amount of care and attention already goes into ensuring that critical pieces of equipment such as forges remain fully functional and in good working order. Problems here can be catastrophic for production, costing time, and vast amounts of money. Worse still, forge failures create a genuine risk of harm to people working in and around those environments.
Far less attention, however, is given to ensuring that auxiliary components are working optimally. At best, these underrated pieces of equipment are maintained according to a strict timetable, meaning they get serviced whether they need it or not. This approach also results in the possibility that a problem with a machine or individual component could go months before discovery. These problems can bring down a production line, and cause secondary damage to the machines involved.
Automated predictive maintenance products such as Senseye's PdM software suite provides a solution, allowing organizations to achieve tangible savings immediately by applying a similar level of care and attention to all production assets.
Rather than asking humans to check each production asset manually, we've created machine learning algorithms that automatically assess the condition of industrial machinery. We achieve this by applying self-learning algorithms to existing data outputs to monitor for their small but significant variations in vibration, pressure, temperature, torque, electrical current, and other sources that indicate deterioration in machine health.
Armed with this insight, producers can implement precisely the right maintenance intervention at the right time. This approach ensures that machinery can run as smoothly as possible, reduces the risk of catastrophic machine failure, and eliminates the inefficiencies and waste associated with over-maintenance. Producers can move from monitoring a handful of critical assets to thousands, maximizing efficiency and control with a comprehensive view of what is happening across their production lines.
The challenges facing the metals and mining sector are not unique, and they are certainly not terminal. It is an industry that has existed for millennia, and will no doubt continue to operate for several more. Given the challenges that this sector faces, however, the winners will be those that can continue to deliver a high-quality product at a competitive price, a task that will require smarter, ever more efficient operations. Predictive maintenance is one area in which producers can achieve tangible savings and efficiencies, while also improving aspects such as safety and environmental performance. The data foundations required to implement this new way of working are already in place for the majority of organizations operating in this sector.