“Better be three hours too soon, than a minute too late.” These famous words by William Shakespeare rightly denotes the importance of delivering early rather than at the last minute. In the manufacturing industry, tardiness can cause heavy losses. In this industry, it is always a wise decision to pre-emptively solve problems, especially when it comes to predictive maintenance. But exactly why is predictive maintenance so vital to the manufacturing industry?
The manufacturing industry usually follows a “run-to-failure” or reactive maintenance approach where they operate the machines and systems before they break. It seems a cost-effective solution at first, especially when the machines are new and require minimal maintenance. However, once wear and tear begin, reactive maintenance is a costly affair. Breakdowns become more frequent along with unplanned downtimes that increase operational costs and reduce productivity. Ultimately, this costs you money. According to the analyst firm Aberdeen Research, 82% of companies in the manufacturing industry have experienced unplanned downtime over the past three years; that unplanned downtime can cost a company as much as $260,000 an hour.
For industries, each time there is an equipment failure, there is downtime. The production process is hampered and repair is logistically difficult if the spare parts aren’t on hand. Add to that the overworked employees who work extra hours to repair and make up for the downtime by filling delayed customer orders. The solution to all this waste is predictive maintenance.
MarketsandMarkets forecasts the global predictive maintenance market will grow from USD $3 billion in 2019 to USD $10.7 billion by 2024. Predictive maintenance analyses data collected from sensors to reduce downtimes and improve machine health. Maintenance is conducted by relying on trends in the data collected from the machines such as energy consumption, flow analysis, pressure analysis, and vibration analysis. The data is then run through algorithms that highlight trends like excessive vibration, high pressure, higher energy consumption and the recommendations required to make the process more efficient and reliable, thereby reducing costs and improving productivity.
For example, let us examine a crusher in a cement industry. When a vibration sensor is installed in a crusher, the vibration is minimal. But, over time, the vibration will increase, leading to wear and tear on the internal parts. This could cause bearing failure, misalignment, cavitation issues, or loosening of bolts. In reactive and preventive maintenance, you will be unable to detect the vibration until just before the crusher breaks down. However, with predictive maintenance, the algorithms monitor your data and inform you well in advance of any machine faults. It will also provide you with recommendations on how to prevent future faults, saving you money on parts and increasing the working life of the equipment.
How will Predictive Maintenance Save Money?
Any equipment purchased for the manufacturing industry has two costs: the fixed cost and the cost of running the machine. Fix cost includes the price of the machine and its commissioning. The second cost is the price of running the machine, including the cost of regular maintenance. The greater the amount of time that the machine runs effectively, the greater its output; this improved efficiency defrays the costs associated with the equipment. By alerting the operator of issues before downtime occurs, predictive maintenance improves machine efficiency, essentially reducing the cost of the machine. Also, predictive maintenance can be configured to alert the purchasing department to proactively purchase parts when it needs maintenance. Not only does this reduce waiting times for repairs, but also avoids the operational costs of stocking unnecessary parts. This feature simplifies your operation’s logistics.
Energy is another large cost to businesses in the manufacturing industry. According to the U.S. Department of Energy, the manufacturing industry consumes about 36% of the country’s electricity. Because it reduces energy consumption-in addition to increasing production, reducing labor costs, reducing machine operational costs, and reducing parts cost-predictive maintenance basically pays for itself. Additionally, predictive maintenance programs have been shown to lead to a tenfold increase in ROI, a 25%-30% reduction in maintenance costs, a 70%-75% decrease of breakdowns, and a 35%-45% reduction in downtime.
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