Unplanned downtime is one of the most challenging and potentially expensive obstacles for manufacturing and industrial plants. Estimates show that the average automotive manufacturer loses $22,000 per minute when the production line stops and the unplanned downtime for industrial manufacturers costs as much as $50 billion annually. These costs heavily impact a company’s bottom line and are best addressed with a sense of urgency.
Maintenance plays a significant role in unplanned downtime, with many organizations working to a more reactive rather than proactive model. Long-term, this model proves costly and inefficient and needs more knowledge-based decision-making and planning.
Greater insight into operations and equipment performance is the key to ensuring efficient business running, avoiding preventable downtime, and reducing operating costs. For many organizations, this might seem unattainable given their sheer number and complexity of production lines. However, with today’s technology, AI-driven monitoring and predictive maintenance solutions help ease this burden. Such solutions provide real-time round-the-clock machinery monitoring that identifies and diagnoses anomalies for proactive asset management.
Predictive maintenance technology can help prevent downtime and optimize resources and costs by enabling maintenance scheduling based on rich, actionable insight. Here are the top five advantages of predictive maintenance:
- Spare Parts Inventory and Cost Management
Preventive maintenance technology can maximize the whole working life of machinery parts, avoiding premature replacement causing wasted resources and money. Past-due replacement is also an issue that could cause additional damage and more extended production stops, in turn costing companies hundreds of thousands of dollars. Using insightful data from predictive maintenance technology also means better management of spare parts inventory, ensuring that the correct parts are on hand when needed and avoiding overstocking parts, which also has associated costs.
- Extended Equipment Life and Reliability
Plant equipment is a costly capital asset to operate and replace. When equipment operates at an optimum level it cost less to run, consumes less energy, and is less likely to break down. Well-maintained machines are more reliable and last longer. Predictive maintenance enables manufacturers to gain a clear understanding of equipment performance and make optimizations accordingly.
- Reducing Energy Consumption
Industrial and manufacturing plants are energy-intensive operations. With rising energy costs and global carbon emissions targets, plants are looking for ways to reduce energy consumption as much as possible. Data-driven manufacturing processes derived from predictive maintenance technology allow companies to drill down to energy consumption at a machine and component level, enabling targeted action and control setting adjustments to ensure optimum performance without excess energy consumption.
- Mitigating Health and Safety Risks
The correct, efficient functioning of machinery impacts health and safety. Timely replacement of worn parts can help prevent serious risks from occurring. Real-time predictive maintenance can also monitor machinery so that operators can make informed decisions on the equipment’s functioning.
- Improving Sustainability
Data-driven insight into plant and equipment operation improves sustainability by reducing waste, optimizing performance, extending equipment lifespan, and reducing energy consumption. All of these elements contribute to an efficient process and help minimize environmental impact.
Arrow works closely with system integrators and hardware and software providers, including Senseye, to deliver AI and ML-powered predictive maintenance solutions for large industrial and manufacturing organizations. Our expertise in asset management solutions combined with our technology portfolio enables the proper data-driven insight and visibility for your business to help minimize downtime and increase efficiency.
