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How much can you justify spending on the Internet of Things (IoT)? For manufacturers, the answer often lies in another question: How much downtime can you afford? For example, in auto manufacturing, just one hour of unexpected downtime can.

If unexpected downtime becomes a chronic problem, there can be additional, even bigger costs — especially in the era of just-in-time manufacturing. For instance, a company that makes brake hoses might lose an automaker’s contract if frequent downtime means the assembly line grinds to a halt. To avoid those costs, manufacturers have always invested heavily in processes and tools to maximize equipment uptime. IoT provides a new set of processes and tools — ones that can minimize planned downtime, too.

A typical system puts IoT sensors on equipment for voltage fluctuations, temperature, vibration and other parameters. That data is fed into a platform for real-time monitoring and analytics, such as Cisco Kinetic. In, I used the example of a plant taking a pump offline every month for preventive maintenance because that’s what the pump manufacturer recommends. But the IoT data might show that it actually takes 90 days for the oil viscosity to approach the threshold that warrants change. This kind of can minimize maintenance costs and maximize uptime, all without risking equipment failure.

Deeper Insights, Including Inside Equipment IoT also maximizes uptime by catching hidden problems before they result in breakdowns. For example, even the most thorough visual inspections can’t peer inside a motor to spot a failing bearing or brushes sparking. But IoT can by tracking fluctuations in power consumption or excessive heat due to arcing. Besides providing real-time alerts on those conditions, IoT also can build a historical database. That way, the company knows which models of pumps, motors and other equipment are highly reliable or prone to breakdowns. This data enables informed decisions about which models to buy or avoid.

A related benefit is smaller inventories of spares and parts because the company has historical data to pinpoint exactly how much it needs to maintain — no more, no less. In a large plant, that could translate into six or seven figures in savings that then can be allocated to revenue-generating processes. There also can be positive ripple effects.