The next time you find yourself waiting for a train to thunder past at a railroad crossing, consider the engineering miracle passing before your eyes. State-of-the-art locomotives are equipped with more than 250 sensors that measure 150,000 data points per minute. The torrent of real-time information generated by the sensors is used to continuously monitor the mechanical health of the vehicle. Once collected, that sensor data can be used to reduce downtime and minimize loss of revenue to companies such as BNSF and Union Pacific.
Now, think about the opportunities for applying that same approach to state-of-the-art factories and manufacturing plants. Individual components, machines, and systems that once were siloed-for example, machines that manufacture cylinder heads in an automotive plant or even the huge automotive robots themselves-can be connected to the Internet through thousands of networked sensors. The massive volumes of data these sensors generate can boost manufacturers’ ability to make automated decisions. Manufacturers can also take action in real time, improve uptime, optimize asset utilization, manage assets remotely, predict breakdowns before they happen, and more. In short, they can vastly improve operational efficiency, among other benefits.
The Internet of Things (IoT) is already changing the face of manufacturing. Factories and plants that are connected to the Internet are more productive, more efficient, and more flexible than their unconnected counterparts. Driving this transformation are global competitive pressures that challenge industrial and manufacturing companies to boost efficiencies in their systems, manage workforce skills gaps, and uncover new business opportunities. Meanwhile, improvements in sensor technologies-including miniaturization, performance, cost, and energy consumption-are making intelligent products more accessible.
For many manufacturers, the concept of predictive maintenance is a good starting point. It is also one of the most widely cited applications of the industrial Internet to date, according to a report by the World Economic Forum.
Today, production machinery is generally maintained based on rotational intervals, triggered by parameters of time, usage, and so on. Although this scheduled maintenance is a step up from the “break-fix” approach to maintenance, it is essentially a one-size-fits-all model that doesn’t take into account how the machinery is actually operated. This operation is often different from what was expected or planned for. What happens, for example, when a production machine’s environment is unusually cold, hot, humid, or dusty? Despite strict maintenance frameworks and schedules, scheduled maintenance can result in malfunctions, downtime, and production outages.
Predictive maintenance uses a combination of sensors, data, and analytics to pinpoint trouble-and take preventive action-before it happens. This proactive approach to maintenance not only identifies a machine’s actual usage and environment, but it can also predict the root cause of potential problems. Any data relevant to the maintenance issue can be sent to the cloud, where an analytics engine can capture “out-of-range” exceptions and zero in on the maintenance needed.
For manufacturing firms, creating and securing data across their entire data infrastructure is the key to unlocking value from their infrastructure and machinery-across the edge, in the data center, and in the public cloud.
If manufacturers want to remain competitive and relevant in the digital era, they need to move toward IoT predictive services to manage the volume, velocity, and value of their data. In the words of Kartik Hosanagar, Wharton professor of Operations, Information, and Decisions, “The operational benefits that IoT will afford cannot be ignored by companies-especially in sectors such as manufacturing and energy.”
Download the white paper The Missing Link in the Internet of Things Value Chain: Data Management to discover how IoT technology pioneers are transforming manufacturing, healthcare, and cities around the world.