Predictive Maintenance & IoT: Match Made in Heaven

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Introduction:

Predictive maintenance is critical for reducing operational costs and preserving a company’s reputation with its customers. Some of the most significant use cases for IoT Analytics are focused on the prediction and prevention of equipment failures. When complex machinery is set up with IoT sensors in the right critical points and provides a steady stream of operating data, it opens up a wide array of possibilities for calibrating the machine’s behavior and predicting future performance and possible problems or failures. In our work with customers across a wide range of industries, predictive maintenance is a well -known scenario, but customers often do not have a rigorous framework or platform for actually implementing it. Below we outline some of the significant examples where a comprehensive IoT Analytics framework and methodology can predict failures or performance problems and thereby deliver significant business value.

Equipment Manufacturing – Field Maintenance

Manufacturers of complex equipment are often committed to providing ongoing and effective predictive maintenance for their equipment in the field. In these cases, the equipment that is being monitored and managed can be located in thousands of locations spread around the globe. Monitoring, predicting, and acting to prevent failures on the complete installed base of machines is an extraordinary analytical challenge that requires new ways to capture, monitor, and act on information in real- time.

Real-time IoT Analytics focused on predictive maintenance can help these organizations plan and target their support and maintenance resources much more efficiently and effectively in order to maintain their equipment in the field at peak operating performance.

Service Delivery

A second major category of examples that highlight the need for effective predictive maintenance involves service oriented companies where the failure of a certain piece of equipment prevents delivery of the core service in question. Within this broad category, there are two major sub-categories.

•  Processing Retail Transactions
Smaller retailers where widely dispersed locations may have only a single checkout terminal point are an excellent candidate for IoT Analytics-based predictive maintenance. Real-time analytics on these critical devices can identify issues that lead to failure and recommend actions to prevent or minimize downtime. For retailers with these single point of failure scenarios, eliminating downtime is essential in order to avoid any lost revenue.

•  Service Companies
Service companies where equipment is used or brought to a particular location to perform the service are another interesting service delivery example. Delivery services that use technology to manage the flow of packages, construction companies that use complex equipment onsite, and transportation companies are three examples of this type of use case. In all these case, as with retail stores, a single piece of equipment can often be a single point of risk and its behavior and maintenance status need to be monitored and managed aggressively to avoid major business problems.

Product Lifecycle Management

A third major category where predictive maintenance based on IoT Analytics can have a significant impact is in the domain of managing product lifecycles.

Product managers and supply chain managers need to know how their products contribute to larger systems and business processes. Doing that requires them to have a detailed knowledge of how products are performing and if that operating performance implies a need for major product updates or even replacement in the field. Data from predictive maintenance based on IoT sensors can provide an early warning system where product performance degradation or even failure can lead to major breakdowns in high value business processes.

Ongoing predictive maintenance enables operations managers to avoid these larger systems risks by giving them the information they need to maintain existing products in the field effectively and also drive the timing and introduction of new products to replace products that are degrading business processes and value.

Conclusion:

The common thread for effective predictive maintenance among all of the above examples is that early knowledge of potential failure conditions enables action much earlier than is normally the case. This capability lowers or eliminates the costs and risks associated with failure or product degradation. Even in cases where failure may still occur the early indicators usually results in substantial savings.

IoT Analytics that unifies all the types of analytics and triggers action is the common recipe for these challenges. Leveraging IoT Analytics to take action in advance is where the real value is created and maintained.

Vitria’s VIA IoT Analytics Platform was built with this precise purpose in mind – unifying data so that intelligent actions can be taken to avoid problems or capitalize on opportunities in a timely manner.

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