Cloud-based field service softwareOur blog Work order management: How to go from corrective to predictive maintenance
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Work order management: How to go from corrective to predictive maintenance

XavierBiseul
Xavier Biseul
May 29, 2018
5 min. read

The Internet of Things and big data allow for real-time analysis and equipment status monitoring. The result? Predictive maintenance solutions lets service managers anticipate faults, extend equipment life, and optimize work order management. Because it is preventive in nature, the approach is based on a change in the maintenance strategy from corrective to predictive maintenance.

Now that the cost of connected objects has dramatically decreased, multiple sensors to measure temperature, humidity, vibration, or pressure are being placed on equipment in ever greater numbers. By working together, field service IoT and big data are enabling the automated detection of equipment failure before it happens, making this after-sales service dream a reality. It is necessary for appropriate maintenance management software.

From 1980s remote monitoring to today’s big data

Manufacturers first introduced sensors on machinery in the late 1980s. Later, in the early 2000s, 24/7 remote monitoring and surveillance systems were deployed. Since then, the computing power and scalability of cloud-based field service management software have been deployed en masse. That’s important because connected objects produce huge volumes of data. Consider this fact: a single sensor recording a measurement each and every second is capable of generating a staggering 31-million readings each year. So, for continuous real-time monitoring and feedback, a high-powered big data infrastructure is a must. This infrastructure ensures efficient use of the various maintenance tasks.

Working in real time

Big data also includes the capability for real-time analysis of time-stamped sensor data. This round-the-clock monitoring allows service managers to detect equipment performance changes, including availability and latency. And, because machine status data can now be gathered at any time and in real time, there’s no more waiting for a technician site visit. This capability enhances asset management significantly.

Welcome to Industry 4.0

The concept of fourth-generation manufacturing, also known as Industry 4.0, brings together the best of IoT, big data, and cloud computing. This coming together can extend a device’s life in two ways: by preventing breakdowns and by improving device function/operation. Moreover, by developing a deep understanding of the equipment and how it functions, the operator can configure it optimally. This, in turn, can reduce energy consumption. The fourth industrial revolution uses maintenance planning tools to ease the process.

The limits of corrective and preventive maintenance

When it comes to the limits of corrective and preventive maintenance strategies, a couple of points need to be made: Predictive maintenance technology is different from corrective and preventive maintenance. What is predictive maintenance? With corrective maintenance, equipment repair occurs once a fault is detected. Typically, the equipment is stopped, or at least slowed down, making service response time and return to normal operations critical. For its part, preventive maintenance means the equipment is maintained at regular intervals. This approach, based on the estimated life cycle of the equipment components or parts, relies on scheduled precautionary replacement of spare parts.

From statistical to real data analysis approach

Unlike the statistical approach, predictive maintenance is based on the analysis of real data. This way, the service manager is sure that the spare part shows no sign of wear and that the replacement can be avoided or postponed. This paradigm shift — from preventive to predictive maintenance — optimizes inventory management, as well as valuable technician time. And, equally important, the predictive maintenance approach allows equipment operators to minimize machine downtime and production slowdowns. This is due to a changing maintenance strategy shifting landscape that is partly the case.

Many benefits but a significant investment

Predictive maintenance technology using alarm company service software can achieve the pinnacle of after-sales service: just-in-time (JIT) service. This is when field technicians are called upon to intervene only when needed ― based on hard evidence. What’s more, predictive maintenance solutions improve the resolution rate at first contact, also known as the First Time Fix Rate. That’s because the deployed field technician knows the equipment and its maintenance history.

To summarize, predictive maintenance has many advantages, including:

  • Reduced incidents
  • Limited risk of serious failure
  • Lengthened equipment service life
  • Better work order planning
  • Reduced equipment downtime
  • Optimized spares inventory management

On the other hand, predictive maintenance technology requires a greater upfront investment than corrective and preventive maintenance. That’s because operators must set up and manage the connected objects, as well as the data processing and data warehousing infrastructure. The right use of maintenance management software can help reduce these start-up costs.

Artificial intelligence (AI) enables the leap forward

According to a four-country study by Vanson Bourne, commissioned by GE Digital, 75% of IT decision-makers and management believe that, by 2020, machine health status will be better managed than the status of human health. Also, Gartner predicts that by 2020, 10% of field service visits will be triggered and dispatched by an AI-enabled device. For this reason, alongside IoT and big data, predictive maintenance AI is the third pillar of the coming after-sales service revolution.

In fact, it’s already possible to schedule work orders on the fly with algorithmic models that take into account technician availability, traffic conditions, and ordering spares. Soon, AI platforms will advise the service manager whether or not to initiate particular work orders.

To get there, what’s needed is greater platform autonomy. It won’t be long before intelligent machinery will be able to self-diagnose and, based on its health status, trigger actions, such as downloading a fix, reconfiguring for reduced output, requesting assistance, or ordering spare parts. It will be as if the machines are speaking their own maintenance language.

 

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