Ensuring Business Continuity with AI

As technology advances, the old adage “Why fix something that ain’t broke” no longer applies.

In today’s world of “always on” manufacturing, where factories and production equipment are running 24/7, any failure causes a major disruption in production that sometimes has the effect of collapsing other downstream businesses. To ensure operational reliability, performing adequate maintenance is essential. Businesses already know that — so it’s not a question of why, but a question of when. (Also read: How Digital Transformation Can Bring Resilience in a Time of Disruption.)

As organizations and operators adopt Internet of Things (IoT) technologies, including various types of robots, cameras and sensors, the amount of data they collect will continue to grow.

In fact, the number of devices worldwide that are connected to each other to collect and analyze data and perform tasks automatically is predicted to nearly triple from 9.7 billion in 2020 to over 29.4 billion in 2030.

Such an exploding amount of data presents a challenge to humans, as our brain cannot analyze the correct information and process it in time. While data provides businesses with an unprecedented amount of insight into their operations, the inability to make sense of it and act accordingly renders profits obsolete.

This is where the use of predictive analytics and artificial intelligence (AI) in care comes into play.

What is Predictive Analytics?

Predictive analytics allows users to predict future trends and events that may be determined from historical data collected.

It predicts possible scenarios and determines individual possibilities, helping to drive strategic decision-making. These forecasts may be for the near future — such as predicting the failure of a piece of machinery over time — or for the future, such as predicting the budget needed to fix annual operations. Forecasting gives businesses the ability to make better decisions and develop more detailed strategies.

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Using AI for Predictive Care

One of the most important features of AI is its ability to digest information from multiple sources simultaneously, calculate the probability of various possible outcomes and make recommendations based on various reasons — all without the need for human input. Such a capability allows predictive analytics to take advantage of the wealth of data available in most modern businesses. (Also read: Top Ways AI Is Improving Business Productivity.)

As the world churns out more data — be it from thousands of IoT sensors, from shipping data that shows the delivery time of raw materials and parts or from open-source weather data collected from weather stations around the world — AI is ripe for the perfect time to help people make sense of it. all information. It can filter the signal from the sea of ​​noise to make possible decisions.

With the right AI configuration, businesses with an AI-enabled workflow, integrated with ERP can act on what they collect from the data.

How does all this affect maintenance? Currently, there are three types of care:

  1. Time-based maintenance.
  2. Active maintenance.
  3. Predictive maintenance.

Time-based maintenance is when the user performs maintenance based on a schedule — usually the expected life cycle of the machine. It’s good in theory, as the user can determine maintenance needs based on other similar devices. However, that’s mostly theoretical, given that each machine works differently depending on a number of factors — including use, location, wear and tear. With a time-based approach, organizations risk doing too much or not enough maintenance on the machine.

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With active maintenance, on the other hand, maintenance is done when needed, which means there will be unplanned downtime that disrupts production operations.

Projection correction solves all these problems. It is a form of condition-based maintenance that monitors the condition of devices and equipment with sensors that provide data that is used to predict when the equipment will need maintenance. Therefore, repairs are scheduled only when certain conditions are met — and before the equipment begins to malfunction.

As AI technology matures and organizations adopt more IoT tools, the use of AI-enabled predictive maintenance is increasing. (Also read: What AI Can Do for Business.)

Predictive Maintenance of Action

While almost all businesses that use equipment that requires regular maintenance can benefit from predictive maintenance (depending on the cost of machine obsolescence), some see greater benefits than others.

Businesses in the field, for example, benefit greatly from predictive maintenance due to the remote nature of their operations. With assets such as oil rigs and wind turbines located in remote locations exposed to severe weather, responding to a failed machine can significantly disrupt production.

Worse, post-launch repairs are costly, as spare parts need to be ordered and repair workers need to be dispatched to those remote locations quickly. With predictive analytics, however, field service organizations can perform necessary maintenance on a wind turbine component before it fails to ensure consistent power production. (Also read: 6 Most Amazing Advances in AI in Agriculture.)

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By analyzing machine vibration, acoustics, and temperature, for example, operators can detect potential problems due to problems such as imbalance, misalignment, wear, insufficient lubrication or air flow.

Another example is an alarm that acts as a signal/error code from a piece of equipment that has gone down. The program can analyze the previous maintenance work done for that type of equipment and that signal/error code. Based on the history, the system determines the last set number of times it has seen that combination (previous repair work and specific signal/error code). A technician will be dispatched in good time before any actual failure, equipped with the working tools recommended by the system to complete the repair. Predictive analytics allows operators to track machine wear and tear and potential problems more accurately, and more importantly, allows them to take action before a machine breaks down.

By using historical trends and weather patterns, combined with information from equipment sensors and forecasted supply chain delivery times, maintenance can be done in advance. The team has more control over where and when repairs are made, instead of rushing to salvage after the fact — allowing them to pick and choose their battles.

The conclusion

While there is no surefire way to predict errors, AI can get us as close to it as we can.

In the same way that people in coastal regions might stock up on bottled water and spare batteries when a storm is in sight, an AI-integrated maintenance system allows businesses to perform the necessary maintenance before any problems manifest themselves as real problems. (Also read: 6 No-Code AI Platforms Accessible to SMBs.)

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