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How automation is enabling modern problem solving

Technology developments in the areas of embedded sensors, IoT, machine learning and artificial intelligence are having a tremendously disruptive impact on the way operations functions, manufacturing and IT are managed within modern companies – but where the really exciting changes are taking place is in the way automation is changing the way we solve problems. Whether problem-solving is more art or science can be debated, but what we can all agree on is that having an upgraded set of tools helps both the artist and scientist be more effective in their craft.

Here are 4 ways that technology and automation are enabling modern-problem solving:

1. Alerting operators in real-time when something goes wrong. It doesn’t matter how sophisticated and refined your problem-solving processes are, you can’t solve a problem until you know that it exists. Technological advances in the areas of cost effective IoT devices, embedded sensors, along with better connectivity and cloud based analytics are enabling companies to expand the coverage of what they can monitor (both in breadth and diversity) – making it more likely that if something happens, at least one sensor registers the event. Machine learning and cloud based analytics enable the expanded volume of sensor data to be processed in real-time to filter out noise and identify the events that are meaningful to provide alerts to system operators. The automated capabilities are enabling operators to manage a larger footprint of systems with the assurance that if something goes wrong, they will be alerted immediately.

2. Tracing the root-cause of the problem. The increased volume of sensor data available, combined with fewer operators and more complex systems can make tracking down the root cause of a problem difficult. Automation in the form of machine learning, diagnostic capabilities, dependency analysis and correlation of sensor data enables real-time information about the state of the systems (sensors and alerts) to be correlated with known information about how the system is configured (the dependencies among components) to help operators more effectively trace down the root-cause of a problem so that they can begin formulating an appropriate response plan.

3. Impact containment. Along with helping to identifying a problem’s root cause, automation is enabling companies to more accurately assess the impact of a problem on their operations and take steps towards impact containment. Machine learning technology is playing a big role in enabling self-diagnosis and self-healing capabilities – using automated pattern identification to implement failover actions, system re-boots and component isolation to allow end-user services to remain available while underlying problems are being addressed.

4. Simulation and testing of solutions. Perhaps the most exciting area where automation is enabling modern problem-solving is in the simulation and virtual testing of solution alternatives – giving problem-solvers the opportunity to try out multiple approaches and evaluate the effectiveness (and potential risks) quickly and without impacting the live business operations. The same simulation capabilities can then be used to test the effectiveness of changes implemented in the live environment and monitor the performance of the solution over time.

Automation is a game-changer for modern problem-solving – enabling not only visibility to real-time operations but the ability to effectively project the impact of potential solutions into the future. As problem-solvers become more comfortable using the new tools available to them, companies will be able to effectively isolate (and avoid) the impact of problems to their operations and focus their resources on solving the underlying issues and enabling long-term success.

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