After-sales service is in a period of technological disruption like the industry has never seen. And leading this charge are artificial intelligence and machine learning. Set to be worth more than $9 billion by 2020, predictive analytics is poised to open up business intelligence doors for manufacturers, and also have a dramatic impact on productivity.

With this in mind, below are a few ways artificial intelligence and machine learning are set to revolutionise productivity for manufacturers.

Streamlining operations

While today’s manufacturing supply chains are becoming more streamlined and informed, there are still instances where additional oversight could be used to identify potential areas for improvement based on delays, production times, results and more.

Automation is “moving out of the realm of competitive advantage and more into the normal course of business.” However, just because current production levels may seem adequate on the surface doesn’t mean things are prepared for the demands of tomorrow. Machine learning helps synthesise production data to identify potential hiccups like machinery performance. Additionally, it gives companies better, real-time oversight so they can make adjustments accordingly. This information will be pivotal in the years ahead, as the manufacturing space continues to consolidate and become more competitive. As a result, expect manufacturers to become more agile.

Forecasting demand

Piggybacking off operations, predictive analytics and AI will save manufacturers a great deal of time when forecasting demand for new machinery and parts. Excel spreadsheets may have been the way of yesteryear for handling inventory and managing distribution. But now, thanks to predictive analytics and machine learning, these tasks have never been more informed and dynamic.

Keeping tabs on where inventory is needed – along with finding the best price – can be a painstaking task. However, manufacturers can leverage predictive analytics and AI to completely automate these processes. This will allow them to devote more time to other areas of the business, and dynamically change pricing and stock levels based on market conditions to get the best results.

Field service 2.0

As more manufacturing businesses shift to a service model focused on maximising product uptime – which focuses on keeping machinery up-and-running and preemptively replacing parts before they fail – proactive maintenance and efficient field service will become more important than ever.

From diagnosis to executing repairs, field service is already one of the most time-consuming tasks for manufacturers and their teams. But with the growth of IoT and the influx of smart parts, there’s an immense amount of data available to field service teams today. With AI and predictive analytics solutions, field service teams can stay more informed about how each piece of machinery is performing, and when proactive maintenance is necessary. In addition, should a breakdown occur, field service teams can use IoT data and machine learning to diagnose failures and quickly make the necessary repairs. This will reduce the number of necessary service trips, and ultimately the amount of time it takes to complete the repair lifecycle.

While they may still be in their infancy, machine learning and AI are some of the most exciting innovations for manufacturers today. And moving forward, they’ll continue to reward the manufacturers who choose to deploy them early.