How to Prove a Robot's ROI with Analytics

Can key performance indicators (KPIs) help you figure out the ROI of your robotic cell? You bet!

Your first robot is an important investment. It can open doors to future automation projects – but only if you can prove that the investment was worth it.

If you can’t prove that the robot has recovered its costs, your company’s commitment to robotics will probably evaporate quicker than water on a hot motor.

But how can you prove that a robot has been a good investment?

We’ve gone over how to calculate the Return on Investment (ROI) for a planned robot purchase. This calculation is useful at the early stages of robotic cell deployment, but it’s only an estimate. It doesn’t prove that a certain ROI has been achieved.

Related: 10 Ways Robotics Can Transform Our Future

To get a more accurate number, you need analytics. This involves using metrics and real operational data. It’s a more accurate method, but it can also take longer if you don’t measure those metrics properly. Dedicated analytics software can speed up the process significantly.

Here’s how to decide which analytics data you will need, gather the data effectively, and analyze it to prove the ROI of your robot.

## How to use analytics

Not all metrics are equal. There are hundreds, if not thousands, of metrics you could use to measure the effectiveness of your business and robotic system, but only some of them are actually helpful for calculating ROI. You should only use KPIs that are directly linked to the performance of the robotic cell.

## Metrics: interesting vs. useful

Make sure you steer clear of metrics that seem interesting, yet are actually useless for measuring robot ROI. Measuring the wrong metric can be worse than measuring no metrics at all, because they’ll give you an inaccurate picture of the robot’s performance.

For example, let’s say you decide to use the “productivity of each cell” metric, so you compare the robot’s productivity with the productivity of the other cells to see if it’s “keeping up.”

Although this might seem like interesting information, it’s probably useless, because different cells will have inherently different productivity levels. In this case, it would be much more useful to compare the robotic cell’s productivity with the productivity of the previous manual cell (from before it was automated).

Wondering how to make sure you choose the most useful metrics?

## Choose the right KPIs

Many of the KPIs we use in business are not directly suited to measuring robot performance. Until recently, there wasn’t much information available about how to apply KPIs to collaborative robots.