Great RPA tool for our manufacturing efforts
September 29, 2023

Great RPA tool for our manufacturing efforts

Kat Karpenko Wozny | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with IBM Robotic Process Automation

We use IBM RPA mainly to automate and stadardize our workflows on the supply chain, inventory, logistics, and warehousing sides. Before we started using it, we didn't have a system to manage these areas in a standardized way, wherea now, all of our intake, data entry, dispatch, etc., processes are more streamlined and error-free.
  • Creating and maintaining reports based on continuous data entry
  • Setting standardization tasks for our production data entry tasks
  • Maintaining standard worklfows for new inventory intake and output
  • It's very expensive, especially for growing operations like ours.
  • It's a bit difficult to set up and start using confidently.
  • It's saved about 50-75 working hours a week from our supply chain ops
  • It reduced the number of errors in these areas, which is helping us monitor everything more accurately.
It was a bit difficult to integrate with our ERP system in the beginning.
I believe we saved about 50-75 working hours a week from the menial tasks that the RPA has helped us automate. But above the time saved, I think it's biggest impact on our success is it's effect on our human error costs.

Do you think IBM Robotic Process Automation delivers good value for the price?

Not sure

Are you happy with IBM Robotic Process Automation's feature set?

Yes

Did IBM Robotic Process Automation live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of IBM Robotic Process Automation go as expected?

No

Would you buy IBM Robotic Process Automation again?

Yes

I think IBM RPA is very suitable if your supply chain workflows have lots of repetitive or time consuming tasks, or tasks that have a lot of room for human error. Reducing these was the main motivation for our purchasing decision. For where it doesn't fit in well, I'm not sure if it fits in well with data from production machines, especially machines like ours that have a wider range of variation.