Our university plans to deploy Microsoft Power Platform. To convince other leaders, we need to present data confirming the time-saving benefits. Does anyone have examples of measured benefits where manual processes were replaced by LCAP?
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We have implemented MPP already 3 years ago. We have recorded consumed time prior and after the optimizations and are transferring these details to our data lake. The differences between both measures are the benefits we are showing with a PBI dashboard. This is showing real time benefits of MPP.
We implemented Microsoft Power Platform across several departments about 18 months ago, and I can share some concrete time-savings we measured. Our admissions processing workflow was previously taking 3.5 hours per application with manual data entry and document routing. After building a Power Automate flow with integrated Forms, we cut this to 45 minutes per application - a 79% reduction that saved approximately 2,800 staff hours in the first year.
Similar success with our facilities maintenance request system: average resolution time dropped from 6 days to 2.3 days after creating a PowerApp that maintenance staff can access on mobile devices. The dashboards provide real-time analytics that helped us identify and eliminate three major bottlenecks. For faculty research grant applications, our administrative burden decreased by 62% by automating approval workflows and document generation. What used to take 4-5 days of back-and-forth now completes within 36 hours consistently.
The biggest surprise was how quickly we achieved ROI - we had calculated a 14-month break-even point, but reached it in just under 9 months due to faster adoption than expected.
Happy to share more details about our implementation approach if helpful. The key for us was starting with clearly defined, measurable processes and getting early wins to build momentum.
We have implemented many power automation measures , and in board reports I have quantified savings. There is no broad brush method to this. As you are building automation models, they inherently should be optimizing something. Case by case you should be able to quantify or qualify the benefit pretty easily. Once you have a pattern for doing this as you go, you should be able to benchmark pretty easily and get to some kind of metrics. Proof is in the pudding (actions better than plans). You will always be able to find early adopters. Work with them, quantify, socialize the results, have them do that for you. Then scale it across the organization.