Latest / The Operator's Manual with Fexingo: Daily Decisions, Tactical Wins, and Business Execution / How One University Cut Transcript Processing Time 70 Percent
Transcript
- Lucas: So there's a university in the Midwest, let's call it Midstate, that processes about 25,000 transcript requests every year. Lucas: For each request, a staff member had to manually type the student's name, ID number, graduation year, and course list from a PDF or form into the system. That's hundreds of keystrokes per request, repeated thousands of times. Luna: And that manual step — how often did it introduce errors? Lucas: About 8 percent of transcripts had a mistake. A wrong course, a misspelled name, a missing semester. Luna: Eight percent — that means one in twelve transcripts required a reprint and a second mailing. Adds up fast. Lucas: Exactly. The average turnaround was five business days. And the registrar's office was constantly backlogged. Lucas: They knew they had a problem, but they assumed the solution would be expensive — maybe a new ERP system or outsourcing. Luna: But they ended up making one change that cost almost nothing, right? Lucas: Right. They built a simple API connection between their student portal and the existing database. When a student submitted a request, the system automatically pulled their verified academic record — no manual entry. Luna: So they eliminated the copy-paste step entirely. How long did that take to build? Lucas: One developer, about two weeks. The integration was straightforward because the data was already in a structured database. Lucas: Once they flipped the switch, turnaround dropped from five days to less than 24 hours for digital requests, and errors essentially disappeared. Luna: Seventy percent reduction in processing time, and the cost was just two weeks of developer time. That's a huge return. Lucas: They also calculated that the manual data entry was eating up 1,200 staff hours a year, which translated to about $75,000 in salary. That's now freed up. Lucas: Now, if you're listening and thinking about your own processes — if today was actually useful to you, the way this show stays ad-free is listener support. Lucas: You can find that at buy me a coffee dot com slash fexingo. It's a small way to keep these conversations going. Luna: Yeah, and we hear from folks who use these case studies in their own teams. That's what keeps it worth doing. Lucas: Alright, back to Midstate. The implementation itself had one hiccup — they initially rolled it out to all students at once and saw a spike in requests because it was suddenly so easy. Luna: So the success created a surge. Did they handle it okay? Lucas: Yes, because the automated process handled the volume without extra staff. They actually saw a 15 percent increase in transcript orders that year, and still maintained the faster turnaround. Luna: That's the kind of problem you want to have. The lesson seems to be: look for places where data is being typed by hand when it already exists in a system. Lucas: Exactly. In many organizations, the biggest bottleneck isn't technology — it's the human step that sits between two databases. Removing that step often costs far less than expected. Lucas: Midstate's registrar told me they had assumed they needed a multi-million dollar system upgrade. But the fix was a few lines of code and a conversation with their IT team. Luna: And now they spend zero time on data entry for transcripts. The staff can focus on advising, exceptions, and actually helping students. Lucas: Right. So the one change was: connect the portal to the database. No new software, no new hardware. Just a smarter workflow. Luna: That's the core of tactical operations — finding the single point of friction that, if removed, unlocks everything else. Lucas: And it applies everywhere. Any process where someone has to re-enter information that's already stored somewhere — that's your 70 percent candidate. Luna: So if you're managing an operation, start by mapping where data moves between systems. If it's hand-carried, that's the spot. Lucas: Midstate's story is a good reminder that the most impactful change is often the one you assume is too small to matter. Luna: Or too big to tackle. They almost didn't try because they thought it required a big project. Lucas: Exactly. Next time you see a repetitive data entry task, ask yourself: what's the cost of a two-week integration?