Makerspace Equipment Rental Redesign

Working as a five-member consulting team (Group 35), we applied the full Business Process Management lifecycle — identification, discovery, analysis, and redesign — to the equipment rental process at UGA's McBay Science Library Makerspace, where students reserve and borrow 3D printers, laser cutters, embroidery machines, and other fabrication equipment.

The makerspace lets students access advanced technology they can't get in a traditional classroom, but the lending process was slow. After mapping the as-is process and analyzing it quantitatively, we found a single non-value-adding bottleneck — a week-long wait for in-person equipment training — and redesigned the process to eliminate it, raising cycle-time efficiency from 37.94% to 56.42%.

Click through all 33 slides below — use the arrows, your keyboard, the thumbnail rail, or fullscreen. The walkthrough that follows highlights the key findings.

We started by placing the makerspace lending process inside UGA's value chain, then scored every core process on strategic importance, health, and feasibility. The Makerspace Equipment Lending Process stood out: strategically important (it enables hands-on learning students can't get in lectures), in poor health (the required training was lengthy and inefficient), and highly feasible to improve using UGA's existing technology — making it the clear candidate for redesign.

To document the as-is process we combined three discovery methods: UGA library and policy documents, the makerspace's own web pages and equipment guides, and online reviews — plus a ~45-minute interview with our process owner, a student assistant at the makerspace. We compiled the notes and modeled the current process in BPMN, including the training sub-process.

We ran both qualitative and quantitative analysis. A value-adding analysis classified each step as value-adding, business-value-adding, or non-value-adding, isolating the culprit: waiting for the scheduled in-person training day — a non-value-adding activity worth a full week of idle time.

Quantitatively, the as-is process had a processing time of 7,647 minutes against a cycle time of 20,160 minutes (≈2 weeks), for a cycle-time efficiency of just 37.94%.

The redesign resequences the in-person training: instead of booking a separate training day a week out, students complete training immediately before using the equipment. That removes the week-long wait entirely. The to-be model drops processing time to 1,617 minutes and shrinks the end-to-end cycle time from two weeks to two days — pushing cycle-time efficiency to 56.42%.

Finally, we planned to communicate the redesign back to the process owner by email and follow up on any questions about the improvements.