VendMax Services Case Study

The Challenge

Ron Patterson inherited VendMax’s route structure when he was promoted to route supervisor three years ago. The routes hadn’t been redesigned in over five years. Each of the 10 technicians owned a territory: North Columbus, East Side, Hilliard, and so on. Within each territory, every machine was serviced on the same weekly cycle regardless of how much product it sold.

The problem was simple and expensive. A vending machine in a busy hospital cafeteria that sold 200 items per day was on the same weekly service schedule as a machine in a quiet corporate lobby that sold 15 items per day. The hospital machine ran empty by Wednesday. The office machine still had 80% of its inventory at the end of the week. Stockout complaints were VendMax’s number one customer service issue.

Ron knew the service frequencies needed to change, but the territory-based routing made it impractical. If he moved a hospital from weekly to twice-per-week service, the technician covering that territory needed an extra half-day of route time. That meant another machine in the same territory got pushed to the following week, which might trigger its own stockout. Every adjustment created a ripple effect that the manual routing couldn’t absorb.

The consequences were mounting:

• Stockout complaints driving client losses: VendMax lost 2-3 client accounts per quarter, almost always preceded by repeated stockout complaints. A hospital administrator who finds empty vending machines during a night shift doesn’t send a polite email. They call the account manager and threaten to switch providers.
• No optimization within territories: Routes were sequenced by the technicians themselves, usually in the order they preferred to drive. One technician started at the farthest location and worked back toward the warehouse. Another started nearby and worked outward. Neither approach was optimized, and Ron had no way to compare efficiency across the team.
• Fixed schedules wasting capacity on low-traffic machines: Roughly 30% of VendMax’s 500 machines needed biweekly service at most. Visiting them weekly meant technicians spent hours restocking machines that didn’t need restocking, while high-demand machines sat empty between visits.
• Variable service times: A basic restock took 15 minutes. A restock with cleaning took 25 minutes. A machine requiring repair could take 45 minutes or more. The territory routes didn’t account for these differences, so a technician with three repair calls in one day fell behind on restocks.

Ron attempted to address the problem with spreadsheets. He built a matrix of all 500 machines, their estimated daily sales volume, and a recommended service frequency. The spreadsheet confirmed what he already knew: high-traffic locations needed 2-3 visits per week, while low-traffic locations could go biweekly. But translating that spreadsheet into actual routes for 10 technicians across Columbus was a project he estimated would take weeks of manual planning, and the result would be outdated within a month as accounts were added or removed.

The Solution

Ron found Upper while looking for route optimization software that supported recurring schedules with variable service frequencies. The critical requirement was the ability to assign different visit cadences to different machines and have the routing algorithm optimize across the entire fleet, not just within fixed territories.

The setup process took two days. Ron imported all 500 machine locations with their service frequency (twice weekly for high-traffic, weekly for standard, biweekly for low-traffic), estimated service time per visit type, and priority level. Upper’s route scheduling feature built recurring route templates that distributed machines across the 10 technicians based on geography rather than legacy territories.

Replacing Territories With Usage-Based Routing

The most fundamental change was abandoning fixed territories entirely. Under the old system, each technician was responsible for every machine in their geographic zone, regardless of service needs. Under Upper’s optimized routing, machines are grouped by service day based on proximity and frequency requirements, with assignments distributed across the team for balanced workloads.

A high-traffic hospital in Westerville and a high-traffic gym in nearby Polaris might now be on the same Tuesday route, even though they were in different legacy territories. A biweekly office machine in Dublin and another in Hilliard appear on alternate Wednesday routes. The routing follows usage patterns and geography, not organizational boundaries drawn five years ago.

The result was immediate. Technicians stopped visiting low-traffic machines unnecessarily. High-traffic machines received the attention they needed. And the geographic clustering eliminated the cross-city drives that had padded route times across the fleet.

Photo Proof That Strengthened Client Relationships

Stockout complaints had eroded trust with VendMax’s clients. Even after switching to frequency-based routing, Ron needed a way to demonstrate that machines were being serviced properly and consistently. Upper’s proof of service feature gave him that documentation.

After every service visit, technicians photograph the fully stocked machine through the Upper app. The photo is timestamped and geotagged, creating a verifiable record of the visit. Ron started sending weekly service reports to key accounts, showing photos of each machine after restocking, along with visit dates and times.

The hospital that had threatened to leave became one of VendMax’s most vocal supporters. The facilities manager told Ron that no other vending provider had ever shown them documentation of actual service visits. The transparency turned a rescue situation into a strengthened partnership.

Using Analytics to Continuously Refine Frequencies

Upper’s route analytics provided Ron with data he’d never had access to before. He could track time spent per machine, total route duration, and service completion rates across the fleet. After three months of data collection, patterns emerged that further refined the routing.

Several machines originally classified as “standard weekly” turned out to need biweekly service based on technician visit times and restocking volumes. Twelve machines that Ron had assumed were low-traffic actually required weekly visits because they were in buildings with seasonal employee surges. The analytics allowed Ron to adjust frequencies based on real data rather than estimates, keeping the routes accurate as conditions changed.

The Impact

The transformation at VendMax played out over six months, with improvements compounding as the usage-based routing matured. Stockout complaints, which had been the company’s most persistent operational problem, dropped by 80%. The remaining stockouts were almost exclusively caused by equipment malfunctions, not missed visits. High-traffic machines were serviced on schedule, and the photo documentation gave account managers concrete proof of service consistency.

Drive time across the fleet decreased by 30%. The reduction came from two sources: eliminating cross-city territory drives and removing unnecessary visits to low-traffic machines. Technicians spent less time on the road and more time servicing machines. The efficiency gain translated directly to capacity, with each technician servicing 15% more machines per day.

That capacity increase had a direct business impact. Ron added 60 new machines to VendMax’s network over the following eight months without hiring additional technicians. The new accounts were absorbed into existing routes through Upper’s optimization, with the algorithm redistributing stops to maintain balanced workloads. Before Upper, adding 60 machines would have required at least one additional technician and vehicle.

Performance Metrics

Client retention improved dramatically. VendMax went eight consecutive months without losing an account, compared to the previous pattern of 2-3 cancellations per quarter. The improvement came from better service consistency and the proactive reporting that Ron introduced with the photo documentation. Several accounts that had been at risk of leaving renewed their contracts with expanded machine counts.

Ron has since started using the analytics data to pitch new business. When prospecting a potential client, he shows the service reports from similar accounts: timestamped photos, visit frequencies, and response times. The data-backed presentation has closed deals with two hospital systems that had previously used competitors.