01 / The previous program
The existing vending bank set the baseline.
The previous program was an eight-machine traditional vending bank.
Machines replaced

Dallas Stars Plano StarCenter case study
NextShelf combined open-shelf shopping with SKU-level sales data, basket analysis, daypart and event patterns, disciplined replenishment, product testing, and custom-built machine-learning models that continuously tuned the assortment to what customers actually bought.

01 / The previous program
The previous program was an eight-machine traditional vending bank.
Machines replaced
02 / The replacement
NextShelf replaced the previous bank with two self-checkout coolers and an open-shelf buying experience built for multi-item purchases.
NextShelf coolers
03 / Data and operating inputs
NextShelf combined open-shelf shopping with SKU-level sales data, basket analysis, daypart and event patterns, disciplined replenishment, product testing, and custom-built machine-learning models that continuously tuned the assortment to what customers actually bought.
Inputs
04 / Decisions made
05 / Measured result
Monthly revenue compared with the previous eight-machine vending bank at the same facility.
Measured against the previous vending program at the same facility.
06 / What this deployment demonstrates
The Dallas Stars Plano StarCenter deployment demonstrates how the buying experience, disciplined replenishment, advanced analytics, and custom-built machine-learning models can work together. The equipment created the storefront; active operation made the program perform.
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