Case Study
De-Risking a High-Precision Product Request
- Validation
- Development
- Automation
- Workflow
Follett, a commercial ice equipment manufacturer, was pursuing a high-volume opportunity with a major coffee chain to automate ice dispensing. What appeared to be a straightforward product request carried hidden risk: the requested level of dispensing precision introduced more complexity than the workflow likely required. Product Insight helped Follett validate what level of performance actually mattered before committing to a production architecture.
Challenge
The requested accuracy — roughly within one to two ice cubes — would have required a far more complex dispense-and-weigh feedback system, despite limited evidence that that level of precision materially improved the workflow. The requirement was already solidifying into a production specification before Follett had validated what level of performance actually mattered.
Solution
We reframed the project from building the production mechanism to testing what level of precision the workflow actually required. Instead of optimizing a production-intent design too early, we developed an intentionally over-capable prototype using a servo-controlled motor system that allowed them to precisely vary dispensing behavior and test the full performance envelope.
Automated testing generated statistically significant data around dispense accuracy, speed, variability, and repeatability across different conditions. This allowed us to identify where tighter precision stopped delivering proportional value. Rather than accepting the original requirement at face value, the prototype became a tool for defining what the downstream production system actually needed to achieve.
Outcome
The project produced a validated development path instead of an over-constrained specification. Prototype systems were deployed in a real test-kitchen environment, allowing the manufacturer and end customer to evaluate workflow and performance in context. The resulting data gave Follett confidence in defining realistic production targets, supplier requirements, and control strategies. By validating the requirement before scaling development, we reduced the risk of unnecessary complexity, costly redesigns, and misaligned product decisions.