A useful way to structure value in product development is the Force Management “Command of the Message” framework. It starts with the customer’s current situation and its business consequences, then defines the improved future state, the measurable outcomes that matter, the capabilities required to get there, and finally why a given solution is better and how that claim is supported. That makes it a strong fit for Product Velocity, because it forces a link between technical work and stakeholder value instead of treating product features as value by themselves. A good public illustration of the framework is the LinkedIn post Force Management “Command of the Message” example. This is just one of many value-based sales frameworks.
The value framework below is a simplified example, not Ameru’s official sales messaging. It is meant to show how the case can be interpreted through a Product Velocity lens. The structure and metrics are grounded in Ameru’s published case material, especially the Heathrow case study and the Betahaus deployment, which provide the factual basis for the scenarios, metrics, and proof points used here.
Before / Negative Consequences
| Before | Negative Consequences |
|---|---|
| Users must decide themselves which bin an item belongs in, often in a hurry or with incomplete information. | High contamination in recycling streams, lower material value, and more waste sent to residual treatment. |
| Waste handling is largely invisible after disposal. Operators do not know what is thrown away, what is misplaced, or where the losses occur. | No reliable basis for improving sorting behavior, reducing waste, or proving ESG impact. |
| Better recycling depends on signage, training, and user discipline. | Performance is inconsistent, hard to scale, and often degrades in busy shared environments. |
After / Positive Consequences
| After | Positive Consequences |
|---|---|
| The bin identifies and sorts the item automatically at the point of disposal. | Lower contamination, higher capture of valuable recyclables, and more consistent sorting quality. |
| Every disposal event is captured as data. | Waste streams become measurable and improvable over time, not just auditable after the fact. |
| The system combines physical sorting with analytics and field learning. | Customers gain both operational savings and a basis for continuous waste optimization. |
Required Capabilities
- Detect and classify common waste items reliably in real usage conditions.
- Physically separate waste into defined streams with stable hardware performance.
- Capture item-level disposal data to support analysis, retraining, and reporting.
Metrics
- Recycling contamination reduction (%)
Example benchmark: 75% reduction in contamination. - Increase in overall recycling rate (%)
Example benchmark: +43.9% improvement in recycling rate. - Waste cost savings per ton (€ / ton)
Example benchmark: €94.07 saved per ton.
How we do it
- Use AI-based visual recognition to classify waste at the moment of disposal.
- Route items into separate internal streams through an automated sorting mechanism.
- Turn disposal data into waste analytics for operators and future model improvement.
Better (vs. alternatives)
- Better than standard multi-bin stations, because the user no longer has to make the sorting decision.
- Better than signage and awareness campaigns alone, because sorting quality does not depend on memory or motivation.
- Better than smart bins without operational analytics, because Ameru improves both disposal and system learning.
Proof Points
- Heathrow Compass Centre: demonstrated 75% contamination reduction, +43.9% recycling uplift, and €94.07/ton savings in a high-traffic food-service environment.
- Betahaus Sofia: showed successful deployment in a coworking environment, including measurable recyclable capture and an operational workflow involving downstream handling.
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