Damm Motion

Inspiration

Our inspiration came from observing a critical bottleneck in Damm's Direct Integral Distribution (DDI) network: the fundamental conflict between warehouse efficiency and delivery efficiency. While grouping products by reference allows for fast warehouse loading, it creates a "slow delivery" scenario where drivers must search for products across the truck during their 15 to 25 daily stops. We wanted to solve this by bridging the gap between warehouse speed and the actual day-to-day reality of the driver

What it does

Damm Smart Truck acts as a "Hybrid Engine" that balances warehouse loading constraints with client delivery sequences to ensure optimal delivery times. Instead of prioritizing only the warehouse or only the client, it generates a unified strategy. The system provides a 3D Visual Guide for loading that ensures "Zero Re-handling" during the route. Importantly, it also features dynamic allocation logic to reserve and manage space for the 60% of products that are returnable empties.

How we built it

We built the solution by integrating multiple data points as inputs: the sequence of the 15-25 route stops, warehouse deployment data, and strict operational constraints like side-curtain access and maximum truck height. At the core of the Hybrid Engine is a Top-Down LIFO (Last-In-First-Out) Spatial Algorithm. This algorithm calculates the exact physical placement of every item, ensuring that the truck functions as an optimized workspace rather than just a storage box.

Challenges we ran into

The biggest challenge was that the solution couldn't just be a routing map; it had to manage physical, real-world dimensions. We had to account for diverse product formats, weights, volumes, and truck capacity limits. Furthermore, reverse logistics posed a major technical hurdle. Because the truck drops off full products and simultaneously collects empty barrels and crates, the cargo space is constantly shifting. Building a dynamic return allocation model to manage this shifting 60% return rate was highly complex.

Accomplishments that we're proud of

We are incredibly proud of achieving a "Zero Re-handling" framework. By stepping away from the extremes—100% warehouse-focused or 100% client-focused grouping—we engineered a hybrid model that respects both realities. Translating this complex spatial and routing algorithm into an accessible 3D Visual Guide is a massive win that directly improves driver ergonomics and time management.

What we learned

We learned that in an extensive network like Damm's HORECA distribution, true optimization goes far beyond finding the shortest driving distance. Operational friction, such as searching for products in a poorly packed truck or struggling with unloading, causes the most significant delays. We realized that effective logistics technology must deeply respect physical operations and the human element—the drivers and warehouse workers.

What's next for Damm Truck

Moving forward, we want to validate our Hybrid Engine against live operational data and conduct a live demo to measure the exact time saved per stop. We also plan to enhance the UX of the 3D Visual Guide, making it an interactive tool for both warehouse operators and drivers. Ultimately, we aim to scale the Damm Smart Truck logic across DDI's network of 33 centers and 470 vehicles, driving better customer service and more sustainable city logistics through reduced unnecessary movements.

Built With

• etc
• next.js
• react
• streamlit