Saka Modernized Car Dealership Logistics with Google Cloud AI
Goal: Seamless Digital Car Sales
Saka, a used car retail chain with 33 locations across Finland, completes approximately 4,000 car sales each month. Around one-third of these transactions are digital. The company aimed to reduce unnecessary vehicle transfers between locations, speed up sales processes, and lower the environmental impact of its operations. The goal was to implement data-driven decision-making to better match vehicle placement with customer demand.
Optimizing Car Logistics
Saka and Codento developed an AI-based logistics solution called “AIwot.” The solution leverages Google Cloud data, AI, and machine learning technologies to analyze Saka’s inventory, customer, and market data, predicting the most suitable location for each car. The system is integrated into Saka’s data warehouse environment and presents its forecasts using existing tools like Google Sheets and other summary dashboards. While Saka’s logistics team makes the final decisions on vehicle placement, AIwot provides them with data-driven predictions to support the process.
Partnership with Codento
Codento’s ability to quickly grasp Saka’s business needs and translate them into a functional AI solution was a key factor in the project’s success. Codento was responsible for developing the AI model, ensuring data quality, and implementing the technical solution. The partnership focused on delivering the solution, which positioned Codento as a key partner in Saka’s digital development.
Results and Future Development
The AIwot solution was developed and deployed in just a few months. It has helped Saka reduce unnecessary vehicle transfers, optimize sales times, and decrease the environmental impact of its operations. The project also laid the groundwork for the wider use of AI within the company. Saka is now exploring new AI solutions to improve customer experience and operational efficiency, with a focus on data quality and developing AI expertise within its teams.
In Saka’s Own Words
“AiWot is another significant step forward in our data journey, helping our teams understand what building these models and making better decisions really means. For us, the customer is always the most important thing, so leveraging AI and data capabilities like Vertex AI and various AI/ML models helps us serve our customers even better. AI also increases employee satisfaction, as complex decisions can be supported with more accurate data and predictions, and tedious tasks can be automated. Aiwot was a great pioneering program that laid the foundation, but we are only at the beginning, and there is still a lot of excitement ahead.”
— Petteri Heinonen, Chief Technology & Data Officer, Saka
The technologies we used:
| Google technology | Usage in Saka’s case |
| Vertex AI | Pipelines are used for data preprocessing, model training, and serving |
| BigQuery | Storing prediction and explainable AI results |
| + Tableau & Sheets | Visualization for predictions |
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