> For the complete documentation index, see [llms.txt](https://docs.wagon.network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.wagon.network/products/data-network-upcoming/use-cases.md). # Use Cases The Wagon Data Layer is designed to serve multiple stakeholders across the supply chain and financial ecosystem. Here are key real-world applications: *** ## 📦 For Logistics & Supply Chain Businesses * **Operational Optimization**\ Analyze fleet usage, idle time, and route efficiency to improve asset performance. * **Predictive Maintenance**\ Use on-chain data trends to forecast when vehicles or equipment may require servicing. * **Inventory & Demand Forecasting**\ Match asset availability with delivery schedules and regional demand. *** ## 💼 For Investors & Credit Analysts * **Risk Evaluation**\ Assess businesses reliability using real-time rental payment data and asset productivity. * **Yield Benchmarking**\ Compare leasing pools by return profile, operational efficiency, and default history. *** ## 📊 For Enterprise & Research Analysts * **Market Insights & Trend Monitoring**\ Track industry-wide metrics like leasing activity, repayment behavior, and asset utilization. * **Macroeconomic Correlation**\ Connect logistics performance data to external indicators like commodity prices or economic cycles. *** ## 🤖 For Developers & Data Scientists * **AI Model Training**\ Use high-quality, anonymized operational datasets to build predictive models. * **Custom Analytics Tools**\ Access structured data via API to build dashboards, alerts, or internal reporting tools. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.wagon.network/products/data-network-upcoming/use-cases.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.