With each sprint, we continuously develop our products, Demand Forecasting and Product Carbon Intelligence. In our latest sprint, our team worked hard to implement significant improvements that we would now like to present to you.
Product Carbon Intelligence
Improved Data Quality through Manual Annotation 🔍
Our team successfully conducted manual annotation of emission factors to create precise benchmark data for our collaboration with AI Singapore. This measure is crucial for optimizing our matching algorithm and has deepened our understanding of our customers' challenges. It confirms the necessity of automation to further enhance efficiency.
Enhanced Accuracy through Rule-Based Matching 🎯
We have implemented a new, rule-based matching system that significantly improves the assignment of emission factors. By selectively filtering emission categories, we reduce confusion and ensure higher-quality assignments. This improvement leads to more accurate data, providing a reliable foundation for more precise sustainability reports and well-informed decisions.
Technological Advances and Improved User Interface 💻
The transition to the Postgres database is nearly complete, ensuring increased stability and scalability. Additionally, we have comprehensively optimized the user interface, including enhanced editable tables for production plans and targeted bug fixes in reporting. These updates contribute to a smoother and more reliable user experience.
Demand Forecasting
Optimized Widget Performance ⚡️
Based on customer feedback, we have specifically improved the performance of our forecasting widgets, resulting in significantly reduced loading times and increased responsiveness. Our tools are now more powerful and user-friendly, allowing customers to retrieve and process data faster, greatly enhancing the efficiency of their forecasts.
Backend Optimizations for Increased Stability ⚒️
We have made substantial improvements to our backend systems, including migrating from Redis streams to hash maps. This adjustment, along with increased data persistence, ensures greater stability and reliability, even under heavy usage. Our system is now better equipped to handle growing demands and support future growth seamlessly.
Advanced Data Processing and Caching 🚀
Our new caching mechanisms significantly reduce data retrieval times. Whether reviewing past forecasts or analyzing new datasets, users benefit from lightning-fast loading times, making our platform more efficient and user-friendly.
Improvements in Ensemble Calculation 📊
We reviewed our methods for ensemble calculation and found that simpler approaches, such as mean and weighted mean calculations, yield the best results. These refinements are now being integrated to improve the accuracy and reliability of our forecasting models, further increasing value for our customers.
We are continuously working to develop innovative tools that not only meet but exceed your expectations. Look forward to more exciting updates and improvements in the coming weeks.
Do you have suggestions or questions? Feel free to share your feedback with us via email at feedback@pacemaker.ai.