Quotation Process Automation with ML
Manufacturing Sector
From Hours-Long Manual Quotes to Minutes with >85% Accurate ML Recommendations
Generation Time: Hours → Minutes
Model Accuracy: >85%
Up to 4 Personalized Offers per Click
Introduction
As part of the strategic evolution from DataSphere to Simov Labs, this case demonstrates a machine learning-driven automation that transformed a slow, error-prone manual quotation process into a fast, accurate, and competitive tool for a manufacturing company.
Business Challenge
A manufacturing client relied on a fully manual quotation generation process without proper documentation, standardization, or data leverage. This caused frequent errors, inconsistencies, long response times compared to competitors, and missed opportunities. The process ignored historical patterns and external factors (e.g., raw material and electricity price fluctuations), reducing accuracy, speed, and ability to close deals effectively.
Implemented Solution
- 1Developed supervised Machine Learning models and recommendation algorithms trained on historical quotation data and patterns to predict optimal pricing and terms.
- 2Achieved model training accuracy of over 85%.
- 3Built and deployed an API to serve the ML models in real-time.
- 4Created a simple, user-friendly web front-end interface for capturing client inputs and generating up to four personalized quotation options with a single click.
- 5Automated output delivery in professional PDF format.
- 6Operationalized the entire solution for seamless, real-time use by the sales/procurement team.
Obtained Results
Drastically reduced quotation generation time from several hours to just a few minutes, enabling faster responses and higher competitiveness.
Improved quotation accuracy by incorporating historical data and external price fluctuations, leading to better deal-closing rates.
Minimized errors and inconsistencies through standardization and automation.
Freed up the procurement/sales team to focus on strategic activities rather than manual calculations.
Achieved rapid user adoption thanks to an intuitive interface, making the tool indispensable in daily workflows.
Delivered up to four tailored offers per client request, increasing flexibility and conversion potential.
Evolution in Simov Labs
This ML-powered quotation automation project is now fully integrated into our portfolio at Simov Labs. We evolve these models into advanced, cloud-native implementations (Snowflake, Databricks, BigQuery) with production-ready AI/ML for dynamic pricing recommendations, real-time external data integration (e.g., commodity prices), automated sensitivity analysis, continuous model retraining, and explicit ROI tracking.
Aligned with DAMA and TDWI governance frameworks, our current solutions emphasize business-first analysis, radical honesty in opportunity assessment, and true partnership—ensuring quotation processes deliver sustainable speed, accuracy, margin protection, and competitive edge in dynamic markets. No promises. Just results.
Ready to automate your quotation process with accurate, AI-driven recommendations that close more deals faster?
Book a free 30-minute consultation. We'll discuss your quotation workflow and where ML automation can deliver the most impact.