Waste Reduction Optimization with Predictive ML
Manufacturing Sector
Up to 50% Waste Reduction per Work Order + 35%+ Improvement in Surplus Levels
Waste Reduced by up to 50% (or more in some cases)
Surplus Levels Improved by over 35%
Predictive Waste Forecasting Before Execution
Introduction
As part of the strategic evolution from DataSphere to Simov Labs, this case demonstrates a machine learning-powered solution that enabled a multinational manufacturing company to predict and proactively reduce production waste, driving significant cost savings and operational efficiency during their digital transformation journey.
Business Challenge
A large manufacturing company operating in more than six countries experienced high levels of material and resource waste across production stages, negatively impacting profitability. There was no predictive tool to forecast waste levels before executing work orders, leading to reactive management, inefficiencies, and higher costs. Plant managers and production teams lacked an efficient system to optimize resource and machinery usage, resulting in preventable losses and suboptimal process control.
Implemented Solution
- 1Developed a supervised Machine Learning model using Random Forest and logistic regression techniques, trained on historical production data and initial work order configurations to predict potential waste levels for each order.
- 2Generated actionable recommendations for optimal materials, measurements, and machinery to minimize waste.
- 3Integrated the solution with the company's ERP system for real-time data extraction, traceability, and reporting.
- 4Implemented automated email alerts for high-risk work orders with elevated waste potential.
- 5Designed user-friendly dashboards and views with clear visualizations, enabling plant managers and supervisors to quickly interpret predictions and apply optimizations.
Obtained Results
Reduced waste per work order by up to 50% (or more in certain cases) through proactive prediction and optimization.
Improved surplus levels by over 35%, preventing overproduction and enhancing overall plant metrics.
Boosted profitability by minimizing material losses and resource inefficiencies.
Enabled faster, data-driven decision-making for production teams with automated alerts and clear recommendations.
Increased operational efficiency and confidence in process control, supporting broader digital transformation goals.
Evolution in Simov Labs
This predictive waste reduction project is now fully integrated into our portfolio at Simov Labs. We evolve these ML models into advanced, cloud-native implementations (Snowflake, Databricks, BigQuery) with production-ready AI/ML for real-time waste forecasting, continuous model retraining, automated optimization recommendations, integration with modern ERPs/IoT, and explicit ROI tracking.
Aligned with DAMA and TDWI governance frameworks, our current solutions emphasize business-first analysis, radical honesty in opportunity assessment, and long-term partnership—ensuring waste reduction delivers sustainable cost savings, resource efficiency, and competitive advantage in manufacturing operations. No promises. Just results.
Ready to predict and eliminate production waste with AI-driven insights that improve margins and efficiency?
Book a free 30-minute consultation. We'll discuss your production metrics and where predictive waste optimization can deliver the most impact.