In a trillion-dollar industry where clothing fit determines the success of sales, we distinguish ourselves by tackling the issue at its root - pre-production variance. Poor fit, which accounts for 70% of returns and results in a £230B loss p/a, is now a solvable problem. At FitCollective, we address poor fit from the outset, before production begins. FitCollective, a UK-based manufacturing optimisation SME, is developing a machine learning (ML)-driven SaaS solution that lowers returns, reduces waste, and boosts profits by identifying and preventing sizing discrepancies across fashion supply chains. By consolidating supply chain data into a unified dataset for ML models and enriching it with our material science expertise, the solution effectively manages the scale, speed, and complexity of fast-fashion supply chains. The ML algorithm identifies discrepancies, simulates sizing variations, and suggests corrective actions for future batches, transforming fit from a subjective decision to a data-driven science. The ProductionOptimiser is the only market solution that improves fit before production. FitCollective predicts a 38% reduction in fit-related returns, a 10% increase in gross profits, and an 80% reduction in fit development time. Furthermore, achieving a 38% better fit is expected to enhance conversion rates both in-store and online and increase customer lifetime value by up to 200%. We are developing the ProductionOptimiser to transform tomorrow's fashion supply chain, ensuring happier customers, boosting sales, and reducing fashion waste.