Modern Metal Mills

Build a software-defined operating system for modern metal mills designed to compress lead times, increase flexibility, and improve margins. Objective: Re-architect scrap-to-metal manufacturing around AI-driven planning, real-time execution, and energy-aware optimization — enabling mills to operate faster, more flexibly, and more profitably. This is not traditional MES or ERP software. It is infrastructure for building next-generation American mills. Core vision: Most mills optimize for tonnage and furnace utilization. Modern mills should optimize for: - Lead time - Margin per run - Flexibility for short and custom orders - Energy efficiency - Setup-time elimination The platform should enable mills to move from 8–30 week lead times toward compressed, software-optimized production cycles. Core system architecture: 1. AI-Driven Production Planning Engine - Demand-aware scheduling that prioritizes speed and margin, not just tonnage - Dynamic melt and rolling plans based on order mix - Short-run optimization to treat small batches as profitable opportunities - Changeover minimization modeling - Real-time schedule adaptation when disruptions occur The system must continuously re-plan as conditions change. 2. Scrap Intelligence and Alloy Optimization - Scrap batch registration with composition metadata - Predictive alloy blending to maximize yield and reduce waste - Dynamic recipe adjustment based on scrap availability and cost - Margin simulation per recipe before furnace charge Optimize for yield and profitability, not just spec compliance. 3. Energy-Aware Operations Layer - Energy cost per ton modeling in real time - Load-shifting recommendations based on power pricing - Integration hooks for on-site generation and storage - Simulation of alternative energy sourcing strategies - Alerts for energy inefficiency events Energy must become a controllable production variable. 4. Real-Time Execution and Traceability - Lot-level event timeline from scrap intake to shipment - Automated deviation detection during melt, cast, roll, or finishing - Exception alerts for out-of-spec chemistry - Automated hold gates for QC anomalies - Full traceability for compliance and customer transparency Every ton should be digitally traceable end-to-end. 5. Throughput and Setup-Time Compression - Changeover tracking and bottleneck analysis - Setup-time reduction insights - Equipment utilization heatmaps - Predictive maintenance signals tied to downtime events - Simulation of process improvements before deployment Goal: eliminate variability and tribal knowledge dependence. 6. Order-to-Ship Optimization - Direct mill quoting capability - Margin-aware order acceptance logic - Inventory forecasting tied to demand variability - Short lead-time commitments backed by real-time production modeling - On-time shipment probability scoring Mills should become responsive manufacturers, not backlog managers. 7. Workforce Augmentation Layer - Operator-friendly interface for desktop and tablet - AI copilot for operations managers - Guided override workflows with role-based approvals - Contextual recommendations during production runs - Institutional knowledge capture to reduce reliance on tribal expertise Roles: - plant-admin - operations-manager - procurement-lead - quality-engineer - logistics-coordinator 8. Industrial Data Architecture Data model: - suppliers - scrap-batches - furnace-runs - alloy-recipes - production-lots - qc-tests - customer-orders - shipments - equipment - downtime-events - energy-metrics - schedule-models - audit-logs System requirements: - Event-sourced traceability for every lot - Exception alerts for spec and schedule deviations - Role-based approvals for recipe overrides and shipment release - IoT/SCADA-ready integration architecture - Deterministic replay of production decisions - High reliability suitable for industrial environments Dashboards: - Lead time compression trend - Yield by recipe - Scrap utilization rate - Energy cost per ton - Margin per production run - On-time shipment rate - Defect trends - Setup-time reduction over time Positioning summary: This is not incremental mill software. It is the operating system for modern, software-defined metal manufacturing. It enables: - Faster lead times - Profitable short runs - Energy-optimized production - Reduced setup time - Improved margins - Fully traceable, flexible manufacturing The goal is to transform legacy mills into modern industrial systems capable of rebuilding domestic metal production at lower cost and higher speed. Create a modern startup design inspired by Y Combinator (YC) companies. Choose one bright primary color and build a clean, minimal color scheme around it. The design should feel bold, simple, and product-focused with strong typography, generous whitespace, and clear hierarchy.