Work / Manufacturing & Logistics
Advanced Manufacturing Planning System

ScheduleIQ

An advanced planning and scheduling system for manufacturers — demand forecasting, constraint-based production scheduling, and capacity optimization via a high-performance C++ planning engine.

C++ Core
Planning Engine
APS First
Market Category
MIT (CE)
License
Docker
Deployment

Overview

ScheduleIQ is a demand forecasting and advanced planning & scheduling (APS) system for manufacturing companies. It generates optimized production schedules using constraint-based planning, time-series forecasting, and lean manufacturing principles — addressing a software category dominated entirely by $500,000+ enterprise contracts (SAP APO, Oracle ASCP, Kinaxis). The Community Edition is MIT-licensed, Docker-deployable, and delivers capabilities that mid-market manufacturers have historically been priced out of.

The Challenge

Manufacturing companies running on Excel-based scheduling or native ERP planning modules consistently leave 15–30% of production capacity underutilized while simultaneously experiencing material shortages and late orders. The master production scheduling problem — allocating capacity, material, and labor across hundreds of concurrent orders subject to dozens of constraints — is computationally intractable without a purpose-built optimization engine. Traditional APS software from SAP, Oracle, and Kinaxis solves the problem but requires half-million-dollar implementations that mid-market manufacturers cannot justify.

What We Built

ScheduleIQ’s C++ planning engine handles the performance-critical optimization algorithms — constraint propagation, capacity leveling, and material requirements planning — at the speed that real-time scheduling requires. The Python integration layer connects to ERP systems (FactoryOS, Odoo, SAP) via REST API for demand and inventory data. Django provides the web application layer and planning board UI, where planners can visualize Gantt charts, drill into capacity bottlenecks, and override algorithmic recommendations. Time-series forecasting uses statistical methods to generate demand plans from historical order data. The Community Edition under MIT license covers the core planning capabilities; Enterprise and Cloud editions add advanced forecasting, S&OP, and commercial support.

Results

  • C++ Core — Planning Engine. Performance-critical optimization in compiled code
  • APS First — Market Category. Proprietary alternative in a category owned by $500K software
  • MIT (CE) — License. Community Edition free — Enterprise tier available
  • Docker — Deployment. Single-command deployment, ERP integration via REST
More Work

Related case studies.

Get Started

Have a project like ScheduleIQ?

Tell us about your problem. We'll tell you honestly how we'd approach it — and whether we're the right team.