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Kinaxis Maestro

Concurrent planning platform from a TSX-listed specialist

Overview

Kinaxis is a supply chain planning company founded and headquartered in Ottawa, Canada. It was founded in June 1984 as Cadence Computer Corporation, launched an in-memory planning engine in 1986, was renamed Kinaxis in 2005, released its first cloud platform the same year and listed on the Toronto Stock Exchange (KXS) in June 2014[2]. Its current platform, Maestro, is described as an AI-infused, end-to-end supply chain platform[2].

Maestro’s distinguishing approach is concurrent planning: demand, supply, inventory and capacity plans run on one in-memory data model, so a change in the forecast propagates immediately and planners can compare scenarios. Kinaxis added AI and machine-learning applications from 2018 and joined planning with execution in 2022[2]. It says it was named a Leader in the 2026 Gartner Magic Quadrant for Supply Chain Planning Solutions for process and discrete industries[2].

Kinaxis reported 2025 revenue of US$548.0 million, up 13%, SaaS revenue of US$362.4 million, up 17%, and year-end ARR of US$433 million, up 20%, with 2026 revenue guidance of US$620–635 million. Razat Gaurav is chief executive[1]. Pricing is quote-based and projects are substantial, making Maestro better suited to large manufacturers than to small distributors.

Kinaxis Maestro features

  • Concurrent demand, supply and inventory planning
  • In-memory scenario simulation
  • AI and machine-learning forecasting
  • Planning linked to execution
  • Open platform for third-party developers
  • Concurrent supply chain planning
  • Demand, supply and inventory planning
  • Sales and operations planning

Kinaxis Maestro pricing

Quote-based SaaS subscription; no published prices.

Kinaxis Maestro strengths and limitations

Strengths

  • Single data model for concurrent planning
  • Strong scenario and what-if capabilities
  • Audited public-company financials
  • Long planning heritage
  • Fast concurrent planning across the network
  • Audited public financial disclosures

Limitations

  • No published pricing
  • Large implementation effort
  • Oriented to big manufacturers
  • Retail store-level forecasting less central
  • No native ERP, WMS or TMS
  • Requires strong data integration

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