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Overview
Verdis is an AI supply chain planning platform covering integrated business planning, optimization, and digital twin scenarios. However, some report that interface and performance improvements are needed. Still, teams value its real-time visibility, scenario planning, and the breadth of analytics that support complex manufacturing and logistics operations.
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Starting Price
Custom
Verdis Specifications
Natural Language Dialogue
Smart Data Discovery
Self-Service Dashboards
Text To Speech And Speech To Text
What Is Verdis?
Verdis is an AI-based decision engine for supply chains that unifies planning and execution across functions. It combines probabilistic demand sensing with optimization for production, inventory, and dispatch, producing feasible plans that respect real-world constraints. A digital twin simulates scenarios to test policies before rollout and quantify risk. Built for manufacturers, retailers, and logistics networks, it helps cut cost-to-serve, improve service levels, and respond to volatility with data-backed decisions.
Verdis Pricing
Disclaimer: The pricing is subject to change.
Verdis Integrations
Who Is Verdis For?
Verdis is ideal for a wide range of industries and sectors, including:\
- Automotive and OEM
- Consumer goods
- Distribution
- Ecommerce
- Retail
- Manufacturing
- Logistics
Is Verdis Right For You?
Verdis suits organizations that need mathematically optimized plans tied to day-to-day execution. If your network faces volatile demand and frequent constraints, its digital twin and scenario planning help test policies, quantify risk, and align supply, production, and distribution. Integrated business planning and optimization can lower cost to serve, improve service levels, and shorten replanning cycles when connected to operational systems.
Still doubtful if Verdis is the right fit for you? Connect with our customer support staff at (661) 384-7070 for further guidance.
Verdis Features
Verdis unifies planning across demand, supply, and finance so teams evaluate tradeoffs in a single model. This supports scenario analysis that balances cost, service, and risk while converting plans into actionable schedules for plants, warehouses, and fleets.
A digital twin enables what if experimentation against realistic constraints. Operations teams can test policies for sourcing, capacity, or transportation and observe KPI impacts before executing, reducing risk in complex networks.
Optimization models generate feasible, efficient production schedules across multiple sites and stock keeping units, honoring resource constraints and changeovers. The result is an error-resistant plan that supports daily execution.
Dispatch and allocation tools optimize loads and routes to reduce freight cost while meeting service targets. Teams gain visibility into tradeoffs and can track realized performance against model expectations.
Dashboards consolidate KPIs with real-time data from connected systems, creating a shared view for planners and executives. This shortens analysis cycles and focuses attention on exceptions that need action.
