Turning Supply Chain Complexity into Simplicity
VORPI AI is a next-generation supply chain platform that combines advanced forecasting, optimization algorithms, and LLMs to maximize end-to-end supply chain performance. It is built around two integrated modules: Intelligence (Sales, Forecasting, Decisions, Planning) and Engine (Vendors, Operations, Resources, Products). Intelligence determines what actions should be taken, while Engine ensures those actions are executed, governed, and tracked across the organization.
At its core, supply chain management revolves around five dimensions: vendors, operations, resources, products, and intelligence. These dimensions capture the fundamental trade-offs firms face when moving from raw materials to final products. VORPI AI is designed around this structure and addresses three major gaps in today’s supply chain software: (1) data aggregation that reduces granularity and weakens forecasting accuracy, (2) rigid optimization tools built on unrealistic assumptions, and (3) reporting systems that obscure critical operational trade-offs. By unifying these elements, VORPI AI enables faster, more informed, and more scalable decision-making.
- Built on the fast Fourier transform and machine-learning regularization
- Operates directly on transactional data while incorporating customer traffic, product-selection, and demand lead-time dynamics
- Built on dynamic optimization and decomposition techniques
- Optimizes end-to-end decisions including procurement, production, and fulfillment across the supply chain
- Built on the VORPI framework across Vendors, Operations, Resources, Products, and Intelligence
- Structures scenario analysis directly around interacting supply-chain trade-offs
- Powered by a database model uniquely tailored to the VORPI framework
- Enables rapid information transfer and continuously updates system trajectories in real time
Built for manufacturers, wholesalers, and retailers
Same software, different decisions. Clear outcomes for each audience.
- Inventory optimization under demand, production lead time, and supply lead time uncertainties
- State-of-the-art demand forecasting with advance order and demand lead time variations
- Seamless integration of sales transactions and MPS/MRP
- Inventory optimization with downstream proliferation of locations
- State-of-the-art demand forecasting with price-dependent product selection
- Strategic inventory placement across distribution and fulfillment centers
- Omni-channel inventory optimization
- State-of-the-art demand forecasting with store and website traffic, product selection, and pricing dynamics
- Streamline replenishment at key locations
Technological Innovation
VORPI AI is built on decomposing the structure and analysis of supply chains. Its unique database and algorithmic architecture minimize inefficiencies and unlock the full potential of LLMs.
Team
Researchers with professional experience focusing on measurable operational performance.
Isik is a tenured professor of operations management and information systems at the Schulich School of Business, York University, where he leads the Supply Chain Analytics Lab. With 19 years of academic and industry experience, he has published 15 top-tier journal articles, authored a book on supply-chain analytics with Springer Nature, and a second book on digital transformation with Cambridge University Press. The analytical tools and frameworks he has developed have been adopted across the pharmaceutical, automotive, agriculture, and finance industries.
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