AI-powered operating system for supply chain management
Unique Architecture
VORPI - 5 dimensions of supply chains
- Modular design capturing VORPI (vendors, operations, resources, products, and intelligence) dynamics
- AI built on core dimensions and trade-offs
Designed for supply chain complexity with transactional granularity
- Data aggregation reduces granularity, leading to information loss and less accurate forecasts.
- Optimization tools are often inflexible and rely on unrealistic assumptions.
- Reporting applications can obscure or conflate critical operational trade-offs.
VORPI AI addresses these issues through research-backed innovations:
Accurate Demand Forecasting
Uncertainty modeling for robust and precise forecasting.
- 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.
Inventory & Fulfillment Optimization
Large-scale optimization under uncertainty.
- Built on dynamic optimization and decomposition techniques.
- Optimizes end-to-end decisions (including procurement, production, and fulfillment) across the supply chain.
Detailed Scenario Analysis
Simplified scenario analysis in complex environments.
- Built on the VORPI framework (Vendors, Operations, Resources, Products, Intelligence), articulating the five fundamental supply chain trade-offs.
- Structures scenario analysis directly around these interacting trade-offs.
Real Time Activity Monitoring
Blending operational monitoring with predictive foresight.
- 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 twin, different decisions. Clear outcomes for each audience.
Manufacturers
Plan and execute under deep uncertainty.
- Inventory optimization under demand, production lead time, and supply lead time uncertainties.
- State-of-art demand forecasting with advance order and demand lead time variations.
- Seamless integration of sales transactions and MPS/MRP.
Wholesalers
Allocate scarce supply across customers.
- Inventory optimization with downstream proliferation of locations.
- State-of-art demand forecasting with price-dependent product selection.
- Strategic inventory placement across distribution and fulfillment centers.
Retailers
Keep products where and when customers buy.
- Omni-channel inventory optimization.
- State-of-art demand forecasting with store and website traffic, product selection, and pricing dynamics.
- Streamline replenishment at key locations.
Major breakthrough in supply chain management
Perfect decomposition of predictive, optimization, and reporting tools.
VORPI AI redefines supply chains in the most simple way.
Modern supply chain management is all about receiving inputs from (1) vendors (e.g., raw material suppliers), carrying out (2) operations that utilize some (3) resources, and move (4) products to the market in an (5) intelligent way to meet customer demand. These five elements are the key dimensions of supply chains, representing different trade-offs decision makers face.
Vendors
In-house vs outsourcing.
Operations
Excess inventory vs shortages.
Resources
Efficiency vs responsiveness.
Products
Standardization vs customization.
Intelligence
Inventory imbalances vs revenue growth.
Decomposition keeps it fast at high granularity
Instead of one opaque model, VORPI AI coordinates modular subproblems (forecasting, inventory, sourcing, fulfillment) through shared constraints and KPIs.
Case studies
Evidence-based research tested extensively in practice.
Success stories
Our case studies went through extensive academic reviews to justify our claims.
- Working capital reduction and accurate demand forecasting in the automotive industry. Read our article published at the Harvard Business Review!
- Inventory and fulfillment optimization in the manufacturing, wholesale, and retail industries. Read our book published by Springer!
- Supply chain design boosting performance. Read our book published by the Cambridge University Press!
Enterprise readiness
Start small, scale confidently.
Pilot-first
Integration-friendly architecture
Tailored to your company's needs
Team
Researchers with professional experience focusing on measurable operational performance.
Our Founder
Isik Bicer, PhD
Founder, VORPI AI • Supply chain analytics & optimization
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.
Run VORPI AI on Your Supply Chain
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