Fixing the Decision Speed Gap in Modern Supply Chains - with Joris Wijpkema of Optilogic

2026-06-15 · Source: The AI in Business Podcast · Field: Business & Management — Operations & Process Management, Corporate Strategy & Leadership · Depth: Intermediate, extended

Summary

Optilogic's Executive Vice President, Joris Wijpkema, highlights that modern supply chain organizations face a "decision speed gap" during disruptions because traditional Advanced Planning Systems (APS) and Enterprise Resource Planning (ERP) systems are designed for execution, not rapid evaluation of structural or network-level changes. He advocates for a dedicated, high-compute modeling layer, an AI-native platform, that can run thousands of supply chain scenarios in minutes. This approach enables teams to quickly assess routing, inventory strategies, and network configurations at granular levels, feeding optimized decisions back into existing systems. This integration of design-grade optimization into daily planning cycles strengthens resilience, builds trust in modeling before crises, and allows for faster, better-aligned responses to market volatility.

Key takeaway

For Directors of AI/ML or Operations Professionals seeking to enhance supply chain resilience, recognize that your existing APS/ERP systems are insufficient for rapid, structural decision-making during disruptions. You must invest in a dedicated, AI-native modeling layer to run thousands of scenarios in minutes, integrating design optimization directly into your planning cycles. This will enable your teams to make faster, data-backed decisions, build organizational trust in modeling, and proactively adapt to market volatility, transforming crises into potential opportunities.

Key insights

Supply chains need a dedicated, high-compute modeling layer to rapidly evaluate thousands of structural scenarios, augmenting traditional planning systems.

Principles

• Traditional APS/ERP systems prioritize execution over structural optimization.
• Resilience requires integrating design-grade optimization into planning.
• Build trust in modeling capabilities before a crisis hits.

Method

Implement a digital twin modeling layer that leverages cloud computing to run thousands of parallel simulations and scenario evaluations, then use algorithms and AI to process data and identify optimal strategies.

In practice

• Identify specific supply chain pain points addressable by digital twin models.
• Convert existing strategic network models into multi-period planning models.
• Establish live data pipelines from ERP/APS to feed design models.

Topics

• Supply Chain Resilience
• Supply Chain Design
• Scenario Evaluation
• Digital Twins
• Advanced Planning Systems
• Network Optimization

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, Operations Professional, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.