our Focus
One of the key challenges in supply chains is improving the accuracy of quality monitoring. We are working on generative learning approaches that can extract rich information from production line records, thereby eliminating the need for a large numbers of samples that were produced under normal operational conditions.
Overview
Autonomous supply chains with agent-based systems
Current Themes
Predicting the formation of “hidden dependencies” in supply networks
People
Predicting risk and disruptions in supply networks
We study Supply Networks by taking a Complex Systems lens, recognizing their emergent nature. In fact, the very beginning of our lab was motivated by this emergent context which led to the data-driven perspective we take today.
Foresight & Scenario Planning
Trend Analysis: Examine emerging clinical technologies, behavior trends, and regulatory shifts.
Scenario Development: Craft multiple “possible futures” to capture various industry trajectories (e.g., technology breakthroughs, policy changes, global health crises).
Vision Definition: Establish a bold, outcome-focused vision of how your organization fits into these future landscapes.
Backward Road mapping
High-Level Milestones: Work backward from each scenario to identify essential achievements over 3, 5, or 10 years.
Tactical Objectives: Translate high-level milestones into specific, time-bound goals (e.g., adopting AI-based clinical workflows, building blockchain ecosystems for supply chain tracking).
Risk Assessment: Identify potential barriers—technical constraints, cultural resistance, regulatory hurdles—and plan mitigation strategies.
Implementation & Agile Iteration
Pilot Projects: Launch controlled experiments to validate feasibility and refine strategies.
Ongoing Feedback Loops: Track progress using real-time dashboards, adjusting plans as market conditions evolve.
Scalable Deployment: Roll out proven initiatives organization-wide, ensuring alignment with your overarching future-back vision.