Data Science Center for Decision Making
The Data Science Center for Decision Making is a joint initiative of Purdue University’s Mitch Daniels School of Business and the College of Engineering, built on the legacy of David Simchi‑Levi’s pioneering Data Science Lab at MIT.
The center’s mission is simple but ambitious: turn data, models and advanced AI into tools that solve high‑impact problems in industry. Anchored at Purdue's business school and Edwardson School of Industrial Engineering, the center is where analytic innovation meets operational reality.
Through partnerships across sectors including airlines, finance, high‑tech, insurance, manufacturing and retail, we help organizations turn data into decisions — and decisions into durable competitive advantage. Our current focus integrates predictive, prescriptive and generative AI to automate and advance autonomy in essential business processes.
The center’s roots trace back to global supply disruptions in 2010 and 2011 — volcanic eruptions, tsunamis and floods that halted production across continents. Those events drove Simchi‑Levi, then at MIT, to found the Data Science Lab, dedicated to understanding and mitigating hidden supply chain risks.
When Ford Motor Company sought his team’s help after those disasters, the goal was to expose vulnerabilities across its supply network. The results were surprising: weaknesses weren’t only in large suppliers but often hidden in small, low‑cost component makers across global markets. The research gave Ford new visibility and later appeared in leading journals and Harvard Business Review — a method soon recognized as one of Ford’s top engineering technologies.
In late 2019, as Simchi‑Levi and collaborator Michelle Wu monitored the fast‑moving COVID virus while traveling in Asia, they used earlier modeling tools to simulate potential impacts. The conclusion was clear: global supply chains could collapse by March 2020 — and they did.
Their analysis drew global attention. Shortly after, the team proposed supply chain stress testing, urging companies to measure their exposure before the next shock hit. The framework caught the interest of policymakers and was later referenced in the U.S. President’s Economic Report as a model for analyzing critical supply chains.
At the heart of this work were two metrics that redefined resilience: Time to Recover (TTR) and Time to Survive (TTS).
Together, they helped leaders identify both fragile and overprotected operations. Short TTS values mark critical risks; long ones reveal excess buffers that tie up capital.
Collaborations with global manufacturer Denso showed why short stoppages can trigger long recovery periods. Even a 10‑day wafer plant shutdown could cause six months of downstream disruption. During the pandemic and semiconductor crisis, these insights proved invaluable for companies balancing cost, continuity and capacity.
The lab’s data‑driven methods soon expanded beyond supply chains to revenue optimization and customer experience.
Dynamic pricing became the next major frontier. Partnering with digital platforms and retailers, the team built algorithms that adapt prices automatically. With Zalando, Europe’s largest online fashion marketplace, the lab developed a pricing engine that updates more than 1.5 million products across 23 countries weekly, matching local demand while improving profitability and customer satisfaction.
Personalization grew as a complementary field. Because of the lab’s work, the airline industry now uses AI‑based systems to tailor recommendations for each traveler — from seating upgrades to hotel offers — based on behavioral data. Both innovations share the same foundation: turning mathematical models and data analysis into practical, scalable business value.
Now based at Purdue, the Data Science Center for Decision Making is charting its next chapter through AI. Its research merges three forms of intelligence to build decision systems that evolve from analysis to autonomy:
Together, these layers enable organizations to move from automation to true autonomy: AI systems that learn, adapt and act with human oversight. As Simchi‑Levi notes, the goal is not to replace human judgment but to extend it — turning planners into strategists who design the objectives, while AI handles execution.
Purdue provides the ideal foundation for this vision. Its combination of engineering strength, manufacturing tradition and leadership in data science fuels the center’s applied research agenda.
Operating at the intersection of academia and industry, the center co‑develops new methods, tests them in real operations and scales proven results. Faculty work side by side with students on projects — from supply chain stress testing to autonomous process design — developing solutions that shape the future of analytics‑driven decision‑making.
As Simchi‑Levi explains, “Purdue offers the perfect convergence of engineering strength, manufacturing leadership and data‑driven innovation. It’s an ideal environment to advance autonomous supply chains, AI‑driven manufacturing and the future of business analytics.”
From natural disasters to the pandemic to the accelerating rise of AI, the journey from MIT’s Data Science Lab to Purdue’s Data Science Center for Decision Making reveals how resilience evolved into intelligence — and intelligence into autonomy.
For industry, the message is clear: the next frontier of decision‑making isn’t surviving disruption, but thriving through it by embedding data, AI, and human insight into every choice a business makes.
