Tariffs, Trade, Turmoil and Rethinking Holiday Shopping

Published: June 28, 2025

Retailers are gearing up for their annual “Christmas in July” sales, but this year, the early holiday cheer comes with a warning: escalating tariff whiplash may lead to higher prices, fewer options, and major delays for shoppers come December. Behind the scenes, businesses are scrambling to forecast demand and lock in orders for the back-to-school and winter holiday seasons — but unpredictability in trade policy is breaking the very tools they rely on to plan.

In this episode, I’m joined by Debdatta Sinha Roy, a principal scientist in O.R. and data science at Oracle Retail Science R&D, to explore how this volatility is creating an invisible crisis within the analytics systems that power global logistics, and what a better path forward could look like. From smarter sourcing strategies to more stable policymaking, we’ll discuss what it will take to bring long-term resilience to today’s fragile supply chains — as retailers and consumers alike are already feeling the impact on the holiday shopping season.

Supply chains rely on predictability. Whether you're forecasting demand, scheduling shipments, or planning production, our analytics models need stable assumptions—for example, lead times, costs, trade regulations. When those inputs fluctuate—like when tariffs are announced, paused, or reversed in rapid succession—the entire foundation of the models collapses. Businesses can adapt to higher costs, but they can't easily plan around volatility. It's like trying to navigate with a GPS where the map keeps changing.

Interviewed this episode:

Debdatta Sinha Roy

Oracle

Debdatta Sinha Roy is a principal research scientist and co-lead for the AI Foundation product in Oracle’s Retail Science R&D team. He started his industry career as a research scientist at Staples’ Supply Chain and Transportation team. Before this, he completed his PhD in Operations Management/Management Science from the Robert H. Smith School of Business, University of Maryland, College Park, where he received the Abraham Golub Memorial Dissertation Proposal Prize in Management Science. He also holds a BS-MS Dual Degree in Mathematics from the Indian Institute of Science Education and Research, Mohali, and received the President’s Gold Medal.

His primary research interest is in data-driven decision-making under uncertainty for applications in retail, supply chain, transportation, logistics, and service operations. The methodologies employed in his research range from data analytics and statistical machine learning to data-driven optimization. His Erdös Number is 3. He has been an active participant in various INFORMS conferences and has also served as a session chair and panelist at distinguished supply chain & logistics conferences.

At Oracle, he oversees the research and development of critical areas in retail science, utilizing various traditional AI and Gen AI methodologies for diverse retail customers, including fashion, grocery, and electronics. At Staples, he redesigned some essential transportation and delivery operations in response to the significant changes in market behavior and demographics caused by COVID-19. At the Smith School, he worked on real-world logistics problems and developed modern solutions considering the challenges posed by technological changes.