EXCLUSIVE Q&A: Amazon exec examines next-gen AI in the supply chain
Machine learning and predictive analytics are among the artificial intelligence technologies retailers can utilize to streamline supply chain operations.
Chain Store Age recently spoke with Justin Honaman, head, worldwide retail, restaurants & consumer goods business development, Amazon, about how in an age of supply chain disruption and uncertainty, leading-edge AI solutions can enable retailers to navigate unexpected events and minimize risks to operational efficiency and profitability.
[READ MORE: EXCLUSIVE: How Amazon sees generative AI affecting retail]
How can predictive analytics help retailers in their supply chain operations?
Predictive analytics can be a powerful ally for retailers navigating the ups and downs of supply chain uncertainty. By tapping into historical data, market trends, and external factors like tariff changes, retailers can get ahead of potential disruptions, shifting demand, and supplier risks.
When predictive analytics is integrated with supply chain management systems, businesses can make smarter, data-driven decisions – whether finding the most cost-effective sourcing strategies, balancing inventory levels, or adjusting prices in real time to remain competitive.
How can retailers mitigate supply chain risk with AI simulations?
Machine learning models help retailers when it comes to spotting and managing risks in the supply chain due to sudden changes in the market. These models can simulate various “what if” scenarios to see how disruptions may play out, giving companies an opportunity to develop contingency plans and avoid a last-minute scramble.
Take, for example, econometric modeling, which brings in economic indicators like inflation, GDP, and exchange rates, alongside trade data, to paint a clearer picture of potential risks. In short, these simulations lead to a more proactive decision-making process that keeps the supply chain on track.
What role can AI play in optimizing supply chain sustainability efforts?
AI can be a transformative tool for achieving supply chain sustainability efforts. By analyzing potential sourcing locations vs their environmental impact, AI can help brands make smarter choices that align with their sustainability goals.
From tracking carbon footprints to ensuring ethical labor practices and minimizing material waste, AI gives companies the real-time insights they need to stay on track with their ESG commitments. In addition, with continuous learning, AI can adapt to new sustainability challenges, making it easier for brands to pivot their supply chain strategies over time.
How can AI help retailers evaluate supply chain partners?
AI makes it easier for retailers to evaluate suppliers by constantly analyzing their track record, industry compliance, and financial stability. This helps brands find reliable partners in different locations while reducing risks and improving efficiency.
Machine learning can also spot patterns in supplier performance over time by analyzing historical data, making it possible to adjust and re-evaluate choices based on real-time insights. This means retailers can stay agile, quickly pivot to better suppliers if needed, and build more resilient supply chains.
What do you see as the biggest trend(s) for the rest of this year in supply chain AI?
With tariff conversations now on the table, retailers are going to rely more on AI and machine learning to stay ahead. These tools can help brands anticipate changes, adjust strategies, and make smarter supply chain decisions.
For example, AI can analyze trade routes and entry ports to find the most cost-effective options, as well as identify suppliers in countries with favorable trade agreements. On top of that, machine learning makes demand forecasting more accurate, which means retailers can better manage inventory, avoid overstocking, and stay agile in response to shifting tariff policies.
