How AI Is Transforming the U.S. Retail Delivery and Supply Chain Landscape
In today's fast-paced retail environment, the U.S. retail delivery and supply chain landscape faces constant pressure to become more efficient and responsive. Artificial intelligence (AI) is emerging as a game-changer, with over 40% of supply chain organizations investing heavily in AI.
In a McKinsey study, executives stated that they expect the highest AI business impact to be in supply chain and operations with cost savings between 10-20%. However, it also found that only 6% of respondents are already effectively using AI for their supply chain today, which leaves a significant discrepancy between the potential of this new technology, its recognition, and its actual application.
Learn the multifaceted ways AI is poised to reshape the retail supply chain through insights into its transformative potential with actual use cases that apply to U.S. retailers.
Demand Forecasting
Accurate demand forecasting is crucial to ensure retailers meet consumer demands without overstocking or under-stocking products. AI enhances demand forecasting by analyzing years of historical data to identify patterns and predict seasonality. Unlike traditional methods, AI can incorporate a high number of variables in real time, using internet data (such as sentiment analysis and economic factors) to refine its predictions.
This allows for forecasting not only at the broad category level but also down to specific SKU levels, enabling retailers to anticipate consumer needs with unprecedented accuracy.
Digital Twin and Inventory Management
The concept of a digital twin — creating a virtual replica of physical assets — has significant implications for inventory management. AI can optimize inventory by using digital twin technology to have a real-time understanding of inventory levels at all times, as well as track moving goods to predict stock levels at any given warehouse or store.
By routing goods to the right place based on real-time demand forecasts, AI reduces the need for excess safety stock. This optimization ensures that products are available where and when needed, minimizing storage costs and improving delivery efficiency.
Real-time Measurement and Feedback Loops
AI's ability to measure actual performance against plans in real time creates a dynamic feedback loop. This continuous monitoring allows retailers to adjust their strategies promptly, correcting any discrepancies in forecasts.
Real-time data collection and analysis ensure that future forecasts become increasingly accurate. This adaptive approach not only enhances demand forecasting but also refines inventory planning, leading to more efficient operations and better resource allocation.
Companies that are able to implement technology-driven feedback loops are, on average, able to increase revenue by 3% to 4%, according to McKinsey. New startups are building AI-driven products to map supply chains and connect previously independent parts of the business, claiming to drive significant efficiency gains for supply chains.
Regional SKU Inventory Optimization
More accurate inventory planning and demand forecasting can significantly optimize regional SKU inventory levels. With AI, retailers can maintain precise inventory levels at local fulfillment centers or even retail stores, particularly for high-demand items. This localized approach improves both cost efficiency and delivery speed, ensuring customers receive their products faster.
This can be achieved through real-time measurement and optimization of existing processes, or retailers can find alternative solutions via scenario analysis to simulate changes to their supply chains.
Reverse Logistics
With online apparel returns estimated to exceed 25%, many retailers look for AI solutions to reverse logistics, particularly in the handling of returns. Traditional return processes can be lengthy and cumbersome, involving in-store visits or extended phone calls. AI automates the return process, allowing consumers to process returns quickly and efficiently as shown by Amazon who started to utilize AI in recognizing damage for both outbound as well as returned items.
Jeremy Wyatt, director of the Amazon Robotics team, has said that checking for damage can be time-consuming given that most items are in fine condition, pointing out that the use of AI in reverse logistics can lead to tremendous efficiency gains for retailers in addition to minimizing fraud patterns and reducing waste.
This streamlined approach to returns not only enhances the customer experience but also reduces operational costs associated with reverse logistics; however, it is yet to be fully realized across retailers in the U.S.
Consumer Insights and Trend Analysis
Understanding consumer behavior is vital for retailers to stay competitive. AI enables retailers to gain deeper insights into consumer preferences through trend analysis and persona building.
By analyzing internal data such as basket size, return rates, and website churn, AI identifies what resonates with different consumer groups. When combined with third-party data like social media sentiment analysis, these insights become even more powerful.
Retailers can use this information for product development and strategic decision-making, such as incorporating sustainable materials or creating functional garments.
According to Intel, accurate forecasting can improve margins for retailers by 1-3% on average, which can make a significant difference in traditionally low-margin industries.
Future Outlook
The integration of AI into the U.S. retail delivery and supply chain landscape offers transformative potential. From demand forecasting and inventory management to reverse logistics and consumer insights, AI enhances efficiency, reduces costs, and improves customer satisfaction.
As AI technology continues to advance, retailers must embrace these innovations to stay competitive and meet the evolving demands of consumers. And while many invest heavily in their supply chain ecosystems, few actually apply AI today to achieve real-world learnings.
The companies that do embrace an active role in deploying AI tools in various steps of their supply chain will likely see exponential advantages. These may either be replicated by others through heavy investment so they can catch up or early adopters might externalize some of their new AI capabilities through IP licensing or consulting businesses, creating entirely new revenue streams.
The future of retail lies in harnessing the power of AI to create smarter, more responsive supply chains that deliver value to both businesses and customers.
Author: Tobias Waldhecker is the senior manager of strategy at Walmart. He is a business and strategy professional with over 10 years of experience across engineering, consulting, and supply chain functions. For the past year and a half, he has led efforts for the strategy team at Walmart U.S. to develop and commercialize advanced logistics and supply chain products for increased efficiency and customer experience.
