In this section, learn about:
How AI drives efficiency in forecasting, inventory, logistics, and pricing.
AI-enhanced customer experiences with personalization and curated searches
Proven impact from brands like Amazon, Ruggable, Coach, and Walmart.
The State of AI in a Rapidly Changing Supply Chain
AI is quickly becoming a part of everyday life, whether it's featured in ads or quietly powering capabilities behind the scenes for major retailers. Chances are, you've already used AI technology without realizing it, both online and in-store.
With AI becoming an accepted but rapidly evolving industry standard, forward-thinking suppliers and retailers are already planning their next technological investments for their businesses.
Keep reading to see how AI is cutting costs, saving time, and streamlining operations across the supply chain: from enhancing customer experiences with visual search and augmented reality to driving efficiencies in the operational core of the supply chain.
How AI Is Advancing the Supply Chain
Enhancing Customer Experiences
Shoppers today expect customization, personalization, and try-before-you-buy experiences, which can help prevent costly returns for retailers. Meeting these expectations makes it essential for retailers and suppliers to focus on enhancing the customer experience.
AI is reshaping product discovery through tools like visual and conversational search, allowing customers to find exactly what they want using natural language. Many consumers are already familiar with AI-powered chatbots and conversational service models, even if these solutions still have room for improvement.
By combining personal preferences, contextual information, and product recommendations, AI delivers highly tailored shopping experiences that not only improve customer satisfaction but also drive sales growth.
AI Examples in Action: Ruggable’s Design Assistant
An interesting feature many home goods retailers are experimenting with are AI design assistants. In Ruggable’s case, they enhance customer search experiences with their Digital Design Assistant.
With this tool, potential customers can upload a photo of the room they’re decorating. The AI design assistant processes visual information like color, furniture shapes, or room size to develop personalized product recommendations. Most importantly, it allows customers to see different rug options in their actual space.
This direct application of AI helps customers discover highly relevant products to their specific decorating needs, making the search and selection process more efficient and personalized.
Demand Forecasting
Demand forecasting uses predictive analytics, prior data, and market research to estimate future customer demand for a product or service. However, for many small and medium-sized businesses, market research can be expensive to obtain and too time-consuming for small teams to process and utilize.
Demand forecasting with AI can change that. Instead of a reactive and historically based forecasting method, AI systems take in multiple variables in real-time, providing highly accurate and detailed demand prediction (also called multimodal AI).
For example, retailers might typically run geographically targeted ads for soccer fans when a star soccer player and their team come to town. With AI, however, the system can also identify another teammate who is popular with a specific local demographic, creating additional sales opportunities that a human planner might have overlooked.
AI Examples in Action: Market Research for Coach
Coach’s holding company, Tapestry, uses the free AI solution called Adobe Firefly to create mockups of potential Coach products. The digitally rendered replicas allow for more flexible conceptualization, higher-quality customer feedback and scaled content for marketing. Before this AI implementation, this expensive kind of product ideation wasn’t viable, especially if teams were unsure of its market viability.
Inventory Management
With improved demand forecasting, retailers can utilize AI to minimize stockouts. This can help reduce product waste and prevent aggressive markdowns of slow-moving goods. Retailers report that AI’s capabilities improve margins by eliminating guesswork.
When the sweet spot lies between overstocking and running out of products, some stores are using AI for decision-making, including tools like digital sensors and smart shelves to minimize waste and spoilage. With 30% of the world’s food supply going to waste at the retail and consumer level, this technology can have a powerful impact.
Related Reading: How to Manage Items and Inventory in Walmart’s Supplier One
AI Examples in Action: Automatic Ordering and Waste Control
When perishable foods go to waste easily, companies want to be mindful of ordering products that might not get used. Rather than reordering food based on historical data or intuition, AI software lets buyers analyze additional data like weather forecasts, local events, nutritional trends, and more to predict demand accurately.
One startup, Wasteless, lets grocery retailers and supermarkets access and use dynamic pricing for perishable items, like dairy products, to become cheaper as the expiration date gets closer. The German government funded a project that uses AI algorithms to control the energy used in production facilities, which keeps food fresh longer and reduces waste.
Supply Chain Strategy and Logistics Optimization
Some of the largest and most impactful changes in the supply chain come in the form of logistics optimization, specifically in distribution and warehousing restocking.
In the context of warehouse automation, AI-driven systems can predict when items need restocking, plan optimal storage layouts, and adjust tasks on the go. This leads to faster order fulfillment and lower costs by minimizing human errors and optimizing the flow of goods. With AI allowing retailers to streamline inventory replenishment, frequent and dynamic restocking can help move products quickly in environments with high foot traffic or perishable goods.
Related Reading: How Suppliers Can Choose the Right Warehousing Partner
AI Examples in Action: Amazon’s Packaging Optimization
Amazon’s AI use helps them meet sustainability goals and increase shipping efficiency in their Ships in Product Packaging (SIPP) Program. This technology precisely determines the most efficient packaging for items. Not only does the system learn and understand the amount and best configuration of packaging supplies, but it also recommends the precise sizing and tape recommendations. This allows items to ship safely in their own packaging, while allowing Amazon to save costs and significantly reduce packaging materials, with this video resource reporting a 41% average decrease in waste.
AI Examples in Action: Live Inventory Monitoring
Retailers like Walmart and Lowe’s keep product shelves stocked by employing cameras, sensors, and interactive displays to monitor inventory levels in real time. When items are running low, these systems automatically trigger notifications to associates for restocking. This allows for a consistent product flow that keeps shelves stocked and sales up.
AI Examples in Action: Vendor Risk Assessments
Large enterprises might work with thousands of separate vendors across their supply chain. If only one vendor is of poor quality or unreliable, these effects ripple across the supply chain. Global electronics manufacturer Lenovo uses AI-powered analytics to predict delivery dates and delays for its 2,000 suppliers. This provides rare proactive insight for chief supply chain officers in a business where it’s complicated to foresee issues.
Related Reading: Vendor Compliance Checklist
Strategic Decision Making: Price Optimization and Assortment Planning
AI helps retailers set the best prices by analyzing sales trends, local demographics, and the impact of promotions. It can spot slow-selling items in physical stores, suggest short-term discounts, and quickly adjust if demand picks up. Retailers can also use AI to run A/B tests with different pricing strategies across stores or online, learning the best approaches. This type of AI enables smarter, faster, data-driven pricing decisions that boost sales, cut waste, and increase profits.
AI Examples in Action: Price Optimization
Amazon is a prime example of a retailer leveraging AI for dynamic pricing. It employs an AI-powered tool called "Amazon Price Optimizer" to adjust product prices multiple times a day. This technology considers factors like customer demand, competitor pricing, sales volume, and product availability to remain competitive while maximizing profits. Reports indicate that this solution has led to a 5% increase in sales and a 2% profit increase for Amazon.
AI Examples in Action: Assortment Planning
AI assists in assortment planning by identifying best-sellers, emerging trends, and complementary goods. This helps retailers choose the right product mix for each store and region, tailoring offerings to local preferences and improving sell-through rates, highlighting underperforming products before they become dead inventory.
Stay Aware of AI Trends and More
At SupplierWiki, we’re big fans of proactivity. With topics ranging from practical to forward-thinking, we want to be your reliable go-to source for supply chain knowledge. Sign up for an account today!
