Retail, AI and automation

Michael Hung , CEO of TradeBeyond, explains how AI and automation are leading retail supply chains into the future

Retail executives are making artificial intelligence the cornerstone of their supply chain improvement strategies to stay ahead of the curve. A new study of senior retail executives, including VPs, directors, and senior managers from leading global retailers and brands, revealed a decisive trend: AI has emerged as the top priority for supply chain managers, with 28% of brands ranking AI implementation as their most critical focusover the next three years. This embrace of AI is driven by the need to enhance efficiency across supply chains, just ahead of other pressing goals such as increasing agility and resilience (26%) and scaling operations to support aggressive growth (22%).

AI had long been anticipated to be a game-changer in retail. Now it is fully arriving in the supply chain sector, creating transformative impacts almost overnight. This technology is rapidly bringing speed and efficiency to a new level, while also becoming a critical tool in retail’s drive toward sustainability, as tightening global regulations have made it increasingly difficult to maintain compliance and efficiency without sophisticated digital systems. For example, by optimizing routes and inventories, AI helps reduce unnecessary fuel consumption and waste. AI-powered tools are revolutionizing traceability by automating chain of custody verification and documentation, ensuring compliance with sustainability regulations. They also transform audit and risk management, analyzing supply chain data to predict risks and improve product quality while reducing costs.

We’re already seeing some of retail’s biggest names leveragingAI in their supply chains with impressive results. Walmart and Amazon are using AI-powered robots in fulfillment centers to manage inventory, process orders, and optimize storage space. They’re also utilizing predictive analytics to forecast demand, ensuring products are available when and where they are needed. Zara is similarly using AI for demand forecasting and inventory management, analyzing sales data, social media trends, and other data sources to predict fashion trends more accurately and adjust accordingly, minimizing overproduction and stockouts. For instance, Amazon's Proteus and Sparrow robots, which incorporate advanced automation and AI-driven capabilities, help with sorting packages and managing inventory, enhancing both efficiency and safety. Meanwhile, Walmart's use of Symbotic's AI-powered system has enabled it to fulfill online orders faster and with greater accuracy. This shift toward automation is allowing these retailers to stay competitive and meet increasing consumer demands.

All this is just the beginning. As AI evolves, it's poised to take on even more complex roles. Future applications are on track to extend into autonomous decision-making where AI systems will not only predict but also make real-time adjustments to supply chains without human intervention. Advanced AI is likely to manage most end-to-end supply chain processes, from raw material acquisition through to customer delivery, allowing the promise of the most efficient supply chain control tower approach to take shape. This deeper integration promises to transform traditional supply chain models into dynamic, predictive networks that can more adeptly respond to global challenges and market fluctuations.

                                        Building the Data Foundation for AI

Brands and retailers are understandably eager to leverage AI in their supply chain operations, but in truth many have not yet created the digital infrastructure to do so. One major obstacle preventing businesses from realizing AI’s potential is the lack of organized, centralized, real-time data. To overcome this, companies need to start creating a central repository of supply chain data at the PO, SKU, and factory levels.

The foundation for optimizing the benefits of AI for any organization lies in the ability to interconnect thousands of data points from multiple data sets across your enterprise, both proprietary, company-specific data as well as external environment data points from a range of outside databases. That requires aggregating all data from early-stage planning through the creation of product specifications, onto sourcing, costing, and logistics, and including detailed information on all suppliers along the supply chain up to the Nth tier. It’s only once businesses have established effective data management that they can begin unlocking AI's full potential.

Digitalizing with a multi-enterprise platform ensures that data is current, accurate, and accessible, setting the foundation for leveraging AI. These platforms provide real-time supply chain visibility, allowing businesses to monitor their supply chains continuously, identify potential issues before they escalate, and make informed decisions based on accurate, up-to-date information. Establishing this robust digital infrastructure is thekey to equipping AI with the data it needs for predictive analytics and automated decision-making.

Already these platforms are deploying AI in innovative ways, and their capabilities are continually expanding. New AI-powered chain of custody tools significantly enhancetraceability by automating documentary verification and documenting the chain of custody of all materials. These toolsproactively assess compliance risks and ensure that every link in the supply chain meets your company’s standards of sustainability and prepare all chain of custody documents necessary to comply with global ESG regulations. By automatically scanning and vetting all documents against multiple databases of blacklisted entities and identifying gaps or missing documentation before shipping, this AI dramatically simplifies compliance with global ESG laws like the Uyghur Forced Labor Prevention Act.

AI is also reimagining quality management. Innovative new AI-powered PO Line Risk Rating functionality optimizes quality inspections by leveraging artificial intelligence to analyze thousands of data points around risk factors such as product type, materials used, and country of origin, assigning a percentage risk score to each purchase order line. These capabilities allow businesses to proactively identify and address high-risk PO product lines, so they can prioritize quality inspections around high-risk items, reducing inspection costs while increasing product quality.

As retail stands on the brink of a digital revolution powered by AI, the opportunities for transformation are immense. Retailers that can effectively integrate AI into their supply chains will not only achieve greater operational efficiencies but will also gain competitive advantages in agility, customer satisfaction, and sustainability. To fully capitalize on AI’s growing potential, brands and retailers must prioritize the digitalization of their supply chain now or risk missing out on critical advancements and falling behind industry leaders.