Retailers can transform supply chains with AI-powered processes

As the retail industry uncovers the transformative value of artificial intelligence, one of the most significant opportunities lies within supply chain optimization. With massive data volumes, disparate systems, and a need for rapid, precise decision-making, AI-powered supply chain automation stands out as a critical application.

Findings from our RSM Middle Market AI Survey 2024: U.S. and Canada reveal that the demand for a comprehensive AI strategy across the value chain is clear. Nearly two-thirds (67%) of middle-market leaders acknowledge the need for external expertise to harness AI fully—a reflection of the technology’s inherent complexities and potential.

Retail executives are prioritizing high-value areas such as productivity, margin improvement and revenue growth, with supply chains often leading the focus. Key financial gains stem from improved product availability and reduced seasonal discounting gained by enhanced demand and supply planning. Additional gains have been realized in reduced supplier costs and reduced production expenses. To unlock these gains, some leaders are turning to innovative AI applications that streamline processes, leveraging data-driven technology solutions. Historically, supply chains are often built around relatively predictable demand and supply patterns. Recent trends, however, are anything but predictable, with multiple disruptions occurring over the last several years. Relying not just on internal data, but also on market-based data, is becoming increasingly important. AI tools can make sense of multiple structured and unstructured data sources.

With rising capital costs, a tight labor market and intensifying competition, maintaining profitability and efficiency is essential. AI data solutions are increasingly central to achieving these goals. Over the past five years, disruptions like COVID-19 underscored the importance of agile supply chains that can swiftly adapt to changing conditions. Retailers equipped with AI-driven automation are better positioned to respond proactively to future disruptions. Take, for instance, the potential supply chain impact of port strikes. AI-driven systems can forecast such disruptions, offering alternative freight routes or advising procurement adjustments to mitigate risks.

Transitioning to AI-driven supply chains

For retailers shifting from traditional supply chain management to advanced, AI-powered systems, the journey involves several key steps:

1. Data strategy: Construct a digital twin of the supply chain, integrating data from procurement, logistics, vendor portals, and Internet of Things devices into a unified data layer.
2. Business case development: Define targeted business cases, pinpointing essential data, specific problems, anticipated cost, and value-driven metrics.
3. Proof of concept: Develop a prototype to validate the AI application and secure approval for further development; scan the market for a packaged software solution before building a model.
4. Development and deployment: Move from concept to live implementation with a focus on scalability and adaptability.
5. Adoption and value measurement: Implement robust change management practices and continuously measure impact to ensure sustainable value.

Key AI-powered use cases for supply chain automation

Here are some of the ways AI can address common supply chain needs:

• Production scheduling and management: Optimize scheduling to maximize yield and profitability, dynamically responding to disruptions while factoring in energy usage, cost of goods sold, on-time item fulfillment (OTIF)  and emissions, ultimately reducing production costs and boosting margins.
• Inventory rebalancing: Proactively manage inventory levels to improve network health, support order fulfillment, and meet OTIF goals—streamlining excess inventory while effectively matching demand.
• Supplier management: Identify supply disruption risk (Tier-n visibility) and proactively manage potential disruptions such as a port strike or a hurricane or other weather event.
• Logistics: Adapt supply and demand strategies in real time to overcome constraints and external challenges, optimizing logistics to improve customer satisfaction and reduce waste.

The takeaway

For retailers aiming to embed AI-driven automation into their supply chains, becoming data-centric is imperative. Information technology is no longer just a support function; it’s the strategic core that drives competitive advantage.

• Begin with a comprehensive data strategy across the organization.
• Build strong business cases to justify AI investments.
• Deploy incrementally to maintain alignment with strategic priorities.