Harnessing the power of AI in distribution operations

November 15, 2024Today’s customers expect more from distributors: more choice, higher availability, and a better service experience. For distributors, delivering these benefits in the face of increased competition and rapidly changing markets puts increased pressure on operational efficiency.

Leading distributors are using artificial intelligence (AI) to create more efficient, agile, and streamlined supply chains and support functions. New tools and approaches are emerging across the distribution value chain, in planning, warehousing, transportation, and the way distributors develop and retain talent.

Off-the-shelf solutions

Embedding AI in operations can create significant value for distributors, including reductions of 20 to 30 percent in inventory, 5 to 20 percent in logistics costs, and 5 to 15 percent in procurement spend. Here are three high-impact use cases that many players in the industry could consider:

1. Planning and inventory. AI can reduce inventory levels by 20 to 30 percent by improving demand forecasting through dynamic segmentation and machine learning, and optimizing inventory through simple and cost-effective tools. A major building products distributor improved fill rates 5 to 8 percent by developing an AI-enabled supply chain control tower that proactively manages inventory levels across its warehouse footprint, identifies potential issues early, and facilitates cross-functional collaboration to accelerate decision making. The control tower includes a generative AI chatbot that provides live answers to questions based on real-time data. This allows the company to spend less time on tedious analysis and more time on making critical decisions.
2. Warehousing. AI-powered tools can unlock 7 to 15 percent additional capacity in warehouse networks by identifying additional daily spare capacity, understanding variability in resource availability, and evaluating opportunities to improve efficiency. A major logistics provider used a “digital twin” powered by AI and machine learning to increase its warehouse capacity by nearly 10 percent without adding new real estate. The system, an industry first, runs simulations to identify optimization levers specific to each warehouse in the network. Using a highly granular approach, the system evaluates the labor and assets (such as forklifts) required to complete warehouse operations on an hour-by-hour basis and analyzes the impact of variability in demand and resource availability. This helps the company understand the true capacity of its facilities and improves decision making by accurately predicting the impact of changes in labor, assets, or material flows.
3. Frontline workforce. Advanced analytics can help reduce costs 15 to 20 percent by providing visibility into the factors that affect employee attraction, attrition, and performance and by recommending specific actions to improve retention and talent development. A major distributor identified at-risk employee clusters by using a custom advanced analytics tool to analyze more than five million data points from truck driver interviews. The company then developed six targeted initiatives to improve driver retention, unlocking a 4 percent EBITDA improvement opportunity.

How to get started

A recent survey of distributors found that about 95 percent are exploring AI use cases across the distribution value chain.¹ However, only about 30 percent say they have sufficient talent within the organization to scale these efforts, and less than 10 percent say they have developed an AI road map and prioritized use cases for deployment.² As distributors embark on their AI journey, it’s important to keep in mind that a successful AI transformation must improve business processes, simplify technology, and enhance the team experience.

To get started, distributors need effective action on three key implementation elements:

1. Prioritize immediate value. Assess current AI capabilities in operations, define pain points across different operational areas, and select one or two low-risk, high-value use cases that can be delivered within three to four months to generate buy-in. Organizations often lack the resources or executive support to deploy multiple use cases simultaneously. Starting with focused experimentation helps avoid missing opportunities or biting off more than you can chew.
2. Create a structured road map. Create a one- to two-year, value-based road map focused on targeted operations to help prioritize future initiatives. Include a timeline that includes quantifiable impacts to ensure a positive ROI throughout the implementation. In parallel, develop a data and technology strategy that supports these initiatives, rather than trying to fix everything, everywhere, all at once.
3. Make AI self-funding and self-sustaining. Reinvest the returns from the initial use cases into the next set of AI initiatives on the road map. This self-funding approach allows the AI transformation to start small while improving data and technology capabilities, upskilling the workforce (including digital talent to deliver and frontline talent to use the new tools), and gaining leadership support as use cases begin to deliver improvements.

By following these steps, distributors can take a structured, strategic approach to AI in operations. With the right approach, AI can transform all aspects of the supply chain, improving the organization’s overall resilience and building competitive advantage.

The authors wish to thank Ann Carver, Blade Clarke, Sanchit Jain, and Tom Svrcek for their contributions to this blog post.

_{¹ McKinsey AI in Distributor Operations sentiment survey, September 2024 (n = 40).
² McKinsey AI in Distributor Operations sentiment survey, September 2024 (n = 40); McKinsey survey of distributor operations, December 2022 (n = 74).}