Artificial intelligence (AI) is more than a buzzword—it’s an evolving technological force reshaping industries, including supply chain management. Keith Moore, CEO of AutoScheduler.AI, recently led an insightful online event exploring AI’s practical applications in supply chains, dispelling common misconceptions, and highlighting real- world success stories.
He discussed the practical applications of AI in supply chain management. He emphasized the importance of understanding AI’s role in learning, predicting, and driving decisions. Moore highlighted specific AI use cases, such as IKEA’s 22% reduction in pick times through optimized routes, Ryder Systems’ 99% accuracy in yard management, and Kenco’s 15% reduction in warehouse travel. He also explained the limitations of generative AI in these contexts and its potential to enhance existing AI systems. He stressed the need for businesses to harness AI’s evolving capabilities for operational efficiency.
Understanding AI in Supply Chain
Keith opened the session by demystifying AI, emphasizing that AI, at its core, is about using math, data, and computation to extract value for businesses. He broke down AI applications into two categories:
AI for Intelligence: This involves understanding what happens in an operation and predicting what will happen next. Machine learning (ML) plays a significant role here by identifying patterns in data to inform decision-making.
AI for Action: This is where AI goes beyond predictions and actively drives decision-making and execution. Optimization techniques, such as reinforcement learning, mixed-integer programming, and operational research, helping companies make the best decisions given their data constraints. The key is that AI is a tool, not magic—it thrives when applied to well-structured problems with sufficient data. Here are some compelling case studies demonstrating
AI’s impact across industries, with a focus on tangible results:
AI in Distribution – Efficiency, Accuracy, Cost Reduction
1. Pick Route Optimization – The IKEA Fuzhou distribution warehouse faced challenges with declining sales and high operational costs. Using a genetic algorithm-based model to optimize picking routes, IKEA Fuzhou improved sorting efficiency and reduced operational costs. This optimization allowed for faster order fulfillment and better resource utilization within the warehouse.
2. Yard & Door Management—Ryder Systems used vision AI to recognize trucks and trailers in their yard across over 10,000 loads, resulting in 99% check-in accuracy.
3. Slotting Optimization – Kenco used AI for slotting optimization. With slotting, putting the fast-moving inventory close to each other is important to reduce pick times and distances. Kenco reduced travel distance by 15%.
4. Labor/Automation Planning & Orchestration – PepsiCo implemented a lot of complex automation and workflows at their sites and realized it was becoming a challenge for their coordinators to manually plan the moves throughout their plant distribution operations. They implemented an AI orchestration system that optimized the inbound and outbound workflows to balance labor and operational flow across shifts. This resulted in a significant improvement in their load-ready times, which is a number that can’t be disclosed, as well as a drastic improvement in productivity per site (12-35%, depending on the level of automation).
AI in Transportation – Faster, better, more predictable decisions
1. Route Optimization – UPS’s On-Road Integrated Optimization and Navigation (ORION) system revolutionized their delivery operations by calculating the most efficient routes for drives. By analyzing data such as package delivery locations, pickup times, and historical route performance, ORION optimizes daily routes, significantly reducing fuel consumption, operational costs, and total miles driven. This optimization enhances efficiency and contributes to environmental sustainability by lowering emissions. Fuel Savings were over 10M gallons annually, with 100M fewer miles driven and up to $400M reduction in operational costs.
2. Predictive Maintenance in Fleet Management – United Road, a vehicle logistics company, leveraged AI to reduce unplanned maintenance events by 50%. By analyzing historical data on truck performance, AI models predicted failures before they occurred, leading to 925 fewer breakdowns annually and an estimated $34 million in savings over five years.
3. Deployment Planning – Kimberly-Clark manufactures products, then deploys them from facilities to the rest of their warehouses, and includes warehouse-to-warehouse moves. Kimberly-Clark had much volatility in their deployment plan, which meant they weren’t shipping trucks filled to the maximum level and weren’t using their preferred carriers. AI incorporates data from ERP, order management, forecast, and transportation costs; it sees what is coming down the pipeline and then builds a smooth plan to significantly reduce volatility in transportation and lower costs. Cost savings were $5M.
4. Autonomous Vehicles – Waymo’s Self-Driving Technology aims to enhance transportation safety and efficiency by reducing human error. The deployment of self-driving cars offers the potential for cost savings in logistics, improved traffic flow, and increased accessibility to transportation services.
AI in Procurement and Planning
1. AI-Powered Demand Forecasting at Walmart
Walmart implemented AI models to improve demand forecasting by incorporating point-of-sale data, historical trends, and seasonal factors. The dynamically retraining AI system reduced stockouts by 16%, significantly increasing on-shelf availability and boosting revenue. AI-driven intelligence can enhance supply chain efficiency and profitability.
2. Generative AI for Contracting at Unilever
Unilever deployed generative AI to streamline legal and contract processing. The AI tool saved 85 employees an average of 30 minutes daily, resulting in over $4 million in annual savings. AI can automate administrative processes, improving efficiency without replacing human judgment.
3. Risk Management at Medtronic
Medtronic utilized AI to detect supply chain disruptions at the tier-four supplier level—several layers removed from direct suppliers. By identifying potential disruptions months in advance, the company maintained uninterrupted production, avoiding costly delays and supply shortages. AI can be used in proactive risk management and resilience planning.
Challenges in AI Adoption
Despite its potential, AI adoption is not without hurdles. Change management is often the biggest obstacle, not the technology itself. Employees may resist AI due to job security concerns, while leadership might have unrealistic expectations about immediate results.
Key Considerations for Successful AI Implementation:
1. Data Quality Matters: AI is only as good as the data it learns from. Examples from Amazon’s failed AI recruiting tool and Zillow’s misguided home pricing model underscore the risks of relying on biased or incomplete data.
2. Start Small, Scale Gradually: Companies should begin with small, measurable AI projects instead of attempting full-scale AI adoption immediately.
3. Clear ROI Metrics: AI projects should be evaluated based on cost savings, efficiency gains, revenue impact, and risk reduction. Aligning stakeholders on success metrics from the outset is crucial.
4. AI Augments, Not Replaces: The best AI implementations enhance human decision-making rather than replacing employees outright. Organizations must educate teams on AI’s role in supporting operations rather than eliminating jobs.
The Future: Agentic AI and Automation
Looking ahead Keith introduced the concept of Agentic AI—the next evolution of automation. Unlike traditional Robotic Process Automation (RPA), which follows predefined rules, Agentic AI operates in an OODA loop (Observe, Orient, Decide, Act). These AI agents can autonomously make and execute decisions, with applications ranging from legal contract automation to supply chain optimization. While fully autonomous AI-driven supply chains are not yet a reality, early applications
in logistics (e.g., AI-driven carrier negotiations and warehouse optimization) signal
where the industry is headed.
AI is already transforming supply chain management by improving forecasting,
optimizing logistics, enhancing risk management, and automating administrative tasks.
However, successful AI adoption requires high-quality data, well-defined objectives, and
strategic change management.
As AI evolves, businesses embracing AI-driven intelligence and automation will gain a
competitive edge in an increasingly complex and dynamic market. Companies like Kimberly-Clark, Walmart, and Unilever have already reaped significant benefits, proving
that AI is not just a futuristic concept—it’s a practical tool delivering real business value
today.
Supply chain leaders looking to implement AI know that AI is not magic, but when applied correctly, it can unlock immense efficiencies and cost savings.
Want to learn more about how AutoScheduler.AI uses AI to improve warehouse efficiencies and productivity? Contact us today at info@autoscheduler.ai.
