Robotics and artificial intelligence can help supply chain operations address challenges such as worker shortages and procurement delays. Optimizing productivity is essential, so companies must supplement human workforces with automation and smart technologies.
Are the promises of these advancements enough to meet business growth and digital transformation goals? Three parts of the supply chain industry — procurement and logistics, data centers, and manufacturing warehouses — particularly stand to benefit from AI and robotics.
Automation can transform factories, warehouses
In 2024, S&S Activewear decided to test how well 340 Geek+ robots and software from Körber Supply Chain (now Infios) could fit into its workflow. They sped up picking efficiency, enhanced inventory slotting, and expedited order fulfillment. This example demonstrates how powerful robotics is in diverse applications.
AI and robotics can also enhance Internet of Things (IoT) capabilities. Sensor-based technologies on production lines and in warehouses make monitoring environmental conditions and productivity metrics easier.
For example, sensors can identify anomalous dips in temperature in sensitive environments. They can also detect changes in belt speed, notifying technicians when it is time to apply lubricants or replace parts. This impact enables predictive maintenance, reduces downtime, and decreases costs associated with extensive part repair or replacement.
Automated guided vehicles (AGVs), autonomous mobile robots (AMRs), and self-driving lift trucks are also critical for supply chain efficiency because they can transport products from production lines to assembly, storage, and distribution without human intervention.
Advanced cameras and robots can sort the materials, and natural language processing (NLP) can help identify the correct locations.
Tasks with increased efficiency: Because of informed maintenance, production machinery will run at peak efficiency for longer. Transportation and organization to warehouses can occur with minimal manual labor, allowing employees to avoid safety concerns and focus on higher-value tasks.
KION Group and Accenture case study: A collaboration between supply chain systems provider KION Group and tech giant Accenture demonstrated how digital twins could visualize new warehouse layouts. The project used NVIDIA’s Omniverse and Mega products to simulate everything from fleets to floor plans.
The blueprints enabled more fluid movement of heavy equipment like automated forklifts. The software can also predict key performance indicators like error rate or safety infractions, improving productivity from multiple angles.
In addition, the AI-powered tech can determine the number of robots needed to fulfill the warehouse’s demand.
Heemskerk case study: Dutch food company Heemskerk adopted many ABB robots in its processing. These included four-axis crate movers and six-axis placers, among others.
The robots improved quality by reducing how much time products spent wasting away on shelves. The changes enabled Heemskerk to transition to just-in-time delivery models, making food fresh for every client.
“Today, the ABB robots are able to process 300 to 400 crates per order,” said Chris Vleeschouwers, sales and marketing manager at Heemskerk. “Previously, about 30 to 40 people were needed to perform the same task.”
AI enables procurement and logistics
AI and robots can effectively automate movement and business processes in procurement and logistics. In logistics, an AI algorithm can optimize routes to expedite delivery times and use less fuel. The model becomes familiar with common routes and weather patterns in the region. The software takes these factors into account when designing paths for drivers.
Procurement professionals can deploy autonomous trucks, AGVs, or drones with computer vision to transport heavy inventory to shipping.
In offices, employees can incorporate intelligent procurement and sourcing practices. Natural language processing (NLP) can help parse supplier contracts to ensure transparency and understanding.
Decision-makers should also experiment with machine learning to see what they can learn from historical inventory and purchasing data. These insights will inform future purchases. Robotic process automation (RPA) could automate repeated orders or submit and reconcile invoices.
Tasks with increased efficiency: Automating fleet operations, workflow management, and client communications will save time and resources.
Hudson&Hayes case study: Incorporating AI for an internal procurement department saved more than 10,000 hours yearly. This example from AI experts Hudson&Hayes shows how tools like analytics and RPA can eliminate cumbersome manual data entry and project delays.
After implementing AI-powered assistants and an analytics-driven dashboard and educating staff on AI capabilities, the company realized how critical the resource was to fixing its biggest pain points. It established a Procurement 360 dashboard and honed its use of SAP Ariba for automation.
Uniper case study: Leaders at energy corporation Uniper tested RPA’s efficacy in their procurement process. It made processes more efficient while reminding the company how essential its people are. The awareness will streamline future operations.
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AI can help data centers save energy
While energy demand is rising for generative AI and robotics, AI-optimized data centers are crucial for predictive maintenance, anomaly detection, and analytics visualizations. They might even reduce carbon emissions, as virtualizations for 100 servers equate to planting 1,500 trees. Older processing methods are more energy- and resource-intensive. These use cases incentivize suppliers to invest more in AI development.
AI in data centers can also monitor supply chain and data center activity while suggesting lean improvements. Advanced software could let stakeholders detect and prevent bottlenecks in factories or warehouses, as well as reduce the energy required by cooling equipment.
Tasks with increased efficiency: AI in supplier data centers could predict potential failures, automatically allocate resources, and improve energy efficiency throughout the value chain.
Amazon case study: Amazon is well-known for its heavily automated and robust warehouses and data centers. To help manage vast inventories and provide fast delivery for customers, the e-commerce company has used AI to optimize its supply chain efficiency. It wanted to improve demand forecasting and streamline logistics at every phase.
Amazon reduced inventory turnover and energy consumption by using AI servers to analyze sales data, social media, and more. Leaders discovered investing in AI-powered data centers made supply chain operations more agile and sustainable.
Gak Sejong case study: South Korea’s Gak Sejong data center wanted to change its backup power solutions. It sourced control systems and generator sets, or gensets, from Cummins and AI from Naver.
The final project will outpace Google, storing over 65 exabytes of data. Each genset was over 30 tons. Transporting them required further collaboration. The effort demonstrated how advanced tech and AI success starts in the upstream process before hitting the data center.
Automation is an asset for supply chain efficiency
AI and robotics are pivotal when defining the most competitive supply chain leaders. These case studies prove they are a consistently valuable incorporation with numerous returns.
Global and external influences — such as geopolitical tensions and the climate crisis — present significant challenges for supply chain companies. Experimenting with these productivity-enhancing technologies can reduce inefficiencies and satisfy every client.
About the author
Lou Farrell is a senior editor at Revolutionized. He has written on the topics of robotics, computing, and technology for years. Farrell has a great passion for the stories he covers, and for writing in general.
