How AI in logistics is transforming operations and efficiency

The growing use of AI in logistics 

The supply chain and logistics industry is under constant pressure. With tight margins, fierce competition and ever-increasing customer demands, even the smallest improvements in operational efficiency can make a huge difference on profitability.  

In recent years, the supply chain and logistics industry has been experimenting with the potential of AI, particularly generative AI, to become more efficient and effective. In 2024, DHL predicted that 65% of logistics organisations would be using AI in at least one part of their operations. 

But has this prediction come true? And how does AI in logistics create greater efficiency? 

How AI in logistics is transforming operations and efficiency 

If you're a leader in supply chain and logistics, you’ve probably experienced the stress that comes with operational bottlenecks. Revenue dips, unhappy customers and loss of productivity.  

AI has the potential to transform just about every part of supply chain and logistics, including demand planning, predictive maintenance, inventory management and route scheduling. This shift is about making smart, data-driven decisions that streamline operations, reduce costs and enhance the overall performance of supply chains.  

AI-powered predictive analytics  

Unexpected delays, inventory issues and equipment failures are headaches for any supply chain and logistics organisation. For years, the industry has had no choice but to react to problems only after they disrupt operations.  

But predictive analytics is changing this. Organisations can now use real-time data together with AI to predict and prevent these issues, before they escalate into operational chaos.  

1. AI in predictive maintenance

AI analyses data, (failure history, usage patterns and real-time condition reports), from vehicles and equipment to predict when a breakdown is likely to occur. It provides insights into the health of equipment and vehicles in real-time, so that organisations can proactively prevent failures before they happen. According to McKinsey, predictive maintenance can lower costs by 10 - 40%, reduce downtime by up to 50% and improve the safety of employees using equipment, making it a worthwhile investment.  

Rolls-Royce uses advanced data analytics and AI predictive maintenance for aircraft engine inspections. This has reduced the time it takes to inspect an aircraft engine by 75% and is estimated to save up to £100 million in inspection costs over the next five years.  

2. Demand forecasting

Managing inventory is a tricky act to balance and can go one of two ways:  

1. Organisations run out of stock, losing out on potential revenue andcreating unhappy customers 
2. Organisations overstock, leading to higher storage costs and product waste 

Traditional forecasting used basic prediction models and historical data which often failed to capture real-time market shifts. AI demand forecasting predicts how much of a product or service is needed by analysing millions of historical sales data points, social media trends, seasonal patterns (e.g. buying trends in summer and Christmas) and market patterns. This leads to smarter inventory management, as organisations report a 10-20% improvement in forecasting accuracy. 

3. Optimised route scheduling 

Traffic congestion, vehicle capacity, poor weather conditions and fluctuating demand patterns create unpredictability. This makes it difficult for organisations to plan the most efficient delivery route, often leading to: 

• Delays and missed delivery windows, leading to unhappy customers 

• Increased fuel consumption and costs due to empty miles and inefficient routing 

• Higher carbon emissions making sustainability goals harder to achieve 

• Poor fleet utilisation, causing inefficiencies in delivery scheduling and resource allocation  

AI-powered route scheduling improves delivery planning by analysing real-time traffic, weather and order data. It adjusts routes in real-time to better meet customer needs, and faster. 

Parcel delivery giant, UPS, uses AI-powered predictive analytics in its ORION system to optimise delivery routes daily. This has saved 10 million gallons of fuel annually, reduced CO₂ emissions by 100,000 metric tonnes and helped predict vehicle breakdowns.  

Aside from improving efficiency, AI in logistics is improving sustainability, which is becoming a crucial aspect of every organisation's business model.  AI in logistics is reducing empty miles, lowering fuel consumption and providing real-time emission tracking.   

The biggest barriers for AI in logistics  

What’s holding the industry back? 

While AI in logistics offers obvious benefits, there are very prominent challenges when it comes to adoption. Besides the perceived upfront costs of implementing AI systems, which deters smaller organisations, legacy systems, data quality and cultural transformation are also challenges that the industry faces. 

Legacy systems and integration  

Reports predict that over 66% of organisations rely on legacy applications for their core operations. This is a cause for concern, as outdated systems increase maintenance costs, compromise security and create challenges when integrating with modern AI technologies. They can lead to data silos that make it difficult for AI solutions to access and analyse real-time data from multiple systems.

Want to overcome this challenge? Learn how to turn your legacy systems into a competitive advantage. 

RPS struggled with legacy systems that were over a decade old, costly to maintain and inefficient. NashTech helped RPS migrate from on-premise systems to a scalable cloud-based solution, significantly lowering maintenance costs, eliminating data silos and improving data integration. 

Data quality  

For AI in logistics to work, high-quality data is critical. AI models need accurate, complete and consistent data to optimise operations and automate decisions, like in route planning and inventory management. But poor-quality data (e.g. outdated supplier information, shipping details and product details) is a significant barrier for supply chain and logistics organisations. On top of this, many struggle with fragmented and siloed data that is spread across multiple systems.  

When data is outdated or inaccurate, it sets off a chain reaction - order volumes become misaligned, customers face delays, delivery times get disrupted and the overall customer experience suffers. 

An American multinational logistics organisation faced data quality challenges due to outdated legacy systems and fragmented data across multiple platforms. By implementing NashTech's scalable data platform and a data mesh architecture, the organisation decentralised data ownership, improving governance, data accuracy and reliability.  

Cultural transformation 

The human side of AI adoption is often overlooked and can be one of the hardest challenges to navigate. Many employees in the supply chain and logistics industry resist change due to concerns over job displacement, especially with AI and automation being integrated into tasks like route planning, warehouse management and delivery tracking. 30% of the workforce worldwide fear that AI might replace their jobs within the next three years.  

To overcome this challenge, supply chain and logistics organisations need strong leadership and change management strategies. Engaging employees early in the process, providing them with the right tools and ensuring they understand the benefits of AI is a good place to start. 

The future of AI in logistics 

Unlike other industries that are rushing toward AI transformation, the supply chain and logistics industry is taking a measured, practical approach to AI adoption. The focus isn't on dramatic transformation but on targeted improvements that can deliver real value.  

Looking ahead, AI will play an increasingly important role in transforming the supply chain and logistics industry. Technologies such as autonomous vehicles and fully automated warehouses will continue to reshape how goods are transported, stored and delivered.  

Ultimately, successful AI implementation isn’t just about technology. It’s about building the right processes, strategies and skills around it. 

Reach out to our experts today to discuss your AI needs. 

NashTech regularly hosts roundtable events for the supply chain and logistics industry. To be invited to the next UK roundtable, reach out to our logistics industry expert and Client Director, Stuart Simpson at stuart.simpson@nashtechglobal.com.