Smarter, Faster, Better: The Promise of AI in Logistics

The following coverage appeared in Business Technology Review, a monthly supplement magazine of Khaleej Times.

Artificial Intelligence (AI) is enabling a 20-minute convenience delivery startup in Egypt to work its unit economics and drive sustainable logistics operations. The technology is helping a leading consumer goods and electronics retailer in the Middle East increase order pickups in 24 hours by 30%, enhance delivery completed within 1 to 4 days by 25%, and cost-efficiently manage a 5x surge in delivery demands. Thanks to AI and analytics, Imerys S. A. reduces up to USD 100K in incidental costs. 

More companies are joining the AI bandwagon, using cutting-edge technologies to enhance resource utilization, boost efficiencies, and increase profitability. A Gartner study says that more than 80% of new supply chain enterprises will employ AI and data science in some capacity by 2025. According to a McKinsey report, companies that embraced AI supply chain solutions early on were able to lower logistics costs by 15%. This is just the beginning of how AI will transform the future of logistics, and we can expect more of its revolutionary effects in the sector.

Generative AI Will Drive Autonomous Supply Chain Operations

Logistics processes depend on multiple variables. A single disruption can cascade the entire supply chain. However, AI can serve as a solution for logistics-powered businesses by accurately identifying the shortcomings in these variables. Generative AI will eventually take this a notch higher by finding optimization pockets and issues and enhancing data quality. Going forward, it will steer users toward the right action or even make autonomous decisions.

The technology can enable businesses to predict demand for a specific area and date precisely. This data will further help suggest or plan the optimal mix of in-house and freelance courier partners to meet elastic delivery demands with the highest certainty.

System-Driven Delivery Personalization For Better Customer Experiences

A Forrester study revealed that companies that deliver exceptional customer experience outshine those that don’t by almost 80%. Fortunately, “data as the new oil” can significantly help here. Mounts of historical data and the power of real-time analytics will take delivery personalization to another level. It will help the delivery platforms learn from customer behavior and patterns to automatically align deliveries at certain times to ensure successful first attempts. Moreover, the system can use customer feedback to gauge if it can further improve the customer’s delivery experience. It will automatically prioritize that customer’s order and optimize workflows to ensure that their next delivery experience compensates for and exceeds expectations.

Improving Delivery Efficiency through Machine Learning

Advanced Machine Learning (ML) algorithms helps further optimize routes, increase delivery productivity, and reduce logistics costs. The system can gradually fine-tune delivery processes over time through previously collected data, enhancing the identification of customer locations, understanding traffic patterns, and execution of efficient deliveries. This increases rider productivity, lower delivery costs, and ascertains on-time order fulfillment.

Moreover, dynamic route optimization and intelligent order clubbing can help reduce miles traveled by up to 7% through accurate routing based on customer location details. It also increases deliveries-per-day-per-driver by 22%, boosts the delivery success rate by 12%, and reduces non-delivery (ND) and return-to-origin (RTO) costs, among others.

Highly Intuitive Chat Interfaces

This will augment the capabilities of conversational AI. Let’s say a customer requests a market vehicle over chat. Here, the system will utilize Natural Language Processing (NLP) to understand the context and automatically submit a vehicle request to the vendor based on the client’s needs. Rather than creating new platforms for a fragmented supply chain ecosystem, optimizing existing and widely acknowledged portals would be more practical. With the help of ML and NLP, businesses can speed up requisitions, saving time and effort for customer success teams.

Warehouse Automation

The technology also helps with the sorting process. Deeper integration of technology with warehouse management systems allows for carrier-based picking and packing. It removes the need for warehouse executives to go back and forth to the staging areas to pick up consignments/parcels. This technology also helps optimize warehouse space utilization. Automation also plays a critical role in improving package labeling, making warehouse systems effective, and faster order fulfillment. Businesses that embrace technology sooner will stand an edge in the industry. 

Integrating AI has already brought significant improvements in the logistics sector. Initiatives across the region, such as the UAE National Artificial Intelligence Strategy 2031 and Saudia Arabia’s National Strategy for Data and AI, will propel further research and unearth newer possibilities for the sector across use cases. All this will open doors for businesses to achieve never-before-seen efficiencies.