AI route optimization has become pivotal for improved customer experience, faster deliveries, lower transportation costs, and reduced fuel consumption.
AI route optimization has become pivotal for improved customer experience, faster deliveries, lower transportation costs, and reduced fuel consumption.
Dense traffic is undermining driver productivity and harming the profitability of businesses. Delivery route planning is facing unprecedented challenges due to high road closures and blockages. In response, companies are increasingly investing in AI route optimization to address these issues. However, the critical factor for businesses is how swiftly they can realize the value of AI-driven logistics solutions across their supply chains.
As the supply chain landscape undergoes a paradigm shift, becoming more intricate and expansive, AI has emerged as a key asset in end-to-end logistics. AI routing technology is being deployed across distribution centers, production facilities, transport vehicles, and storage areas to reduce errors, enhance warehouse efficiency, and minimize operating costs. The benefits of AI in logistics are substantial, including the ability to spot trends, outliers, and sequences within large datasets. AI route calculation helps sift through data to identify inventory levels, purchase orders, transportation milestones, quality check logs, and delivery records. This intelligent routing solution exemplifies AI logistics optimization, transforming logistics companies into a smart ecosystem through AI route planning and AI routing benefits.
What is AI routing?
Gone are the days when logistics managers would use pen and paper to plan and dispatch delivery routes. That is not all. Imagine grappling with weather conditions, fluctuating order volumes, road closures, and vehicle breakdowns. AI routing is the panacea! It uses machine learning, predictive analytics, and other AI technologies to optimize routes.
Today’s supply chain business has a host of challenges that stand in the way of streamlining its logistics operations. Some of them include:
- Route complexities: The complexity of pickup and many delivery points may pose challenges to wave planning, shipment planning, and driver assignments.
- Traffic congestion: Finding alternative routes to avoid road closures and congested areas demands AI route optimization.
- Inaccurate delivery addresses: Inaccurate delivery addresses or the possibility of misrouting can cause bottlenecks. AI algorithms can collate and assess data, including delivery address and delivery time to optimize routes.
- Warehouse inefficiencies: Slow picking times and picking errors can slow down operations, leading to increased costs and reduced productivity. If you struggle to find out how long you have had a product, it will be difficult to fill and pack orders. AI routing optimization can boost productivity, real-time asset tracking, and visibility.
- Regulatory constraints: The supply chain process can grapple with regulatory constraints. AI routing technologies, such as IoT devices, sensors, and RFID tags can be used to propel transparency and collect data in real-time for analysis.
- Capacity limitations: Optimizing routes can be an uphill task — estimating distances and capacity limitations can be a headache for businesses. The advent of AI in route optimization has helped enterprises increase order capacity, balance capacity, and help allocate resources.
- Driver availability: Challenges of driver availability linger during all-mile delivery. AI route planning could be the answer to the conundrum as it will help you understand how many drivers you need to hire in advance.
- Delivery time windows: This can be a tricky period as inefficient routes can lead to longer delivery times and reduce customer satisfaction. AI routing software can help you comply with delivery windows and streamline the supply chain.
- Sustainability requirements: With every organization emphasizing sustainability initiatives to underscore their environmental, social, and governance (ESG) goals, it becomes a daunting task for organizations to combat toxic waste and hazardous air emissions. Companies can use AI in the supply chain to plan delivery routes, reducing fuel consumption and lowering carbon emissions.
How does AI make a difference in route planning compared to traditional methods?
From hand-drawn maps and manual planning to trial-and-error methods, traditional approaches to route planning have had numerous downsides. AI routing technology has emerged as a silver lining, offering significant AI routing benefits such as improved customer experience, faster deliveries, lower transportation costs, and reduced fuel consumption. By optimizing freight capacity and increasing the number of deliveries completed on time, AI route optimization enhances operational efficiency. In contrast, traditional route planning is often prone to inaccuracies and human errors, making it increasingly obsolete.
Think of AI routing as a superhero who streamlines supply chain management and logistics. Here is the kicker suggesting why there is broad daylight between AI routing and traditional routing:
Traditional routing: Imagine you are in the Internet Explorer era. It is that awkward moment when traditional routing grapples with complex and large-scale networks and there are high chances of a dip in performance with the rise in the number of variables. Nevertheless, traditional routing utilizes pre-defined rules and constraints and works with static and historical data involving manual input.
The light at the end of the tunnel seems distant as the routing is infamous for being less flexible in adapting to disruptions or sudden changes. While it can handle basic constraints, such as time windows and vehicle capacity, it may face the heat with complex and interdependent constraints.
AI Routing: How about bestowing the moniker “Goliath” to AI routing? Yes, it deserves the accolades. AI routing dynamically adjusts routes based on real-time information, quickly responds to unexpected events or changes in conditions and learns from past experiences to improve future decisions. It has the innate ability to manage multiple, complex, and interrelated constraints and balance conflicting objectives more effectively.
AI routing uses advanced machine learning algorithms, often including neural networks for pattern recognition and predictive analysis. What’s more? It is highly scalable and can handle complex networks with numerous variables, while performance often goes a notch higher with more data and larger problems. The intelligent routing solution also incorporates diverse data sources, including traffic, weather, and historical performance, and is capable of automatic data ingestion and processing. 
AI route optimization generates the most time-saving routes, minimizing the distance traveled and fuel consumption while boosting on-time deliveries (OTD). Real-time monitoring and data analysis enable logistics companies to respond effectively and swiftly to last-minute delivery requests, vehicle breakdowns, weather conditions, and road congestion.
AI-driven logistics has transformed middle-mile logistics through intelligent routing solutions, allowing for dynamic route optimization. This approach significantly reduces travel time and fuel consumption by leveraging real-time data analysis and predictive analytics, leading to enhanced efficiency, cost savings, and improved reliability in transporting goods between warehouses and distribution centers.
AI in logistics also plays a crucial role in collating data on emissions and fuel consumption, helping companies improve their sustainability profiles. Through AI route planning and AI logistics solutions, businesses can achieve a more sustainable and efficient logistics operation.
Why do enterprises need AI and ML in route optimization?
Conventional route planning methods demand hours of manpower to plan routes for even a few stops, considering various cost, business, strategic, and on-ground constraints. AI route optimization, combined with machine learning (ML) in route planning, enables delivery managers to schedule and plan routes in minutes, which would otherwise take hours using manual delivery plans. It can also handle and assess new algorithms against large volumes of data without fear of other workloads. Furthermore, the innate ability of AI routing to adapt to volatile conditions in real-time, including weather and traffic snarl, puts a premium on bespoke solutions.
AI routing technology and ML can facilitate zone-based route planning, an intelligent routing solution that prevents the overlap of delivery vehicles within service areas. This approach ensures that an optimal number of vehicles are deployed based on the quantity of orders, avoiding excessive vehicle utilization. Additionally, ML algorithms can assess weather forecasts, and current traffic conditions, and make real-time adjustments, enhancing AI logistics optimization and contributing to more efficient AI-driven logistics operations.
Decoding AI in the supply chain: Some interesting statistics
According to Gartner, over 75% of commercial supply chain management applications will incorporate AI by 2026. This reflects a growing trend towards AI-driven logistics, which is expected to significantly impact various aspects of supply chain operations. Gartner also predicts a five-fold increase in real-time decision execution within the digital supply chain by 2028.
McKinsey highlights that AI-powered supply chain management has enabled early adopters to improve logistics costs by 15%, service levels by 65%, and inventory levels by 35%. AI in logistics has become highly sought after for various applications, including load planning, fraud detection, inventory management, predictive analytics, shipment monitoring, and supplier selection. For example, AI route optimization through automated inventory management can prevent stock shortages by analyzing data patterns and optimizing inventory levels.
Pando has demonstrated how AI logistics solutions can drive optimization and cost savings, enhance customer experience, and streamline procurement processes. By leveraging AI route planning and AI routing technology, Pando has helped Castrol achieve significant benefits in supply chain management and operational efficiency.
Ticking the AI logistics optimization box
AI-driven logistics software solutions can determine the optimal route for trucks, predict arrival times, and adjust travel speeds along those routes. By utilizing AI route optimization, these solutions can estimate peak demand locations and prevent any single site from becoming overloaded.
According to recent McKinsey research, over 90% of U.S. online shoppers expect free shipping within two to three days. Additionally, more than 50% will look elsewhere if delivery times are too long. To reduce delivery times and enhance customer satisfaction, leveraging intelligent routing solutions is crucial. A cost-efficient route plan, made possible through AI logistics solutions, ensures timely deliveries and a superior customer experience. Choosing the right tech partner for AI route optimization is essential for achieving these goals and maximizing AI routing benefits in your supply chain operations.
Pando: The way forward for AI in route optimization
AI route planning has revolutionized route optimization by leveraging real-time traffic information and accurate travel time predictions. This advancement is pivotal in the supply chain, offering significant benefits not just in last-mile delivery efficiency but also in predictive forecasting and overall supply chain agility. AI algorithms serve as a crucial tool for brands seeking to generate optimal routes, thereby enhancing operational efficiency and ensuring a seamless delivery experience for customers.
Pando's route optimization solution epitomizes this innovation in logistics. By integrating AI into warehouse, order, and transport management systems, Pando harnesses the power of real-time data to dynamically adjust routes based on current traffic conditions. This capability not only minimizes delivery time but also reduces fuel consumption and vehicle wear and tear, leading to cost savings and environmental benefits. Pando's approach exemplifies how AI-driven logistics optimization can be a game-changer, offering companies a competitive edge in today's fast-paced market.
Interested in Pando’s Route Optimization offerings? Schedule a demo with us!
