Discover how AI copilots enhance transportation management software with efficient logistics and user-friendly features, driving competitiveness through automated decisions and real-time insights.
Discover how AI copilots enhance transportation management software with efficient logistics and user-friendly features, driving competitiveness through automated decisions and real-time insights.
With the rise of AI, it’s no longer just humans behind the wheel of transportation management. Digitization of the logistics sector is now closely being followed by yet another evolution of AI — Generative AI. Supply chain organizations are already embracing these advancements. According to a report by EY, nearly 40% of them are actively investing in Generative AI technology. Many of these efforts are directed towards learning how logistics teams can leverage AI to streamline operations.
According to Forbes, AI-powered copilot could be one of the most significant transformations in the logistics industry. AI copilots in Transportation Management Software (TMS) are quickly becoming essential to tackle the growing challenges of cost, efficiency, and real-time decision-making.
What are AI copilots in TMS?
AI copilot is an advanced generative intelligence that can assist teams in making decisions across the freight procure-to-pay cycle. It is a conversational interface where your team can interact with the system and accelerate tasks like selecting carriers, optimizing shipping routes, and managing invoices.
Copilots use large language models to analyze vast amounts of data, identify trends based on historical patterns, and provide recommendations based on real-time insights. In short, a copilot augments TMS solutions for modern logistics.
Why does your logistics team need AI copilot?
Logistics teams often grapple with time-consuming manual tasks. While some of these tasks are mundane enough to drain their productivity, others are too complex to be sorted out with pen and paper. An AI copilot is the answer to both of these extreme conditions. It can automate repetitive tasks, improve resource allocation, and enable more informed choices.
- Tackle Unpredictability and Delays
It is possible that you perfectly plan out the freight procure-to-pay cycle. However, its success often hinges on factors beyond your control, like unpredictable weather conditions and unforeseen events. AI copilots ensure flexibility and allow teams to adjust plans and avoid such issues proactively.
- Efficient route optimization
Route optimization plays a crucial role in minimizing costs and delivery times. AI copilots can analyze various factors, including traffic, fuel costs, and shipping deadlines, to recommend the most efficient routes. Pando’s AI copilot goes a step further from recommending the right procurement strategy to automate it.
- Compliance and documentation
A robust TMS solution must incorporate logistics management tools to handle the entire procure-to-pay life cycle. This includes handling compliance requirements and managing shipping documents. AI copilots automate the creation and management of these tedious documentation processes. This reduces the risk of errors and allows your team to invest their energy in other tasks.
- Dispatch optimization
Manual load-to-driver assignments can often lead to inefficiencies, as they rely on broad geographic knowledge rather than precise data. An AI copilot optimizes dispatch decisions by analyzing carrier performance, factoring in traffic patterns and weather forecasts, and accounting for the specifications of each vehicle. This intelligent approach boosts delivery success, ensuring smoother operations and higher delivery rates.
- Fuel management
Standard route planning can only suggest a handful of fixed fuel stops along the way. On the other hand, an AI copilot identifies optimal refueling points based on live fuel prices and suggests route adjustments for better fuel economy. The AI can also incorporate charging schedules for fleets using electric vehicles.
- Customer communication
Customers often need to track the delivery updates manually. This is not only time-consuming but often inaccurate as well. With an AI copilot, customers receive automated ETA updates and proactive notifications in case of any delays. The copilot adapts to each customer’s preferred communication channel, further building trust and improving customer satisfaction.
- Delivery planning
Fixed pickup and delivery windows can lead to inefficient waiting times for drivers and delayed schedules. An AI copilot reduces these inefficiencies with dynamic dock scheduling, ensuring that load preparation aligns closely with the arrival of trucks. It further reduces wait times by seizing cross-dock opportunities that save both time and resources.
- Load consolidation
Traditional load planning based on geographic grouping limits the potential for more efficient deliveries. An AI copilot optimizes load consolidation by identifying multi-stop routing options, backhaul opportunities, and compatible load combinations. This dynamic approach improves load efficiency, allowing logistics teams to maximize each trip and cut down on empty miles.
- Regulatory compliance
Manually tracking hours of service is not only labor-intensive but also prone to errors. An AI copilot automates this process, tracking driver hours in real time and ensuring they align with regulatory limits. It also automates documentation management, reducing the risk of human error and enabling smooth, compliant operations.
- Last-mile optimization
Fixed delivery zones can limit the flexibility of efficient last-mile logistics. An AI copilot adjusts to dynamic delivery territories, suggesting alternative delivery points and optimizing time windows based on real-time conditions. This makes last-mile delivery faster, more responsive, and better suited to meet customer demands.
- Carbon footprint management
Basic mileage calculations for carbon tracking overlook many factors that contribute to a company’s environmental impact. An AI copilot offers a better solution by monitoring real-time emissions and identifying alternative fuels. It can also assign sustainability scores to different routes. The scores empower logistics teams to make choices that reduce their carbon footprint and support sustainable business practices.
How is AI copilot transforming TMS?
AI copilots are revolutionizing TMS by enhancing user experience, automating decision-making, and providing real-time insights. Let’s understand how this integration of AI copilot with TMS is helping businesses elevate overall performance, improve customer satisfaction, and respond more efficiently to unforeseen disruptions.
1. Enhanced user experience
AI copilot in TMS enhances user experience by making interactions more efficient and responsive. Take Pando’s copilot as an example. Once you log into your executive dashboard, you get a detailed summary of the entire freight spend, active bids, and the contracts that are soon expiring. Pando’s copilot analyzes the current market value to suggest which contracts you must extend. It can further automate routine tasks like automatically notifying carriers of the extended contract while also updating the carrier agreement with new expiration dates or other changes.
This transportation software automation enables you to have full control to either accept or reject the recommendations. According to Castrol, "Through their integration of advanced AI technology, logistics expertise, and top-tier talent, Pando has become a key partner for us. Their solutions streamline our freight procurement to payment processes, driving meaningful changes in our supply chain toward greater agility and sustainability." By minimizing manual intervention and enabling smarter decision-making, AI copilots ensure a smoother, faster, and more informed user journey in the TMS ecosystem.
Case study: Transforming customer support
Challenge: Castrol faced unique supply chain complexities due to its dual B2B and B2C business model. They served over 100,000 touchpoints, including OEMs, industrial clients, and retail customers. This required efficient operations across a sprawling distribution network. Volatility in raw material prices and unreliable international logistics further resulted in complex challenges. As a result, cost control and on-time deliveries became increasingly difficult. Additionally, manual workflows across 50 logistics partners and thousands of transactions created inefficiencies and slowed down the whole process.
Solution: To address these issues, Castrol partnered with Pando to implement a Transportation Management System (TMS). The solution focused on digitizing manual workflows, enhancing customer support, and optimizing operations. By integrating AI-driven automation, Castrol enabled real-time visibility for its customers, streamlining the order-to-payment cycle. The partnership emphasized a collaborative approach, ensuring alignment with business goals and delivering a seamless transition from manual to digital processes.
Result: The AI-powered TMS transformed Castrol's customer support by providing real-time shipment visibility and improving the overall experience. Customers benefited from a B2C-like experience in a B2B setup, simplifying order tracking and receipt processes. Automated workflows eliminated inefficiencies, reducing manual intervention and errors. Additionally, Castrol achieved significant cost savings through optimized truck utilization, route planning, and load consolidation.
2. Automated decision making
Automating complex decisions is one of the best offerings of AI in transportation management. Tasks such as route optimization, load balancing, and carrier selection can be handled with minimal manual input. According to a report by Accenture, generative AI technology, like copilot, could impact 43% of all working hours across end-to-end supply chain activities. This can further result in streamlined operations and reduced costs across the board.
Case study: How automated decision-making helped streamline payment processes
Challenge: A confectionary giant faced significant inefficiencies due to its reliance on manual decision-making processes within its supply chain operations. Critical tasks like freight cost optimization, route planning, carrier selection, and compliance management were labor-intensive and prone to errors. This approach limited the company’s ability to make timely and accurate decisions, leading to increased costs, delays in delivery schedules, and inconsistencies in invoice reconciliation and payment processing. The lack of real-time visibility further compounded these challenges, making it difficult to address issues proactively or adapt to changing logistics demands.
Solution: By implementing Pando Fulfillment Cloud, the company transformed its supply chain decision-making through automation. AI-powered algorithms enabled precise load and route optimization, ensuring that freight costs were minimized while maximizing truck utilization. Automated carrier selection considered factors like SLAs, lane performance, and budget constraints, eliminating manual guesswork. The platform provided real-time insights into shipment statuses and predictive analytics to preempt potential issues. Digital documentation and invoicing streamlined compliance and payment processes. These automated solutions empowered the organization to make fast, data-driven decisions at every stage of their logistics operations.
Result: Automated decision-making delivered immediate and lasting benefits for the company. Freight costs dropped significantly due to optimized routes and better vehicle utilization, while invoice reconciliation and payment cycles were accelerated, reducing manual errors and disputes. Real-time tracking and predictive insights allowed the company to address shipment delays proactively, improving customer satisfaction. The streamlined processes not only enhanced operational efficiency but also supported sustainability goals by minimizing resource wastage. Through Pando’s automated solutions, the organization achieved a scalable, efficient, and future-ready supply chain.
3. Real-time analytics and insights
AI copilots excel in leveraging real-time data to deliver actionable insights. It can continuously analyze data streams from multiple sources to keep your logistics team updated with real-time information. AI copilot empowers your team to keep track of carrier performance, route conditions, and shipment statuses. As per Accuride, “With Pando, we can show them on a map exactly where the container is at any given moment.” The ability to monitor operations in real time, combined with predictive analytics, ensures a more resilient and efficient supply chain.
Case study: Enhancing procurement with real-time insights through AI
Challenge: Accuride, a $1.3 billion global manufacturer of wheels and wheel-end products, faced significant challenges in its supply chain management during the pandemic. Freight rates soared, container shortages and shipping delays became frequent, and manual processes created inefficiencies in freight visibility and invoice management. The company lacked real-time visibility into its shipments, leading to costly detention fees, unverified freight charges, and an inability to track and optimize deliveries effectively. With fragmented communication across multiple vendors and manual tracking methods, Accuride needed a solution to streamline its processes, enhance visibility, and reduce operational costs.
Solution: Accuride turned to Pando’s real-time analytics and insights capabilities to gain comprehensive visibility across its supply chain. The platform allowed the company to consolidate its contracts, validate rates, and make optimized routing decisions by factoring in both cost and transit times. Real-time shipment tracking and milestone updates empowered Accuride to monitor shipments from port entry to final delivery. Automated alerts and digital documentation facilitated faster processing through customs and ports, reducing bottlenecks and detention fees. Pando's freight audit features ensured that Accuride was only charged for agreed-upon services, and the system provided exception reporting to highlight discrepancies and opportunities for cost savings.
Result: The integration of real-time analytics significantly improved Accuride’s supply chain efficiency. The company achieved a $500 reduction in accessorial fees per container, leading to substantial savings across 3,000 containers annually. The ability to track shipments in real-time allowed Accuride to respond promptly to customer inquiries, improving service levels and customer satisfaction. Additionally, enhanced financial tools within Pando enabled the company to pinpoint opportunities for cost reductions and process improvements. With better control over its logistics operations, Accuride improved decision-making, reduced manual errors, and ensured faster, more reliable deliveries to customers, both internal and external.
4. Driving sustainability across the supply chain
As per the UN Global Compact report, 63% of CEOs find it challenging to measure ESG (Environmental, Social, and Governance) data across the value chain. This largely occurs due to the prevalence of unstructured data with multiple suppliers, logistics partners, and varying regional regulations. This is where AI in transportation management can make a significant difference. AI copilot can aggregate, track, and analyze ESG metrics from diverse sources.
Case study: Accelerating supply chain decarbonization with AI copilots
Challenge: A global pharmaceutical company faced a significant challenge in tracking the carbon reduction targets of its extensive supplier network. They had to monitor thousands of suppliers manually and collect data on Science-Based Targets (SBTs) of their suppliers. The labor-intensive and slow process made it difficult to measure the company’s overall environmental impact and meet decarbonization goals efficiently.
Solution: The company adopted a generative AI copilot designed to accelerate the collection and analysis of sustainability data. The AI solution was programmed to comb through thousands of supplier websites and gather real-time information on their SBT progress. The company was able to get up-to-date information within minutes through this automated process.
Result: In just one hour, the AI copilot delivered reliable insights confirming that the company had already surpassed its supplier SBT targets. The automation not only reduced time and effort but also empowered the company to make faster decisions regarding its decarbonization efforts.
Enhance operational efficiency and sustainability with Pando TMS
AI copilots in Transportation Management Software are transforming logistics operations, offering significant benefits such as real-time insights, efficient route optimization, and enhanced decision-making. These tools streamline manual processes, reduce errors, and enable logistics teams to proactively address challenges, ultimately driving cost savings and improved customer satisfaction.
As AI continues to evolve, the integration of generative intelligence in TMS will become indispensable for companies aiming to stay competitive. From improving sustainability practices to automating compliance and documentation, AI copilots are setting a new standard for efficiency and innovation in the logistics industry.
If your current TMS isn't meeting your needs, consider a TMS like Pando, which includes an AI copilot to enable seamless supply chain and logistics planning for your team and enhance the delivery experience for your customers. Book a demo today.
