Transforming Transportation with AI in Logistics

For the logistics industry, the journey towards AI-powered solutions is beginning. 

Problem-solving powerhouse Greg Price, CEO and Co-founder of Shipwell was recently invited to join the Association for Supply Chain Management PowerLearn series to talk about why more transportation organizations are looking to AI to find workflow efficiencies and growth opportunities. But it’s bigger than that: AI is already fundamentally changing the way the work gets done. Here are some takeaways from the conversation and how you can start using artificial intelligence in your logistics strategy. 

Why AI, and why now?

Errors in supply chain operations can be tremendously costly–and that’s a big reason why the logistics industry has been slower to embrace new technologies compared to others. For instance, leaving a container at a port for an extended period can lead to demurrage fees of up to $50,000–and with a poorly implemented AI logistics solution, you may not realize it until the charge has already been applied. Despite examples like this, organizations are balancing apprehension with enthusiasm about AI's potential. 

The emergence of agentic AI is set to revamp supply chain operations by automating routine functions. These agents work around the clock, taking on tasks such as:

• Exception Management: Automatically identifying and resolving discrepancies in shipments, reducing the need for human intervention and speeding up the resolution process.
• Execution: Streamlining order fulfillment processes by managing inventory levels, tracking shipments in real-time, and ensuring timely deliveries without human oversight.
• Planning: Using historical data and predictive analytics to optimize supply chain schedules, improving efficiency and reducing delays.
• Corrective Actions: Instantly recommending and implementing corrective measures when unexpected issues arise, such as rerouting shipments or adjusting inventory levels.

As these AI agents take care of central, often straightforward functions, supply chain professionals will be freed up to focus on higher-level strategic tasks leading to innovation and improved overall productivity. And with AI projected to automate up to 70% of business activities by 2030, companies must focus on reskilling their workforce to effectively harness AI’s capabilities, especially in a business context. By embracing AI and its capabilities now, the logistics industry can not only mitigate costly errors but also transform how supply chains operate in the future. 

“You can innovate very quickly, but if a company can’t actually ingest and actually execute [a strategy that includes AI Agents], it’s very hard to put it into practice.” – Greg Price, CEO & Co-founder

Consider your framework

There is a huge amount of information out there around AI in logistics. When picking a starting point for your department, it helps to start with what you already know. 

A typical supply chain operation consists of seven divisions:

• Planning: Forecasting demand and aligning resources to meet future needs efficiently.
• Execution: Managing the physical movement of goods and fulfilling customer service requirements.
• Procurement: Sourcing and acquiring goods, services, and capacity.
• Risk and Compliance: Identifying and mitigating risks while adhering to regulations.
• Strategy: Designing and optimizing supply chain networks.
• Finance: Managing costs, profitability, and financial sustainability.
• Analytics and Visibility: Providing insights into supply chain performance and operations.

Within each of these, AI can potentially:

• Automate repetitive and manual tasks
• Augment human decision-making, or
• Generate insights to improve performance.

Considering each logistics division's pain points, and whether they’re best supported by automation, augmentation or generative insights will not only help you better understand AI, but where your organization may find the greatest opportunity to utilize it.

Know the difference between automation, AI workflows, and AI Agents (Agentic AI)

Advances in technology mean new terminology. Let’s break down what some of these actually mean–with some relatable examples from the shipping industry to boot.

While automation excels in speed and reliability, leveraging AI workflows and AI agents in logistics brings adaptability and human-like reasoning. At Shipwell, we’re leveraging these technologies to revolutionize supply chain efficiency, combining the strengths of each to deliver smarter, more responsive systems. 

AI Agents will automate key logistics functions

While automation and AI workflows might be integrated in some supply chain operations, agentic AI is still in its very early stages. Its potential to revolutionize supply chain planning and execution however, is immense. A few of the traditional logistics roles predicted to become highly automated by AI are:

Planning

• Demand Planner: A role that analyzes historical data, market trends, and seasonality to forecast future transportation needs. This role also develops transportation plans that optimize resource allocation (vehicles, drivers, routes) while meeting service level agreements (SLAs) and profit targets. AI can analyze historical data, market trends, and sales forecasts to predict future demand. 
• Scheduler: Creates daily or weekly schedules for drivers and vehicles, considering factors like delivery windows, driver availability, and vehicle capacity. Monitors schedule adherence to make real-time adjustments as needed. AI can automate scheduling and dispatching, optimizing for efficiency and cost reduction while considering real-time events like traffic and weather.

Execution

• Warehouse Operator: Manages the flow of goods in and out of the warehouse, including receiving, storing, picking, packing, and shipping. AI-powered robots and automation systems can significantly automate warehouse operations, improving efficiency and reducing manual labor.

Finance

• Freight Auditor: Reviews freight invoices for accuracy, identifies discrepancies, and processes payments. AI can automate compliance monitoring, flag potential violations, and generate reports.
• Logistics Cost Analyst: Tracks and analyzes transportation costs, identifies cost drivers, and develops cost reduction strategies. AI can analyze historical data and external factors to predict potential risks and suggest mitigation strategies.

With all of this automation taking place - the role of human expertise and oversight to enhance performance, reliability, and ethical considerations must be increasingly recognized. Keeping a human in the loop is absolutely necessary to ensure accuracy and account for nuances.

Getting started with AI in logistics

While many organizations are piloting AI initiatives, few have fully integrated these technologies into their operations. To start using AI in logistics, consider:

1. Assess current processes & identify opportunities: Begin by evaluating your existing supply chain processes to identify areas that could benefit most from AI integration. Pro-tip: Focus on processes that are data-intensive, repetitive, or prone to human error.
2. Assess risks & rewards: Carefully evaluate the potential risks and rewards of AI implementation in each process. 
3. Evaluate existing technology platforms: Take stock of your current technology infrastructure to determine which platforms can support AI integration. Pro-tip: Reach out to your tech vendors to understand and talk through their AI capabilities.
4. Ensure data quality & compatibility: Ensure that your data is clean, well-organized, and compatible with the AI systems you plan to use/implement. This may involve data cleansing, integration of disparate data sources, and establishing data governance protocols.
5. Start small with pilot programs: Begin your AI journey with small-scale pilot projects. This approach allows you to test AI tools, understand their impact, and make necessary adjustments before scaling up.
6. Scale successful implementations: Once you've validated the success of your pilot programs, begin scaling the most effective AI implementations across your organization. Use the lessons learned from your initial projects to guide this expansion.

There are several other areas – like strong ethical frameworks and building a diversely skilled AI team – to keep in mind while building an approach. For a more detailed list on how to get started, download the complete AI Implementation checklist.

Winning with AI: start small and scale smart

The adoption of AI in supply chain management is still in its early innings. Companies that focus on building the right infrastructure, ensuring data quality, and starting with small-scale pilots before scaling up will be well positioned for success.

AI—particularly agentic AI—has the potential to transform the supply chain industry by automating repetitive tasks, improving decision-making, and unlocking new growth opportunities. However, businesses must approach adoption carefully to address challenges and maximize ROI. By carefully considering an AI implementation framework and starting small–you’re already well on your way to improved operations and logistics excellence.

‍