The future for AI in logistics is incredibly promising: McKinsey reports that firms will derive between $1.3trn and $2trn a year in economic value from using AI in supply chains and manufacturing. And implementation continues to be bright. PWC reports that 27 percent of organizations have already implemented AI and 20 percent plan to deploy this year.

Beyond the hype, supply chain adoption of AI is taking off because more companies are realizing the technology’s tangible value: solving the inherent complexities of managing a global logistics network. When implemented correctly, AI algorithms help supply chain and logistics teams make smarter, more agile planning decisions and predict and quickly resolve problems that arise. This AI-enabled proactivity raises the quality of service by meeting customer expectations for on-time and in-full deliveries while creating widespread efficiency gains through backorder avoidance and automated compliance processing. Naturally, this reduces costs and headaches across the logistics network.

But what’s really exciting about AI is its unlimited potential: on its own, AI offers a solution to one piece of logistics’ complexity problem. When bundled with other emerging technologies like IoT, analytics, automated workflows, digital integrations and more, the algorithms amass even more power because there is additional data and insight to leverage for predictive insights, giving organizations a more complete picture of the global network. This transparency is important because the way we think about supply chain and logistics planning is fundamentally shifting.

Use cases for AI in supply chain and logistics today

The supply chain has always been data-rich and analytical, but it's way less linear today due to its global scale, the multitude of partners, the horde of disparate data systems and more. Our customers often tell us their current supply chains have an incredible amount of data that isn’t being leveraged or realized.

The way companies handle their data is ripe for disruption, and the use cases for AI prove that the time to shift strategies is now. Here are a few AI use cases seeing serious traction across the market:

  • Exception Management: AI can predict which priority shipments are estimated for a late delivery into regional transportation hubs, or the percentage of shipments having an exception, by automatically analyzing real-time trends in the logistics network such as process compliance issues and damages, incomplete documentation, partner issues, custom clearance delays and more. This type of data helps logistics teams proactively manage and avoid, or alleviate, delays and excess costs – a benefit that alleviates a major pain point in logistics today.
  • Automated Workflows: Once an exception is discovered, AI can identify a path to resolution and fix the issue based on actions and rules that were previously set up by humans in an automated workflow. By suggesting the appropriate action at the right time, large-scale damage and costs can be avoided well before an issue occurs. AI turns data into invaluable intelligence that elevates supply chain performance.
  • Capacity Management: AI is also being leveraged to address capacity management and optimization. For example, by using AI to forecast demand and predict consumption, logistics leaders can more intelligently allocate shipping space and lanes to move products to the end consumer faster and more affordable. This type of intelligence helps companies avoid under or overstocking shipping containers to meet their customers’ needs. FedEx’s plans to increase and manage capacity and resources to meet demand in a more cost-effective and timely manner is a great example of this in action.

Getting started with AI: Where and how to act now

Implementing AI in a way that produces lasting and impactful results requires companies to take a digitization-first approach to everything they do. At the core of this shift is creating an environment where data is electronically generated, captured and stored. AI is only as good as the data it’s fed – and it needs lots of data to do its job well.

But you don’t have to be a data scientist or tech innovator to get started. Readily-available information from legacy systems or email exchanges can go a long way. The key is having a platform that can ingest and aggregate structured and unstructured data into a ‘single-version-of-the-truth’, which increases end-to-end visibility and collaboration among supply chain partners.

“Fast-fail” pilots are also good starting points. Attaining success and avoiding costly hiccups requires organizations to build a cross-functional internal team with deep expertise and find partners that can support quick proof of value projects. Accelerating discovery is key for securing quick wins and evaluating future AI deployments.

AI is taming supply chain complexity and powering a digital transformation that brings more proactivity, efficiency, and results. When AI is paired with the expertise of humankind, more value and insight can be produced than ever before.

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