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Ever wonder how your Amazon Prime packages show up at your door mere hours after you place an order?

A complex series of operations connects suppliers to manufacturers to wholesalers to retailers to you, the end consumer. Oversight of this process is called supply chain management (SCM). Within SCM, logistics is the portion that handles the movement of goods. Ecommerce giants like Amazon specialize in logistics, while consumer packaged goods leaders like Unilever provide full-spectrum supply chain management services.

Like every other data-driven industry, logistics and supply chain companies are investing in transformational AI solutions to tackle their most pressing pain points. Both small and large enterprises are dabbling in innovations ranging from machine learning to robotics.

A breakdown in logistics breaks the supply chain, so companies constantly seek out improved ways to manage inventory, predict pricing, and streamline operations. Chad Lindbloom, CIO of C.H. Robinson, a Fortune 500 multi-modal transportation company, shares with us the top use cases he’s using AI to tackle.


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The largest portion of C.H. Robinson’s business is North American truck freight. A portion of their customers pre-commit to regular business and outsource portions or all of their logistics needs. The remainder are one-off transactions, for which the company is a surge provider for unplanned freight.

Surprisingly for a transportation company, C.H. Robinson owns no vehicles. They are instead what’s called a “freight broker,” an operational and financial middleman between buyers who want to move freight and suppliers of vehicles who can do the job. The supplier base is incredibly fragmented, ranging from one man with a truck to massive fleets of co-owned vehicles. Despite these capacity challenges, CHR must commit to move freight for a customer at a specific price in advance. Sometimes they’re asked to quote a price for a last-minute, same-day load. Other times they commit up to 2 years in advance.

How much does a load cost?

Price prediction is thus their biggest business challenge. “The pricing in our industry varies seasonally, by day of the week, by lane, by time of the day,” explains Lindbloom. A “lane” is an origin-destination pair, such as Toledo, Ohio to New York, NY. Note that reversing the lane, from NYC to Toledo, requires a different price because urban centers don’t generate high volumes of goods that must be moved back to manufacturing zones.

Many vendors, such as Watson Supply Chain, ToolsGroup, and TransVoyant, offer logistics and supply chain software with AI baked in, but the complexities and nuances of C.H. Robinson’s massive business require them to build in-house technology tailored to their specific needs. Pricing was previously done by human experts with deep domain experience and historical market knowledge.

Prior to becoming CIO, Lindbloom spent 25 years in finance, with 15 years as CFO. Combining financial with technical expertise, he and his team have built machine learning models for price prediction that resemble those built by automated traders on Wall Street. These models examine historical freight pricing data along with concurrent parameters such as the weather, traffic, and socioeconomic challenges to estimate the fair transactional price on a spot basis.

AI doesn’t always outperform market experts, whom Lindbloom believes will not be fully replaced. “In some cases, humans come up with the better price. In most cases, the technology helps them home in on the fair market price,” he points out.

He also adds that a key benefit of effective algorithms is democratization and accessibility of information. Instead of relying on a few industry experts to produce estimates, more employees can use machine intelligence to ensure they’re quoting within market so they don’t lose the sale, and within capacity so they don’t botch the execution.

Where are all the trucks?

The second important use case is securing and managing the supplier inventory, the vast and fragmented array of trucks available to transport loads.

CHR commits to a transport price for freight buyers before they know the exact pricing and availability of requisite vehicles. The company relies on strategic human relationships, specifically a vast trading network across employees, to find the right truck with the right capacity for the load.

For every lane, CHR runs background analytics to examine which carriers have moved freight at what price and service level. Fragile, expensive, or time-sensitive freight requires a much higher service level. Pooling together these various factors allows CHR to optimize matching freight to the best mover.

Can we expect the unexpected?

Managing disruptions is the third important business task that can be improved with AI. Hurricanes, carrier bankruptcies, and employee strikes all have the potential to cause massive damages to the logistics business.

Predicting such disruptions and training AI to learn from contingency plans developed by humans enables automated corrective action in the future. To do so, CHR pulls together sources of information to analyze the impact of past disruptions, such as a carrier strike in France or a hurricane in the northeast United States. If a distribution center is threatened with adverse weather, for example, freight can be re-routed to a safer one.

Part of the data collection entails detailed surveys that track how human employees handled disruptions and the outcomes of their management. Lindbloom hopes that eventually systems can be trained to automatically take optimal actions after learning from humans.

How do we build the technology?

“We are constantly looking at what’s in the marketplace, and we believe we build better technology,” says Lindbloom. Due to a critical need for reliability, CHR builds and manages its own data centers, only going to the cloud if extra computing power is needed. Owning data center resources allows CHR to spin up environments very quickly as needed, but also to commit idle systems to research and development.

In addition to flexibility, owning data centers enables privacy and control. “We are a cloud provider of transportation management system to our customers,” emphasizes Lindbloom. “We have all the same technology as the core cloud providers, but we know where all the data is, we can control it, and we make confidentiality promises to customers. Many of them are more comfortable using us.”

“Technology is such a differentiating factor in our industry,” Lindbloom concludes. Other giants in logistics and supply chain agree and have also committed substantial dollars to AI initiatives: DHL aims to reduce costs with autonomous cars, Active Ants builds wearable technology to optimize warehouse tasks, Locus Robotics develops warehouse robots, and Honda leverages smartphone applications for real-time shipment tracking.

Where will we go from here?

DHL’s 2016 Logistics Trend Radar predicts that artificial intelligence investments will continue to surge for both domestic and international logistics. Increasingly more companies plan to invest in in-house development for AI applications in predictive analytics, operations and management, augmented reality, robotics, and industrial IoT.

Lindbloom has words of wisdom for those who want to replicate CHR’s success with AI: “Many of the things you’re going to try probably won’t produce value. Be willing to experiment and fail fast. Try to solve the same questions with multiple different models. Multivariate-type testing is key.”

Additionally, he cautions against overfocusing on AI and encourages executives to define clear business use cases first. “Have the business challenges drive your development, instead of data scientists and engineers pushing AI into the business.”

Mariya Yao is the Head of Research & Design at TOPBOTS, a strategy & research firm for enterprise artificial intelligence and bots. 

This story originally appeared on Copyright 2017

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