As with other data-driven industries, supply chain businesses are increasingly incorporating disruptive artificial intelligence solutions to deal with their biggest challenges. Enterprises of all sizes are dabbling in AI innovations ranging from machine learning algorithms to automated robotics.

A failure in logistics severely hurts any supply chain operation, so businesses are continuously looking for better ways to handle disruptions, manage inventory, forecast pricing and simplify operations.

Taking care of disruptions is a critical logistics task that can actually be addressed effectively with AI. For instance, hurricanes, bankruptcies and worker strikes can all disrupt logistics, and training AI to learn from past contingency plans for these incidents can allow for automated corrective action. So, if a distribution center is confronted with catastrophic weather, shipping can be rerouted to a safer facility.

Experts expect artificial intelligence investments to rise for both domestic and international operations. Many businesses have announced plans to invest in AI applications capable of predictive analytics, augmented reality and robotics.

Despite the promise of AI, supply chain operations should avoid over-focusing on the technology. Management first needs to define clear business uses and problems that could be addressed by AI. Company difficulties should drive your development, rather than having the cart pull the horse, with engineers pushing the technology onto the company.

Case study: Freight broker

A “freight broker” is a kind of middleman that connects buyers who want to ship freight, and suppliers of vehicles that can provide the delivery. The supplier base for a freight broker can be quite fragmented, covering anything from a single man with a truck to significant fleets of vehicles.

In spite of these capacity difficulties, a freight broker must move freight for a predetermined price. At times, brokers are instructed to quote a price on a last-minute on a same-day load. In other cases, a broker will commit to a shipment more than a year ahead of time.

One of the biggest challenges for a freight broker is price prediction. Pricing has typically been done by human experts with many years of experience and historical market knowledge. However, new algorithm-based models are being tested to perform this same critical function. These models look at historical freight pricing information along with live data like the weather, traffic and socio-economic difficulties to determine the fair transactional price.

For every shipment, background analytics can investigate which carriers have shipped freight at various price and service levels. Combining together a variety of factors allows an algorithm to optimize coordinating freight to the best moving service.

While AI doesn’t always perform better than human experts, a crucial benefit of effective algorithms, like those that determine price, is democratization and convenience of information. Rather than counting on a handful of industry experts to create estimates, more workers can use machine intelligence to make certain they’re pricing within market so they don’t forfeit the sale, and within capability so they don’t fall short on the execution.

Work With a Top Supply Chain Recruiter

At ZDA, we keep a finger on the pulse of the latest supply chain developments so we can better serve our clients. If your company is currently looking for an in-the-know talent acquisition firm, please contact a top supply chain recruiter today!