Artificial intelligence is ready for primetime


This month the McKinsey Global Institute released a discussion paper called “Notes from the AI Frontier: Insights from Hundreds of Use Cases” that measures the recent progress of artificial intelligence business applications and identifies industries and use cases where AI has the greatest potential to unlock value.

There are a number of different ‘problem types’ identified by the analysts, each of which are suited to particular deep learning or AI techniques. These problem types include classification (based on a set of training data, categorizing new inputs as belong to one of a set of categories), continuous estimation (based on a set of training data, estimate the next numeric value in a sequence), clustering (a system creates a set of categories for which individual data instances have a set of common or similar characteristics), optimization, anomaly detection, ranking, recommendations, and data generation.

The two most important takeaways are that the best AI systems have consistently outperformed humans since 2015, and specifically in the transport and logistics industry, AI can improve performance over other analytics techniques by 89%. In other words, the technology is maturing and becoming commercially viable, and transportation and logistics stands to gain more from AI than almost any other industry.

McKinsey also found that AI will create $400-500B in value for transport and logistics alone, representing 4.9-6.4% of industry revenues. 

“Application of AI techniques such as continuous estimation to logistics can add substantial value across many sectors,” the analysts wrote. “AI can optimize routing of delivery traffic, thereby improving fuel efficiency and reducing delivery times. One European trucking company has reduced fuel costs by 15 percent, for example. By using sensors that monitor both vehicle performance and driver behavior, drivers receive real-time coaching, including when to speed up or slow down, optimizing fuel consumption and reducing maintenance costs. In another example, an airline uses AI to predict congestion and weather-related problems in order to avoid costly cancellations. For an airline with 100,000 flights per day, a 1 percent reduction in cancellations can make a material difference.”

“Applying some of these advanced analytics can actually give the small carriers just as many community-based opportunities as a large carrier. That’s actually going to allow us to grow together,” said Tim Leonard, CTO of TMW Systems, in a recent interview. 

“We’re looking at the business model of full automation,” Leonard told FreightWaves. “I know within trucking that’s kind of far-fetched, but there are specific applications that machine learning components can perform well at. Today large enterprises have a huge set of algorithms, and what we’re looking at is the introduction of algorithms as a service, which sets us up for machine learning,” Leonard added.

DHL and IBM’s new white paper “Artificial Intelligence in Logistics” stated that “In an increasingly complex and competitive business world, companies that operate global supply chains are under unprecedented pressure to deliver higher service levels at flat or even lower costs. At the same time internal functions of global corporations such as accounting, finance, human resources, legal, and information technology are plagued by large amounts of detail-oriented, repetitive tasks. Here, AI presents a significant opportunity to save time, reduce costs and increase productivity and accuracy with cognitive automation.”

Predictive demand and capacity planning that uses artificial intelligence to scour online browsing data, YouTube views, and social media conversations could calculate probabilities of a spike in demand for a specific product and predict the next fidget spinner-like fad. IBM’s Watson has learned how to perform visual inspection of logistics assets like railroad cars, and computer vision tools are being trained to manage inventory.

What’s important for executives at this stage, though, is to precisely define the business problems that can be solved with AI, and ask if the problem actually demands an AI solution. The report warns that “A cautious stocktake of business value drivers such as cost reduction, improved customer experience, and efficiency gains through better insights is required before starting an AI project.”

“As supply chain leaders continue their digital transformation journey, AI will become a bigger and inherent part of day-to-day business, accelerating the path towards a proactive, predictive, automated, and personalized future for logistics. Ultimately, AI will place a premium on human intuition, interaction, and connection allowing people to contribute to more meaningful work,” DHL and IBM concluded.