What is the Role of Artificial Intelligence in Logistics?
Thanks to its ability to gather gigantic, disparate data sets, and then swiftly analyze the information to provide insights, the role of artificial intelligence in logistics is a valuable one as it helps consumer goods leaders make decisions more quickly, accurately, and efficiently. By receiving a better understanding of their demand planning, inventory management, route optimization, and customer service needs, integrating AI in the supply chain enables leaders to proactively head off problems before they occur and recalibrate operations as needed.
The use of AI in supply chain and logistics is still emerging, but 19% percent of consumer goods executives said logistics optimization was one of their top AI/ML use cases in the 2023 Retail and Consumer Goods Analytics Study.
“I often say that the great thing about AI is you can do so much with it,” said Levi Strauss & Co.’s chief global strategy and AI officer, Katia Walsh. “And the bad thing about AI is that you can do so much with it, which is where focus matters.”
How is artificial intelligence used in logistics?
Artificial intelligence in logistics can play out in myriad methods, from demand forecasting to last-mile delivery to product returns. Applications include but aren’t limited to:
- Demand planning and forecasting
- Automating manual office tasks
- Customer service chatbots
- Inventory management
- Fulfillment and last-mile delivery route optimization
- Warehouse management
- Reverse logistics
- Optimizing labor scheduling
- Predicting manufacturing downtime and maintenance
What are the advantages of AI in logistics?
Ninety-three percent of logistics professionals spend nearly half of their working day solely dedicated to addressing problems, according to a study by Container xChange. Integrating AI in the supply chain can increase decision-making speed by providing necessary insights quickly, offering recommendations to take action, and freeing up employees’ time to focus on higher-value work.
By upgrading its CRM to a unified platform that leverages artificial intelligence, Massimo Zanetti Beverage USA decreased the number of messages required to solve problems and increased efficiency in employee management. As a result, the company is able to close business more quickly and provide a higher level of customer service, according to Julia Girard, business development manager, strategic accounts.
Girard is bullish on the advantages of AI, as it can free up employees to tackle more complex customer management tasks and build sales pipelines more quickly. “I can see the AI feature being integrated into more aspects of the system to help us reach customers faster,” she says. “I could see AI becoming our full-time assistant to help fulfill basic customer day-to-day interactions and communication — maybe even help with lead generation for new opportunities.”
What are examples of AI use cases?
Although widespread use of AI is still growing, there have been many examples of AI in logistics across the consumer goods industry, often through test-and-learns. Levi’s has been one such front-runner in the use of artificial intelligence and reports using it across many business functions, including within merchandise planning and inventory management. The company developed a livestreaming repository of data that’s used to create machine learning models and algorithms to predict the demand for every product type in different channels around the world.
A Levi’s distribution employee, meanwhile, developed a machine learning model that predicts the likelihood of equipment failure in that worker’s particular center — a very personal pain point that demonstrated the benefits of AI in the supply chain.
The apparel company also developed BOOST (Business Optimization of Shipping and Transport), an AI- and ML-driven e-commerce solution that optimizes inventory management to improve order fulfillment. It’s designed to make independent, informed decisions based on all elements of the fulfillment processes — including shipping, packing, and labor — and is expected to improve operational efficiency, decrease consumer costs, and provide more streamlined customer experiences.
What are some ways that companies are using analytics or AI for transportation?
Yelloh is using machine learning, a subset of artificial intelligence, to optimize operations for its fleet of mobile store vehicles. The company, formerly known as Schwan’s Home Delivery, started as a milk delivery route over 70 years ago but now sells a curated selection of consumer goods that consumers can order via a mobile app.
The company leverages machine learning, predictive analytics, and personalization engines to provide precision routing information to its drivers and tailored product recommendations to consumers. As a result of these investments, Yelloh has increased both customer and employee efficiency and satisfaction.
“Through machine learning and understanding the right purchasing patterns of customers, we can gauge their household storage, consumption, and seasonality patterns, allowing us to connect digitally,” says Kevin Boyum, Yelloh chief strategy officer. “We moved from physical door-knocking to digital door-knocking — from a pure push model to using data and personalization.”