How is AI Being Used in Supply Chain?
Artificial intelligence can be used to quickly gather and analyze massive amounts of data flowing from a large number of sources. As a result, AI in the supply chain is a prime candidate for the tech investments that can move the needle in improving business performance.
How Artificial Intelligence is used in supply chains
The applications of artificial intelligence in logistics and supply chain are wide and varied; while adoption is still growing across the consumer goods industry, the supply chain remains one of the top functions AI and machine learning are being applied, according to the CGT/RIS News Retail and Consumer Goods Analytics Study. When asked where they were using it, the top of the list was stuffed with AI in supply chain use cases:
One example in action includes Amazon, which is using AI in some of its warehouses to reduce the number of damaged items shipped to consumers. The company not only expects this to improve customer service, but it also foresees a reduction in the amount of time spent picking and packing orders by automating this manual process.
Perhaps unsurprisingly, Amazon is a bit of a front-runner when it comes to both AI and IoT in the supply chain; the company also leverages IoT in a number of ways, including within fleet management and monitoring its carbon footprint.
Other companies using artificial intelligence in supply chain management include Kraft Heinz, which is using it as part of its “KH Hive” demand forecasting. The technology can forecast sales at the SKU, location, and down to the daily level, according to the company. Seventy percent of its customers leverage the platform, and the company improved distribution forecast accuracy by 5 percentage points, which it expects to double in 2023.
What are the challenges of using AI in supply chains?
As with all technologies, there are some disadvantages of AI in supply chain. For one thing, data can be a big barrier when leveraging AI, says Amber Salley, senior director analyst at Gartner, particularly regarding machine learning. With machine learning, one needs a large volume of data in order to have the large number of observations required to identify patterns, and many organizations might not have the volume of data needed at the right level of granularity to receive value out of the AI outputs.
“Another big challenge is the skill sets of the planners or the resources themselves and getting them to rethink what the supply chain will be with the use of AI to enable more automation,” she says. “That is also included with identifying what the skill sets of the supply chain professionals need to be and how you might need to upskill the skill sets of professionals is a big one.”
What’s more, companies just might not know what to do with the output that they have, she adds.
What is an example of AI in logistics?
Maersk is using artificial intelligence to build a “predictive cargo arrive mode,” according to CNBC, which the shipping and logistics company expects to improve scheduled reliability for its customers.
“Reliability is a big deal, even post-pandemic so that they can plan their supply chain, their inventories better, and bring their costs down,” Navneet Kapoor, chief technology and information officer, tells CNBC.
How AI can make supply chains more sustainable
Artificial intelligence can play a role in helping make supply chains more sustainable, resilient, and collaborative. In fact, Gartner reports that high-performing supply chains are 19% on average more likely to have capabilities in place to achieve their sustainability goals.
Methods in which this can be accomplished include:
Demand forecasting and planning: Visibility into the types and amount of products consumer goods manufacturers should make and sell enables them to adjust operations accordingly to limit excess goods.
Inventory management: Visibility into the ideal locations where products should be shipped and sold can also decrease excess inventory and food waste.
Energy efficiency: AI can be used to monitor and optimize energy usage in factories, as well as predict machinery downtime.
Transportation optimization: AI can optimize delivery routes to reduce the amount of fuel used, decreasing harmful emissions.
Developing a more sustainable supply chain is imperative for today’s consumer goods leaders, who need to remain in compliance with stricter regulatory mandates and deliver on consumers’ desire for more eco-friendly products. It is, to be sure, a tall order.
“Sustainability is…a broadly-impacting initiative with so much work to do. We have to reformulate whole product lines; rethink whole supply chains,” says Scott Isaacson, manager of data science for Citrine Informatics. “There is a need to have a tool that lets us go faster because if we just eat this elephant one forkful at a time, it'll take too long — somebody's going to beat us to market or we're going to fall behind the regulations. There's real consequences for going too slow because the consumer preferences and the regulations are moving so quickly.”