What is an Example of Artificial Intelligence in Logistics?
Given its versatility, it can be helpful to really detail the use cases of AI in the supply chain and logistics to connect the dots between the theoretical benefits and the real-life applications. To be sure, there’s no shortage of AI logistics use cases, whether it’s through demand forecasting, enhancing warehouse efficiencies, or improving route planning. In all of these cases, AI in the supply chain can translate to streamlining, predicting, and automating key aspects of logistics operations.
Consumer goods companies are increasingly leaning into the benefits of AI in logistics. In fact, 19% percent of consumer goods executives cited logistics optimization as one of their top use cases of AI and machine learning in the 2023 Retail and Consumer Goods Analytics Study.
What is logistics intelligence?
Logistics intelligence involves using data and analytical tools to optimize logistics processes. AI is able to collate vast amounts of data – from historical sales, social media, consumer behaviors, and more – as well as analyze the impacts that factors such as seasonality, holidays, and even weather might have on inventory levels.
By learning continuously, the AI model can adjust for demand fluctuations and vastly improve forecasting accuracy. According to RIS News, 90% of supply chain executives list inaccurate inventory data as being “very important” or “somewhat important” when resolving inventory management challenges, illustrating how these insights are a top priority for supply chain and logistics leaders.
How is artificial intelligence used in logistics and supply chain management?
Artificial intelligence in logistics and supply chain management can play out in a multitude of ways. Supply chain teams are charged with the task of adapting to market conditions, reducing costs, and ensuring manufacturers have everything needed. This adoption is picking up speed. According to IDC’s Futurescape report, by 2023, 50% of all supply chain forecasts will be automated using AI.
This is all the more important today, given that warehouse space is increasingly at a premium, with the vacancy rate for U.S. warehouses sitting at just 3% last year, according to eMarketer. Additionally, retailers are cutting back on their warehouse space and pairing down logistics teams in response to changing consumer behaviors. This means the pressure is on to finetune forecasting while also ensuring goods get to where they need to be.
These teams use AI to provide prescriptive recommendations, bolster forecast accuracy, and provide improved visibility into inventory management efforts. Mattress company Serta Simmons, for example, recently added an AI-driven integrated demand and supply planning solutions to its existing advanced planning systems, allowing the company to better plan out the acquisition of raw materials needed to manufacture its mattresses.
How is AI improving logistics?
In its simplest form, logistics is the practice of getting a product from one place to another. In reality, however, the process is far more complex. In fact, according to Gartner, logistics costs account for nearly 80% of overall supply chain expenditures for certain companies. Weaving artificial intelligence into logistics can help companies to save money, streamline processes, and improve forecasting accuracy through greater visibility into supply chain operations.
“AI will increase efficiency in all aspects of logistics to the point where it won't seem [like] any effort at all,” says Larry Sherrod, senior manager at Peloton Consulting Group.
What companies use AI for supply chain?
As the supply chain becomes even more complex, consumer goods companies are exploring the use of AI in logistics. Here are just a few examples:
Colgate-Palmolive is piloting decision intelligence, a subset of data science, to improve product deployment decisions across its Hill's Science Diet and Hill's Prescription Diet fulfillment network. The solution automates decision-making and forecasts demand to precisely designate optimal product allocation, ultimately improving stock deployment operations across its fulfillment networks.
Kimberly-Clark has sought to use AI for supply chain management to get around the issue of order bunching. The company adopted an AI-enabled tool to automate distribution planning and deployment processes and improve scheduling to avoid order loads piling up on particular days of the week — specifically spikes on Monday-Wednesday, followed by quieter weekends. As well as unifying disparate systems, the solution makes recommendations that Kimberly-Clark can execute more efficiently. As a result of the implementation, the company has reportedly reduced variability daily by 40%.
Discussing how the company's adoption of AI is improving logistics, Kimberly-Clark’s VP of global logistics said: “We started thinking, ‘Well, it’s easy enough for our transport and distribution team just to move orders around.’ But they didn't have the ability to do that without understanding, first, do we have the right stock in place, and on the right days, or what the customer's ability was to take a different delivery day.”
Coca-Cola: Istanbul-based bottler Coca-Cola İçecek (CCI) built a digital replica of its manufacturing plants, known as a digital twin, that used advanced analytics and artificial intelligence to help identify machine failures. The company leveraged the technology within its bottling plants to receive a holistic view of its manufacturing process and ultimately improve communication between the facility operators and IoT devices.