AI in Supply Chain and Logistics
Artificial intelligence can be applied across nearly all business functions in the consumer goods industry, and the supply chain is no exception. While it has not reached mainstream adoption, CPGs are increasingly leveraging AI in the supply chain in order to make faster and more accurate decisions. By gathering and analyzing disparate data sets at speeds humans aren’t capable of, an AI-powered supply chain has the potential to serve as a competitive advantage for companies.
In fact, when asked in a Gartner survey which emerging technologies their organizations are investing or planning to invest in, 24% of supply chain leaders chose artificial intelligence as their number one choice, according to information emailed to CGT. What’s more, 35% of them slotted it within their top three choices.
But where does artificial intelligence make sense to use within the supply chain? And what are some of the companies that are investing in it? Let’s dig in…
How is AI being used in the supply chain?
While maturity levels vary for artificial intelligence in logistics and the supply chain, it has the potential to facilitate more accurate and efficient decision-making in demand forecasting, inventory management, customer service, logistics, transportation, warehouse management, labor management, and more.
The top use cases for consumer goods manufacturers using AI/ML are crowded with supply chain activities.
“AI is enabling businesses to have added intelligence and incremental predictions in their supply chains,” says Manish Ghosh, corporate VP, industry strategy, consumer industries at Blue Yonder. “While planning and forecasting were instrumental prior to the pandemic, recent developments in AI have dramatically enhanced these capabilities.”
How does artificial intelligence improve the supply chain?
There’s a myriad potential benefits of AI in logistics and supply chain management. For example, artificial intelligence and machine learning can be used to:
- Predict the type and number of products a CPG should manufacture to minimize excess inventory in order to fulfill seasonal demand
- Identify factory equipment that isn’t being properly optimized in order to reduce energy waste
- Predict when equipment will require maintenance so companies can be more proactive about repairing and replacing them
- Speedily resolve common customer service complaints through the use of chatbots
- Identify patterns in these complaints to in turn locate problems in product manufacturing
- Optimize labor scheduling to improve employee efficiency and satisfaction
- Enable route optimization to reduce delivery times and decrease transportation emissions
In short, AI is used to automate and optimize decision-making. “In particular, technologies like machine learning paired with more traditional optimization techniques are able to maximize desired outcomes in all logistics operations by modeling constraints and trade-offs,” says Keith Moore, CEO of AutoScheduler.
How AI is revolutionizing supply chain management?
For one thing, it’s changing the user experience for supply chain personnel, says Larry Sherrod, senior manager at Peloton Consulting Group. “It is providing better data management, more efficient processes, and [will] one day enhance customer satisfaction through better visibility and optimization across the supply chain.”
Indeed, supply chain technology is advancing to become table stakes. “Like any industry, the most successful CPG businesses exhibit excellence in supply chain management,’’ says Adam Clark, VP of IT business shared services at Tyson Foods. “Access to near-real-time actionable insights and machine learning-driven recommendations and alerts is crucial to achieving this. Companies that are not investing in this capability will soon find it difficult to compete.”
Which companies use AI in the supply chain?
So who’s using artificial intelligence in logistics and the supply chain? Quite a few, though some use cases remain in pilot stages.
Tyson Foods: The food company is investing in a supply chain control tower that serves as a vehicle for an aggregate of insights, KPIs, and AI-driven recommendations across all supply chain disciplines. “Building out advanced analytic capabilities and data flows that feed the control tower is foundational,” says Clark. “It’s an aggregate view/capability layered on and entirely dependent on the insights and intelligence you feed it.”
Coca-Cola: Coca-Cola İçecek (CCI), an Istanbul bottler, built a digital twin of its manufacturing plants that used advanced analytics and artificial intelligence to help identify machine failures. By leveraging the technology within its bottling plants, the company received a holistic view of its manufacturing process and ultimately improved communication between the facility operators and IoT devices.
They also developed a solution for a key step in an everyday production line and manufacturing sanitation process that afforded CCI visibility into the process in almost real-time. These efforts ultimately reduced annual energy use by 20% and annual water use by 9%, according to Amazon Web Services, its partner for the initiative.
Procter & Gamble: The company is implementing artificial intelligence, machine learning, and edge computing to digitize and integrate data from over 100 manufacturing sites around the world. As part of this, it seeks to leverage machine learning and data storage platforms to improve energy use across its paper machines by determining which machines require maintenance and where they can reduce energy use.
P&G has also piloted a focus on its paper towels that predicted finished sheet lengths to deliver more accurate products to consumers. Sensors on the manufacturing line, paired with algorithms, machine learning, and predictive analytics, improved manufacturing efficiency.
Danone: AI supported its decision-making capabilities and improved real-time collaboration between the company’s commercial, operational, and finance teams. Danone was able to run its planning processes across every function and time horizon on the integrated platform, as well as perform real-time scenario planning to reduce decision-making timelines.
Unilever: The company is using satellite imaging, AI, and geolocation data as part of its bid to achieve a deforestation-free supply chain for palm oil, paper and board, tea, soy, and cocoa. In partnership with Google Cloud, Unilever says it’s combined almost four decades of continuous satellite imagery and machine learning to monitor mills, landscapes, and farms, enabling it to estimate which farms and plantations are supplying the mills.
Thanks to AI, the company can also detect changes in tree cover to determine the amount of carbon stored by forests and the associated climate threat if trees are felled.
Colgate-Palmolive: The company is piloting decision intelligence, which is a subset of data science, to improve product deployment decisions across its Hill's Science Diet and Hill's Prescription Diet fulfillment network. Designed to automate decision-making and forecast demand to precisely designate optimal product allocation, the technology is expected to improve stock deployment operations across its fulfillment networks.
How is artificial intelligence used in the logistics industry?
If logistics is narrowly defined here to represent the execution branch of the supply chain, artificial intelligence is most commonly used in the consumer goods industry for the consolidation of information and the automation of complex decision-making, according to Keith Moore, CEO of AutoScheduler, a provider of warehouse resource planning and optimization technology that works with such companies as Procter & Gamble.
When asked about their top three use cases of artificial intelligence and machine learning 19% of consumer goods executives cited logistics optimization as one of them in the 2023 Retail and Consumer Goods Analytics Study.
What is an example of artificial intelligence in logistics?
A prime logistics use case includes Kimberly-Clark, which deployed an AI-fueled platform across all North American operations in order to reduce “order bunching” — the challenge of order loads stacking up on certain days of the week. Order bunching can contribute to poor on-delivery rates and labor issues.
The company leveraged artificial intelligence to automate its distribution planning and deployment process and was able to connect disparate systems and receive easier-to-execute recommendations.
Within this process, Kimberly-Clark received greater visibility into where it was underutilizing the cubic intensity of its trailers, providing its distribution and customer service teams with the ability to be more proactive. The technology investment and accompanying process changes helped the CPG reduce variability daily by 40%, most pointedly in locations where production plants ship to the company’s distribution centers.
The company substantially improved on-time delivery and customer service performance, according to Scott DeGroot, Kimberly-Clark VP of global logistics, and reduced North American distribution costs by several million dollars.
How AI can make supply chains more sustainable
There are a number of ways that AI can help make supply chains more sustainable:
Demand forecasting and planning: By enabling consumer goods companies to have a firm grasp on the types and amount of products they should make and sell, they can adjust their operations accordingly to limit excess goods. Manufacturing fewer goods equates to fewer items in landfills.
Inventory management: Similarly, having visibility into the ideal locations where products should be shipped and sold can translate to a reduction in excess inventory. For food manufacturers and retailers, this can impact food waste reduction.
Energy efficiency: In addition to reducing waste from goods, AI can be used in monitoring and optimizing energy usage in factories. It can also be used to predict when machinery might need to be repaired, preventing downtime and excess maintenance costs.
Transportation optimization: Similarly to energy optimization in factories, AI in transportation can optimize delivery routes to reduce the amount of fuel used, decreasing harmful emissions.
Though leveraging AI in the supply chain means a greater investment up front, it can pay off in the long term by improving productivity to reduce costs. High-performing supply chains are 19% on average more likely to have capabilities in place to achieve their sustainability goals, according to Gartner.
“Through digital transformation … and making it visible and usable through analytics or artificial intelligence in real-time to all parties that need it, organizations can make faster and better decisions that lead to improved business and sustainability outcomes, such as the rightsizing and appropriate allocation of inventories and the reductions in fuel, energy, packaging, and other material usage,” summarizes Jordan Speer, research director of product sourcing, fulfillment, and sustainability at IDC.
What is the future of AI in logistics and supply chain?
First, expect to see more AI-powered robots in warehouses. John Harmon, CFA, managing director of technology research at Coresight Research, anticipates that industrial robots and sophisticated AI-based systems will be key assets in warehouses to manage inventory, improve visibility and make deliveries across the supply chain.
In fact, almost every aspect of the supply chain will be impacted by robotics and AI by 2030, he predicts, with significant benefits in warehouses and the last mile.
“From a pure technology perspective, AI’s role in standing up future supply chains that are capable of continuous improvement in performance at a significantly lower total cost of ownership is limitless,” agrees Adheer Bahulkar, global supply chain lead of Accenture’s consumer goods and industry practice. “Perhaps the only limits are what us humans will impose on the role of AI. Organizations will need to move from the inertia that comes from lack of digital fluency.”
And what about the role of generative AI in the supply chain? While this much-buzzed-about technology is still in the exploratory stage for many consumer goods companies, some expect it to play a role within the supply chain as it becomes refined.
“In a few years’ time, we could even see supply chain teams granting more trust toward their intelligent, AI-powered co-pilots,” says Manish Ghosh, corporate VP, industry strategy, consumer industries at Blue Yonder. “If teams observe specific patterns in their problem-solving — [such as] a shipment is delayed [so] take this particular action and activate these particular planning algorithms — they can begin letting their co-pilots take the initiative with similar occurrences in the future.”
Supply chain planners may be able to delegate the planning to their “co-pilot” and grant final approval when the plan is ready for execution, he says, boosting productivity.
Bahulkar agrees on the potential of generative AI in the future of the supply chain. “We’ve all seen how effective ChatGPT can be at conversing, answering questions, and summarizing information in a natural, engaging, and relevant way,” he says. “It’s easy to see how this could be extended to Supply Chain — responding to queries, helping to create new procurement contracts, offering recommendations, and more.