How is AI and Machine Learning Changing the Way We Manage the Supply Chain?

Shifting consumer demands, geopolitical uncertainty, labor constraints, and residual pandemic-era behaviors can all have a negative impact on supply chain management. As a result, more companies are leveraging AI in the supply chain to improve their supply chain resilience. 

In fact, when consumer goods executives were asked about their top use cases of artificial intelligence and machine learning, supply chain activities made their way to the top of the list: 

Which of the following uses of artificial intelligence and/or machine learning are being currently leveraged in your organization?

• Pricing: 26%
• Demand planning and forecasting: 26%
• Inventory planning: 23%
• Logistics optimization: 19%
• Supply chain planning and execution: 19%
• Warehouse management: 10%

Source: CGT/RIS News Retail and Consumer Goods Analytics Study

How does AI affect supply chain performance?

Thanks to its ability to gather and analyze data at speeds humans aren’t capable of, using artificial intelligence in supply chain management can provide supply chain leaders with a much deeper visibility into their end-to-end operations. As a result, it can improve supply chain performance by enabling leaders to be more proactive with their decision making to improve efficiency and reduce costs. 

How AI is changing supply chain management

There are many benefits of AI in supply chain, and the technology is transforming SCM by providing companies with the ability to gather large amounts of data from disparate sources and quickly analyze it to: 

• Align supply with demand to prevent stockouts and pivot to meet new consumer demands
• Optimize warehouse, fulfillment, and transportation operations
• Automate manual office tasks 
• Expedite customer service inquiries
• Anticipate raw material shortages based on weather patterns and other external factors
• Recommend actions to improve business outcomes  

Use cases include everything from demand planning to route optimization to labor scheduling to warehouse robotics. With AI/ML, consumer goods leaders can dramatically increase visibility into their supply chains to proactively head off disruption to reduce costs, and improve customer experiences. 

For example, apparel company Tapestry optimized its product allocation processes during the all-important holiday season by using artificial intelligence to forecast customer demand and better position inventory and stores. It led to an increase of inventory availability and helped ensure products were in the right place at the right time for strong customer experiences, according to CEO Joanne Crevoiserat. 

Which companies use AI in supply chain? 

While use of AI and machine learning in the supply chain is still growing, there are many companies that are testing or have deployed the technology. Within the consumer goods industry: 

Procter & Gamble: The company shared plans to implement AI, machine learning, and edge computing to digitize and integrate data from 100-plus 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. 

One machine learning in supply chain case study: P&G has piloted a use case focusing on its paper towels that predicted finished sheet lengths to deliver more accurate products to consumers. Via sensors on the manufacturing line, the technology leverages algorithms, machine learning, and predictive analytics to improve efficiency. 

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. 

They also developed a solution for a key step in an everyday production line and manufacturing sanitation process that afforded CCI with 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 its partner in the initiative, AWS. 

Danone: The food and beverage company used artificial intelligence to support decision-making capabilities and improve real-time collaboration between the company’s commercial, operational, and finance teams. This technology was designed so Danone could 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. 

What is an example of a use case for machine learning?

It’s not just manufacturers looking to invest in AI/ML in their supply chains. When retailers were queried on their top supply chain technology investments, artificial intelligence and machine learning topped the list, cited by 38% of respondents in the Retail and Consumer Goods Analytics Study.  

Levi’s is a popular example of a manufacturer and retailer that’s using artificial intelligence in logistics. The company’s proprietary (Business Optimization of Shipping and Transport) e-commerce solution leverages AI and ML to optimize inventory management and improve order fulfillment. The technology is engineered to make independent, informed decisions drawing from all fulfillment stages, such as shipping, packing, and labor. 

As a result of the technology, Levi’s expects to provide better customer experiences, boost operational efficiency, and trim the costs it passes on to shoppers.