The Role of Artificial Intelligence (AI) in Solving New Business Complexities
How supply chain AI solutions are helping companies make sense of their data
Modern supply chains have grown in scope and size and so has the amount of data that businesses are collecting from them. This data is invaluable because it helps supply chain professionals make the right decisions and take the right course of action in a given situation. However, without the right supply chain AI solutions to help make sense of it, that data remains unstructured and virtually unusable.
Supply chain AI solutions play a pivotal role in transforming how businesses analyze and leverage this data, so let’s explore how this is accomplished.
Analyzing data using supply chain AI solutions
As businesses collect vast amounts of information across supply chain operations, customer interactions, and market activities, the ability to effectively analyze this data becomes a critical competitive advantage. There are three ways that AI processes data from your supply chain, and while each method is important, they are most effective when used in concert.
- Automation: Processes what were once time-consuming manual tasks without any human interaction.
- Operations research: Uses fixed algorithms to perform calculations and provide structured frameworks for complex decision-making.
- Artificial intelligence: Seeks to understand patterns and uncover insights that would remain hidden to conventional approaches.
Together, these three complementary disciplines form a powerful analytical foundation that enables businesses to transform raw data into strategic action.
Four types of supply chain AI solutions
Before making an investment in AI, it’s important to understand exactly how this technology works with the data from the supply chain. There are four types of supply chain AI solutions, each unique in how they process and analyze data.
Supervised learning
finds patterns across disparate data sets, determines what "good" looks like, and improves its ability to predict outcomes.
Unsupervised learning
spots patterns in supply chain data that are invisible to the naked eye. Can detect anomalies, unusual behaviors, and inefficiencies.
Reinforcement learning
experiments with different scenarios and learns through trial and error which actions lead to better outcomes.
Generative AI
uses large language models to translate unstructured data and new content based on that direction.
Together, these approaches form a comprehensive toolkit that allows organizations to extract actionable insights from vast datasets, automate repetitive analytical tasks, identify opportunities invisible to traditional methods, and ultimately drive more informed strategic decisions in an increasingly data-rich business landscape.
Download our infographic to get the full scoop on supply chain AI solutions
Understanding how to effectively integrate and deploy these tools across an organization can dramatically improve efficiency, decision quality, and innovation potential. Download our infographic to learn how AI can help your business make the most of its data.
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