As a supply chain professional, you’ve likely encountered Artificial Intelligence (AI) and may even be using it as a personal productivity tool. But here’s the crucial question: can AI truly help us navigate the complexities of modern supply chains? The harsh reality is that AI’s effectiveness is often crippled by incomplete or ambiguous data, resulting in unreliable insights and a lack of transparency. That’s where Knowledge Graph technology comes in – a powerful capability that addresses AI’s limitations and unlocks its true potential to solve real-world supply chain problems.

In this article, I’ll first explain how knowledge graphs and AI are a powerful combination that can tackle the toughest challenges in supply chains. More specifically, knowledge graph tech bolsters AI to provide reliable answers to complex, multi-domain problems, enabling rapid, informed decision-making across the supply chain. To provide examples of what Knowledge Graph AI can do, I’ll share with you twelve use cases from across the supply chain. This includes planning to procurement to supply chain operations to final delivery. So, let’s get started!

What is Knowledge Graph AI?

Knowledge Graph AI is a powerful combination of information technology that mimics how the human brain works. First, this tech combination includes Artificial Intelligence (AI) that performs complex tasks that typically require human intelligence. Then there is knowledge graph tech that stores data very similarly to how the human brain stores information and knowledge. More specifically, knowledge graphs consist of a data-centric semantic network that stores digital entities and their relationships. Also, Knowledge Graph tech can contextually link to both unstructured and structured data such as pdf documents and relational databases to name a few. As a result, graph tech provides a powerful resource for AI to extract meaning and context out of data.

Without a doubt, knowledge graph tech shores up AI’s greatest weaknesses. Namely, it enables AI to be trustworthy, explainable, reliable, and accurate. To list, Knowledge Graph tech dramatically strengthens AI in the following areas:

New Capabilities Knowledge Graph AI Offers Businesses

Knowledge Graph Tech: Enabling A More Discerning Perspective For AI

• Fact Verification. With Knowledge Graph tech, AI becomes more trustworthy, delivering results that are fact checked.
• A Framework for Contextual Understanding. Knowledge graphs put data in context for AI via linking and semantic metadata.
• Fact Ranking. Moreover, AI can prioritize results by ranking them against a structured knowledge graph.
• Linking Related Entities. When AI provides results, Knowledge Graph tech can also help discover additional facts as well as provide better, explainable insights. 
• Contextually Linking Data From Disparate Data Sources. Lastly, Knowledge Graph tech enables AI to better answer multi-hop questions across many different functional domains and data types.

For a more detailed explanation of Knowledge Graph AI, see my article, Knowledge Graph Tech: Enabling A More Discerning Perspective For AI. Also, for an example of a Knowledge Graph mapping, see diagram below of a supply chain goods movement process.

Supply Chain Example of a Knowledge Graph Mapping

Twelve Supply Chain Use Cases for Knowledge Graph AI.

Knowledge Graph AI can revolutionize supply chains by providing deep insights and enabling context-aware decision-making. The following twelve use cases demonstrate how Knowledge Graph tech can accelerate informed decision-making, facilitate collaboration, and turn knowledge into action, going beyond simple data analytics tools.

1. Leverage Up-To-Date Facts to Enhance AI-Powered Delivery Route Optimization.

First, Knowledge Graph AI can optimize delivery routes. It can do this by mapping the relationships between delivery points, traffic patterns, and weather conditions. For example, a logistics company can use a knowledge graph and AI to dynamically adjust routes based on real-time traffic updates and historical delivery data. As a result, this ensures the most efficient and timely deliveries. The knowledge graph can also incorporate customer preferences and past delivery performance to further refine routes. Additionally, especially with Large Language Models (LLM) AI, knowledge graphs help AI avoid hallucinations. Graph tech does this by providing context to data so that AI can stay on task.

2. Optimize Inventory Management by Drawing Insights and the Latest Information From Across the Supply Chain.

Multi-Hop Reasoning For Supply Chains: This Is The Way To Make Better Decisions And Avoid Unintended Consequences

Also, Knowledge Graph AI can optimize inventory management. It does this by creating a comprehensive view of product demand, supplier lead times, and market trends. For example, a retail company can use knowledge graphs and AI to better predict demand spikes and adjust inventory levels. This is possible because knowledge graphs can link data from sales, supplier performance, and market analytics, enabling quick answers to complex problems. Indeed, graph tech can identify correlations and patterns that traditional systems might miss. As a result, this ensures that popular items are always in stock while minimizing overstock. Without a doubt, this multi-hop linkage across all supply functions and data silos enables superior insights to optimize inventory.

For more information on Multi-Hop Reasoning, see my article, Multi-Hop Reasoning For Supply Chains: This Is The Way To Make Better Decisions And Avoid Unintended Consequences.

3. AI Warehouse Planning Gets Smarter with Better Context.

Here, Knowledge Graph AI can enhance warehouse planning by better predicting demand and optimizing storage layouts. For example, a warehouse manager can use knowledge graph AI to improve its predictions of which products will be in high demand so that they can be placed in easily accessible locations. AI can do this because the graph tech links data from sales forecasts, historical order patterns, and seasonal trends. As a result, this provides a holistic view of inventory needs. Also, this smarter, contextual view can include key characteristics such as weight, storage requirements, and material costs matched to incoming goods and demands.

4. AI Can Help Automate Compliance and Reinforce Best Practices With a Verified, Up-To-Date Knowledge Base.

In this use case, Knowledge Graph AI can recommend, and even automate, compliance and best practices. This tech combo can do this by continuously monitoring such things as regulatory changes and supplier performance. For example, a pharmaceutical company can use Knowledge Graph AI to track regulatory updates and ensure all suppliers meet strict compliance standards. Moreover, this linked data can be in different formats like PDFs and SQL tables. By keeping the knowledge graph continuously updated, this helps AI to rapidly analyze and interpret changing regulations and internal corporate policies. Thus, supply chain operations remain compliant without sacrificing efficiency.

5. Knowledge Graph AI Personalizes Last-Mile Delivery with Diverse Data Insights.

Knowledge Graph AI can personalize customer experiences by analyzing data on customer preferences and delivery history. For example, a delivery service can use knowledge graphs to offer preferred delivery times and locations based on individual customer habits. In this case, the graph tech can link disparate data from customer profiles, social media, past delivery performance, and real-time traffic conditions. As a result, this provides a seamless and personalized delivery experience. Additionally, the knowledge graph’s ability to infer patterns and provide an intelligent data structure helps AI discover hidden customer preferences, further increasing customer satisfaction. Lastly, knowledge graphs can also enhance man-machine interactions by linking aliases and synonyms to customer conversations. Thus, AI applications can better understand and respond to customers.

6. Unified Data Insights for Timely AI Decision Support.

An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes

Here, Knowledge Graph AI can provide executives with on-demand, data-driven insights to make informed decisions. For example, a supply chain manager can use knowledge graphs to quickly identify and address bottlenecks in the supply chain. In this case, the graph tech can link data from various sources, such as production schedules, inventory levels, corporate policy, IoT devices, and supplier performance. This results in a unified view of the supply chain for both AI and decision-makers. Also, knowledge graph tech helps AI to be more explainable for decision-makers to understand better why AI is making a certain recommendation. Thus, AI becomes more trustworthy. For more on Decision Systems for supply chains, click here.

7. Links Diverse Data Sources and Types to Enhance AI Predictive Maintenance.

In this use case, Knowledge Graph AI can enhance predictive maintenance by analyzing data from diverse sources such as vehicle sensors and maintenance records. Also, it can parse text from documents such as warranty and service documents to add to knowledge graphs. For example, a logistics company can use knowledge graph AI to predict when a vehicle is likely to need maintenance. It does this by identifying patterns in sensor data and historical maintenance logs. Specifically, the graph tech can map the relationships between vehicle performance, maintenance history, warranties, maintenance staff availability, and environmental conditions. The end result is reduced downtime and lower maintenance costs.

8. Knowledge Graph AI: Better Detecting Logistics Fraud and Security Threats.

Additionally, Knowledge Graph AI can help detect and prevent fraud by analyzing patterns and anomalies in logistics data. For example, a shipping company can use Knowledge Graph AI to identify suspicious activities, such as unauthorized access to cargo. It does this by using knowledge graphs to map the relationships between different data points, such as shipment tracking, access logs, and historical data. Indeed, by knowledge graph tech placing suspicious events in context, the event’s relationships with various entities are magnified. Thus, this helps AI to determine where action is needed. Also, this allows for proactive measures to enhance security and prevent fraud. Further, it avoids false positives.

9. Contextual AI: The Key to Streamlining Data Integration Projects.

For example, Knowledge Graph AI can support, document, and add shared meaning for data integration projects. This is because knowledge graphs provide context to data exchanged between systems. This includes both meta-data about the data elements and contextual links between data entities. As a result, Knowledge Graph AI can both support standards development and data integration implementations. For more details, see my article, Semantic Digital Interoperability: This Is The Ultimate Way To Make Supply Chains Seamless.

Semantic Digital Interoperability: This Is The Ultimate Way To Make Supply Chains Seamless

10. Connecting the Dots: Smarter Shipment Visibility with Knowledge Graph AI.

Also, Knowledge Graph AI can enhance traceability by mapping the entire supply chain, from raw materials to finished products. For example, a food company can use Knowledge Graph AI to track the origin and journey of ingredients. This ensures compliance with food safety regulations and building consumer trust by providing detailed product information. Specifically, knowledge graphs can map the relationships between suppliers, production processes, and distribution channels. Also, knowledge graphs help to place new events in context such as assisting with identifying anomalies and out of tolerance situations. Further, knowledge graph tech can serve as an audit trail for traceability purposes.

11. Knowledge Graph AI: Pinpointing Critical Supply Chain Issues and Best Mitigation Options.

In this case, Knowledge Graph AI can help identify and mitigate supply chain disruptions by analyzing data from multiple sources. Moreover with Knowledge Graph Tech, AI can better rank facts to identify the most critical problems and best solutions. For example, a retail company can use a Knowledge Graph AI to monitor news feeds, social media, and supplier performance data to identify potential disruptions. Moreover, the graph tech can map the relationships between different data points, allowing the company to take proactive measures to secure alternative suppliers or adjust production schedules. Also, knowledge graphs can help with identifying relationships between physical entities and capabilities to better identify critical shortcomings and best alternatives.

12. Enables AI to Rapidly Integrate Factual Internet-of-Things (IoT) Data for Real-time Monitoring of Supply Chains.

Lastly, Knowledge Graph AI can integrate and analyze IoT data rapidly, providing insights into equipment performance and environmental conditions. For example, a logistics company can use a knowledge graph to monitor the temperature and humidity of refrigerated trucks, ensuring the quality of temperature-sensitive goods during transit. Also, the graph tech can map the relationships between sensor data, environmental factors, and vehicle performance, enabling real-time adjustments and alerts. 

More Knowledge Graph AI References.

Below are more references on the power of knowledge graph tech and AI working together.

• See Maya Natarajan article, The Future of AI: Machine Learning and Knowledge Graphs.
• Kurt Cagle’s posting, Knowledge Graphs and Supply Chains.
• Tony Seale insightful posting on Why Use Knowledge Graphs?
• Also, see my article on AI weaknesses, AI Impact On Business Decisions – Know AI’s Unique Challenge To Overcome Its High Number Of Weaknesses.
• Lastly, see my article, Knowledge Graph Tech: Enabling A More Discerning Perspective For AI, on how knowledge on how Knowledge Graphs enable AI.

Knowledge Management Breakthrough – The Best Way With AI, Data, And Graph Tech

Need help with an innovative supply chain solution that leverages emerging information technologies? I’m Randy McClure, and I’ve spent many years helping logistics organizations to make the most of new information technologies. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in new strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your supply chain or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.

For more from SC Tech Insights, see the latest articles on Data Analytics, AI, and Supply Chains.