Modern supply chains are a tangled web of moving parts, data streams, and constant changes. Keeping up with everything can feel overwhelming. To add to the challenge, supply chain leaders are now swamped with countless examples of artificial intelligence (AI) solutions claiming to solve all their problems. This situation is tricky because, while many AI solutions can definitely benefit supply chains, figuring out which ones to trust is the hard part. On the bright side, AI is now a powerful tool that can analyze huge amounts of data on-demand, detect patterns that humans often miss, and make quick recommendations.

So in this article, I’ll give you a brief history of AI so that you understand what AI is and what it isn’t. While AI has been in the research labs for some time, it hasn’t been very useful for most businesses until now. Today, advances in high-speed internet, fast computers, and cheap data storage have changed this, making AI something businesses can now use and see returns from. To detail, I’ll share nine examples of AI technologies that can significantly enhance supply chains.

Artificial Intelligence – What Is it?

Basically, Artificial Intelligence (AI) is where machines simulate human intelligence processes. To elaborate, below is a more comprehensive definition of artificial intelligence:

“… a machine’s ability to perform the cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, and even exercising creativity.”

McKinsey

Artificial Intelligence – A Short History.

With the advent of computer technology in the twentieth century, Artificial Intelligence (AI) had its small beginnings. At first It was thought of as a novelty. Consequently, for many decades AI stayed for the most part within the research community. Now AI is transforming our lives. This is because of unprecedented advances in computer processing, high-speed internet, and affordable data storage have given AI the power to access vast data sets and redefine industries. Now, AI is pushing the boundaries of what is possible. See below for a short timeline of the evolution of AI.

The AI Evolution: From Silly Novelty To Being Mainstream

The AI Evolution

1. The Prelude to the AI Evolution: Myths, Novelty Automation and Mathematics
2. 1950s – From Theory to Digital Computers
3. 1980 – 1990s – The AI Evolution of Expert Systems that Simulates Human Knowledge
4. 2000 to 2020 – Revolutionary Deep Learning AI Powered by Fast, Cloud Computing
5. 2020 and Beyond: AI Explodes with Large Language Models, Autonomous Agents, and Advanced Robotics

For a more detailed account of the history of AI, see my article, The AI Evolution: From Silly Novelty To Being Mainstream.

“Artificial intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur …”

Mark Cuban

9 Examples of Artificial Intelligence Technology By Type.

Artificial intelligence (AI) is a broad field with many applications across industries. From predicting future trends to automating decisions, AI technologies are transforming how businesses operate. In this section, I’ll look at nine examples of AI technology, categorized by type, and provide examples of their use cases in supply chains. 

1. Forecasting Systems: Uses Predictive and What-If Analysis.

AI-powered forecasting systems use predictive analysis and machine learning algorithms to analyze historical data and make forecasts about future events. Further, this type of AI is able to handle large volumes of data to include Internet of Things (IoT) device data and complex variables compared to traditional software. Additionally, AI can vastly improve accuracy by learning from historical data patterns and continuously refining their predictions. Indeed, AI now has self-correcting capabilities, thanks to machine learning, versus the rules-based design of traditional software and expert systems.

In supply chains, these AI-powered systems can better forecast demand than traditional forecasting software. This includes helping companies adjust production and inventory accordingly. Further, these forecasting systems improve companies ability to model different scenarios and their potential outcomes. For more references on AI-based forecasting systems, see ThroughPut.AI’s Supply Chain Forecasting with AI and GEP’s Data-Driven Insights to Supercharge Supply Chains.

2. Expert Systems: Knowledge and Rules-Based Advisors.

Expert systems mimic the decision-making ability of a human expert in a particular domain. These systems are based on knowledge and rules-based inference engines to reason and make recommendations. The end result of these software programs is to provide an explanation or advice to the users. Examples of AI expert system applications include: medical diagnosis, chemical analysis, and financial management. Now, many rules-based expert systems are being superseded by more advanced AI capabilities such as machine learning. For a detailed discussion on expert systems and how they work, see GeekForGeeks’ article, Expert Systems.

Robotics In Logistics – Stunning Technology With Unlimited Operational Applications

3. Autonomous AI: Intelligent Robotics and AI Agents.

Both smart robots and AI agents perform tasks that typically require human intelligence. Also, they can operate with varying degrees of autonomy, from being fully autonomous to requiring human oversight. For instance, in supply chains intelligent robots are used in warehouses to pick and pack items. Further, companies like Amazon use robots to navigate large warehouses, locate items, and bring them to human pickers. For more details on how supply chains can use intelligent robots, see my article, Robotics In Logistics.

Also, AI agents will soon start being an indispensable tool for businesses, seamlessly augmenting human professional and technical teams. Moreover, these advanced systems are not just about automating mundane tasks; they are transforming the way we analyze data, make decisions, and manage our daily operations. Moreover, these AI agents are much more “Intelligent” than the “classic” ChatGPT of 2023. Additionally, these AI agents are much different than AI Assistants and traditional Bots. Incredibly, AI agents also have the capability to learn and adapt. For a more details, see my article, The New AI Agent Business Specialist: You Need to Know Their Skill, Expertise, and Comparative Performance. This article looks at AI agent components and types of AI agents.

The New AI Agent Business Specialist: You Need to Know Their Skill, Expertise, and Comparative Performance

4. Machine Learning (ML) Systems: Learns and Adapts Without Following Explicit Instructions.

AI Impact On Business Decisions – Know How To Best Apply To Get The Most Benefits

Machine learning (ML), a type of artificial intelligence (AI), enables computers to learn from data, identify patterns, and adapt without being explicitly programmed. Further, by it analyzing large amounts of historical data, ML algorithms can identify trends and insights that would be difficult or impossible for humans to recognize. Thus, businesses and supply chains can use ML for a wide range of general-purpose tasks to include:

Machine Learning Use Cases

• Automate tasks such as inventory management, demand forecasting, and order fulfillment. Thus, it reduces costs and improves efficiency. 
• Forecasts demand (previously discussed under Forecasting Systems). For instance, this includes supply chain forecasting tasks such as predicting customer demand,  what-if analysis, and predictive maintenance.  
• Optimizes supply chain processes and identifies areas for improvement. For instance in transportation, ML can determine the most efficient delivery routes by analyzing real-time traffic data. Thus, this reduces fuel costs and ensures on-time deliveries. 
• Supports supplier selection by analyzing historical data, market performance, and seasonal variations to identify the best vendors.
• Used for fraud detection. For instance, ecommerce operations can use ML to spot suspicious transactions. 

For a more detailed discussion of Machine Learning use cases for supply chains, see Arindam Mukherjee’s article, Machine learning techniques in supply chain management and GEP’s article, Machine Learning (ML) in Procurement and Supply Chain.

“As artificial intelligence evolves, we must remember that its power lies not in replacing human intelligence, but in augmenting it. The true potential of AI lies in its ability to amplify human creativity and ingenuity.”

Ginni Rometty

5. Generative AI Applications: Prompt-Based Content Generators.

Generative AI and Large Language Models (LLM) like ChatGPT create new content based on a prompt or input. Specifically, these applications can generate art, music, verbal communication, and the written word. In supply chains, generative AI can be used to create content that supports operations. For example, a company can use a generative AI tool to create instructional videos for warehouse workers based on standard operating procedures. Another example is in customer service, where generative AI can be used to create personalized responses to customer inquiries based on their history and the nature of their inquiry. 

Also, with the growth of Large Language Models (LLMs) such as ChatGPT, this advanced AI can directly support end users in knowledge-based tasks. Indeed, what is remarkable about an LLM is its ability to offer an interactive user interface (UI) for non-technical users. See my article, AI Impact On Business Decisions – Know How To Best Apply To Get The Most Benefits for a detailed explanation and examples.

6. Virtual AI Assistants: Versatile, Conversational Helper to Carry Out Knowledge-Based Tasks.

AI Chatbot Technology – Its Staggering Benefits And How To Best Use

Virtual AI assistants are computer programs that use natural language processing (NLP) to understand and respond to voice or text commands. Examples of these virtual assistants include  Amazon Echo, Siri, Google Assistant, Google Home, Amazon Alexa. As AI evolves, these virtual AI assistants are becoming more useful. For example, a warehouse worker can ask a virtual assistant to look up the location of an item in the warehouse or to check the status of a shipment. Further, the assistant can access the relevant systems and provide the information back to the worker.

Additionally, software developers are now further enhancing virtual AI assistant technology with LLMs (previously discussed under Generative AI Applications). For a more detailed discussion on the use cases for virtual AI assistants, see my article on AI chatbot technology.

7. Computer Vision Systems: Real-Time Capability to Interpret and Respond to Visual Data.

Computer vision is a general-purpose AI technology that enables computers to interpret and understand visual information from the world around it. Specifically, these systems use cameras and machine learning algorithms to identify objects, people, and activities. Indeed, computer vision AI systems provide a powerful capability across the various functions of the supply chain. For example:

Supply Chain Use Cases for Computer Vision AI

Computer Vision AI: The Unlimited Ways To Use This Awesome Tech To Empower Supply Chains

• Identify Volumetric Properties. These systems can detect the dimensions of goods such as detecting parcel dimensions on conveyor systems. 
• Monitoring Occupancy of Storage and Traffic Areas. For instance, monitoring can include pallets stored in warehouses, vehicles parked in parking lots, and packages that are loaded onto delivery trucks.
• Automated Handling Systems (previously discussed under Autonomous AI). For example, smart robots that perform unloading operations can use object recognition to detect individual goods, packages, sacks, and more. 
• Optimize Manual Picking and Packing. Computer vision helps to minimize human error with manual tasks. For instance, vision systems help to assist in the picking and packing operations to reduce error rates and save costs.

For more supply chain use cases for computer vision systems, see my article, Computer Vision AI: The Unlimited Ways To Use This Awesome Tech To Empower Supply Chains.

8. Recommendation Engines: Predict Users’ Choices, Offer Suggestions.

Recommendation engines are AI systems that suggest products or services for users based on their past behavior and preferences. These systems use machine learning algorithms to analyze user data and make personalized recommendations. Indeed, all of us have come in contact with AI recommendation engines in our daily life. For example, Facebook — “People You May Know”; Netflix — “Other Movies You May Enjoy”; Amazon — “Customers who bought this item also bought …”; and Waze — “Best Route”.

In supply chains, recommendation engines can be used for a variety of tasks. For example, a B2B ecommerce site can use a recommendation engine to suggest related products to customers based on what they are currently viewing or have purchased in the past. 

9. Decision Platforms: Data-Driven, AI-Enabled Systems to Support, Augment, Automate Business Decisions.

Decision platforms use AI and machine learning to support, augment, and automate business decisions. These platforms can analyze large datasets, identify patterns and trends, and make recommendations or take actions. Specifically, the software provides situational awareness, decision support, and generates recommendations. key capabilities include:

Decision Intelligence AI Capabilities

• Provides Cross-Organizational Situational Awareness Directly to the Decision Maker. Here, a decision platform leverages data from execution systems, process automation, administration & financial systems, and sensor data. Moreover, the decision maker working directly with the software can make better decisions that positively impacts the whole organization, not favoring one department over another.
• Works in Concert With the Decision Maker’s Op Tempo. In this case, the system adapts itself to both the timing and types of decisions required. Hence, the system automatically adjusts to the decision maker’s op tempo, be it real-time or periodically such as daily, weekly or monthly.
• Provides Decision Traceability. Also, the platform can record recommendations that were accepted and measures the effectiveness of decisions to support future decision-making.
• Provides Opportunity to Automate Decision Flows. Also, decision-makers can automate the recommendations of the decision platform.

Now, let’s look at an example of a supply chain decision platform. Here, logistics executives would work directly with the decision platform to gain situational awareness needed for good decision-making. For instance,  the decision platform would leverage data from supply chain execution systems such as TMS, WMS, and order fulfillment. Additionally, other possible data sources would include sensor data, CRM, financial systems, and partner systems. Lastly, decision platforms are part of a rising IT domain the industry calls Decision Intelligence. For more information on this exciting AI-powered capability, see my article, This Is What Decision Intelligence Technology Is And Know What Its Not.

More References.

For more examples and details on Artificial Intelligence technologies, here are some more references:

• OpenText’s What is Artificial Intelligence (AI)?
• IBM’s What is Artificial Intelligence
• TutorialsPoint’s Artificial Intelligence – Overview
• IEP’s Artificial Intelligence
• BuiltIn’s Artificial Intelligence Examples Shaking Up Business Across Industries
• HashStudioz’s 10 Helpful Everyday Examples of Artificial Intelligence
• CONVIN’s 10 Examples of Artificial Intelligence in 2024
• Artificial Intelligence Act’s Types of AI

Logistics Technology 2024 Trends: The Best Advice To Empower Your Supply Chain Right

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 information from Supply Chain Tech Insights, see articles on AI, data analytics, and robotics.