Where Does Artificial Intelligence (AI) Fit In the Supply Chain? Part 1: The Past

AI in the Supply Chain

It's no secret that artificial intelligence (AI) is owning the headlines. The next generation of AI is expected to cause widespread impact far beyond just technology organizations. Companies across nearly every industry are scrambling to decipher how and when AI will impact their businesses, especially those with supply chains - manufacturers, logistics providers, wholesalers, distributors, and retailers.  

To determine how the future of AI will influence the supply chain, we must first understand the history of AI and how it led us to where we are today. In this three-part Cleo blog series, we will cover at a high level, the past, present, and future of AI, and how the innovative technologies are expected to advance business operations, processes, products, and services offerings, and more, across the supply chain.


What Is Traditional AI?

Traditional AI goes by a lot of different names. It is not uncommon for traditional AI to be referred to as:

  • Conventional AI
  • Classical AI
  • Symbolic AI
  • Weak AI
  • Narrow AI

So, what is traditional AI? Traditional AI first appeared in the 1950s and refers to the early approach to artificial intelligence that was prevalent before the rise of machine learning and deep learning methods. Traditional AI is characterized by use of symbolic representations and rule-based systems to mimic human intelligence and solve complex problems.

What does this actually mean? Well, traditional AI systems are designed to respond to a particular set of inputs. These systems learn from data and make decisions or predictions based on that data. 

For example, imagine you need a recipe for a cake. You go to Google and search "The world's best chocolate cake recipe." Google then gathers millions of results for the search query. Google then ranks the recipe results based on what it predicts will be the most beneficial to you. In this scenario, Google is not inventing new chocolate cake recipes, but rather it is combing through all the options that are already available. Google then identifies which recipe results to present to the user, based on how it was trained and programmed.

Key features of traditional AI include:

  • Symbolic Representation
  • Rule-Based Systems
  • Expert Systems
  • Knowledge Engineering.
  • Limited Learning
  • Common Algorithms

While traditional AI has been successful in certain applications, it has limitations when dealing with complex, data-rich tasks where patterns are not easily codified by rules and symbols. This limitation led to the rise of machine learning and deep learning approaches, which have been particularly effective in tasks like image recognition, natural language processing, and recommendation systems. These newer approaches rely on data-driven learning from large datasets, rather than explicit rule-based programming.


What Is Machine Learning?

Machine learning (ML) is a subset of AI that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It is a data-driven approach where algorithms identify patterns and relationships in datasets to improve their performance on specific tasks.

Machine learning has diverse applications, from image recognition and natural language processing to recommendation engines and autonomous vehicles. It has the power to automate tasks, extract insights from data, and improve decision-making across various industries.

So how does machine learning work? Here's an example:

  1. Data and Patterns: Imagine you have a friend who loves guessing the weather. To get better at it, your friend starts keeping a diary of weather conditions (sunny, rainy, cloudy) and the temperature for each day. This diary is like the "data" we use in machine learning.
  2. Learning from Data: Your friend starts to notice patterns. For instance, on days when it's sunny, the temperature tends to be higher. On rainy days, the temperature is often lower. This is similar to what a machine learning model does - it looks at data and learns patterns, associations, or relationships.
  3. Making Predictions: Now, if your friend looks outside and sees a clear blue sky and checks the temperature, they can make an educated guess about the weather. They might say, "It's sunny, so it's probably warm today." This is like the "prediction" made by a machine learning model. It uses what it learned from data to make predictions or decisions.
  4. Improvement Over Time: Your friend keeps recording weather data and improving their guessing skills. They might notice more subtle patterns, like how wind speed or humidity affects the weather. Similarly, machine learning models get better as they are exposed to more data and learn from it.
  5. Applications: In the end, your friend becomes quite good at predicting the weather based on their observations. Machine learning is like that friend but on a much larger scale. It is used in various applications, like predicting diseases from medical data, recognizing faces in photos, recommending movies, and even self-driving cars, all by learning from vast amounts of data.

So, machine learning is like teaching computers to find patterns in data and make educated guesses or decisions based on what they have learned. It is similar to how humans learn from experiences and become better at tasks over time.

AI and Machine Learning in business practices


How Do Traditional AI and Machine Learning Work Together?

Traditional AI and machine learning can be integrated to create more robust AI systems. This works because traditional AI, which is characterized by rule-based systems and expert knowledge, provides a structured foundation and domain expertise. Meanwhile, machine learning enhances this foundation and expertise by enabling data-driven learning and adaptation. In summary, traditional AI sets the rules and guidelines based on established knowledge, while machine learning optimizes these rules and refines decision-making through continuous learning from available data. 

In the logistics industry, as well most any supply chain-driven business, traditional AI and machine learning collaborate to optimize operations and/or to improve supply chain execution. Traditional AI provides rule-based systems that manage complex logistics processes, such as route planning, resource allocation, and inventory management—based on established industry standards and regulations. Machine learning complements these systems by analyzing vast amounts of historical data to identify patterns, predict demand fluctuations, and optimize routes dynamically.

For example, traditional AI may set the logistics rules and compliance guidelines, while machine learning models continuously adapt these rules based on real-time data, like traffic conditions, weather, and demand spikes. This synergy allows logistics companies to enhance efficiency, reduce costs, and improve delivery times by leveraging the domain expertise of traditional AI and the data-driven decision-making capabilities of machine learning.


The importance of traditional AI and machine learning lies in their complementary roles in addressing a wide spectrum of challenges, as well as their ability to transform businesses by advancing operations, processes, and product/service offerings. Together, they enable the development of intelligent systems that can automate complex tasks, make informed predictions, and adapt to evolving conditions.

Traditional AI and machine learning are just the beginning though. The world's rapidly changing technological landscape has led to tremendous improvements in both technologies, including the advent of present-day AI, referred to as generative AI. To learn about generative AI, continue to part two in this innovation blog series.

If you have questions about what was covered in this blog, whether it's about AI, innovation at Cleo, API and EDI integrations in the supply chain, or Cleo's products and services, contact us at sales@cleo.com or +1.815.282.7695. Lastly, you can explore additional educational resources through our resource library.

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