Artificial Intelligence and Machine Learning are revolutionizing supply chain management by enabling smarter, more responsive, and adaptive planning systems. As supply chains become increasingly complex and dynamic, traditional planning methods struggle to keep pace with the demands of modern business environments.
This article explores the cutting-edge AI and ML technologies that are transforming supply chain planning, from advanced search algorithms and neural networks to self-optimizing systems that continuously learn and improve. We’ll examine how these sophisticated techniques are being applied to solve real-world supply chain challenges, providing unprecedented levels of efficiency, accuracy, and adaptability in planning operations.
AI Search Techniques
Constraint Propagation – This is used in order to quickly arrive at a near-optimal solution. An example of this approach is the 8-Queen problem in chess.
Divide and Conquer – Using this approach the system sets up guidelines of constraints at various levels of detail in order to arrive at solutions that are feasible. Thus, the search space keeps getting smaller and closer to the final solution.
Taboo search – This is a useful search technique where the system keeps learning from its previous attempt in order to shorten the length of the next search. In other words, it leaves markers behind to let the next search to know branches that would NOT have a feasible solution. (We refer to this as self-optimizing learning technique—see below)
Gradient Descent – This is a complex search process that is used in order to arrive at an optimal solution where the search space is exponentially large. This search is used in order to optimize inventory levels and safety stock at the right locations to ensure quick response and optimal delivery performance.
Expert Systems – Eyelit Technologies’ attribute-based planning (ABP) technique allows the creation of intelligent constraints using Boolean expressions that are parsed by the engine. The expressions are in the form of IF_THEN_ELSE so that one can allow user-defined business constraints as needed for ANY object in the supply chain. As it can be seen this makes the application very flexible and adaptable to any environment and more importantly as the business changes, the application also changes with the business as needed.
Use of Machine Learning
Neural Nets – NNs are very effective machine learning tools that are applicable in many different areas of supply chain including demand planning and forecasting. At Eyelit Technologies we use NNs for finding multi-variate causes of events and use that in order to have a more precise prediction of the outcome knowing the potential cause(s).
Self-Correcting Models – Supply chains are a living organism. They are changing all the time. A static model of the supply chain becomes irrelevant after a while because the real world has changed from its original assumptions that we made about availability of equipment, processing times, delivery leadtimes during different months of the year and so on. To this end Eyelit Technologies uses learning techniques in order to constantly update the model of the supply chain to represent a true Digital Twin (what we call a Digital Mirror©)
Self-Improving Policies – As the supply chain changes and or business priorities and environment changes, one needs to adapt to the new way of doing business and optimize decisions. At Eyelit Technologies, we use ML in order to make good decisions on inventory settings, customer prioritizations, best mix of products so that a much better delivery performance is obtained.
Self-Optimizing Algorithms – ML is used to improve the algorithms themselves. There are a variety of ways to speed up search techniques such the one mentioned earlier using Taboo search. This approach allows the system to find short cuts to finding the right solution and develop plans much faster by exploiting the structure of the problem at hand.
RBP and Swarm Technology
Eyelit Technologies deploys Swarm technology in order to automate business processes in a scalable fashion. Supply chain planning and execution is essentially a distributed system.
To this end it does not make sense for one big centralized system to respond to changes in a timely manner. Furthermore, it does not make sense to do batch planning. Distributed agents can sense, act and learn from their experiences and respond in an event-driven (or continuous) fashion.
We believe this is the next generation of IBP platform to enable distributed and intelligent processes to collaborate with one another and grow to be more intelligent making the whole system more effective and adaptive.
Let’s make accurate plans together!