## 2 Materials and Methods
### Related Works
[75] used artificial intelligence (AI) to show how supply chains (SCs) can respond dynamically to unstable conditions, reducing the need for small-to-medium-sized businesses (SMEs) to make potentially costly decisions. This work explores the effect of AI on SC risk management for SMEs, building on a resource-based perspective. Based on information gathered from executives, managers, and senior managers of SMEs, a structural model consisting of AI-risk management capabilities, SC reengineering prowess, and supply chain agility (SCA) was created and evaluated. Artificial neural networks (ANNs) and partial least squares-based structural equation modeling (PLS-SEM) are the key methodologies used in this study (ANN). According to the findings, SC re-engineering flexibility and capacities are influenced by the application of AI for risk management. Efficiency is further impacted and mediated by reengineering abilities. Reliability was found for models A and B when PLS-SEM and ANN were compared. The SC's current levels of demand uncertainty put managers under pressure to make difficult trade-off decisions in a short amount of time. AI makes it feasible to model different situations to provide answers to important issues that outdated infrastructures cannot. In this work, non-linear correlations in the model were discovered and a multi-construct adaptability idea was combined. The limitation of the study is that other factors affecting the acceptance of technology, like culture, managerial dedication, and technological innovation, were not taken into account by the model. [PERSON] [76] applied different machine learning techniques for supply chain management. To close the supply-demand imbalance, the researcher created a complete system that uses an expandable deep neural network architecture. Depending on transaction records from already processed transactions, the architecture is able to examine a number of customized input items and proactively identify supply and demand trends. Based on a set of adaptable features, a general training model is created to forecast future demand, which is then put to the test. Integrating layers are applied to transfer high-dimensional features onto a small subdomain, resulting in a more compact representation, to bring together incoming data. The training framework of the model is made up of fully joined layers with connected activation functions. The limitation of the work is the low performance of the machine learning models used. [PERSON], [PERSON], and [PERSON] [77] presented a study on a drug refilling optimization problem, a general model for drug inventory management in a hospital. To solve the optimization challenge of a drug shortage, they investigated the hospital's drug supply chain model and developed Dynamic Refilling dRug Optimization (DR2O). They simulate an objective function that seeks to reduce the restocking costs, which include the price of the drug itself, the cost of storing it, and the consequence of a deficit. They also took budgetary limitations and supply limits into account while refilling medications, such as storage capability. They also proposed the Deep Reinforcement Learning Model for Drug Inventory (DRLD), a deep learning approach based on RL and DNN in which the pharmaceutical problem is represented as a state in a Markov Decision Process (MDP). They searched for a proper measure to decide whether to supply based on each state in order to reduce the objective cost function. They created an online approach to system control based on the MDP model, where reward and Q-matrices are established to assess an action match for each state. They presented a DNN model that can approximate the Q-values after training and can understand the behavior of the system because of the large search state space in RL. Lastly, they looked at using a detailed simulation to carry out their work. They specifically compared their strategy to three baseline strategies, which included support functionality, ski rental, and max-min.