Designing the causal structure of the intelligent distributed supply chain management model in the pharmaceutical industry using the fuzzy cognitive mapping method

New technologies have profoundly changed how people communicate and interact with their surroundings. These technologies affect every industry. Now, according to the standard definition, we define the supply chain as a set of interconnected activities that include coordination, planning, and control of products and services between suppliers and customers. Looking at technological advances, we realize that these traditional structures are no longer self-sufficient because digitalization has affected almost all aspects of human life, especially supply chain processes. The technologies of the fourth generation of the industry represent the industrial revolution that has combined the Internet of Things with automatic systems such as artificial intelligence and its subset of machine learning, which are self-adjusting and self-learning. Such technological systems can change the supply chain from a centralized state to a distributed state, in fact, the deployment of these technologies provides distribution and decentralization for supply chains, in this The distributed and decentralized supply chain components were identified through interviews with experts in the research field, and then the relationship and structure of its members were analyzed using the fuzzy cognitive mapping method. In the traditional supply chain, there are limitations to achieving the real price of the products. On the other hand, it takes several days for payments to be made between the manufacturer and the supplier or the buyer and the seller. From the point of view of consumers, there are many reasons why the supply chain of products is very important from their point of view. Recently, the ability of consumers to evaluate and check the sourcing of raw materials and their ethical dimensions and what happened to the products in the production process has been very limited. All decisions made in the supply chain, from demand forecasting to communication with suppliers, distributors, and consumers, are all based on a set of information whose accuracy and validity are very high. It depends on the reliability of the central monitoring unit or the third intermediary department that provides information and intelligence needs in the production, storage, distribution, sales, and tracking processes.
Therefore, the first question that is always raised in the supply chain is whether it is possible to trust the information shared by the members of the supply chain. The use of sensors in the supply chain and logistics to track products is a phenomenon that every year its application in the real world seems more serious [3]. When geographic locations are added to the data, more accurate information will be obtained that can determine the time and place of unwanted events. Sensors can quickly send malfunction alerts to prevent potential damage if the conditions do not match the pre-defined status. The information obtained from the Internet of Things can be processed visually through a web-based interface. Even if such systems do not have the ability to connect and send their data for any reason, they can save them and transfer them whenever they connect to the system again. Analyzing big data obtained from the Internet of Things systems is one of the problems of using this technology. To solve this problem and use big data and provide useful information for decision analysis, prediction and classification, researchers suggest the use of artificial intelligence technology such as deep learning and reinforcement learning. With new technologies such as IoT and RFID, the information related to the received products, the amount of storage space, and the time of receiving and sending the shipments are automatically recorded in the storage centers, and all this information is stored in block systems. China is stored in a way that is visible to all members of the supply chain.