Optimizing Smart Supply Chain with Vendor Managed Inventory through the Internet of Things

Document Type : Research Paper


1 Ph.D. Candidate, Department of Industrial Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Associate Prof., Department of New Business, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran.

3 Associate Prof., Department of Industrial Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

4 Associate Prof., Department of Industrial Engineering, Faculty of Management, University of Tehran, Tehran, Iran.



Objective: The purpose of this research was to provide a mathematical model for smart supply chains with Vendor Managed Inventory (VMI) through the Internet of Things (IoT) technology to overcome traditional supply chain challenges and solve problems such as uncertainty, high costs, and changes in customer demands. Creating an intelligent and agile supply chain is essential to tackle these challenges and problems. Generally, customers are still skeptical and doubtful of the smart supply chain, and this skepticism is more likely to arise in the financial context. To improve efficiency, all organizations and institutions need to adapt themselves to external and internal changes, maintain supply chain inventory, and be flexible with customer demands.
Methods: Previous studies discussed intelligent supply chains, IoT, and their combination and described the advantages and disadvantages of using these technologies. Despite much research in this field, no mathematical modeling has been done for a smart supply chain that incorporates technology and automation. Based on such needs and objectives, this study sought to design a two-objective mathematical model, with a four-level supply chain. In this model, there is a direct relationship from suppliers to manufacturers, from producers to retailers, and from retailers to customers. The supply chain was intelligently designed, and the chain levels used Wireless Sensor Network (WSN) technologies, Radio Frequency Identification (RFID), blockchain, and Internet sales.
Results: The designed model in this research was validated by GAMS software. The researchers identified 10 problems of small and medium dimensions in the model. They presented the results in the form of objective function values ​​and solution time. The basic problem was solved using the -constraint method. The results obtained from solving the model and the Pareto diagram were presented in detail. The optimal implementation of the mentioned technologies, as well as the time and cost response to this implementation, proved the efficiency of the model.
Conclusion: The present study put forward a two-objective mathematical planning model. The first and second objectives of this model were to minimize the cost and decrease the time of the supply chain, respectively. By solving the basic problem, using the -constraint method, the values ​​of the objective functions and the Pareto diagram showed desired results. Solving the model in small and medium-sized dimensions using GAMS software, the values of the objective functions and the solution time were achieved. Also, the results proved the validity of the provided model suggesting that it can be used in larger dimensions. However, due to the lack of memory in GAMS software, for large dimensions, the suggested mathematical model for the smart supply chain needs to be checked using MATLAB software and meta-heuristic algorithms.


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