Document Type : Research Article
Authors
1
Department of Industrial Management, University of Tehran, Kish International Campus, Tehran, Iran.
2
Department of Industrial Management and Information Technology, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.
3
Department of Public Policy, Faculty of Management, University of Tehran, Tehran, Iran.
4
Department of Management, Faculty of Management and Financial, Khatam University, Tehran, Iran., Tehran, Iran.
Abstract
Improving the responsiveness to customers’ orders is the goal of this research. Balancing the manual and automated production lines using proper equipment is crucial for the case study to reduce production costs and increase the quantity. The main problem in this research is responding to orders and demand using the proper equipment and machines in the production line. Using automated equipment can improve productivity and responses, but using advanced technology is not necessarily effective and should be organized in proportion to demands. As in this case study, there are complex cause-and-effects relations; using System Dynamics is very effective. Different productivity factors, delays, orders, etc., are considered to determine the proper production method and equipment in the long term. After that, behavioral reproduction and extreme condition testing validate the model and compare the automated production line equipment percentage usage. According to the volume of orders, the optimum case is suggested. Then, different scenarios related to customer volume are analysed, and finally, the policies are examined. As the simulation results in current conditions and market forecasting indicate, 67.2% of production line equipment should be automated. Any customer order changes can increase or decrease the results, which should be calculated accurately. Considering all the influential factors, the presented model helps the production managers have the best policy for choosing the production method. Using this method, considering some changes in the variables can be used in different industries as optimizing the production line equipment is the main key finding of this research.
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