سال:1402 | دوره: | شماره: |تعداد مقالات:6

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.

Finished products and manufacturing plants are some elements of the production system in the supply chain, and there are other manufacturing plants. They produce work in process and finished products and hold them in warehouses. So, they need to plan and control production and inventories. Isolated planning and control by different manufacturers increase inventories in them, and then they must plan and control integratory. This paper presents an iterative approach for solving the optimal control problem with bounded control variables. The projection function constructs the iterative method to approximate the control law. Employing the approximation of control law, the approximation of state and the co-state variables are obtained. For this purpose, we apply the Hamiltonian of the optimal control problem. From the Hamiltonian, the approximation of control law and then the approximation of state law is obtained. A simple example is given to compare the results with another published paper. Also, a case study on production planning in a three-stock reverse logistics system with deteriorating items is derived to show the method's performance.

Commercialization of innovative products in small and medium-sized enterprises (SMEs) faces many challenges. In this study, the factors related to the existing challenges and resolutions are identified with the help of the multi-layer fuzzy cognitive mapping (ML-FCM) method. The most effective criterion is introduced by examining the centrality. Also, the challenges and the existing resolutions to overcome these challenges are specified, and the most effective ones are determined. The present study addresses the practical experience of Sanat Prozheh Toos Company, which operates in the design and production of mechanical noise pollution control equipment (e. g., Silencers). The data is collected based on the organization's documents and experts' opinions. Research findings confirm that among the challenges of commercialization of innovative products associated with the case study, management challenge has the highest degree of effectiveness and centrality,moreover, among the ways of overcoming these challenges, organizational integration has the highest degree of centrality. Thus, the findings provide policy and management suggestions for SMEs policymakers and managers in commercializing advanced technologies.

A data governance policy is a foundational document providing instructions to manage data assets and organizational information effectively. Within the field of data governance, data access is one of the most important aspects of data management and includes considerations such as the extent of access, how to access data, access position, and data control and application. This research focuses on the banking industry, as its multiple stakeholders, diverse attitudes, and intangible aspects have created a problematic situation. To better understand and improve the current situation, soft systems methodology (SSM) provides a rich picture of the complex situation of data access in the bank, extracts key system definitions, and leads to a correct understanding of purposeful activities. After identifying these purposeful activities, a support policy for each set of activities is evaluated based on the literature in the field of data governance, specifically regarding data access. A mapping is established between activities and the fundamental principles of the data governance policy. One important innovation of this research is that, instead of directly utilizing SSM in the policy development process, it describes the situation and fundamental actions to provide the foundation for the policy. In conclusion, the data access problem has been identified as having various dimensions that can be grouped into six categories: data application, risk, processing, infrastructure, route, and access. These categories have been used to develop 13 support policy rules.

Effective management of engineering changes in the Automotive industry is an essential ability in new product development, and products evolve in an environment with an iterative nature and increasing changes from the idea stage to the final product and shortening the life cycle of products and also the duration of product Launch from The idea-to-market stage is a severe requirement of a competitive environment. First, the complexity of the product development environment, the challenges of engineering changes and the need to control and governance its effects in a competitive environment in the vehicle development process are described. Then by literature review, an approach to modeling engineering change management using two hard and soft models has been introduced. in our modeling approach, the assumptions of cybernetics and system dynamics approaches have been practiced in building causal loop diagrams that integrated SOFT effects with HARD dimension parameters. Using this approach to develop simulation scenarios, strategic managers have better insight into effective management of engineering changes to find appropriate policies configuration to control its effects in a competitive environment.

Management in health organizations is always interested to know whether their employees are satisfied with the type of job that they are doing. The trend of job satisfaction is of great concern to all employers all around the industries. The health industry is one of those industries where job satisfaction plays a vital role in its performance and providing quality service to patients. Knowing that many studies have been done about job satisfaction in the health industry, only a few are paying attention to the dynamic impact of factors on each other, considering feedback loops for real modeling of the problem. This paper proposes a multi-stage model for evaluating job satisfaction by system dynamics in a big hospital in Iran. Firstly, key influenced factors of job satisfaction are listed based on the Job Descriptive Index. In the second stage, after designing influence diagramming, three scenarios are developed for examining the impacts of two crucial financial and nonfinancial rewards factors. Finally, we analyzed the result of the running flow diagram of each scenario. The results show that applying both financial and nonfinancial rewards simultaneously can increase job satisfaction and the organisation's income via applying one of them.