نشریه مدیریت صنعتی
سال پانزدهم شماره 50 (پاییز 1402)

Organizational structures are purposefully crafted around the strategic objectives of companies, incorporating well-defined processes that serve as the foundation for employee activities and communication. The processes and activities of organizations and companies are implemented through organizational roles and positions that are assigned to employees based on the structure and goals. However, the crucial question lies in how effectively official structures can address people's communication needs and facilitate the implementation of activities. Within numerous organizations, an informal structure emerges in parallel to the official hierarchy, leading to the execution of various activities based on the relationships established within this informal framework. Because of various reasons, employees in some organizations are forced to carry out organizational processes through informal communication and in temporary work teams. The presence of an informal structure in numerous organizations is an unavoidable phenomenon. However, the primary concern lies in effectively monitoring and appropriately managing this structure and the relationships that arise from it. This study mainly seeks to measure the degree of similarity and difference between the formal and informal structures of the organization.

In the present study, the official structures were extracted based on the organizational chart. The informal structures were based on data logs and information from different sources and bases. Reports of events taking place via the work interactions of people in the process of supplier selection in a project-oriented company were collected in one of the complex construction projects. The gathered data was used as an informal network using the social network analysis method. The degree of similarity and difference between the formal and informal structure of the company was measured through social network criteria and the Rand index.

Based on the information obtained, the informal working groups or communities resulting from the real interactions of people were identified and analyzed. Also, these communities were compared with departments and formal units based on the hierarchical structure of the organization. In addition, by using the Rand index, the similarity of these two structures was estimated to stand at 0.76%. This method can be a basis for comparing the official structure and the real structure of companies. The ideal degree of similarity or difference between the two structures varies depending on the nature, culture, and mission of each company.

Using this method and identifying and analyzing the causes of the differences between these two groups of communication structures can help organizations improve their structures, make them more agile, and compensate for some structural deficiencies. By using this way of analyzing and comparing the results in these two groups, it is possible to examine the organizational structure, strengthen effective working groups and teams, support effective communication, and even prevent weak communication.

Railway cargo transportation has gained popularity as a preferred mode of conveyance, owing to its benefits over alternative methods. These advantages include reduced pollution in comparison to road transport, shorter transit times compared to maritime shipping, and cost-effectiveness in contrast to air transport. As a result, transport companies are more willing to use rail freight transport, especially for international transportation. One of the challenges facing the Islamic Republic of Iran Railways is to rank these companies, which are influential in various decisions such as granting discounts and facilities, handing over foreign wagons, prioritizing requests, and handling complaints. Therefore, this paper aims to develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies.

Considering the dynamic nature of the problem, the dynamic multi-criteria decision-making model was implemented based on the real data of international freight transportation (including export, import, and transit) by international rail transport companies in five years. To estimate future data for international rail transport companies, the fuzzy inference system was used. A fuzzy inference system is a mathematical tool that can handle uncertainty and imprecision in data.

Considering the dual uncertainty factors initially, companies' activities were evaluated using a fuzzy inference system, followed by the dynamics of a dynamic multi-criteria decision-making model the prioritization of 15 international rail transport companies for the Islamic Republic of Iran Railways was established. The results indicated that dealing with the problem of ranking international rail transport companies dynamically will achieve a more favorable outcome. The applicability of the presented method to benefit from the fuzzy inference system in the dynamic multi-criteria decision-making model is one of the other results.

This study could develop a dynamic multi-criteria decision-making model to rank Iran's international rail transport companies. The proposed dynamic multi-criteria decision-making model includes several implications. Firstly, it provides a comprehensive and systematic approach to evaluate and rank international rail transport companies based on multiple criteria and factors. This approach can help decision-makers to make informed decisions and allocate resources effectively. Secondly, the use of the fuzzy inference system to estimate future data is a significant contribution to the field of transportation. This mathematical tool can handle uncertainty and imprecision in data, which is common in transportation systems. This can help decision-makers to anticipate future trends and plan accordingly. Moreover, the proposed model can be customized and applied to other realms and fields. The model can be adapted to the specific needs and requirements of each problem, making it a versatile and adaptable tool for decision-making. By using this model, decision-makers can take into account multiple criteria and factors, such as cost, time, and environmental impact, and evaluate different scenarios to find the most optimal solution.

Food security holds such strategic significance that ensuring it plays a pivotal role in a nation's economic and political self-reliance. The growing reliance on foreign governments for essential food supply considerably undermines a country's political and economic leverage on the global stage. Regrettably, Iran finds itself grappling with over 90 percent dependency on the oilseeds sector, consequently amplifying the nation's susceptibility. Thus, the current study addresses this critical concern, vital for a nation's sustenance. Accordingly, this study comprises the development and cultivation plan of oilseeds in the Islamic Republic of Iran intending to portray a deep-rooted power system that has been formed in the food security system that hinders the optimal implementation strategies. Consequently, the paper aims to provide a model of key stakeholders in the field of food security by employing the Problem Structuring Method (PSM), in search of better analysis of policies and supporting strategies.
 
The study employs stakeholder analysis to introduce the actual stakeholders followed by cognitive mapping, considering the chaotic nature of the issue and structuring the problem for providing an agreed and committed solution.  To this aim, FC mapper software is employed for concept mapping, interrelation, and important analysis. Oilseeds Stakeholders were chosen according to the library research method and upstream documents. It was followed by semi-structured interviews with 25 food security experts who have been selected through snowball sampling, for structuring the mentioned stakeholders in a comprehensive model.
 
First, in the qualitative part, according to documents a list of 90 stakeholders was proposed. Then, Through the process of conducting expert interviews and utilizing cognitive mapping, while also taking into account additional factors like the objectives, responsibilities, and authorities of the relevant stakeholders, the aim was to ascertain the outcomes of the oilseed development and cultivation plan. Also, the degree of influence, effectiveness, and centrality of all influencing factors were determined which declares their priority as well. Accordingly, the Iranian Ministry of Agricultural is the most influential stakeholder with a value of 0.67, and food security and economic independence with a value of 0.51 is the most influential outcome among other outcomes.
 
The research focuses on the systematic formulation of the oilseed cultivation challenge in Iran, culminating in the development of a model to recognize stakeholders, delineate their objectives, responsibilities, and authorities, and analyze the ramifications of the implementation plan using the PSM. Identifying stakeholders as well as their power and interest, goals, and consequences, in a comprehensive model leads to significant implications for the management and maki-policy of the food security system and plays a more effective role in improving the conditions. The obtained outcomes were endorsed and utilized by the Hekmat Institute of Research Policy and Strategic Studies, which provided financial backing for the study.

In recent years, there has been a growing global concern regarding the escalating production of waste. As countries continue to industrialize, the challenge of managing and disposing of waste properly has become a daily issue. Consequently, the fate of industrial goods and products has become a topic of significant interest to consumers. Waste management operations and network design in the automotive industry differ in certain aspects from those in other industries. This difference basically comes from the complex structure of the supply chain in the automotive industry. A large number of sectors are involved in the supply chain, which makes it difficult to control and manage the reverse network. In addition, the high customization in cars means that the parts or components are not the same, and for this reason, it is difficult to predict the recycling of parts or materials. Also, the world is currently confronted not only with the challenges of environmental preservation and sustainability but also with rapid technological advancements in digitization and automation.‎ This study aims to develop an industrial waste reverse supply chain network ‎mathematical model (Iranian automotive industry) using the vehicular ad hoc ‎network (VANET).

To maximize the economic benefits and minimize the environmental and social ‎impacts, a multi-objective (multicriteria) mixed integer programming (MOMIP) ‎facility location mathematical model was developed in the present study for a ‎sustainable supply chain. The economic goal includes aspects such as income and costs within the supply chain, the environmental goal focuses on factors like carbon emissions during transportation and operations, and the social goal encompasses various elements such as annual accident rates, the well-being of drivers, the residential locations of the workforce, and workforce recruitment and termination. Also, to calculate more accurately, the amount of carbon emissions based on the duration of transportation in the supply chain used vehicular ad hoc ‎network (VANET). Real data for the Iranian automotive industry and ‎GAMS were used for model solution.

The proposed multi-objective mathematical model was solved using the ‎enhanced epsilon constraint method in GAMS based on data obtained from the ‎Iranian automotive industry. The results demonstrate the model's validation accuracy and indicate that the proposed model exhibits strong efficiency, making it well-suited for the Iranian case study.

The results showed that the reverse supply chain is efficient over time by ‎considering the recycling of waste products at the same time as the economic, ‎environmental, and social dimensions, as well as taking into account VANET. The model could determine the centers of dismantling facility, processing facility, and recycling according to the objectives of selection and the flow of materials between the centers. ‎Sensitivity analysis also showed that changes to the parameter of culture ‎making to advertise used car sales have a significant effect on the objective ‎functions.

Supply chain management is a modern organizational management mode that organizes and plans information, capital flow, and business partnerships in the supply chain and requires complete business and market information (Quinn et al., 2012). However, the cost of acquiring supply chain companies and product information by traditional methods is very high. Information technology provides the power for companies to implement supply chain management and share the supply chain easily, and all companies in the supply chain can create value through information management (Shawaki et al., 2023). The utilization of intelligent approaches to predict prices and demand quantities enhances supplier delivery performance. It also refines demand forecasting accuracy, improves factory planning precision, forecasts demand for new products, and minimizes supplier risks, transportation costs, inventory, operational expenses, and time (Tirklai et al., 2021). In supply chain management, accurate forecasting of demand reflects the price. It is a critical issue that can reduce inventory costs and achieve the desired service level (Zouqaq et al., 2020). Intelligent supply chain pricing approaches can help supply chain companies to adapt the quality of their product offerings in supply chain management according to the knowledge gained (Kotsiopoulos et al., 2021). Identifying and modeling steel market fluctuations is very important in the steel industry and supply chain management. Considering the vertical chain in this industry and the interaction between the players of this industry, game theory has been used to model the optimal price. Neural network models were employed to replicate the game, as interaction and repeated gameplay are required for achieving balance among players. Taking into account Iran's unique circumstances, notably its confrontations with substantial sanctions in the metal industry, the sanctions variable was integrated as an adjusting factor in the pricing model for this sector.

This is a practical study. The research time frame for predicting steel prices and calculating the sanctions index spans from 2011 to 2020, with quarterly data. The MATLAB software was used.

Three Bayesian neural networks, support vectors, and Grassberg's anti-diffusion were used to predict the price of steel. The results showed that the Grossberg anti-diffusion model is more accurate in predicting steel prices. Next, the predicted price entered the game theory process and the Nash equilibrium point of the model was determined. According to the country's specific conditions, the sanctions variable was introduced in the game theory model. The results showed that the inclusion of sanctions in the model led to price increases and production reductions within the steel industry. The present study delved into price fluctuations resulting from shifts in supply and demand, particularly in the context of sanctions. The findings reveal that a reduction in supply coupled with escalated sanctions led to substantial price hikes, surpassing the impact of supply changes. Consequently, steel exhibits a heightened susceptibility to input constraints, where any disruption in its supply chain triggers significant price spikes, thus unsettling the market. This amplifies the sensitivity of supply chain management for steel. Consequently, a systemic and dynamic approach is essential for market regulation policies, raw material supply, transportation strategies, and warehousing considerations. It should be noted that the use of intelligent approaches and machine learning can play a significant role in coordinating such issues.

Considering that Stackelberg's approach was used in the current research, the sequence of players' entry into the game holds significance with respect to the Nash equilibrium. The development of market entry monitoring rules and regulations in this industry should be investigated because the steel industry is one of the industries that face high entry and exit costs. As a result, Policymakers and industry managers should monitor the entry and exit of players within this sector. They should endeavor to establish norms and regulations governing interactions among market participants to foster a structured and well-defined competitive environment.

Short product life cycles, unpredictable demand patterns, and ever-decreasing time-to-market have put manufacturing companies under pressure. To face all complex production scenarios, these companies tend to the implementation of Cellular Manufacturing Systems (CMS) to reduce production costs, increase flexibility, and respond quickly to market demands. The cellular manufacturing system facilitates the control and management of the production system by dividing a large system into several small subsystems. The design of a cellular manufacturing system includes cell formation, group layout, group scheduling, and resource allocation. The first issue in designing a cellular system is the grouping of machines to produce a family of parts in production cells referred to as cell formation. The creation of efficient cells aims to achieve maximum performance of intracellular machines (intra-cellular processes) and minimize the transfer of parts from one cell to another (inter-cellular processes). The second problem is the problem of group layout, which includes the placement of cells in the workshop relative to each other (intercellular layout) and the layout of machines within the cells (intracellular layout). The optimal grouping of machines in cells, the efficient placement of cells relative to each other, as well as the machines inside cells affect the cost of intracellular movements and intercellular movements of parts. Considering that in a static cellular manufacturing system, the demand is considered constant in all periods, however, due to the advancement of technology, the environment of an industry is faced with turbulence in the types of products and demand. To overcome these problems, a dynamic cellular manufacturing system was introduced, which forms optimal cells in each period according to the demand conditions and changes in product composition. This study addresses the simultaneous challenges of a dynamic cellular manufacturing system (DCMS) with unreliable machines and production planning and intercellular layout problems. The proposed model seeks to minimize the costs of Inter and intra-cellula movement, reconfiguration, machine breakdown, part production, keeping parts in the warehouse, and back-ordering parts in production cycles.

At first, a mixed integer nonlinear programming mathematical model for the considered problem was presented, Next, linearized and validated with a case study in GAMS software with a GUROBI solver. In the following, the impact of moving machines between periods and the sensitivity analysis of the MTBF parameter were discussed.

Flexibility in routing, optimal location of cells, and optimal grouping of machines reduced production costs, and also by moving two machines m1 and m3, production costs improved by 353,870 Tomans.

The reconfiguration of machine cells in the new period improves the cost of production and also the model is flexible in routing part production. In this study, MTBF sensitivity analysis showed that the number of failures affects the system's performance.