نشریه مدیریت صنعتی
سال پانزدهم شماره 49 (تابستان 1402)

In recent years, industries worldwide have been striving to adopt a groundbreaking accomplishment of the fourth industrial revolution known as smart manufacturing. This advanced manufacturing system is based on Industry 4.0 technologies to improve systems' reliability, efficiency, and productivity. To have the highest level of performance and success, organizations need to pay constant attention to some variables and conditions, which are called Critical Success Factors (CSFs). Critical Success Factors play a significant role in all projects. Implementing smart manufacturing requires the identification of Critical Success Factors to be implemented effectively. Managers need to know the relationship between components of technology and the critical success factors, as well as the stakeholders who are involved or exert influence over these components. Therefore, the purpose of this study is to collect the Critical Success Factors of smart manufacturing implementation and also to determine the Critical Success Factors' relationship with components of technology and stakeholders of organizations, based on the extant research.
Collecting all the Critical Success Factors will be possible through a comprehensive and systematic literature review. In a systematic literature review, in addition to answering research questions, gaps in a field can be identified and introduced to researchers. To conduct a systematic literature review in this study, keywords were first selected based on the research objectives and questions. Then, articles were collected by searching selected databases. After collecting the articles, relevant articles were identified according to the selected criteria. Finally, the research questions were answered by using the selected related articles. In addition to answering the research questions, the citation analysis of the selected articles was also done.
In this study, 62 relevant research studies were identified. Through the examination of the selected research, 22 critical success factors were recognized. These factors were further classified into the domains of Technology- Organization Environment (TOE Model). By conducting a thorough analysis of the content in the selected studies, the interconnections between the critical success factors, stakeholders, and components of technology were thoroughly investigated.
The findings of this study can provide valuable assistance to researchers in gaining insights into existing gaps and addressing them appropriately. Moreover, the research outcomes can prove beneficial to managers as they endeavor to implement intelligent production methods. By effectively harnessing the identified critical success factors, managers can ensure that their endeavors are impactful and successful. This study offers a comprehensive understanding of the crucial factors that contribute to success in the chosen domain. It serves as a guide for future research endeavors and provides actionable insights for practitioners aiming to optimize their strategies. The study underscores the importance of considering stakeholder engagement and technological aspects in conjunction with organizational structures

One of the important issues in project management is project scheduling. Since projects are faced with high uncertainty, project scheduling is of high importance under uncertain conditions. The purpose of this research is to present a robust multi-objective model to optimize project scheduling with limited resources by considering uncertainty, in which activities have several execution modes with uncertain duration, costs, and resources.
After reviewing the extant theoretical literature on project scheduling, the assumptions, parameters, and variables of the mathematical model were determined. Then, considering the goals and limitations of the project scheduling problem in deterministic conditions, a mathematical model was developed. This model was transformed into a single-objective model by the epsilon constraint method. To consider the uncertainty in the parameters of the problem, robust optimization, and Bertsimas and Sim's approach were used. Also, Robust optimization of the single-objective model was developed to consider the uncertainty.
According to the obtained results, while the duration of the project increases with the increase in the tolerance of non-deterministic parameters; the percentage of project duration changes decreases for higher values ​​of robust parameters.
The robust parameters are with negative coefficient in the objective function, so their increase leads to a decrease in the objective function value. The highest reduction of the objective function is when the robust parameter is changed from two to one. This coefficient decreases with the increasing value of the corresponding parameter i.e., as the value of stability parameters increases, its effect on changing the value of the objective function decreases. The cost parameter was changed between -40% to +40% for the value of gamma 10 (the state where 10 activities of the project are non-deterministic) for different variation values of parameters of non-deterministic activities. Its effect on the values of the objective function indicated that the variation of the cost parameter in the range of 0 to +40% turns it into an unnecessary constraint and that its change has no effect on the value of the objective function (project duration). Also, in the range of 0 to -40%, the cost reduction caused a decrease in the value of the objective function (increasing the project duration) and the maximum impact of the reduction of the project budget related to the situation where the uncertain parameters of time and cost change by 40% and 50%.

Numerous variables are involved in green supply chain management and investigating any intervening variable can influence a diverse set of others. Blockchain, as an emerging technology of the fourth generation of the industry, comes with a variety of distinctive characteristics. The analysis of the integration between blockchain and green supply chain management will be highly intricate. Therefore, it is essential to establish a model to comprehend it effectively.
This study was carried out in two phases. In the first phase, the required information was collected, extracted, analyzed, and combined through a systematic review with a focus on the key questions. After identifying the primary existing sources, based on the entry conditions, some items were removed according to the determined conditions. Finally, 18 articles were thoroughly reviewed, and the variables were extracted. In the second phase, the validation, refinement, and examination of the relationship among variables were discussed through questionnaires. The combined method of DEMATEL and Interpretive Structural Modeling (ISM) were used.
Variables from the green supply chain, likely to be influenced by blockchain were extracted, refined, and modeled in 10 levels, ranging from the most influential to the least influential ones. Environment-related variables, along with business viability, are the most influential.
According to the achieved model and the degree of influence and impact of the variables, the main driver for moving towards green practices and utilizing blockchain include "the need to obtain environmental certification (government pressure)," "buying green products (inclination)," and "giving importance to the social responsibility of the green company." The outcome also depends on "business continuity" and environmental variables. An important aspect regarding the relationship between blockchain and the green supply chain is the location of the variables influenced by blockchain in the interpretive structural model. These variables were placed in the middle levels of the model, indicating that before implementing blockchain technology, it is crucial to assess the influencing variables and determine their value. The assessment can determine whether the establishment or non-establishment of blockchain technology is justified. If these variables indicate the need for changes in the intermediate variables of the model, blockchain technology can be used as a tool or enabler in the supply chain to drive the desired changes based on the specified influencing variables. Ultimately, as the intermediate variables in the model change, the dependent or influencing variables in the model can also be affected.

As economic enterprises that seek to earn a profit, banks collect and accept financial resources from depositors in the form of financial intermediaries. They allocate the resources to economic activities (investors) by providing various facilities. Therefore, banks play a very sensitive and important role in the economic system of a country and have a significant effect in regulating the economic relationships in society. Banks need to pay attention to the accurate allocation of facilities and resources. Accordingly, in today's financial landscape, effective asset and debt management has become a crucial component for the success of banks and other financial institutions. It is now essential to optimize the provision and allocation of financial resources in the banking system to reduce the costs of collecting financial resources and increase income via the distribution of financial resources in the form of various facilities. Introducing a multi-objective mathematical model and considering the limitations of the problem, this study seeks to present a mathematical model for equipping and optimal allocation of financial resources.
After reviewing the literature and previous studies conducted in the field of the research topic, using the opinions of banking experts, the researchers defined the objectives of the study as maximizing the income from the allocation of resources and minimizing the costs from the collection of resources. The first objective was defined to maximize the income from the payment of facilities (allocation of financial resources) and the second objective was defined to minimize the costs of the collection of financial resources (equipment of financial resources). The objectives were determined according to the rules and regulations of the Iran Central Bank as well as the financial regulations governing the business of the Iranian banking system. After defining and coding the model, the problem was solved using meta-heuristic algorithms. Finally, the effectiveness of the algorithms in providing the desired answer was assessed. Due to the NP-HARD nature of the problem, three meta-heuristic algorithms NSGAII, MOPSO, and CUCKOO were chosen to solve the problem. Due to the high efficiency of the MATLAB program, this software was used to perform the coding and calculations.
The results of the research indicated that the credit and operational risks of the bank will be reduced in case the banking system's resources are equipped and allocated scientifically. The achieved results from the evaluation of the algorithms confirmed the superiority of the mass particle swarm algorithm (MOPSO) over the other two algorithms. Long calculation time was found to be one of the disadvantages of genetic algorithms. Also, in the evaluation of various indices, the cuckoo algorithm did not demonstrate any significant advantage over the other two algorithms under scrutiny.
The results showed that thanks to its optimal allocation of financial resources, the model presented by this study can significantly increase the profitability of banks.

Despite the presence of legislation aimed at promoting the development of oil-rich and less-developed regions in Iran, the Western Ilam province continues to face significant deprivation in various areas, despite its abundant oil and gas resources. The primary focus of this study revolves around the intricate management of poverty through the implementation of a comprehensive intervention plan. The objective of this study is to identify policies for reducing deprivation through sustainable development in the oil and gas regions of Ilam province.

For this purpose and based on the sustainable development of deprived oil-rich areas, the dynamic model of the poverty system was designed with the participation of regional policymakers. After validating the model, the simulation was carried out on a twenty-year horizon. According to the behavior of the variables and the sensitivity analysis of the model, poverty alleviation policies were identified based on the sustainable development of deprived areas, including economic, social, and regional environmental sustainability. The results of the comparison and the behavior of the target variables were analyzed.

The findings from the simulation of the model revealed the effectiveness of selected combined policies including: 1) Providing government and non-governmental financial resources and investing in regional development and poverty alleviation; 2) Investing in job opportunities within the oil and gas industry; 3) Investing in job opportunities within the agricultural sector; 4) Investing in job opportunities within the tourism sector and international border customs; 5) Enhancing accountability and transparency to combat political corruption and undue influence in government resource allocation within the province; 6) Empowering the agricultural workforce in line with available job opportunities in the agricultural sector; 7) Empowering the workforce of the oil and gas industry in accordance with the job opportunities available in the industry to reduce non-native employment; 8) Empowering the workforce of the service sector according to the job opportunities of tourism and international border customs; 9) Investing in the productivity of water resources to manage the demand for water resources in agriculture and the oil and gas industry; and 10) Strengthening monitoring of the implementation of environmental requirements and the imposing penalties for environmental offenses for industrial development operators in the region along with the requirement to publish the sustainability report of oil, gas and petrochemical companies as the best poverty alleviation policies. These policies have been identified as the best poverty alleviation policies based on sustainable development in deprived oil-rich regions of Ilam.

The present study could demonstrate the effectiveness of the proposed practical model for managing the complexity of poverty and implementing sustainable development policies in deprived areas, specifically focusing on the oil-rich regions of Ilam Province.

The swift expansion of intricate projects in the global construction industry has prompted numerous investigations in the last twenty years, highlighting the significance of comprehending project intricacy for the triumph of construction project management. Identifying, assessing, and ranking procedures in response to risk is a crucial yet currently difficult aspect of project management to handle intricate projects at every phase of their existence effectively. Project managers have consistently focused on complexity and its linked hazards since it is a significant factor in project cost and time delays. This study explores the correlation between project complexity and modeling its outcomes' risks.
The study employed a deductive, positivistic methodology. The literature review examined the history and definition of complex projects. The risk factors were identified based on the underlying causes of the project's complexity. To achieve the best possible outcome in financial terms, a comprehensive model was proposed that considered the type of project contract with different risk response activities. The model was then tested by analyzing the risks associated with a sample project, and cost-response index graphs were generated for each risk individually and aggregated.
This research aimed to examine the current state and developments in project complexity research and to provide valuable insights for scholars and practitioners. The study's findings indicated that risks do not impact all projects equally. It was found that the actual effects of a risk event depend not only on the event itself but also on the management actions taken to address the contingency and their timing. These factors can influence the severity of the problems caused by the event and its ripple effects throughout the project organization.
According to the results of this field study, risks do not uniformly affect all projects. The actual impact of a risk event is contingent not only on the nature of the event itself but also on the managerial response to the contingency and its timing. These factors can influence the severity of problems caused by the event and the cascading effects within the project organization. While no single set of guidelines can guarantee project success, it is essential to recognize that the process is not random. A better understanding of the organizational dynamics that affect project performance and the factors contributing to risks in complex projects is a crucial precondition for creating a cross-functional solid team capable of managing risks before they negatively impact project outcomes. Therefore, this study can represent the first attempt to investigate the relationship between project complexity, risk consequences, and financial goals in complex construction projects. Among the various criteria contributing to complexity, the project's content, organization, and external environment were identified as the most significant risk generators in complex construction projects.