modeling and simulation
Fereshteh Koushki
Abstract
It is inevitable for a manager to consider the performance effects of each component of a multi-stage financial equity capital. These components serve as inputs in the first stage to raise investments. The investments, as outputs of the first stage, become inputs for the second stage and are used in ...
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It is inevitable for a manager to consider the performance effects of each component of a multi-stage financial equity capital. These components serve as inputs in the first stage to raise investments. The investments, as outputs of the first stage, become inputs for the second stage and are used in bank services, such as bank facilities, which are outputs of the second stage. Therefore, when evaluating bank performance, the connectivity between the stages must be considered; otherwise, efficiency may not be calculated correctly. Traditional methods often assess multi-stage systems as black boxes, neglecting the potential connectivity that may exist among the stages. We delve into the system and propose models to improve overall efficiency and the efficiency of each stage. Additionally, the continuity and relationships among stages introduce numerous variables and constraints to linear programming for evaluating the entire system. A centralized approach calculates the efficiency score of units simultaneously by solving only one linear programming problem, significantly reducing computational complexity. This approach, especially in large organizations, is commonly employed by central managers. In this paper, we introduce a centralized method for evaluating units with a multi-stage structure. We apply the proposed models to evaluate the efficiencies of bank branches and insurance companies, demonstrating the superiority of the improved network approach and centralized method in enhancing overall system efficiency. Bank branches typically have a two-stage structure, involving labor, physical capital, and other factors.IntroductionBank branches operate under the supervision of a central management team. The central manager, acting as the decision-maker, allocates resources such as labor and financial equity capital as inputs for these branches. The goal is to optimize the overall efficiency of the branches by minimizing the total consumption of resources while maximizing the desired outputs, such as security investments. A common approach to enhancing the performance of banks involves evaluating each branch separately. However, this method does not guarantee the minimization of total resource consumption and can be time-consuming. Since all bank branches are under the control of central management, the decision-maker can optimize the efficiency scores of branches by allocating resources to them simultaneously. This approach, known as centralized Data Envelopment Analysis (DEA), is particularly relevant when certain variables are controlled by a central authority, such as a Head Office, rather than individual unit managers. DEA is a mathematical programming technique used to assess the performance of homogeneous Decision Making Units (DMUs). However, in cases where DMUs have a network structure, such as banks, where the outputs of one division or sub-process serve as inputs for the next sub-process, traditional DEA models treat two-stage DMUs as black boxes and overlook potential connectivity among the stages. In our approach, we consider the internal activities within the system and propose a non-radial model to optimize multi-stage DMUs by taking into account the connectivity among the stages. Furthermore, in previous network DEA models, constraints related to intermediate activities were treated as inequalities, which, as we will demonstrate in this paper, can lead to contradictions in optimality. We address this issue by carefully considering the connectivity among stages. The presence of connectivity among stages introduces numerous variables and constraints to the corresponding model. This model, when used to measure the overall efficiency scores of all DMUs, would traditionally require solving as many problems as there are DMUs, which can be highly time-consuming. In our paper, we introduce a centralized approach that measures the efficiency scores of multi-stage structure DMUs by solving only one linear programming problem. We have applied these proposed models to evaluate bank branches and insurance companies. This approach provides a more comprehensive and efficient way to assess and improve the performance of multi-stage organizations like banks, taking into account the interconnected nature of their operations.MethodologyWe employ the Data Envelopment Analysis approach to evaluate systems with a multi-stage structure, often referred to as a network structure. Traditional DEA models treat two-stage DMUs as black boxes and overlook the potential for connectivity among these stages. In contrast, we delve into the internal activities of the system and propose a model that optimizes multi-stage DMUs by considering the interconnections among the stages. Moreover, in previous models designed to assess network systems, constraints related to intermediate activities were typically treated as inequalities, which could lead to inconsistencies in optimization. In our approach, we enhance these constraints associated with intermediate activities to ensure more robust optimization. Additionally, we apply a centralized approach to allocate resources to DMUs, allowing for the simultaneous optimization of the efficiency scores of all DMUs through the solution of a single linear programming problem. This centralized method streamlines resource allocation and improves the overall efficiency of the DMUs.ResultsWe evaluated 20 bank branches, treating them as 20 DMUs with a two-stage structure. In the first stage, inputs included paid interest, personnel costs, paid interest related to foreign currency transactions, and personnel costs related to foreign currency transactions. The first stage produced intermediate outputs in the form of raised funds and raised funds related to foreign currency transactions. In the second stage, the outputs consisted of loans and common incomes. Notably, some loans in the second stage might become non-performing, where borrowers are unable to make full or even partial repayments. To address this, we considered non-performing loans as undesirable or bad outputs and transformed them into inverse values to treat them as good outputs. To calculate the efficiency scores of the bank branches, we employed both our improved network model and the traditional DEA approach. Our network-based method revealed that many of the bank branches under evaluation were inefficient, in contrast to the traditional method, which inaccurately identified many of the bank branches as efficient. Subsequently, we extended our network method to a centralized case, significantly reducing computation time. The network-based assessment of bank branches took nearly 5 seconds, whereas solving the centralized model required only 0.1 second. In addition to evaluating bank branches, we applied our methods to assess insurance companies. The results demonstrated that our model provided more accurate efficiency scores compared to previous network-based approaches.ConclusionIn multi-stage production systems, the production process comprises several stages. Banks, for example, operate with a network structure in which labor, physical capital, and financial equity capital serve as inputs in the first stage to generate deposits as intermediate outputs. In the second stage, these banks utilize the deposits obtained from the first stage to create loans and security investments. We have introduced models to assess the efficiency of each stage, whether it's the first, intermediate, or final stage, individually. Additionally, we have developed a non-radial SBM model designed for evaluating DMUs with multi-stage structures. The Centralized DEA approach is a valuable method for central managers, particularly in large organizations like bank branches, to allocate resources effectively. We have extended our network-based method to a centralized approach, allowing us to calculate efficiency scores by solving just one linear programming problem. The results obtained from applying our proposed models to evaluate bank branches and insurance companies, both exhibiting network structures as DMUs, demonstrate the superiority of the network centralized approach over previous models.
modeling and simulation
mahboobeh golestanizadeh; Akbar Eetebarian; amirreza naghsh; reza ebrahim zadeh
Abstract
This study aims to design a model for measuring the level of maturity of business intelligence in electronic businesses, specifically for Internet Service Provider (ISP) companies. The study adopts a qualitative approach based on the phenomenological approach. A total of 10 specialists, experts, and ...
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This study aims to design a model for measuring the level of maturity of business intelligence in electronic businesses, specifically for Internet Service Provider (ISP) companies. The study adopts a qualitative approach based on the phenomenological approach. A total of 10 specialists, experts, and managers from electronic businesses involved in providing Internet services are selected as participants using the maximal differentiation method. Data are collected through in-depth and semi-structured interviews and analyzed using Colaizzi's method. The findings are classified into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique. Subsequently, a model consisting of 33 dimensions and 232 indicators is designed based on the relevant literature and the researcher's viewpoint, with confirmation from experts. Finally, the model is validated using confirmatory factor analysis in Smart PLS software.IntroductionDue to the fact that businesses face numerous challenges, such as the need for increased responsiveness and transparency towards customers, the growing number of tasks and organizational activities, and rapid technological changes, they require mechanisms capable of real-time data analysis and integration. Business intelligence serves as one of these mechanisms. Additionally, businesses need to assess and evaluate their current performance, compare their existing processes, tools, and methods with the best practices, and measure indicators of predictability, control, and effectiveness to effectively implement business intelligence. Therefore, they require a model to gauge the maturity level of business intelligence within their organization. Consequently, the objective of this research is to present a model for measuring the maturity level of business intelligence in electronic businesses.Materials and MethodsSince the researcher aims to extract the components of business intelligence maturity based on people's mentalities and experiences, the phenomenological method, specifically Colaizzi's method, was employed. To achieve this, 10 experts from Internet service provider companies were interviewed and selected using the maximal differentiation sampling method. The analysis of these interviews resulted in the extraction of 277 significant codes. Given the research's focus on measuring the maturity level of business intelligence, 40 experts were then asked to classify the obtained concepts into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique and snowball sampling method. After three rounds of Delphi, 232 codes remained out of the initial total of 277 codes. These 232 indicators were then categorized into 33 dimensions based on the definitions, functions of business intelligence, and the perceived concepts of each indicator. Subsequently, the researcher designed a measurement model for the maturity level of business intelligence in electronic businesses specifically tailored for Internet service providers. Finally, the designed model was validated through confirmatory factor analysis using SmartPLS software.Discussion and ResultsThis research has developed a model that enables companies, especially Internet service providers, to assess their current business state and their progress towards their goals. The model facilitates the decision-making process for e-business managers. With 5 levels, 33 dimensions, and 232 indicators encompassing technical, managerial, and human aspects, the model effectively enhances business capabilities and establishes a foundation for improving and advancing the level of maturity within the business. It is important to note that the model's Level 1 (Primary maturity) includes one dimension titled "reporting" with five indicators. Level 2 (Repeatable maturity) comprises five dimensions: advertising (eight indicators), management and performance evaluation (seven indicators), control (three indicators), documentation (five indicators), and automation (two indicators). Level 3 (Defined maturity) consists of six dimensions: access level (four indicators), customer orientation (16 indicators), process management (eight indicators), standardization of processes (10 indicators), improvement of information quality (five indicators), and improvement of service level (28 indicators). Level 4 (Managed maturity) encompasses 13 dimensions: assessment and analysis skills (14 indicators), business development and organizational processes (nine indicators), organizational management (12 indicators), organizational training (nine indicators), human resource management (16 indicators), organizational value (five indicators), security (two indicators), support (five indicators), business strategies (three indicators), management and development of essentials (11 indicators), business performance management (five indicators), policy making (four indicators), and cost-benefit (two indicators). Lastly, Level 5 (Optimized maturity) includes eight dimensions: predictive analysis (six indicators), dashboard (two indicators), knowledge management (six indicators), innovation (four indicators), competitive advantage (six indicators), technology development (four indicators), expansion of investment (three indicators), and data mining (three indicators).ConclusionsThis research has designed a model to facilitate the decision-making process of e-business managers, particularly those in Internet service providers. The model enables companies to assess their current business state and their progress towards their goals. The model encompasses 5 levels, 33 dimensions, and 232 different indicators, taking into account technical, managerial, and human aspects. With this comprehensive approach, the model has the potential to enhance business capabilities and establish a solid groundwork for improving and advancing the maturity level of the business. Internet service provider companies not only gain an understanding of their business intelligence maturity level and have the opportunity to elevate it through long-term planning, but they also empower themselves to navigate future changes and meet evolving customer expectations. The business intelligence maturity model introduced in this study serves as a framework for continuous improvement in their business activities. It provides a foundation and context for controlling processes and facilitates the ongoing enhancement of their operations.This study aims to design a model for measuring the level of maturity of business intelligence in electronic businesses, specifically for Internet Service Provider (ISP) companies. The study adopts a qualitative approach based on the phenomenological approach. A total of 10 specialists, experts, and managers from electronic businesses involved in providing Internet services are selected as participants using the maximal differentiation method. Data are collected through in-depth and semi-structured interviews and analyzed using Colaizzi's method. The findings are classified into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique. Subsequently, a model consisting of 33 dimensions and 232 indicators is designed based on the relevant literature and the researcher's viewpoint, with confirmation from experts. Finally, the model is validated using confirmatory factor analysis in Smart PLS software.IntroductionDue to the fact that businesses face numerous challenges, such as the need for increased responsiveness and transparency towards customers, the growing number of tasks and organizational activities, and rapid technological changes, they require mechanisms capable of real-time data analysis and integration. Business intelligence serves as one of these mechanisms. Additionally, businesses need to assess and evaluate their current performance, compare their existing processes, tools, and methods with the best practices, and measure indicators of predictability, control, and effectiveness to effectively implement business intelligence. Therefore, they require a model to gauge the maturity level of business intelligence within their organization. Consequently, the objective of this research is to present a model for measuring the maturity level of business intelligence in electronic businesses.Materials and MethodsSince the researcher aims to extract the components of business intelligence maturity based on people's mentalities and experiences, the phenomenological method, specifically Colaizzi's method, was employed. To achieve this, 10 experts from Internet service provider companies were interviewed and selected using the maximal differentiation sampling method. The analysis of these interviews resulted in the extraction of 277 significant codes. Given the research's focus on measuring the maturity level of business intelligence, 40 experts were then asked to classify the obtained concepts into five levels of business intelligence maturity (Level 1: Primary maturity, Level 2: Repeatable maturity, Level 3: Defined maturity, Level 4: Managed maturity, Level 5: Optimized maturity) using the Delphi technique and snowball sampling method. After three rounds of Delphi, 232 codes remained out of the initial total of 277 codes. These 232 indicators were then categorized into 33 dimensions based on the definitions, functions of business intelligence, and the perceived concepts of each indicator. Subsequently, the researcher designed a measurement model for the maturity level of business intelligence in electronic businesses specifically tailored for Internet service providers. Finally, the designed model was validated through confirmatory factor analysis using SmartPLS software.Discussion and ResultsThis research has developed a model that enables companies, especially Internet service providers, to assess their current business state and their progress towards their goals. The model facilitates the decision-making process for e-business managers. With 5 levels, 33 dimensions, and 232 indicators encompassing technical, managerial, and human aspects, the model effectively enhances business capabilities and establishes a foundation for improving and advancing the level of maturity within the business. It is important to note that the model's Level 1 (Primary maturity) includes one dimension titled "reporting" with five indicators. Level 2 (Repeatable maturity) comprises five dimensions: advertising (eight indicators), management and performance evaluation (seven indicators), control (three indicators), documentation (five indicators), and automation (two indicators). Level 3 (Defined maturity) consists of six dimensions: access level (four indicators), customer orientation (16 indicators), process management (eight indicators), standardization of processes (10 indicators), improvement of information quality (five indicators), and improvement of service level (28 indicators). Level 4 (Managed maturity) encompasses 13 dimensions: assessment and analysis skills (14 indicators), business development and organizational processes (nine indicators), organizational management (12 indicators), organizational training (nine indicators), human resource management (16 indicators), organizational value (five indicators), security (two indicators), support (five indicators), business strategies (three indicators), management and development of essentials (11 indicators), business performance management (five indicators), policy making (four indicators), and cost-benefit (two indicators). Lastly, Level 5 (Optimized maturity) includes eight dimensions: predictive analysis (six indicators), dashboard (two indicators), knowledge management (six indicators), innovation (four indicators), competitive advantage (six indicators), technology development (four indicators), expansion of investment (three indicators), and data mining (three indicators).ConclusionsThis research has designed a model to facilitate the decision-making process of e-business managers, particularly those in Internet service providers. The model enables companies to assess their current business state and their progress towards their goals. The model encompasses 5 levels, 33 dimensions, and 232 different indicators, taking into account technical, managerial, and human aspects. With this comprehensive approach, the model has the potential to enhance business capabilities and establish a solid groundwork for improving and advancing the maturity level of the business. Internet service provider companies not only gain an understanding of their business intelligence maturity level and have the opportunity to elevate it through long-term planning, but they also empower themselves to navigate future changes and meet evolving customer expectations. The business intelligence maturity model introduced in this study serves as a framework for continuous improvement in their business activities. It provides a foundation and context for controlling processes and facilitates the ongoing enhancement of their operations.
modeling and simulation
Mohammad Reza Atefi; Reza Radfar; Ezzatollah Asgharizadeh
Abstract
Purpose – Organization managers tend to use an optimal and precise method to evaluate the performance of their organization by understanding the organization dynamics. The immediate research goal was to propose a dynamic model for the performance evaluation of a LARG supply chain with the balanced ...
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Purpose – Organization managers tend to use an optimal and precise method to evaluate the performance of their organization by understanding the organization dynamics. The immediate research goal was to propose a dynamic model for the performance evaluation of a LARG supply chain with the balanced scorecard (BSC) approach. Design/methodology/approach – In this study, dynamic simulations are carried out for the performance evaluation of a supply chain. At first, a strategy map was designed, and measures are identified for each strategic objective considering the LARG supply chain measures. Afterward, a quantitative dynamic model was designed to identify the mathematical relationships among them. Findings – The proposed model is implemented in a company operating in the automotive industry. Based on the company’s strategic objectives, scenarios were designed and analyzed to evaluate the performance of the LARG supply chain with the balanced scorecard approach. Research limitations/implications – The BSC- based LARG supply chain evaluation has been studied for the auto part manufacturer sector. The different industry may lead to different results as the model designed important in each sector may differ as well as how each model is designed.Originality/value – The dynamic model enables managers to identify the determinants of the supply chain performance and set the scene for the necessary decisions by analyzing the possible scenarios in advance.
modeling and simulation
Mohsen JavidMoayed; عباس Toloei Eshlaghy; Mohammad Ali Afshar kazemi
Abstract
Nowadays, more successful businesses are those that keep their customers satisfied and in addition to the macro level of their policies, also pay serious attention to the micro level and details of the market. In this article, in order to study the influential factors in the mobile phone market, the ...
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Nowadays, more successful businesses are those that keep their customers satisfied and in addition to the macro level of their policies, also pay serious attention to the micro level and details of the market. In this article, in order to study the influential factors in the mobile phone market, the dynamic system method with the discrete event method has been used in combination.In this paper, the first two mobile companies Hamrahe Aval and Irancell as the basic players in the Iranian mobile phone market are considered as two rivals. Since in recognizing and analyzing the influential factors on market share, operational and strategic levels affect each other, after specifying the impressive factors at each level, from the discrete event approach at the operational level, and the dynamic system approach at the strategic level and their combination has been used to indicate a compound model of the mobile phone market.Based on findings any change in the operational and strategic levels of each competitor will have a serious influence on the rate of increase / reduce of willingness on their services and the consequently increase or decrease in customers. On the other hand, it indicates how a more specified level of detail can be noticed by combining discrete event simulation methods and a dynamic system.In comparison the suggested combined model investigates more details of events than simple simulation models, so, it can be used to examine different ways for decision-making.
modeling and simulation
Salman Abbasi Siar; Mohammad Ali Keramati; MohammadReza Motadel
Abstract
Because of the dissemination of impulse buying behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in impulse buying to be taken into account by the researchers and managers of the stores. The purpose ...
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Because of the dissemination of impulse buying behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in impulse buying to be taken into account by the researchers and managers of the stores. The purpose of this paper is agent–based simulation impulse buying behavior consumer (customers) considering the discount, the optimal time of customer presence in stores from the point of view the store managers and learning from previous buying. The population and the statistical sample of the present study include 15 academic professors who are expert in impulse buying and marketing topics. The present study is executive in terms of purpose. It is mathematical in terms of data type and modeling method. This model examines the existing reality of consumer buying behavior to develop impulse buying models in the agent-based simulation environment by Netlogo software. After reviewing the theoretical foundations and research background 5 dimensions and seventeen indicators have been identified by fuzzy screening method. The results showed that the factors considered in this study describe the impulse buying behavior of consumers as an economic analysis based on consumer relations and customer-product relationship. This achievement by simulating customer behavior at the time of purchase strives to provide valuable information for managers shareholders and store decision makers.
modeling and simulation
Navid Nadimi; Abbas Toloei Eshlaghy; Mohammad Ali Afshar kazemi
Abstract
With the tremendous progress in communications in the world, the transformation andbehavior of mobile operators and their digitalization, which in the past were onlyservice providers, as well as the creation of different experiences for customers, isinevitable.The purpose of this study is to create a ...
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With the tremendous progress in communications in the world, the transformation andbehavior of mobile operators and their digitalization, which in the past were onlyservice providers, as well as the creation of different experiences for customers, isinevitable.The purpose of this study is to create a hybrid simulation of systemdynamics and agent based model in order to analyze the revenue of the first operator inthe country to enter the field of digital platform and development of nativeapplications. Using the model proposed, first operators need to enter the digital areaand produce native applications was expressed. Then, the factors that affect the mobileecosystem which, affect the production of applications and the development ofrequired platforms were described. By utilizing hybrid simulation of system dynamicsand agent based modeling, the income of mobile operator in entering and not enteringthe digital arena and producing native applications were examined. The results showthat with the entry of the operator into the field of production of native applicationsand the adoption of digital approach, consumers tended to use more data services, butdue to different tariffs for data and voice, the operator's income up to 2 Next years willnot change much.
