Mansureh Mohammadi; Mohammad Reza Gholamian
Abstract
This paper focuses on supply chain coordination under a revenue sharing contract. Demand plays a crucial role in modeling inventory systems, particularly in the retail industry where products need to be brought in and taken out of retail stores within specified timeframes. The question addressed is how ...
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This paper focuses on supply chain coordination under a revenue sharing contract. Demand plays a crucial role in modeling inventory systems, particularly in the retail industry where products need to be brought in and taken out of retail stores within specified timeframes. The question addressed is how to allocate shelf space effectively. Optimal shelf space allocation in retail significantly impacts product sales and profitability. The research demonstrates that by considering factors such as price, allocated shelf space, product brand image, and advertising, a new demand function can be developed. The study explores decentralized, centralized, and coordinated structures using a Stackelberg game model. The findings show that a revenue sharing contract leads to a win-win outcome in the supply chain. Additionally, a numerical example is provided to illustrate the model, and sensitivity analysis is performed on key parameters. The results highlight the significant impact of price elasticity on demand, emphasizing the need to pay close attention to this parameter in real-world applications.IntroductionCoordinating manufacturers and retailers has always been a challenge in decentralized supply chains, as each member seeks individual profit. One practical mechanism for coordination is the revenue-sharing contract, where manufacturers set the wholesale price and retailers pay it. Additionally, retailers share a portion of their profits with manufacturers to incentivize participation in coordinated structures. However, the percentage must be chosen carefully to maximize the profits of both chain members compared to a decentralized structure. In today's competitive world, securing shelf space and determining optimal pricing, particularly in chain stores, has become increasingly intense. Companies can increase their profits by considering various factors that influence customer behavior. This study investigates how manufacturers and retailers in a supply chain leverage advertising and brand image to drive demand while setting prices and allocating shelf space for their products. This new model effectively captures the concept of competition in the market. On the other hand, in today's highly competitive business world, the competition to secure shelf space and determine pricing in department stores has intensified. Pricing and shelf space decisions are influenced by the demand within the supply chain. By considering various factors that impact customer behavior and incorporating them into the model, even though it introduces complexity and challenges in solving the model, the results become more realistic and enhance the model's effectiveness. Customers place great importance on accessing products at the right time and place, making optimal allocation of shelf space in stores and effective shelf space planning crucial steps towards improving and increasing sales in retail stores.Materials and MethodsA two-echelon supply chain, comprising a manufacturer as the leader and a retailer as the follower, is the focus of this study, and Stackelberg game theory is applied to model and analyze this system. The investigated model considers decentralized, centralized, and coordinated structures. Through this research, various factors such as price, allocated shelf space, and the impact of product brand image and advertising are taken into account, leading to the development of a new demand function. The application of the Stackelberg game in the developed model demonstrates how a revenue sharing contract can result in a win-win outcome within the supply chain. Real-world performance analysis of the proposed models is conducted using a dataset from Golrang Holding Supply Chain Network and Ofogh Kourosh Chain Stores Company in Iran.Discussion and ResultsThe proposed problem is modeled under three structures: (1) a decentralized structure where each member of the supply chain (SC) makes independent decisions to optimize its own profit, (2) a centralized structure where a central administrator maximizes the overall SC profit, and (3) a coordinated structure achieved through the design of a revenue-sharing contract. The revenue-sharing contract encourages SC members to transition from the decentralized structure to the centralized one. The results demonstrate that the use of the revenue-sharing contract leads to the sum of retailer and manufacturer profits being equal to the total profit in the centralized structure, with each member achieving higher profit compared to the decentralized structure. Consequently, the revenue-sharing contract facilitates the coordination of the desired supply chain.ConclusionsThis research is based on a real-life case study in the retail industry. The findings of this study are applicable to various retail sectors, including dairy, protein, grocery, cosmetics, fresh fruits, vegetables, and more. Traditionally, price has been viewed as a revenue generation tool. However, it is now recognized that price plays a crucial role not only in generating revenue but also in ensuring customer satisfaction. Therefore, it is important to coordinate and align pricing decisions with factors related to customer management, such as brand image and advertising. The main objective of this research is to design a new model with a multiplicative demand function that considers factors such as price, shelf space, brand image, and advertising. Additionally, the implementation of a revenue-sharing contract has improved system performance. The developed models were solved using Mathematica software, and numerical examples were provided to demonstrate their real-world application and the solution method. The numerical examples revealed that the centralized and coordinated structures experienced price declines compared to the decentralized model, leading to increased profitability in these structures. Furthermore, sensitivity analysis was conducted on key model parameters, highlighting the significance of the price elasticity parameter on demand. This parameter should be given greater consideration in real-world applications. While the study focused on a supply chain structure involving one manufacturer and one retailer, future research can explore supply chains with multiple retailers. It is also possible to combine various supply chain coordination contracts or compare different coordination contracts with each other.
Industrial management
Mina Kazemian; Mohamad Ali Afshar Kazemi; Kiamars Fathi Hafshejani; Mohammad reza Motadel
Abstract
IntroductionThe field of supply chain management has focused on crucial topics such as coordination, cooperation, and competition among chain members. Game theory has emerged as a valuable tool for examining supply chain management issues, as it analyzes various situations and their impact on supply ...
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IntroductionThe field of supply chain management has focused on crucial topics such as coordination, cooperation, and competition among chain members. Game theory has emerged as a valuable tool for examining supply chain management issues, as it analyzes various situations and their impact on supply chain performance (Naimi Sediq et al., 2013; Shafi'i et al., 2018). While every action and performance within the supply chain influences the outcomes of the game, it does not solely determine them. The goal is to balance the parties involved in the supply chain and create satisfaction for the end customer (Metinfer et al., 2018).Although extensive research has been conducted in supply chain management within the steel industry, the impact of sanctions on Nash equilibria and the application of three approaches (Cournot, Stackelberg, and collusion) to achieve game balance in different scenarios have not been thoroughly investigated. This research aims to fill this gap by addressing the mentioned research question. The current study focuses on determining the optimal price using an intelligent decision-making system based on game theory within the steel industry, considering the presence or absence of the sanctions variable.Our country currently possesses several relative advantages in terms of steel production conditions, including abundant and affordable energy, iron ore and refractory raw materials, considerable steel production experience, and a skilled and cost-effective workforce. By acquiring new production technology, these advantages enable our country to play a competitive and influential role in the global steel market. However, the steel industry faces significant challenges such as price fluctuations, extreme price disparities across regions, and delayed delivery due to a lack of efficient supply chain management. Therefore, the main research question aims to provide a model that incorporates key variables, such as the supply and demand of final and intermediate products in the steelmaking industry and the future trends in production and quantity changes.Research methodThis article introduces a composite model that combines artificial neural networks and game theory to assist stakeholders in the steel industry in determining optimal production levels and price levels. To predict the price of steel, three techniques were employed: Bayesian neural networks, support vectors, and Grassberg anti-diffusion. Additionally, to address the issue of binary identification in the neural network, three different network structures were introduced: feedforward network structure, competitive network structure, and backward associative memory network structure.Research findingsThe first scenario is the non-cooperative game (Cournot model scenario) where each participant aims to maximize their profit and would not deviate from their strategy as it would lead to a reduction in their profits. The second scenario is the sequential non-cooperative game (Stackelberg model scenario), in which two chains engage in a confrontation of the Stackelberg game type. The leader's goal is to determine the best strategy while considering all rational strategies that follower players can employ to maximize their income. This scenario demonstrates that the rate of price and profit increase is lower compared to sequential and cooperative game modes. The third scenario is the cooperative game (collusion model scenario). In this scenario, the allocation of profits among the cooperating members is crucial to ensure the stability of their cooperation. The Grassberg anti-diffusion method exhibits higher accuracy due to its higher true positive (TP) and true negative (TN) values compared to other algorithms. Additionally, it has fewer false positives (FP) and false negatives (FN) because a higher TP and TN indicate more accurate predictions in the tested dataset, while FP and FN represent incorrect predictions. The inclusion of the sanctions variable as a moderating factor in the steel price forecasting model accounts for the potential reduction in production and increased cost price. Through the model, it was discovered that the Grossberg method yields more accurate steel price forecasting. Consequently, price forecasting in the model is based on the Grossberg method.Research resultsThe results indicate that transitioning from the Cournot game to the Stackelberg game and from the Stackelberg game to the collusion game in the steel industry's supply chain leads to a $6 increase in price per ton and a product supply ranging from 1500 to 4000 tons. In other words, as collusion in the steel market intensifies, more products are introduced into the market, resulting in an increase in product prices and a decrease in the welfare of steel consumers. According to the findings, increased competition in the industry reduces the profitability and production levels of producers while enhancing consumer welfare. Conversely, higher levels of monopoly exhibit the opposite effect. To maintain a balanced supply chain in the steel industry and prevent potential problems, it is recommended to adopt the Stackelberg game approach, which aligns more closely with reality. It is worth noting that the order in which players enter the game impacts the Nash equilibrium. Therefore, exploring market entry monitoring regulations and rules in this industry becomes crucial since the steel industry involves high entry and exit costs. Policymakers and industry managers should consider monitoring the entry and exit of players, formulate game standards and rules among market participants. Based on the results, the primary recommendation of this research is to increase competition intensity and adopt the Cournot approach in the industry to reduce prices and increase production. Additionally, enhancing international relations and diplomatic efforts will mitigate the impact of sanctions on the industry, leading to cost price improvements and an increase in the level of comparative advantage at the international level.
maedeh mosayeb motlagh; Parham Azimi; maghsoud Amiri
Abstract
This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate ...
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This paper investigates unreliable multi-product assembly lines with mixed (serial-parallel) layout model in which machines failures and repairing probabilities are considered. The aim of this study is to develop a multi-objective mathematical model consisting the maximization of the throughput rate of the system and the minimization of the total cost of reducing mean processing times and the total buffer capacities with respect to the optimal values of the mean processing time of each product in each workstation and the buffer capacity between workstations. For this purpose, in order to configure the structure of the mathematical model, Simulation, Design of Experiments and Response Surface Methodology are used and to solve it, the meta-heuristic algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Ranked Genetic Algorithm (NRGA) are implemented. The validity of the multi-objective mathematical model and the application of the proposed methodology for solving the model is examined on a case study. Finally, the performance of the algorithms used in this study is evaluated. The results show that the proposed multi-objective mathematical model is valid for optimizing unreliable production lines and has the ability to achieve optimal (near optimal) solutions in other similar problems with larger scale and more complexity.IntroductionA production line consists of a sequence of workstations, in each of which parts are processed by machines. In this setup, each workstation includes a number of similar or dissimilar parallel machines, and a buffer is placed between any two consecutive workstations. In production lines, the buffer capacity and processing time of machinery have a significant impact on the system's performance. The presence of buffers helps the system to maintain production despite possible conditions or accidents, such as machinery failure or changes in processing time. Previous research has investigated production lines without any possibility of machinery failure, referred to as "safe production lines." However, in real production lines, machinery failure is inevitable. Therefore, several studies have focused on "uncertain production lines,"assuming the existence of a probability of failure in a deterministic or exponential distribution. This research examines uncertain production lines with a combined layout, resulting from the combination of parallel deployment of machines within each workstation, if necessary, and serial deployment of workstations. The objective of this research is to determine the optimal values (or values close to optimal) of the average processing time of each product in each workstation, as well as the volume of buffers, as decision variables. The approach aims to maximize the system's output while minimizing the costs associated with reducing the processing time of workstations and minimizing the total volume of buffers between stations. Moreover, simulation can be applied without interrupting the production line or consuming significant resources. In this research, due to the high cost and time involved, implementing the proposed changes on the system is not cost-effective for investigating the changes in the production system's output rate. Therefore, the simulation technique has been utilized to optimize the production line.Research methodThe present study aims to develop a multi-objective mathematical model, based on simulation, to optimize multi-product production lines. In the first step, the structure of the multi-objective mathematical model is defined, along with the basic assumptions. To adopt a realistic approach in the model structure, the simulation technique has been employed to address the first objective function, which is maximizing the output rate of the production line. To achieve this, the desired production system is simulated. The design of experiments is used to generate scenarios for implementation in the simulated model, and the response surface methodology is utilized to analyze the relationship between the input variables (such as the average processing time of each product type in each workstation and the buffer volume between stations) and the response variable (production rate).ResultsTo implement the proposed methodology based on the designed multi-objective programming model, a case study of a three-product production line with 9 workstations and 8 buffers was conducted. Subsequently, to compare the performance of the optimization algorithms, five indicators were used: distance from the ideal solution, maximum dispersion, access rate, spacing, and time. For this purpose, 30 random problems, similar to the mathematical model of the case study, were generated and solved. Based on the results obtained, both algorithms exhibited similar performance in all indices, except for the maximum dispersion index.ConclusionsIn this article, the structure of a multi-objective mathematical model was sought in uncertain multi-product production lines with the combined arrangement of machines in series-parallel (parallel installation of machines in workstations if needed and installation of workstations in series). The objective was to determine the optimal values of the average processing time of each type of product in each workstation and the buffer volume of each station, with the goals of maximizing the production rate, minimizing the costs resulting from reducing the processing time, and the total volume of inter-station buffers simultaneously. To investigate the changes in the output rate of the production system, due to the high cost and time, it was deemed not cost-effective to implement the proposed changes on the system. Therefore, the combination of simulation techniques, design of experiments, and response surface methodology was used to fit the relevant metamodel. In the proposed approach of this research, taking a realistic view of production line modeling, the probability of machinery failure, as well as the possibility of repairability and return to the system, were considered in the form of statistical distribution functions. Additionally, all time parameters, including the arrival time between the parts, the start-up time of all the machines, the processing time, the time between two failures, and the repair time of the machines, were non-deterministic and subject to statistical distributions. Finally, to solve the structured mathematical model, two meta-heuristic algorithms (NSGA-II) and (NRGA) were considered.
milad aghaee; Alireza Jazini
Abstract
Today, the geographical distribution of crime, particularly in Tehran, is so unpredictable that accurately predicting its location and timing has become impossible. Consequently, the utilization of proactive and retrospective models has become essential for the prevention police, ensuring their ability ...
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Today, the geographical distribution of crime, particularly in Tehran, is so unpredictable that accurately predicting its location and timing has become impossible. Consequently, the utilization of proactive and retrospective models has become essential for the prevention police, ensuring their ability to maintain maximum proactive and reactive operational power. In this context, logistics serves as a pivotal element supporting police missions and operations, thereby playing a significant role in enhancing the overall performance of the preventive police and establishing sustainable security. Given the nature and sensitivity of the prevention police mission, it is inevitable to encounter insecure factors that frequently manifest as sudden and unexpected disruptions. These disruptions can range from natural disasters, fires, and cyberattacks to economic shocks, illegal gatherings, violent crimes, and drug-related incidents. Consequently, there is a pressing need for the preventive police logistics system to embrace novel approaches and strategies in order to effectively adapt to the dynamic and varied nature of the police mission environment. Due to the nature and sensitivity of police organizations' missions, the utilization of logistics-based approaches that facilitate fast and agile movement, as well as logistics recovery during crises and disruptions, is crucial for effective logistics management in these organizations. Therefore, the objective of this research is to propose a hybrid model for agile and resilient logistics in police organizations. MethodologyThis research is applied-developmental and descriptive-analytical in nature. The research population consists of managers, expert commanders of the police force in operational command, expert university professors specializing in police logistics, and experienced logistics managers with at least a bachelor's degree and a minimum of 15 years of management experience at high levels within the police and military support. Data collection is conducted through semi-structured interviews, with the input of 22 experts selected using a purposeful judgmental method. The thematic analysis method is employed for data analysis. Additionally, in this research, the reliability of the findings was confirmed through the method of audit by a referee. This involved sending the coding to a referee who possesses knowledge in the research subject. At each stage of implementing the thematic analysis method, the referee's opinions were obtained and incorporated. The Attride-Stirling methodology was employed, which encompassed the identification of basic, constructive, and comprehensive themes. To ensure internal validity, a combination of triangulation methods, member checks, paired checks, and bias elimination techniques were employed. Additionally, several strategies were implemented to safeguard accurate performance and ensure external validity, including collecting data from multiple information sources, consistently comparing data during analysis, preventing initial assumptions from influencing conclusions, and avoiding hasty judgments. FindingsThe research findings, in terms of model design, reveal that the police agile and resilient logistics model encompasses 178 indicators, 30 components, and 6 fundamental dimensions, which include organization and management, technology, logistics measures, supply and distribution, agility capabilities, and capabilities. A total of 89 basic themes related to agility and 58 basic themes related to resilience were extracted from the conducted interviews and analyzed using the thematic analysis method. These findings led to the identification of six main dimensions in the police agile and resilient logistics model: organization and management, technology, logistics measures, supply and distribution, agility capabilities, and resilience capabilities. ConclusionWe are currently residing in an era where organizational managers, particularly logistics managers in police organizations, strive to deliver timely and suitable logistics services to fulfill their requirements. This enables them to consistently enhance their performance and improve customer satisfaction within their operational units. In this context, agility and resilience approaches emerge as crucial strategies that can play a highly functional role in empowering logistics as an effective tool towards achieving this objective. The integrated approach of the agile and resilient logistics model, as intended in this research, highlights the overlap between the two concepts of agility and resilience. Although agility has a historical precedence over resilience, the theoretical foundations of these two approaches demonstrate common ground. In fact, resilience, as an approach, considers a return to the initial state or an even better state, which is not achievable without taking into account the elements of agility. The hybrid model of agile and resilient logistics, based on the research findings, combines managerial and technical elements. It not only encompasses indicators from theoretical foundations and previous research but also addresses indicators specific to the operational environment of the police, particularly their activities within cities. Providing logistics services to police operations within urban areas, especially in large cities with distinct traffic patterns, diverse populations, various types of crimes, and ethnic and cultural characteristics, poses significant logistical challenges for police organizations.
supply chain management
fateme khanzadi; Reza Radfar; nazanini pilevari salmasi
Abstract
Nowdays, supply chain specialists are looking for the integrated development of the supply chain model in order to increase the competitiveness, effectiveness and reduce the problems in the supply chain. They always seek to identify and develop this process so that they can cover more aspects of the ...
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Nowdays, supply chain specialists are looking for the integrated development of the supply chain model in order to increase the competitiveness, effectiveness and reduce the problems in the supply chain. They always seek to identify and develop this process so that they can cover more aspects of the chain. Adding effectiveness indicators along with LARG indicators and using the basics of the dynamic system to improve the efficiency of the supply chain is one of the innovations of this study. At first, by using research literature and studies, 12 headings of indicators were selected as LARG-E indicators. Then, with the Fuzzy Delphi method, the relationships and importance of each of these components were determined, and more important variables were modeled for further investigation. With using the concepts of dynamic systems, causal loops were drawn. Then, to check the function of the model, dynamic hypotheses were developed with the opinion of experts. In the next step, the flow diagram of the model and also the validation tests of the proposed model were done. Finally, by examining the outputs obtained from the proposed scenarios, it was found that most of variables have better behavior in LARG-E approach.IntroductionIn recent years, with the addition of various competitions in the world markets, many researches have been conducted to use new technologies and researches in order to improve the production process and increase the effectiveness of these competitions as much as possible (Mohghar et al., 2017). All the goals that work in this field increase the competitiveness of the organization. This competition is by reducing costs, being present in the market and satisfying the customer. To increase profits, protect the environment, keep the markets stable and meet the expectations of customers, organizations should be provided using the existing environments in a set of customers (Pisha et al., 2016). Use chain management requires the use of new facilities and improvements to previous findings such as lean, agile, resilience and green to increase speed and competitiveness, selection and decision-making to achieve the organization and maximum effectiveness.Today, supply chain specialists are looking for the integrated development of the supply chain model to increase the effectiveness and efficiency of the supply chain in order to increase competitiveness and reduce supply chain problems. In this case, there is a consensus among experts that there is no comprehensive model. All the mentioned cases make it inevitable to design a comprehensive and effective model for the supply chain. The verifiable issue is the conflicts and the non-alignment of all the indicators of the paradigms with each other. LARG paradigms, without considering the spirit of effectiveness in each supply chain, cannot fully protect it against continuous changes in the competitive market arena. A comprehensive model that pays particular attention to effectiveness while implementing LARG paradigms has not been examined in the literature review and the consensus of experts. Therefore, in this research, we are looking to design a comprehensive model in a LARG-effective manner so that the effect of various LARG-effective indicators on the performance of the supply chain can be investigated. The integration of LARG paradigms has been studied a lot so far, but its development is based on the concepts of innovation effectiveness of this research, and in this way, the dynamic system approach was used.Materials and MethodsTo formulate a LARG supply chain, first the framework, indicators and variables of each LARG paradigm were extracted from the research literature, then in order to develop them with effective concepts, the effective supply chain was studied. In order to implement the fuzzy Delphi approach, based on the effectiveness indicators extracted from the subject literature and LARG supply chain approaches, operational indicators were provided to the experts participating in the research in the form of a questionnaire via email and after initial coordination. After collecting the completed questionnaires, fuzzification operations, fuzzy averaging and then de-fuzzification were performed. The results were brought to the attention of the participants and they were asked to apply their desired changes according to the obtained results. This approach reached the saturation stage in the third round and there was no change in the opinions of the participants and the consensus of the panel experts was the final and trusted output of the Delphi method. Finally, according to these weights, 9 quantitative variables had the highest importance and were used for dynamic modeling. The simulation stage is done with the help of software and Nasim. According to the features of modeling based on system dynamics, this approach was chosen as the main research tool in this study because there are linear relationships between the variables and there are nested feedbacks between the variables of the subsystems, the importance of simultaneously improving the performance in different layers of the producer, supplier and distributor. Which is one of the goals of this research, with this approach, it can be a very suitable tool for decision-making by the senior managers of the organization.Discussion and ResultsOrganizations are trying to improve their competitiveness by adopting Lean, Resilient, Green and Agile strategies; But as it was said, the implementation of these paradigms, which sometimes have conflicting results, requires a new integration and index to align the goals. So far, many researches have been done by merging two or more paradigms, the combination of all 4 paradigms called LARG has greatly helped to improve the performance of supply chains, but in this research, in order to improve the conflicts between paradigms, a new concept of spiritual effectiveness was given to the supply chain. Understanding the dynamics of applying the above four strategies and their effectiveness was done using the dynamic systems approach. In this research, the indicators of the LARG supply chain were defined based on theoretical foundations and interviews with experts; then the effectiveness indicators were placed next to them. These indicators were implemented in the printing and ink industry. In this way, an effective LARG integrated system was defined; then, using a dynamic model, dynamic hypotheses were first defined and state and flow diagrams were drawn. After correctness of the model and validation of the model, two scenarios were examined for 8 important variables. After applying the scenarios, the performance of LARG and effective LARG was compared. By applying each scenario in the designed model, it was possible to check the effect of new indicators on the variables and their behavior.ConclusionsAs a result, if the components of the effective supply chain are properly integrated with the LARG concepts, they integrate the conflict that may exist between the LARG paradigms and play the role of synchronization and improvement as a ruler and standard. The effective management of the LARG supply chain may not be defined as an independent variable, but it is a result of variables and indicators that improved performance in most cases.
supply chain management
Ali Mahmoodirad; Ali Tahmasebi Notareki; Sadegh Niroomand
Abstract
The closed-loop supply chain is used in practice for several reasons. Firstly, returning products to the production cycle is important for its operation. Secondly, sustainability in the supply chain is a topic of interest for researchers, as the environmental impacts of industries are significant. In ...
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The closed-loop supply chain is used in practice for several reasons. Firstly, returning products to the production cycle is important for its operation. Secondly, sustainability in the supply chain is a topic of interest for researchers, as the environmental impacts of industries are significant. In this paper, a multi-objective integer fuzzy mathematical programming model is presented to design a sustainable closed-loop supply chain under uncertain conditions. The proposed model aims to maximize profit and social impacts, while minimizing gas emissions into the environment. Since decision makers face uncertainty and doubt, trapezoidal intuitionistic fuzzy numbers are employed to determine the parameter values in the model. To convert the objective functions and model constraints into crisp forms, the expected value and the intuitionistic credibility measure are developed for the objectives and constraints, respectively. Finally, an interactive fuzzy programming approach is utilized to solve the crisp multi-objective problem. Three numerical examples are designed and solved to validate the model and assess the efficiency of the proposed solution method.IntroductionSupply chain management encompasses techniques aimed at coordinating all aspects of the supply chain, from raw material procurement to product delivery or recovery, with the objective of minimizing total costs while addressing conflicts among chain partners. Once raw materials have traversed the forward chain and been transformed into products or services, they may require repair, transformation, or proper disposal, which occurs within the reverse chain. The integration of forward and reverse supply chain methods gives rise to a closed-loop supply chain.Today, one of the primary concerns for organizational managers in supply chain network design is the presence of uncertainties, such as disruptions and uncertain input parameters. Uncertainties can have adverse effects on supply chain performance and decision-making at various network levels, including tactical, strategic, and operational decisions. As probabilistic planning necessitates historical data, which may not always be available or accurate, the theory of fuzzy sets can serve as a suitable option for expressing ambiguity and lack of certainty in parameters. In recent years, environmental factors have received increasing attention. There has been a growing recognition of the importance of environmental effects and the need to incorporate these effects alongside traditional indicators in supply chain design. Environmental considerations are crucial not only for compliance with government regulations but also for improving the organization's social standing from the customers' perspective. Moreover, with the rise of global warming and the accumulation of waste (both renewable and non-renewable, as well as electronic waste and ozone-depleting gases), the importance of managing and controlling these factors has become even more prominent. Despite the significance of environmental issues, there remains a noticeable gap in the supply chain literature concerning the provision of mathematical models based on real-world conditions and efficient solution methods for this problem. This paper focuses on the design of a sustainable closed-loop logistics network that aims to maximize profitability and social factors while minimizing environmental factors. The proposed integrated network considers multi-product and multi-state customer demand under conditions of uncertainty. The significance of this research lies in simultaneously addressing economic, environmental, and social considerations in the modeling process, as previous studies have mostly focused on single or dual objectives. Another innovative aspect of this article is the consideration of parameters in the form of intuitive fuzzy numbers for the design of a sustainable supply chain network.Materials and MethodsIn this research, a comprehensive model addressing the problem of sustainable closed-loop supply chain under intuitionistic fuzzy uncertainty is selected through library studies and internet research. Subsequently, the model is transformed into a deterministic multi-objective model utilizing the intuitionistic credibility measure. Recognizing that decision makers face not only uncertainty but also doubts, trapezoidal intuitionistic fuzzy numbers are employed to determine parameter values within the proposed model. To convert the objective functions and model constraints into their crisp equivalents, the expected value and intuitionistic credibility measure are respectively developed for the objective functions and constraints.FindingsBased on the selected confidence levels and numerical examples, the following observations can be made: In numerical example 1, the first objective function demonstrated that the ABS, SO, and TH methods performed best, respectively. However, in the second objective function, the order shifted to SO, ABS, and TH. Interestingly, all three methods performed equally in the third objective function. The performance of the solution methods in numerical example 2 mirrored that of numerical example 1. Moving on to numerical example 3, the first objective function indicated that the SO, TH, and ABS methods were the most effective, respectively. The order remained similar in the second objective function, and once again, all three methods performed equally in the third objective function. These results indicate the relative superiority of the SO solution method compared to the other methods employed. Additionally, concerning the execution time of the solution methods, numerical examples 1 and 2 exhibited nearly equal execution times for the methods. However, in numerical example 3, the SO, TH, and ABS methods displayed the best performance in terms of execution time, respectively. These findings further emphasize the relative superiority of the SO solution method compared to the others in terms of execution time. It is worth noting that the execution time of each method alone increases significantly with the problem's dimensions across all numerical examples.ConclusionsThis paper presents a multi-objective fuzzy optimization model for the design problem of a sustainable closed-loop supply chain. The model takes into account the concept of sustainability and aims to maximize the income and minimize the costs of the entire supply chain, while also minimizing environmental effects and maximizing social effects. The parameters are considered uncertain and are represented by intuitionistic trapezoidal fuzzy numbers. To handle this uncertainty, the model is transformed into a deterministic multi-objective optimization model using the expected value definition and a chance constraint based on the size of intuitionistic. The obtained deterministic multi-objective model is then solved using the interactive fuzzy mathematical programming method.
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.
