Alireza Moumivand; Adel Azar; Abbas Toloie Eshlaghy
Abstract
Soft OR from pluralist paradigm has effective approaches to structure and improve problem situations with different stakeholders’ worldviews and conflict of interests. The approaches, structure and improve messy situations. In this study, we used Soft System Methodology (SSM) as a popular soft ...
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Soft OR from pluralist paradigm has effective approaches to structure and improve problem situations with different stakeholders’ worldviews and conflict of interests. The approaches, structure and improve messy situations. In this study, we used Soft System Methodology (SSM) as a popular soft approach at two levels. First, we applied Soft System Methodology process (SSMp) to plan the systemic intervention process in the employee promotion System of an oil and gas company. Then, Soft System Methodology content (SSMc) was used to investigate the content of the company's employee promotion system that caused employees’ dissatisfaction. By considering managers’ and employees’ worldviews, three root definitions were made. At this phase, fair condition to present different points of view was applied. So, employees with different organizational power levels expressed their opinions openly. Finally, stakeholders’ participation through the discussion helped to build an agreed model and a root definition, and agreed actions for addressing defects of the employee promotion system were proposed.
Mohammadtaghi Moharrami; Mohammad Kazem Sayadi; Meysam Rafei
Abstract
Nowadays, due to the pollution that businesses and various industries impose to the environment, the adoption of strategies and policies by governments to improve the environmental performance of the supply chain has received more attention. The green supply chain will have many benefits, such as saving ...
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Nowadays, due to the pollution that businesses and various industries impose to the environment, the adoption of strategies and policies by governments to improve the environmental performance of the supply chain has received more attention. The green supply chain will have many benefits, such as saving energy resources, reducing pollutants, and so on. Government intervention to develop these chains takes various forms, such as subsidies, taxes, licensing, and advertising. In this study, two manufacturers with green and non-green supply chains compete in a competitive market and sell their products through a joint retailer, and the government intervenes as a leader in the Stackelberg game. These chains are designed based on the selection of agent-based pricing and wholesale pricing methods in four different models. In these models, the government advertises for green products in the first and second models and imposes taxes on the producer of non-green products in the third and fourth models, seeking to maximize social welfare and improving the environment. In order to analyze and compare the models, the game theory approach was used. The results show that in general, government intervention improves the environmental situation and social welfare, and in the case of advertising has a better effect on the overall market trend and also on social welfare than the tax imposing strategy.IntroductionToday, with the rapid growth of industries worldwide, the environmental impact and ecological effects of products have become significant concerns. There is a growing awareness of the environmental consequences and associated risks to human health resulting from industrial activities. Consequently, research on green supply chain management has seen a significant increase. As public awareness about environmental issues continues to rise and concerns about the future of our planet intensify, customers are increasingly inclined to purchase environmentally friendly products. This shift in consumer behavior has prompted manufacturers and businesses to reassess their production processes and adapt to changing customer preferences and new government policies. The primary objective of this research is to investigate the role of government intervention in influencing the demand for green and non-green products through factor-oriented green and non-green supply chains. Additionally, the study aims to identify government policies that can facilitate the development and adoption of green products. The findings of this research can be utilized by governments to promote the use of environmentally friendly goods and enhance environmental protection efforts.Materials and methodsThe approach of this research involves modeling and analysis. The research considers multiple models, each consisting of two supply chains with two manufacturers and a common retailer. One manufacturer produces a green product (environmentally friendly), while the other produces a non-green product (not environmentally friendly). Throughout the research, all comparative models adhere to this structure, with the first supply chain focusing on the production of green products and the second supply chain delivering non-green products to customers. All the analyses conducted in this research are mathematically analyzed and utilize game theory to validate the model results and analyze them. Since the model results are mathematically proven, there is no need to collect real-world data. Instead, hypothetical data are used in the examples to illustrate the various aspects of the problem. In this research, all the models are designed based on the Stackelberg game, and the government takes the initiative in determining its objectives.ResultsIn order to compare the models and analyze the results, we first considered a fixed strategy (advertisement or taxation) for the government. This allowed us to investigate the effect of pricing type on profit, demand, and social welfare. We compared the first model with the second model and also compared the third and fourth models together. Furthermore, we compared the advertising strategy models with the taxation strategy models, examining each strategy within the supply chains. The results indicate that the second model generates the highest level of social welfare and benefits for society, while also resulting in the greatest profit for producers and retailers. Following that, the first model exhibits more social welfare compared to the third and fourth models. Additionally, the profit of the green product producer in the first model significantly surpasses that of the non-green product producer. This difference in profitability serves as an incentive for producers to transition to green product production. Although the profit disparity between producers in the third and fourth models is more substantial and encourages the greater promotion of green product production, it leads to lower satisfaction and well-being.ConclusionsThe results demonstrate the high sensitivity of producers' and retailers' profits to the pricing of their products. The product price is influenced by factors such as whether the supply chain is factor-oriented or wholesale, as well as the type of government intervention. When consumers make purchasing decisions, they consider not only the price but also other parameters, such as the environmental friendliness of the product. In other words, the choice of a product is determined by a set of conditions and is not solely dependent on price fluctuations. The pricing method, whether factor-oriented or wholesale, significantly impacts the profitability of supply chain members and has implications for social welfare and environmental improvement. Different types of government intervention, such as cultural initiatives or taxation, can also lead to changes in the results
perfomance management
Leila Parhizkar Miyandehi; Alireza Amirteimoori; Sohrab Kordrostami; Mansour Soufi
Abstract
Estimating the revenue efficiency of entities being evaluated is crucial as it provides valuable information about organizations, assuming that the output prices are known. This research introduces a new definition of optimal scale size (OSS) based on maximizing the average revenue efficiency (ARE). ...
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Estimating the revenue efficiency of entities being evaluated is crucial as it provides valuable information about organizations, assuming that the output prices are known. This research introduces a new definition of optimal scale size (OSS) based on maximizing the average revenue efficiency (ARE). Additionally, the ARE is defined for both convex and non-convex sets, independent of returns to scale and the assumption that the vector of input-output prices of units is uniform. Moreover, to address the presence of uncertain data in real-world applications, the introduced ARE model is extended to evaluate systems with random inputs and outputs, along with approaches for its calculation. Finally, the proposed method is applied in an experimental example, calculating the ARE for a dataset of postal areas in Iran.IntroductionThe concept of optimal scale size has been extensively studied in the field of data envelopment analysis. Cesaroni and Giovannola's research on non-convex FDH technology reveals that the optimal scale size is a point in the production possibility set that minimizes average cost efficiency. Average cost efficiency, a new measure combining scale and allocation efficiencies, provides a more accurate performance assessment compared to cost and scale efficiencies. When evaluating units with known output prices instead of input prices, assessing revenue efficiency can offer more valuable insights. This paper extends the research on cost evaluation to revenue evaluation. It introduces the concepts of average revenue efficiency and optimal scale size based on revenue maximization. The optimal scale size based on revenue maximization is defined as the point in the production possibility set that maximizes the average radial income for the unit under investigation. Average revenue efficiency serves as an evaluation measure of unit revenue, surpassing revenue and scale efficiencies in accuracy. The paper examines methods for calculating average revenue efficiency in both convex and non-convex technologies. It demonstrates that the average revenue efficiency model in convex technology with variable returns to scale is equivalent to the revenue model with constant returns to scale. Furthermore, the relationship between optimal scale size points based on revenue maximization and the most productive scale size is determined. Next, the paper presents the average revenue efficiency model for stochastic sets with the presence of stochastic data. An experimental example is used to calculate the average revenue efficiency and obtain the optimal scale size for a set of postal areas in Iran.Materials and MethodsThe study builds upon Cesaroni and Giovannola's method for calculating average cost efficiency and optimal scale size to develop models for average revenue efficiency and optimal scale size based on revenue. It also utilizes chance-constrained probabilistic models with a deterministic objective function in DEA literature to present average revenue efficiency for stochastic sets. The model is transformed from stochastic to deterministic and then converted into a linear model using the error structure method.Discussion and ResultsThis paper introduces average revenue efficiency and optimal revenue scale size, demonstrating the equivalence between the average revenue efficiency models in convex technology with variable returns to scale and those with constant returns to scale. It also presents the average revenue efficiency model for stochastic sets, enabling the calculation of average revenue efficiency and optimal revenue scale size for units with random inputs and outputs.ConclusionIn many real-world scenarios, particularly when output prices are known, evaluating revenue efficiency holds greater significance than cost efficiency. This study develops the concepts of average cost efficiency and optimal scale size for revenue evaluation, expanding upon the existing literature on data envelopment analysis. The paper demonstrates how average revenue efficiency can be calculated as a valuable and accurate measure of efficiency in convex and non-convex technologies, without making assumptions about returns to scale. By assuming the randomness of input and output variables and employing chance-constrained models, a quadratic deterministic model is presented to calculate average revenue efficiency. It is then transformed into a linear model assuming uncorrelated variables, enabling the determination of average revenue efficiency and optimal scale size based on revenue maximization for random units. The proposed models are applied to a real-world sample, evaluating the average revenue efficiency of twelve postal units. The results highlight the models' ability to provide a more accurate evaluation of revenue efficiency and identify the best revenue scale size as the reference for inefficient units.
Hamidreza Fallah Lajimi; Sara Majidi
Abstract
Supplier segmentation is considered one of the key activities in supplier relationship management for companies with multiple suppliers, which can serve as a competitive advantage. Supplier segmentation has garnered significant attention from researchers in recent decades. The aim of this study is to ...
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Supplier segmentation is considered one of the key activities in supplier relationship management for companies with multiple suppliers, which can serve as a competitive advantage. Supplier segmentation has garnered significant attention from researchers in recent decades. The aim of this study is to systematically review the research on supplier segmentation to determine the future trends in this field of research. To achieve this goal, a systematic literature review and co-citation network analysis are simultaneously employed. After defining the search protocol and criteria for article selection, a total of 48 articles were ultimately chosen. The selected articles were evaluated and analyzed in accordance with the steps of systematic literature review and co-citation network analysis methods. The results of the analysis indicate researchers' interest in portfolio approaches, decision-making techniques, and the two-dimensional model of profit and supply in supplier segmentation research. Through comprehensive examination and analysis of the research, future research trends were predicted, and it was determined that this field requires further investigation in supply chain paradigms, the impact of supplier segmentation on performance, and the analysis of overall supplier relationship management, of which supplier segmentation is an integral component.IntroductionManaging supplier relationships is a collection of activities related to the interaction between the buying company and its suppliers. It is one of the key factors in the success of organizations and holds great importance. Supplier relationship management helps organizations establish strong and effective relationships with their suppliers. By maintaining close and continuous communication with suppliers, an organization can facilitate collaboration and coordination in the supply chain and achieve better and more accurate outcomes. By establishing close relationships and mutual trust with suppliers, an organization can reduce risks associated with its suppliers and carry out critical plans during ongoing organizational operations. Supplier relationship management, by creating a strong and mutually beneficial partnership, enables collaboration and strategic partnerships between the organization and the supplier. This allows both parties to achieve greater efficiency, reduce costs, and implement joint improvements. With accurate and up-to-date information about suppliers, an organization can identify and manage potential risks, thus reducing unnecessary costs and achieving greater efficiency. In recent decades, the participation of suppliers in providing products and services that meet customer needs has increased. Therefore, managing relationships with suppliers plays a key and vital role in the supply chain. Companies adopt strategies to evaluate, select, and manage relationships with suppliers. Collaborating with a number of suppliers, each with their own competitive advantages, is certainly challenging without a systematic approach. In many studies in this field, evaluation, ranking, and selection of suppliers have been conducted. However, managing supplier relationships remains a major challenge for companies. Supplier segmentation plays a key role in enhancing the operational capabilities of a company in supply management, which in turn creates value and mutual benefits in supplier relationships. Examining existing research in this area can greatly contribute to understanding the importance of this subject and its role in the supply chain. However, a systematic review of the literature on supplier segmentation has not been conducted. This research aims to provide a comprehensive and systematic review of supplier segmentation in previous studies, which dates back to the 1980s. In addition to introducing and presenting models and approaches, this article examines the techniques used in supplier segmentation and analyzes future trends in this field.Materials and methodsThe present study provides a comprehensive analysis in the field of supplier segmentation by employing two research methods. This research examines systematic supplier segmentation using a combined approach of Systematic Literature Review (SLR) and Content Network Analysis (CNA). Secondly, the existing approaches, models, and techniques in supplier segmentation research are discussed and analyzed in detail. Finally, the prediction of future studies in this subject is analyzed and examined.Discussion and resultsIn the systematic review of literature, a total of 48 articles were selected for analysis. The publication trend of the articles has shown a further increase after 2007, with approximately 62% of the articles in this field being published after 2007. Currently, this upward trend continues. Existing approaches to supplier segmentation can be divided into three categories: Process approach, where supplier segmentation is based on key characteristics of customer segmentation. Portfolio approach, pioneered by Kralljic, which provides a comprehensive portfolio approach for purchasing and supplier segmentation. The variables of profit impact, supply risk, and the types of segments created are determined based on the portfolio approach. Collaborative approach, where the level of collaboration determines the type of relationship. Supplier segmentation is performed using any approach and a dimension-based model. The dimensions of Kralljic's model (supply risk and profit impact) are the basis for 14 studies in the field of supplier segmentation. Among the existing approaches, the portfolio approach and the portfolio-collaborative approach have received more attention from researchers. Kralljic's portfolio approach is still used as a reference and standard approach. In recent years, multi-attribute decision-making techniques (MADM) have been widely used by researchers for supplier segmentation. The popularity of these techniques may stem from the need for less data and more realistic results compared to statistical techniques. Initially, most articles in this field were conceptual and focused on the concept of segmentation. After 2005, this trend shifted, and more articles became practical. Between 2005 and 2012, research in this field used statistical techniques and clustering for supplier segmentation. From 2012 onwards, this trend changed, and most research is now conducted using multi-attribute decision-making methods. The reason for this shift can be attributed to the need for less data and more realistic results. In terms of dimensions used in segmentation, it can be said that multiple dimensions have been used in various studies, with most of them being used only once. Among these dimensions, the risk-profit impact and capability-willingness dimensions have the highest application in research. The risk-profit impact dimensions have been consistently used by researchers since their introduction in 1983. However, since 2012, with the introduction of the capability-willingness dimensions, these dimensions have gained more popularity, indicating a significant growing trend.ConclusionsThe overall findings indicate that in recent years, the trend of studies and research in supplier segmentation is increasing. This suggests that the importance of supplier segmentation in the overall performance of supply chains for companies and organizations is growing, and companies recognize that part of their success and competitive advantage lies in the supplier management domain, of which supplier segmentation is a part. Supplier segmentation is one of the key components of supplier relationship management, and implementing or improving supplier segmentation can lead to gaining an advantage over competitors, as resources are not wasted and are utilized in the right place. By examining the articles from the beginning to 2018, it was possible to provide an overview of the evolutionary stages of key concepts in supplier segmentation and to gain a clearer understanding of the current state of the subject, which can be beneficial for future research advancements. However, there are still significant research gaps that can be explored by researchers in the future.
Morteza Mohajer Bajgiran; Alireza Pooya; Zahra Naji Azimi; Somayeh fadaei
Abstract
Management and warehousing operations are essential parts of manufacturing and service organizations. Warehousing is a significant component of an organization's activities, which incurs high costs and deserves more attention from researchers in this field. The aim of this research is to investigate ...
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Management and warehousing operations are essential parts of manufacturing and service organizations. Warehousing is a significant component of an organization's activities, which incurs high costs and deserves more attention from researchers in this field. The aim of this research is to investigate the storage problem based on item clustering while considering all factors that affect the storage of bulky and varied products in a warehouse. The main objective of this research is to reduce transportation costs for collecting and delivering orders and to achieve more efficient use of storage space. The K-means technique is employed to solve the clustering problem, and the Generalized Allocation mathematical programming model is used to address the assignment of item categories to storage locations. This model is an integer programming model that aims to minimize transportation costs for collecting and delivering orders. This research provides a comprehensive approach to clustering and item allocation by identifying and considering effective indexes and utilizing the generalized allocation mathematical planning model to formulate and solve the problem optimally. Company managers can utilize this model to reduce their inventory costs. The innovation of this research lies in the use of clustering for the allocation of storage sites to warehouse items, followed by mathematical modeling. The proposed model was implemented at Mashhad Housebuilding Company, and several simulated problems were solved using GAMS software for validation.IntroductionThe issue of storage and warehousing is one of the main axes of industries and companies. If the warehouse is properly managed, the efficiency and productivity of the organization can be increased optimally. Warehousing is one of the main costly components in the organization's activities and deserves more attention from researchers in this field. Therefore, the main goal of this research is to reduce transportation costs during the collection and delivery of orders and more effective use of warehouse space. For this purpose, the warehouse of the Mashhad house-building factory was studied. The warehouse of the Mashhad house building factory incurs a lot of costs to collect the orders, which is the result of the improper arrangement of the warehouse. Therefore, in the current research, to achieve a suitable deployment plan and reduce storage costs, the objectives are: 1) to identify the influential criteria in the clustering of items and the model for assigning clusters to their storage location according to the studied warehouse and clustering of warehouse items, 2) to provide a model for improvement The arrangement of the group of items is followed by considering the identified criteria, parameters, and limitations.Materials and MethodsIn the present research, first, according to ABC analysis, the items are divided into three groups (A, B, and C) based on their importance in terms of storage volume and circulation. Group A items are selected for clustering, while for groups B and C, a virtual cluster is considered in the allocation problem. Influential indicators for item clustering in the warehouse were determined through content analysis. The relationship of these indicators with the problem model and their importance were identified using a questionnaire. Cummins cluster analysis was employed for item clustering. Subsequently, a generalized allocation mathematical programming model was utilized to allocate groups of items to their storage locations. This model considered limitations such as warehouse access space, interdependence between groups, demand volume, physical dimensions of items and storage locations, and crane movement during order delivery. The objective of the model was to minimize transportation costs during order collection and delivery. The problem addressed in this research is commonly known as the Warehouse Location Allocation Problem (SLAP).Discussion and ResultsIn this research, 20 clusters were obtained based on 15 indicators, resulting in a total of 154 goods items. ANOVA analysis was conducted on the obtained clusters to examine the impact of each factor on warehouse arrangement. The F statistic value indicated a significant difference among all clusters and indicators The cluster analysis results revealed that the first cluster comprised various types of footings, with the "level of activity" index scoring higher than other indices. This cluster ranked second in terms of this index. The second cluster consisted of roof products, with a higher score in the "need for quick access" index compared to other indicators. Additionally, it obtained the highest scores in the indicators of quick access requirement, demand level, consumption similarity, product activity level, and average item references in the second step, the allocation problem was formulated with two dimensions: the item cluster and its storage location. The first dimension encompassed group A items, including five clusters of frequently used items and 20 clusters resulting from the clustering process, as well as groups B and C inventory items, represented by one cluster consisting of all items from these groups and virtual items. The second dimension consisted of storage locations, with the entire storage space divided into equal areas and a total of 360 storage locations considered in the model to validate the model, the problem was solved in smaller dimensions, and the warehouse manager manually arranged the clusters in the same dimensions. The objective function value was calculated in this case and compared to the value obtained from the mathematical allocation model. The results demonstrated that the researcher's mathematical model achieved over 70% improvement compared to manual arrangement. It should be noted that the actual warehouse conditions were less efficient than the manual arrangement provided by the warehouse manager, as the items had already been clustered, and the warehouse supervisor arranged the problem manually using the data from the warehouse clustering.ConclusionsThe grouping of goods, based on the results obtained from cluster analysis, ensures that items are placed together according to important criteria such as demand, access requirements, and employee safety, among others. This arrangement creates clusters of related products, forming families of goods. This approach minimizes the search time for requested products and enables timely order fulfillment in this research, the area of each cluster was determined by considering the maximum inventory of each product. Additionally, a confidence factor was applied to account for cluster area, allowing for sufficient space in case of additional inventory. This approach ensures efficient search and timely delivery of requested products. It is recommended that if a new product is introduced to the factory's product lineup, managers should conduct cluster analysis to determine its appropriate group. If the area of that category exceeds the area considered in the present research, the allocation issue should be revisited to accommodate the new addition.
Shima Salehi; Mohammad Taghi Taghavifard; Ghanbar Abbaspour esfeden; a alirezaee
Abstract
The integration of supply chain decisions aims to reduce costs and delivery time for customers. However, uncertainty in supply chain parameters, particularly demand, can disrupt this integration. The increased interest in probabilistic planning and simulation models in supply chain modeling is a response ...
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The integration of supply chain decisions aims to reduce costs and delivery time for customers. However, uncertainty in supply chain parameters, particularly demand, can disrupt this integration. The increased interest in probabilistic planning and simulation models in supply chain modeling is a response to this demand uncertainty. Therefore, the main objective of this study was to develop a multi-level, multi-product, multi-period supply chain network model that considers conflicting objectives such as cost minimization, delivery time minimization, and system-wide reliability maximization. The supply chain network under investigation consisted of four levels or subsystems: suppliers, manufacturers, distributors, and retailers. In this study, it was assumed that demand follows a random probabilistic distribution function. Consequently, simulation techniques were employed to estimate costs, including shipping costs, lost sales costs, and other expenses. After developing the multi-objective model, various scenarios were created based on different perspectives of inventory levels, namely minimum inventory, maximum inventory, and average inventory level. For each scenario, objective-related values were estimated. Ultimately, based on the Pareto optimal solutions obtained for each case of the model, the Vickor decision-making method was used to rank the answers and select the best solution from the proposed model. The results indicated that the second scenario, considering the average inventory level, was identified as the optimal solution for the described model.IntroductionToday, supply chain management (SCM) encompasses the entire production planning process for the supply chain, from raw material suppliers to the final customer. This has become a focal point for numerous researchers. In most supply chain designs, the objective has been to transfer products from one layer to another in order to meet strategic, tactical, and operational demands while minimizing complications arising from interrelationships and uncertainties across the chain. These challenges have posed significant decision-making hurdles in the supply chain domain. Supply chains can be regarded as complex systems wherein various factors interact with each other, resulting in emergent properties. Designing a versatile supply chain to address conflicting and diverse objectives requires considering them simultaneously and striking a balance among different criteria. The dynamic and intricate nature of the supply chain introduces a high level of uncertainty, thereby impacting the decision-making process in supply chain planning and influencing overall network performance. Based on the aforementioned issues, the focus of investigation includes the following: The examined supply chain network comprises four levels or subsystems, namely suppliers, manufacturers, distributors, and retailers. Raw materials are sourced from suppliers and sent to production factories, where each product is manufactured using a specific combination of raw materials. The products are then transported from manufacturers to distribution centers, and subsequently forwarded to retailers. The market is divided into different regions, and customer demands are fulfilled through visits to the retailers. Demand is assumed to be random and follows a probability distribution pattern. Consequently, simulation techniques are employed to estimate costs, including transportation costs, lost sales costs, and other expenses. Scenarios are created based on different perspectives at each level, focusing on inventory levels (minimum, maximum, and average). For each scenario, the values associated with the investigated objectives are estimated.Materials and methods In this research, data collection involved the examination of relevant literature, including articles published in international journals, books, and treatises. Documentary studies were conducted to gather information. To analyze the collected data, simulation and multi-objective programming concepts and methods were employed. Minitab and ED software were utilized for statistical analysis and simulation purposes.ConclusionsConsidering that the model can be solved under different conditions, including the current situation and various scenarios, the answers obtained for each state are Pareto optimal. This means that it is not possible to determine a single best answer for each state of the model. Therefore, before comparing the scenarios with each other, the Pareto optimal answers for each scenario should be ranked to identify the best options. In this research, a model for designing the supply chain network was presented, taking into account demand randomness. To better understand the proposed model and demonstrate its practicality, numerical examples were examined and evaluated using different scenarios and the Lingo software. It is important to note that the developed model in this study is independent of the number of facilities at each level of the supply chain and the parameter values. Therefore, the general form of this model can be applied to any production environment that aligns with the patterns presented in this research. The proposed model initially employed the design of experiments to estimate the mathematical relationship related to the cost objective function. After developing the multi-objective model, the Lingo software was used to solve the sample problem and evaluate the results under different scenarios. Finally, based on the Victor decision-making method, the Pareto optimal solutions for each state of the model were used to rank the answers and determine the best mode for the proposed models. Based on the obtained results, the third option or the second scenario is suggested as the preferred choice for the described model, considering the index values associated with each option
Meisam Jafari Eskandari; Hani Emami-Solot
Abstract
In this research, a model for a sustainable closed-loop supply chain with economic, social and environmental considerations, along with the risk arising from uncertainty in parameters, is presented. Stochastic programming has been used for modeling this problem and also using the scale of value Exposure ...
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In this research, a model for a sustainable closed-loop supply chain with economic, social and environmental considerations, along with the risk arising from uncertainty in parameters, is presented. Stochastic programming has been used for modeling this problem and also using the scale of value Exposure to conditional risk is measured by risk. The aim of this model is to maximize network design benefits, reduce unemployment and increase job opportunities resulting from the construction of facilities and minimize the production of carbon produced through intranets, production centers, recycling, repair, re-production. Other goals include minimizing the risk posed by uncertainty in transportation costs and customer demand. In the end, in order to demonstrate the efficiency of the model, an example is solved with certainty and uncertainty with the risk measurement criterion, and the pareto optimal solutions are compared. Results show that, with increasing risk, the profit from the supply chain network has decreased and should be costlier to face the risk.IntroductionToday, the necessity and importance of corporate responsibility and the social impact of companies have led managers and planners to give special attention to these aspects in their organization's missions, visions, and strategies. Corporate social responsibility encompasses the influence of a company's activities on various social groups, including employee rights, workplace safety, favorable working conditions, and job creation, among others. Furthermore, the significance of environmental standards and organizations' efforts to reduce pollution and promote efficient waste management and recycling practices have become crucial for organizational success, considering legal requirements and customer expectations. In recent years, the integration of reverse logistics, social responsibility, and environmental objectives in supply chain management has gained increasing attention due to factors such as resource reduction, pollution mitigation, environmental pressures, customer demands, and transportation costs in a competitive market. This integration, known as the closed-loop supply chain network, aims to ensure sustainability. Additionally, risk management within the supply chain has become a vital concern for supply chain management, considering the uncertainties prevailing in the global economy and trends such as increased outsourcing and advancements in information technology. The growing interest in achieving sustainability as an effective strategy for addressing challenges in the global supply chain has led to extensive research in the field of sustainable closed-loop supply chain management. However, previous studies in this area have lacked a comprehensive measure for assessing risk. Therefore, it is essential to address this issue, which involves considering stability goals in a closed-loop supply chain alongside risk management in uncertain conditions. The necessity for such research is evident, given the complexity of global supply chains and the increased vulnerability and risk exposure faced by organizations.Materials and MethodsGiven the existing gaps in the literature and the presence of uncertainty in real-world data, a mathematical model was proposed to help decision-makers reduce risk by considering identified risks and utilizing a comprehensive and effective risk measurement scale. In the designed model and forward network, suppliers are responsible for procuring raw materials. The manufactured products are then delivered to the market's customers through distributor networks. In the reverse flow of products, returned items are categorized into two groups: separable and non-separable products, after collection and inspection. Products that can be disassembled are sent to separation centers where they are transformed into components. The components are further divided into recoverable and non-recoverable categories. Non-recoverable components are transferred to disposal centers for safe disposal, while recoverable components are sent to inspection, cleaning, and sorting centers. After inspection and cleaning, the products are classified into repairable, remanufacturable, and recyclable groups. In the remanufacturing process, reusable components, after inspection, cleaning, and sorting, are sent to factories based on the production center's capacity. They are then combined with other parts to create new products that reenter the distribution cycle. In the recycling process, separated recyclable components are transported to recycling centers for direct production of raw materials, based on the capacity of the recycling centers, after collection and inspection.Discussion and ResultsModel 1 represents the initial approach, where scenario analysis for future conditions is not utilized, and the average values of uncertain parameters are taken into account. On the other hand, Model 2 incorporates various scenarios of future conditions. It is a linear model that considers possible future conditions as well. Model 1 exhibits lower costs compared to Model 2. The predictability of this problem arises from the fact that the risk associated with future market conditions was largely disregarded in Model 1. However, in Model 2, the consideration of introduced triple conditions for possible future outcomes necessitates a higher cost. Nevertheless, this higher cost brings us closer to real-world approximation and facilitates better decision-making in supply chain management when confronted with risks.ConclusionIn this article, we conducted a literature review on the topic of risk models in supply chains and identified existing gaps. We found that most of the work in this field has certain weaknesses. Firstly, the focus has primarily been on risks in conventional and single-objective supply chains, neglecting the consideration of new risks and uncertainties that may arise in sustainable supply chains. To address this, we proposed a model for risk management in sustainable closed-loop supply chains. Secondly, we noticed that most of the existing studies lack a suitable and effective scale for measuring risk, particularly in the design of sustainable closed-loop supply chains. Drawing from the financial literature, we introduced the CVaR scale to fill this gap. Lastly, we developed and analyzed a model based on research gaps, using a case study in the home appliance industry as an example. The examination of the model's results, along with comparisons to real-world outcomes and previous research, validates the credibility of the proposed model.
