Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.6.002
Ahmet Bahadır Şimşek, Zekiye Göktekin, Büşra Geliç
The current research addresses the inventory classification problem of community pharmacies, which have a dual role as both a vital component of the pharmaceutical supply chain and a typical retail store. Despite the existing literature indicating that pharmacists may lack knowledge on inventory management, it seems that the MCIC literature is weak in explaining how pharmacists can benefit from MCDA techniques in all aspects. To bridge this gap, the study aims to demonstrate that pharmacists can utilize MCDA techniques to gain deeper insights beyond mere classification in the context of inventory management. Real-world data from a community pharmacy in Turkey was classified using the EDAS method. Sensitivity analysis was performed for MCDA inputs, about which pharmacists may lack information. Scenario findings based on criterion weights and threshold values offer important managerial implications for pharmacists. This study provides a critical contribution to the literature on inventory management in community pharmacies by highlighting the potential of MCDA techniques to support decision-making beyond mere classification. The sensitivity analysis also sheds light on areas where pharmacists may lack knowledge and suggests ways to address these gaps. Overall, the study underscores the need for pharmacists to have a deeper understanding of inventory management and highlights the potential benefits of MCDA techniques in addressing this challenge.
{"title":"Exploring managerial insights through multi criteria decision making techniques in pharmacy inventory classification problem","authors":"Ahmet Bahadır Şimşek, Zekiye Göktekin, Büşra Geliç","doi":"10.5267/j.dsl.2023.6.002","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.6.002","url":null,"abstract":"The current research addresses the inventory classification problem of community pharmacies, which have a dual role as both a vital component of the pharmaceutical supply chain and a typical retail store. Despite the existing literature indicating that pharmacists may lack knowledge on inventory management, it seems that the MCIC literature is weak in explaining how pharmacists can benefit from MCDA techniques in all aspects. To bridge this gap, the study aims to demonstrate that pharmacists can utilize MCDA techniques to gain deeper insights beyond mere classification in the context of inventory management. Real-world data from a community pharmacy in Turkey was classified using the EDAS method. Sensitivity analysis was performed for MCDA inputs, about which pharmacists may lack information. Scenario findings based on criterion weights and threshold values offer important managerial implications for pharmacists. This study provides a critical contribution to the literature on inventory management in community pharmacies by highlighting the potential of MCDA techniques to support decision-making beyond mere classification. The sensitivity analysis also sheds light on areas where pharmacists may lack knowledge and suggests ways to address these gaps. Overall, the study underscores the need for pharmacists to have a deeper understanding of inventory management and highlights the potential benefits of MCDA techniques in addressing this challenge.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2022.9.003
N. Ariyani, A. Fauzi, Farhat Umar
Tourist village program is one development priority program for rural development. Despite numerous opportunities to develop tourist villages such as the availability of natural resources and high demand for tourist villages recently, some challenges are still faced to develop tourist villages, especially in a developing country such as Indonesia. Governance problems, infrastructure, and effective partnership are among other factors that remain challenging in developing tourist villages. This study attempts to identify factors that determine the state of tourist villages in Indonesia and determine the appropriate strategies for better tourist village development. Using the case of tourist villages in Kedung Ombo, Central Java, a water based attractive tourist village, this study uses both machine learning and multicriteria approaches by means of Promethee in order to address the objective of the study. This study shows that government support, application of information technology, infrastructure, local participation, partnership, and attractive variations, are among the determinant factors that affect tourist village development. The study also reveals that the appropriate strategies for tourist village development include, improving infrastructure, institutional strengthening, and capacity building. This study could be used to assist local national as well as sub-national governments to effectively manage tourist villages in Indonesia.
{"title":"Predicting determinant factors and development strategy for tourist villages","authors":"N. Ariyani, A. Fauzi, Farhat Umar","doi":"10.5267/j.dsl.2022.9.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.9.003","url":null,"abstract":"Tourist village program is one development priority program for rural development. Despite numerous opportunities to develop tourist villages such as the availability of natural resources and high demand for tourist villages recently, some challenges are still faced to develop tourist villages, especially in a developing country such as Indonesia. Governance problems, infrastructure, and effective partnership are among other factors that remain challenging in developing tourist villages. This study attempts to identify factors that determine the state of tourist villages in Indonesia and determine the appropriate strategies for better tourist village development. Using the case of tourist villages in Kedung Ombo, Central Java, a water based attractive tourist village, this study uses both machine learning and multicriteria approaches by means of Promethee in order to address the objective of the study. This study shows that government support, application of information technology, infrastructure, local participation, partnership, and attractive variations, are among the determinant factors that affect tourist village development. The study also reveals that the appropriate strategies for tourist village development include, improving infrastructure, institutional strengthening, and capacity building. This study could be used to assist local national as well as sub-national governments to effectively manage tourist villages in Indonesia.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"1 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91038352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.1.004
Atıl Kurt, M. Azizoglu, F. C. Çetinkaya
This study considers an integrated inventory planning and distribution problem based on an applied case at the Turkish Red Crescent’s Central Anatolian Regional Blood Center. We define two echelons, the first echelon being the regional blood center and the second echelon being the districts. The blood products are perishable so that the outdated products are disposed of at the end of their lives. We aim to minimize the cost of inventory keeping at both echelons, the shortage, and disposal amounts at the second echelon. We consider two distribution strategies: all deliveries are realized by the regional blood center (current implementation), and the deliveries are directly from the regional blood center or the other districts. We develop a mixed-integer linear programming model for each strategy. Our experimental results show that the decentralized strategy brings significant cost reductions over the centralized strategy. The mathematical model for the centralized distribution strategy can handle large-sized instances. On the other hand, the model for the decentralized distribution strategy is more complex and could not handle large-sized instances in our pre-specified termination limit of two hours. For large-sized instances of the decentralized distribution strategy, we design a decomposition-based heuristic algorithm that benefits from the optimal solutions of the original model and finds near-optimal solutions very quickly.
{"title":"An integrated inventory and distribution planning problem for the blood products: An application for the Turkish Red Crescent","authors":"Atıl Kurt, M. Azizoglu, F. C. Çetinkaya","doi":"10.5267/j.dsl.2023.1.004","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.1.004","url":null,"abstract":"This study considers an integrated inventory planning and distribution problem based on an applied case at the Turkish Red Crescent’s Central Anatolian Regional Blood Center. We define two echelons, the first echelon being the regional blood center and the second echelon being the districts. The blood products are perishable so that the outdated products are disposed of at the end of their lives. We aim to minimize the cost of inventory keeping at both echelons, the shortage, and disposal amounts at the second echelon. We consider two distribution strategies: all deliveries are realized by the regional blood center (current implementation), and the deliveries are directly from the regional blood center or the other districts. We develop a mixed-integer linear programming model for each strategy. Our experimental results show that the decentralized strategy brings significant cost reductions over the centralized strategy. The mathematical model for the centralized distribution strategy can handle large-sized instances. On the other hand, the model for the decentralized distribution strategy is more complex and could not handle large-sized instances in our pre-specified termination limit of two hours. For large-sized instances of the decentralized distribution strategy, we design a decomposition-based heuristic algorithm that benefits from the optimal solutions of the original model and finds near-optimal solutions very quickly.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"110 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87699240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2022.11.003
A. Mohammadi
E-commerce is one of the industries most affected by big data, from collection to analytics in the highly competitive market. Previous research on big data analytics in E-commerce focused only on particular applications, and there is still a gap in presenting a framework to evaluate big data applications from a challenges-values point of view. This study employs a three-phase methodology to evaluate big data applications in E-commerce with respect to big data challenges and values using a hybrid multi-criteria decision-making technique that combines BWM and fuzzy TOPSIS. The results showed process challenge and the strategic value obtained the highest weight for challenges and values criteria. Financial fraud detection is relatively the most challenging, and online review analytics is the most valuable application of big data in E-commerce among identified applications. Evaluating big data applications based on cost and benefit criteria is practical for E-commerce managers and experts to make decisions on implementation priorities to overcome the challenges and make the most of values. Moreover, the proposed approach is not only limited to big data analytics in E-commerce and can also be applied in other industries to evaluate emerging technology applications.
{"title":"Analytical evaluation of big data applications in E-commerce: A mixed method approach","authors":"A. Mohammadi","doi":"10.5267/j.dsl.2022.11.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.11.003","url":null,"abstract":"E-commerce is one of the industries most affected by big data, from collection to analytics in the highly competitive market. Previous research on big data analytics in E-commerce focused only on particular applications, and there is still a gap in presenting a framework to evaluate big data applications from a challenges-values point of view. This study employs a three-phase methodology to evaluate big data applications in E-commerce with respect to big data challenges and values using a hybrid multi-criteria decision-making technique that combines BWM and fuzzy TOPSIS. The results showed process challenge and the strategic value obtained the highest weight for challenges and values criteria. Financial fraud detection is relatively the most challenging, and online review analytics is the most valuable application of big data in E-commerce among identified applications. Evaluating big data applications based on cost and benefit criteria is practical for E-commerce managers and experts to make decisions on implementation priorities to overcome the challenges and make the most of values. Moreover, the proposed approach is not only limited to big data analytics in E-commerce and can also be applied in other industries to evaluate emerging technology applications.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"52 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73569872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.4.009
Garima Bisht, A. Pal
Uncertainty is an important factor in the decision-making process. Hesitant Pythagorean fuzzy sets (HPFS), a combination of Pythagorean and hesitant fuzzy sets, proved as a significant tool to handle uncertainty. Well-defined operational laws and attribute weights play an important role in decision-making. Thus, the paper aims to develop new Trigonometric Operational Laws, a weight determination method, and a novel score function for group decision-making (GDM) problems in the HPF environment. The approach is presented in three phases. The first phase defines new operational laws with sine trigonometric function incorporating its special properties like periodicity, symmetricity, and restricted range hence compared with previously defined aggregation operators they are more likely to satisfy the decision maker preferences. Properties of trigonometric operational laws (TOL) are studied and various aggregation operators are defined. To measure the relationship between arguments, the operators are combined with the Generalized Heronian Mean operator. The flexibility of operators is increased by the use of a real parameter λ to express the risk preference of experts. The second phase defines a novel weight determination method, which separately considers the membership and non-membership degrees hence reducing the information loss and the third phase conquers the shortcomings of previously defined score functions by defining a novel score function in HPFS. To further increase the flexibility of defined operators they are extended in the environment with unknown or incomplete attribute weights. The effectiveness of the GDM model is verified with the help of a supplier selection problem. A detailed comparative analysis demonstrates the superiority, and sensitivity analysis verifies the stability of the proposed approach.
{"title":"A novel multi-criteria group decision-making approach using aggregation operators and weight determination method for supplier selection problem in hesitant Pythagorean fuzzy environment","authors":"Garima Bisht, A. Pal","doi":"10.5267/j.dsl.2023.4.009","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.4.009","url":null,"abstract":"Uncertainty is an important factor in the decision-making process. Hesitant Pythagorean fuzzy sets (HPFS), a combination of Pythagorean and hesitant fuzzy sets, proved as a significant tool to handle uncertainty. Well-defined operational laws and attribute weights play an important role in decision-making. Thus, the paper aims to develop new Trigonometric Operational Laws, a weight determination method, and a novel score function for group decision-making (GDM) problems in the HPF environment. The approach is presented in three phases. The first phase defines new operational laws with sine trigonometric function incorporating its special properties like periodicity, symmetricity, and restricted range hence compared with previously defined aggregation operators they are more likely to satisfy the decision maker preferences. Properties of trigonometric operational laws (TOL) are studied and various aggregation operators are defined. To measure the relationship between arguments, the operators are combined with the Generalized Heronian Mean operator. The flexibility of operators is increased by the use of a real parameter λ to express the risk preference of experts. The second phase defines a novel weight determination method, which separately considers the membership and non-membership degrees hence reducing the information loss and the third phase conquers the shortcomings of previously defined score functions by defining a novel score function in HPFS. To further increase the flexibility of defined operators they are extended in the environment with unknown or incomplete attribute weights. The effectiveness of the GDM model is verified with the help of a supplier selection problem. A detailed comparative analysis demonstrates the superiority, and sensitivity analysis verifies the stability of the proposed approach.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"195 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74723845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.1.009
Awni Rawashdeh, Layla Abaalkhail, Mashael Bakhit
This paper examines the role of vision as a mediating variable of the relationship between organizational factors and IoT adoption in audit firms in the US. Using a combination of analyses based on structural equation modeling (SEM) and artificial neural network (ANN) technology as the primary research methodology. Seven hypotheses were accepted, including one related to the impact of vision on IoT adoption. In general, all accepted hypotheses had a positive effect on IoT adoption. In addition to the direct positive impact of vision on IoT technology adoption, the magnitude of that effect varied depending on the context of each hypothesis. Drawing evidence from the results, this study demonstrates that vision was a partial mediating variable in the relationship between the organizational factor and IoT adoption. As a result, the model can help audit firms adopt IoT technology successfully. On the other hand, it makes essential recommendations for implementing IoT technology in light of the role that vision plays as a mediating variable in this model. The Technology-Organization-Environment (TOE) framework and Diffusion of Innovation theory (DOI) are combined with the vision to improve model predictive power.
{"title":"A two-stage SEM-artificial neural network analysis of the organizational effects of Internet of things adoption in auditing firms","authors":"Awni Rawashdeh, Layla Abaalkhail, Mashael Bakhit","doi":"10.5267/j.dsl.2023.1.009","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.1.009","url":null,"abstract":"This paper examines the role of vision as a mediating variable of the relationship between organizational factors and IoT adoption in audit firms in the US. Using a combination of analyses based on structural equation modeling (SEM) and artificial neural network (ANN) technology as the primary research methodology. Seven hypotheses were accepted, including one related to the impact of vision on IoT adoption. In general, all accepted hypotheses had a positive effect on IoT adoption. In addition to the direct positive impact of vision on IoT technology adoption, the magnitude of that effect varied depending on the context of each hypothesis. Drawing evidence from the results, this study demonstrates that vision was a partial mediating variable in the relationship between the organizational factor and IoT adoption. As a result, the model can help audit firms adopt IoT technology successfully. On the other hand, it makes essential recommendations for implementing IoT technology in light of the role that vision plays as a mediating variable in this model. The Technology-Organization-Environment (TOE) framework and Diffusion of Innovation theory (DOI) are combined with the vision to improve model predictive power.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"45 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74125969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2023.5.002
Hartayuni Sain, H. Kuswanto, S. W. Purnami, S. Rahayu
Support vector machine (SVM) has become one of most developed methods for classification, focusing on cross-sectional analysis. However, classification of time series data is an important issue in statistics and data mining. Classification of time series data using SVMs that focus on cross-sectional data leads to improper classification, and hence, the SVM needs to be extended for handling time series dataset. As with cross-section data, the problem of imbalanced data is also common in time series data. Fuzzy method has been proven to be capable of overcoming the case of imbalanced data. In this paper, we developed a Fuzzy Support Vector Machine (FSVM) model to classify time series data with imbalanced class. The proposed method puts the fuzzy membership function on the constraint function. Through simulation studies, this research aims to assess the performance of the developed FSVM in classifying time series data. Based on the classification accuracy criteria, we prove that the proposed FSVM method outperforms the standard SVM method for the classification of multiclass time series data.
{"title":"Fuzzy support vector machine for classification of time series data: A simulation study","authors":"Hartayuni Sain, H. Kuswanto, S. W. Purnami, S. Rahayu","doi":"10.5267/j.dsl.2023.5.002","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.5.002","url":null,"abstract":"Support vector machine (SVM) has become one of most developed methods for classification, focusing on cross-sectional analysis. However, classification of time series data is an important issue in statistics and data mining. Classification of time series data using SVMs that focus on cross-sectional data leads to improper classification, and hence, the SVM needs to be extended for handling time series dataset. As with cross-section data, the problem of imbalanced data is also common in time series data. Fuzzy method has been proven to be capable of overcoming the case of imbalanced data. In this paper, we developed a Fuzzy Support Vector Machine (FSVM) model to classify time series data with imbalanced class. The proposed method puts the fuzzy membership function on the constraint function. Through simulation studies, this research aims to assess the performance of the developed FSVM in classifying time series data. Based on the classification accuracy criteria, we prove that the proposed FSVM method outperforms the standard SVM method for the classification of multiclass time series data.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"41 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84974419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study aims to identify and evaluate the influence of the factors affecting the implementation of occupational health and safety of employees and employers and its impact on occupational health and safety and legalize risk assessment in small and medium-sized manufacturing enterprises in central Vietnam through the survey among 246 business representatives and data processing through the software SPSS 20 and AMOS 20. The results show that there are 3 factors affecting the implementation of occupational health, safety and hygiene. In order: (1) Safety regulations and instructions; (2) Occupational health, safety policy and (3) Occupational health and safety training. There is no relationship between the impacts on the implementation of occupational health and safety of the employer. In addition, a very interesting finding about the relationship of factors implementing occupational health and safety of employers was a positive influence on the legalization of risk assessment activities. On the basis of these results, employees, business owners and state management agencies will have grounds to offer useful solutions in risk assessment in order to better perform safety work and occupational health.
{"title":"Risk assessment, implementation of occupational health, safety and hygiene in small and medium manufacturing enterprises: A case study in central Vietnam","authors":"Gia-Kien Vu, Tuan-Anh Le, Toan-Pham Ngoc, Tram-Huyen Thi Nguyen, Minh-Quan Ha-Nguyen","doi":"10.5267/j.dsl.2023.7.002","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.7.002","url":null,"abstract":"This study aims to identify and evaluate the influence of the factors affecting the implementation of occupational health and safety of employees and employers and its impact on occupational health and safety and legalize risk assessment in small and medium-sized manufacturing enterprises in central Vietnam through the survey among 246 business representatives and data processing through the software SPSS 20 and AMOS 20. The results show that there are 3 factors affecting the implementation of occupational health, safety and hygiene. In order: (1) Safety regulations and instructions; (2) Occupational health, safety policy and (3) Occupational health and safety training. There is no relationship between the impacts on the implementation of occupational health and safety of the employer. In addition, a very interesting finding about the relationship of factors implementing occupational health and safety of employers was a positive influence on the legalization of risk assessment activities. On the basis of these results, employees, business owners and state management agencies will have grounds to offer useful solutions in risk assessment in order to better perform safety work and occupational health.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a capital market investment, stocks have risks that must be managed. Therefore, investors should consider the returns and risks of investment products. This study aims to estimate the risk of insurance companies' loss when investing. The method used to estimate the level of risk is Value at Risk (VaR) based on Extreme Value Theory (EVT). The data used is secondary data in the form of daily stock closing prices from two insurance companies, AXA General Insurance and BRI Insurance, from January 2016 to January 2022. The data were used to estimate the risk value according to the EVT principle. As a result, Insurance AXA General Insurance, with 5.91% liquidity, has the lowest VaR value with a 99% confidence level, while BRI Insurance has 5.04%. We concluded from these results that AXA General Insurance has a lower investment risk. It means that each company has a different risk value. Therefore, investors should know these risk factors when choosing a company.
作为一种资本市场投资，股票有风险，必须加以管理。因此，投资者应考虑投资产品的收益和风险。本研究旨在评估保险公司投资时的损失风险。评估风险水平的方法是基于极值理论(EVT)的风险值(VaR)。使用的数据是二级数据，以两家保险公司AXA General insurance和BRI insurance从2016年1月到2022年1月的每日股票收盘价形式。根据EVT原理对数据进行风险值估计。由此可见，流动性为5.91%的安盛保险的VaR值最低，置信水平为99%，而BRI保险的VaR值为5.04%。我们从这些结果中得出结论，安盛保险的投资风险较低。这意味着每个公司都有不同的风险价值。因此，投资者在选择公司时应该了解这些风险因素。
{"title":"Estimating the Value-at-Risk (VaR) in stock investment of insurance companies: An application of the extreme value theory","authors":"Riaman Riaman, Amarulla Octavian, Sudradjat Supian, Sukono Sukono, Jumadil Saputra","doi":"10.5267/j.dsl.2023.7.001","DOIUrl":"https://doi.org/10.5267/j.dsl.2023.7.001","url":null,"abstract":"As a capital market investment, stocks have risks that must be managed. Therefore, investors should consider the returns and risks of investment products. This study aims to estimate the risk of insurance companies' loss when investing. The method used to estimate the level of risk is Value at Risk (VaR) based on Extreme Value Theory (EVT). The data used is secondary data in the form of daily stock closing prices from two insurance companies, AXA General Insurance and BRI Insurance, from January 2016 to January 2022. The data were used to estimate the risk value according to the EVT principle. As a result, Insurance AXA General Insurance, with 5.91% liquidity, has the lowest VaR value with a 99% confidence level, while BRI Insurance has 5.04%. We concluded from these results that AXA General Insurance has a lower investment risk. It means that each company has a different risk value. Therefore, investors should know these risk factors when choosing a company.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.5267/j.dsl.2022.11.001
Francesco Zammori, Giovanni Romagnoli, Serena Filippelli
With Newsvendor Problem (NvP) we refer to a specific class of inventory management problems, valid for a single item with stochastic demand over a single period. In the standard version, the newsvendor is allowed to issue a single order, before he or she can observe the actual demand. Since the newsvendor can face both overage and underage costs, due to lost sales or residual stock, the objective is to define the optimal order size that maximizes the expected profit. In this paper, we consider a specific version of the NvP, in which the buyer has the opportunity to make a last and single order for opportunistic reasons. Specifically, we consider discontinued, collectible items, for which demand will not vanish and whose value might appreciate. Hence, the objective is to define the optimal quantity that should be purchased, just before the item is retired from the market or sold-out, and that should be sold as soon as the price rises over a predefined target level. An optimal solution, maximizing the expected profit, is obtained both in case of negligible and non-negligible stockholding costs. In the latter case, to obtain the optimal solution in implicit form, some simplifying assumptions are needed. Hence, a thorough numerical analysis is finally performed, as a way to empirically demonstrate both the robustness and the accuracy of the model, in several scenarios differentiated in terms of costs and customers’ demand.
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