{"title":"用区间数据研究制造企业的范围经济和成本效益","authors":"Elham Zaker Harofteh, Faranak Hosseinzadeh Saljooghi","doi":"10.1007/s44196-023-00340-4","DOIUrl":null,"url":null,"abstract":"Abstract The success requirement of managers’ progress, development and performance improvement lie in their attention to product variety and company effectiveness. Economies of scope (ES) examine the advantages of production or the services diversification of a company based on cost versus production by companies that produce the same products or services separately. Data Envelopment Analysis (DEA) is known as a suitable method for evaluating ES and cost effectiveness. DEA models are introduced with certain input and output costs, while many companies and manufacturing industries in different sectors of production and service provision may not have accurate information on available costs and outputs because of calculation errors, old information, and multiple repeated measurements. The estimation DEA for ES and cost effectiveness are sensitive to changes, also some parameters, such as cost and price, are fluctuated. Therefore, it is a requirement to focus on the interval DEA. Our most important goals in this article are: (1) we develop new DEA models to measure the ES and cost effectiveness of decision-making units (DMUs) under data uncertainty. These models will become non-linear and non-convex models; hence, (2) we identify an appropriate range for ES and cost effectiveness of DMUs from the optimistic and pessimistic viewpoints, allowing decision-makers can use the upper and lower limits or their combination depending on the optimistic and pessimistic viewpoints, (3) we apply our developed models to assess the ES and cost-effectiveness performance of 24 institutions, considering data uncertainties that may affect the quality and reliability of the results. (4) The proposed models’ features have been analyzed, and the impact of interval data on cost effectiveness and ES has been evaluated. The application description of the proposed models for determining ES and cost effectiveness shows that a company can exhibit economies of scope without necessarily being Cost Effectiveness.","PeriodicalId":54967,"journal":{"name":"International Journal of Computational Intelligence Systems","volume":"66 1","pages":"0"},"PeriodicalIF":2.9000,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the Economies of Scope and Cost Effectiveness in Manufacturing Companies with Interval Data\",\"authors\":\"Elham Zaker Harofteh, Faranak Hosseinzadeh Saljooghi\",\"doi\":\"10.1007/s44196-023-00340-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The success requirement of managers’ progress, development and performance improvement lie in their attention to product variety and company effectiveness. Economies of scope (ES) examine the advantages of production or the services diversification of a company based on cost versus production by companies that produce the same products or services separately. Data Envelopment Analysis (DEA) is known as a suitable method for evaluating ES and cost effectiveness. DEA models are introduced with certain input and output costs, while many companies and manufacturing industries in different sectors of production and service provision may not have accurate information on available costs and outputs because of calculation errors, old information, and multiple repeated measurements. The estimation DEA for ES and cost effectiveness are sensitive to changes, also some parameters, such as cost and price, are fluctuated. Therefore, it is a requirement to focus on the interval DEA. Our most important goals in this article are: (1) we develop new DEA models to measure the ES and cost effectiveness of decision-making units (DMUs) under data uncertainty. These models will become non-linear and non-convex models; hence, (2) we identify an appropriate range for ES and cost effectiveness of DMUs from the optimistic and pessimistic viewpoints, allowing decision-makers can use the upper and lower limits or their combination depending on the optimistic and pessimistic viewpoints, (3) we apply our developed models to assess the ES and cost-effectiveness performance of 24 institutions, considering data uncertainties that may affect the quality and reliability of the results. (4) The proposed models’ features have been analyzed, and the impact of interval data on cost effectiveness and ES has been evaluated. The application description of the proposed models for determining ES and cost effectiveness shows that a company can exhibit economies of scope without necessarily being Cost Effectiveness.\",\"PeriodicalId\":54967,\"journal\":{\"name\":\"International Journal of Computational Intelligence Systems\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2023-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computational Intelligence Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s44196-023-00340-4\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computational Intelligence Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s44196-023-00340-4","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Investigating the Economies of Scope and Cost Effectiveness in Manufacturing Companies with Interval Data
Abstract The success requirement of managers’ progress, development and performance improvement lie in their attention to product variety and company effectiveness. Economies of scope (ES) examine the advantages of production or the services diversification of a company based on cost versus production by companies that produce the same products or services separately. Data Envelopment Analysis (DEA) is known as a suitable method for evaluating ES and cost effectiveness. DEA models are introduced with certain input and output costs, while many companies and manufacturing industries in different sectors of production and service provision may not have accurate information on available costs and outputs because of calculation errors, old information, and multiple repeated measurements. The estimation DEA for ES and cost effectiveness are sensitive to changes, also some parameters, such as cost and price, are fluctuated. Therefore, it is a requirement to focus on the interval DEA. Our most important goals in this article are: (1) we develop new DEA models to measure the ES and cost effectiveness of decision-making units (DMUs) under data uncertainty. These models will become non-linear and non-convex models; hence, (2) we identify an appropriate range for ES and cost effectiveness of DMUs from the optimistic and pessimistic viewpoints, allowing decision-makers can use the upper and lower limits or their combination depending on the optimistic and pessimistic viewpoints, (3) we apply our developed models to assess the ES and cost-effectiveness performance of 24 institutions, considering data uncertainties that may affect the quality and reliability of the results. (4) The proposed models’ features have been analyzed, and the impact of interval data on cost effectiveness and ES has been evaluated. The application description of the proposed models for determining ES and cost effectiveness shows that a company can exhibit economies of scope without necessarily being Cost Effectiveness.
期刊介绍:
The International Journal of Computational Intelligence Systems publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics:
-Autonomous reasoning-
Bio-informatics-
Cloud computing-
Condition monitoring-
Data science-
Data mining-
Data visualization-
Decision support systems-
Fault diagnosis-
Intelligent information retrieval-
Human-machine interaction and interfaces-
Image processing-
Internet and networks-
Noise analysis-
Pattern recognition-
Prediction systems-
Power (nuclear) safety systems-
Process and system control-
Real-time systems-
Risk analysis and safety-related issues-
Robotics-
Signal and image processing-
IoT and smart environments-
Systems integration-
System control-
System modelling and optimization-
Telecommunications-
Time series prediction-
Warning systems-
Virtual reality-
Web intelligence-
Deep learning