Pub Date : 2020-09-11DOI: 10.1108/s0276-8976202020
Alan Markee
Book file PDF easily for everyone and every device. You can download and read online Applications of Management Science file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Applications of Management Science book. Happy reading Applications of Management Science Bookeveryone. Download file Free Book PDF Applications of Management Science at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The Complete PDF Book Library. It's free to register here to get Book file PDF Applications of Management Science.
{"title":"Applications of Management Science","authors":"Alan Markee","doi":"10.1108/s0276-8976202020","DOIUrl":"https://doi.org/10.1108/s0276-8976202020","url":null,"abstract":"Book file PDF easily for everyone and every device. You can download and read online Applications of Management Science file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Applications of Management Science book. Happy reading Applications of Management Science Bookeveryone. Download file Free Book PDF Applications of Management Science at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The Complete PDF Book Library. It's free to register here to get Book file PDF Applications of Management Science.","PeriodicalId":244858,"journal":{"name":"Applications of Management Science","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129630668","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}
The conceptual foundations, principles and mechanisms of territorial branding concerning the prospects of rural development in different countries are the subject of the study. The systematization and study of territorial branding problems and experience of the rural areas in Ukraine is the purpose of the paper. The main method of research was the study of the unique experience of individual rural communities. The methodology of the study foresaw the study of the prospects of rural development through the determining the role of territorial branding. Monitoring the potential of territorial branding for rural areas in Ukraine with using SWOT-analysis has shown the uniqueness of risks, limitations and prospects. It had been established that the conditions of neutralization of weaknesses and risks mean the combination of economic (primarily investment), cultural and political initiatives, where a significant role belongs to the effects of community self-organization. At the same time, the prospects are formed from the presence of unique institutional assets, natural, climatic and economic conditions, possible perception of this idea by the rural population which does not contradict the basic cultural values. The emphasis is placed on the fact that the realization of rural development in Ukraine as a national policy should take into account that Ukrainian rural communities remain "difficult", mostly depressed economies, where the level of economic activity is traditionally low and unemployment is high. At the same time, studying the experience of the effectiveness of territorial branding had allowed to generalize and to classify the factors of brand-forming idea in Ukraine. These factors are: 1) a unique institutional history; 2) landscape and recreational potential; 3) special economic behavior of local inhabitants; 4) investment attractiveness of the territory; 5) unique economic specialization of the territory; 6) tourism activity; 7) the role of local government. Significant socio-economic effect of these examples is fixed. The area of application of these results is the activity of local authorities at rural communities, non-governmental organizations and universities, regulatory policy in terms of decentralization.
{"title":"Territorial Branding as an Instrument for Competitiveness of Rural Development","authors":"O. V. Moroz, N. Karachyna, T. Vakar, A. Vitiuk","doi":"10.36941/ajis-2020-0052","DOIUrl":"https://doi.org/10.36941/ajis-2020-0052","url":null,"abstract":"The conceptual foundations, principles and mechanisms of territorial branding concerning the prospects of rural development in different countries are the subject of the study. The systematization and study of territorial branding problems and experience of the rural areas in Ukraine is the purpose of the paper. The main method of research was the study of the unique experience of individual rural communities. The methodology of the study foresaw the study of the prospects of rural development through the determining the role of territorial branding. Monitoring the potential of territorial branding for rural areas in Ukraine with using SWOT-analysis has shown the uniqueness of risks, limitations and prospects. It had been established that the conditions of neutralization of weaknesses and risks mean the combination of economic (primarily investment), cultural and political initiatives, where a significant role belongs to the effects of community self-organization. At the same time, the prospects are formed from the presence of unique institutional assets, natural, climatic and economic conditions, possible perception of this idea by the rural population which does not contradict the basic cultural values. The emphasis is placed on the fact that the realization of rural development in Ukraine as a national policy should take into account that Ukrainian rural communities remain \"difficult\", mostly depressed economies, where the level of economic activity is traditionally low and unemployment is high. At the same time, studying the experience of the effectiveness of territorial branding had allowed to generalize and to classify the factors of brand-forming idea in Ukraine. These factors are: 1) a unique institutional history; 2) landscape and recreational potential; 3) special economic behavior of local inhabitants; 4) investment attractiveness of the territory; 5) unique economic specialization of the territory; 6) tourism activity; 7) the role of local government. Significant socio-economic effect of these examples is fixed. The area of application of these results is the activity of local authorities at rural communities, non-governmental organizations and universities, regulatory policy in terms of decentralization.","PeriodicalId":244858,"journal":{"name":"Applications of Management Science","volume":"386 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121774429","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 : 2018-07-25DOI: 10.1108/S0276-897620180000019005
Bandar A. Alkhayyal, S. Gupta
Abstract This chapter studies the integration of quantitative and qualitative attributes of a particular issue in the strategic “designing” level of the reverse supply chain (RSC) process in a multicriteria decision-making environment. The study employs an analytical network process (ANP) to determine the performance indices of the collection centers derived through qualitative criteria from the remanufacturing facilities that are interested in buying used products. The evaluating criteria are comprised as a four-level hierarchy: the first level contains the objective of evaluating the collection centers, the second level involves the main evaluation criteria taken from the perspective of a remanufacturing facility, the third level contains the subcriteria under the main evaluation criteria, and the fourth level has the collection centers. ANP is presented herein as a matrix that comprises a list of all facets listed horizontally and vertically. This particular method is of value when key elements of a decision are difficult to quantify and contrast, and thus the identification of important facets and their incorporation into a linear physical programing (LPP) environment is of value. To determine the quality of end-of-life (EOL) products for transport from collection centers to remanufacturing facilities, a physical programming approach is adopted. Four criteria and their satisfaction are focused upon: (1) maximizing the total value of purchase; (2) minimizing the total cost of transportation; (3) minimizing the disposal cost; and (4) minimizing the purchase cost. A numerical example is considered in which three collection center locations are evaluated to identify the optimal collection center.
{"title":"A Linear Physical Programming Approach for Evaluating Collection Centers for End-of-Life Products","authors":"Bandar A. Alkhayyal, S. Gupta","doi":"10.1108/S0276-897620180000019005","DOIUrl":"https://doi.org/10.1108/S0276-897620180000019005","url":null,"abstract":"Abstract \u0000This chapter studies the integration of quantitative and qualitative attributes of a particular issue in the strategic “designing” level of the reverse supply chain (RSC) process in a multicriteria decision-making environment. The study employs an analytical network process (ANP) to determine the performance indices of the collection centers derived through qualitative criteria from the remanufacturing facilities that are interested in buying used products. The evaluating criteria are comprised as a four-level hierarchy: the first level contains the objective of evaluating the collection centers, the second level involves the main evaluation criteria taken from the perspective of a remanufacturing facility, the third level contains the subcriteria under the main evaluation criteria, and the fourth level has the collection centers. ANP is presented herein as a matrix that comprises a list of all facets listed horizontally and vertically. This particular method is of value when key elements of a decision are difficult to quantify and contrast, and thus the identification of important facets and their incorporation into a linear physical programing (LPP) environment is of value. To determine the quality of end-of-life (EOL) products for transport from collection centers to remanufacturing facilities, a physical programming approach is adopted. Four criteria and their satisfaction are focused upon: (1) maximizing the total value of purchase; (2) minimizing the total cost of transportation; (3) minimizing the disposal cost; and (4) minimizing the purchase cost. A numerical example is considered in which three collection center locations are evaluated to identify the optimal collection center.","PeriodicalId":244858,"journal":{"name":"Applications of Management Science","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116193599","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 : 2018-07-25DOI: 10.1108/S0276-897620180000019008
R. Klimberg, S. Ratick
Abstract During the past several decades, the decision-making process and the decision-makers’ role in it have changed dramatically. Because of this, the use of analytical tools, such as Excel, have become an essential component of most organizations. The analytical tools in Excel can provide today’s decision-maker with a competitive advantage. We will illustrate several powerful Excel tools that facilitate the decision support process.
{"title":"Decision Support Capabilities in Excel","authors":"R. Klimberg, S. Ratick","doi":"10.1108/S0276-897620180000019008","DOIUrl":"https://doi.org/10.1108/S0276-897620180000019008","url":null,"abstract":"Abstract \u0000During the past several decades, the decision-making process and the decision-makers’ role in it have changed dramatically. Because of this, the use of analytical tools, such as Excel, have become an essential component of most organizations. The analytical tools in Excel can provide today’s decision-maker with a competitive advantage. We will illustrate several powerful Excel tools that facilitate the decision support process.","PeriodicalId":244858,"journal":{"name":"Applications of Management Science","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134368946","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 : 2018-07-25DOI: 10.1108/S0276-897620180000019002
R. Malhotra, D. Malhotra
Abstract Real estate investment trusts (REITs) provide a mechanism through which investors can participate in the real estate market with liquidity and transparency. In this study, we benchmark the performance of 11 residential REITs for the period 2009–2013. The study tracks the performance of residential REITs through the economic crisis period. The data envelopment analysis (DEA) model uses well-performing units (efficiency of 1% or 100%) that are closest to the underperforming unit on the efficiency frontier as a “role model” (peer units) for the underperforming unit. In addition, the DEA model also calculates by how much a nonperforming unit should increase the output level or decrease the inputs level to be on the efficiency frontier (100%) (slack values). Thus, the DEA model identifies the underperforming units and the most feasible path to move to efficiency frontier. The DEA model identifies the peer units that are closely related to these units and calculates the value of the slack variables required to achieve the same efficiency level as their peers.
{"title":"An Empirical Analysis of the Performance of Residential Real Estate Investment Funds","authors":"R. Malhotra, D. Malhotra","doi":"10.1108/S0276-897620180000019002","DOIUrl":"https://doi.org/10.1108/S0276-897620180000019002","url":null,"abstract":"Abstract \u0000Real estate investment trusts (REITs) provide a mechanism through which investors can participate in the real estate market with liquidity and transparency. In this study, we benchmark the performance of 11 residential REITs for the period 2009–2013. The study tracks the performance of residential REITs through the economic crisis period. The data envelopment analysis (DEA) model uses well-performing units (efficiency of 1% or 100%) that are closest to the underperforming unit on the efficiency frontier as a “role model” (peer units) for the underperforming unit. In addition, the DEA model also calculates by how much a nonperforming unit should increase the output level or decrease the inputs level to be on the efficiency frontier (100%) (slack values). Thus, the DEA model identifies the underperforming units and the most feasible path to move to efficiency frontier. The DEA model identifies the peer units that are closely related to these units and calculates the value of the slack variables required to achieve the same efficiency level as their peers.","PeriodicalId":244858,"journal":{"name":"Applications of Management Science","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128474254","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 : 2018-07-25DOI: 10.1108/S0276-897620180000019003
R. Malhotra, D. Malhotra, Elizabeth Mariotz, Raymond Poteau
Abstract In this chapter, we evaluate the dollar amount spent on advertising relative to sales, profit margin, and growth rates to study the effectiveness of advertising in today’s retail environment, and whether it leads directly to higher sales and increased profits affording positive earnings for the investor. The study illustrates the use of data envelopment analysis (DEA) technique to benchmark 16 apparel firms to evaluate the effectiveness of their advertising dollars on the sales, profit margin, growth, return on assets (ROA), return on equity (ROE), and return on investment (ROI).
{"title":"Evaluating the Impact of Advertising on Sales and Profitability in The Apparel Industry","authors":"R. Malhotra, D. Malhotra, Elizabeth Mariotz, Raymond Poteau","doi":"10.1108/S0276-897620180000019003","DOIUrl":"https://doi.org/10.1108/S0276-897620180000019003","url":null,"abstract":"Abstract \u0000In this chapter, we evaluate the dollar amount spent on advertising relative to sales, profit margin, and growth rates to study the effectiveness of advertising in today’s retail environment, and whether it leads directly to higher sales and increased profits affording positive earnings for the investor. The study illustrates the use of data envelopment analysis (DEA) technique to benchmark 16 apparel firms to evaluate the effectiveness of their advertising dollars on the sales, profit margin, growth, return on assets (ROA), return on equity (ROE), and return on investment (ROI).","PeriodicalId":244858,"journal":{"name":"Applications of Management Science","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122524908","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 : 2018-07-25DOI: 10.1108/S0276-897620180000019004
K. Lawrence, D. R. Pai, Sheila M. Lawrence
Abstract This chapter develops a productivity analysis of the US telecommunications industry using a data envelopment analysis (DEA) approach. The study concerns itself with eight telecommunications companies. Output variables used are market price, return on equity, and debt equity ratio. The input variables are sales to profit, return on equity, and debt ratio to capital.
{"title":"Productivity in the US Telecommunications Industry: A DEA Approach","authors":"K. Lawrence, D. R. Pai, Sheila M. Lawrence","doi":"10.1108/S0276-897620180000019004","DOIUrl":"https://doi.org/10.1108/S0276-897620180000019004","url":null,"abstract":"Abstract \u0000This chapter develops a productivity analysis of the US telecommunications industry using a data envelopment analysis (DEA) approach. The study concerns itself with eight telecommunications companies. Output variables used are market price, return on equity, and debt equity ratio. The input variables are sales to profit, return on equity, and debt ratio to capital.","PeriodicalId":244858,"journal":{"name":"Applications of Management Science","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124399455","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 : 2018-07-25DOI: 10.1108/S0276-897620180000019001
Mauro Falasca, J. Kros
Abstract As the pressure to win and generate revenue and as the allegations of out-of-control spending continue to increase, there exists much interest in intercollegiate athletics. While researchers in the past have investigated specific issues related to athletics success, revenue generation, and graduation rates, no previous studies have attempted to evaluate these factors simultaneously. This chapter discusses the development of a data envelopment analysis (DEA) model aimed at measuring how efficient university athletic departments are in terms of the use of resources to achieve athletics success, generate revenue, and promote academic success and on-time graduation. Data from National Collegiate Athletic Association (NCAA) Division I Football Bowl Subdivision (FBS) universities are used to evaluate the relative efficiency of the institutions. The model identifies a series of “best-practice” universities which are used to calculate efficient target resource levels for inefficient institutions. The value of the proposed methodology to decision makers is discussed.
{"title":"Intercollegiate Athletics Efficiency: A Two-Stage DEA Approach","authors":"Mauro Falasca, J. Kros","doi":"10.1108/S0276-897620180000019001","DOIUrl":"https://doi.org/10.1108/S0276-897620180000019001","url":null,"abstract":"Abstract \u0000As the pressure to win and generate revenue and as the allegations of out-of-control spending continue to increase, there exists much interest in intercollegiate athletics. While researchers in the past have investigated specific issues related to athletics success, revenue generation, and graduation rates, no previous studies have attempted to evaluate these factors simultaneously. This chapter discusses the development of a data envelopment analysis (DEA) model aimed at measuring how efficient university athletic departments are in terms of the use of resources to achieve athletics success, generate revenue, and promote academic success and on-time graduation. Data from National Collegiate Athletic Association (NCAA) Division I Football Bowl Subdivision (FBS) universities are used to evaluate the relative efficiency of the institutions. The model identifies a series of “best-practice” universities which are used to calculate efficient target resource levels for inefficient institutions. The value of the proposed methodology to decision makers is discussed.","PeriodicalId":244858,"journal":{"name":"Applications of Management Science","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116401605","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 : 2018-07-25DOI: 10.1108/S0276-897620180000019014
Amit Mitra
Abstract Two-dimensional warranty policies exist for certain consumer products, such as automobiles. Here, warranty is specified in terms of the time since the sale of the product as well as mileage incurred during that period. Thus, at the time of purchasing the product, the manufacturer may offer a warranty of three years or 30,000 miles, whichever occurs first. Failures in the product within this specified period of time or mileage will be covered by the manufacturer. In this chapter, we consider the scenario of enterprise warranty programs, where customers are given the option of extending the original warranty. Thus, the buyer could be given an option to purchase a five year—50,000 mile warranty, whichever occurs first. Of course, the buyer will be expected to pay a premium to purchase this extended warranty. Such enterprise warranty programs are also found in other consumer durables, such as refrigerators, washers, dryers, and cooking ranges. This chapter explores determination of the decision variables, such as product price, warranty time, and usage limit under the original conditions and further, for the enterprise warranty, that is, the extended warranty time and extended usage limit, as well as the premium to be charged to the buyer who selects the extended warranty. Mathematical models are developed based on maximizing the expected unit profit by selecting an enterprise warranty program. Additionally, some other objectives are also considered based on the proportional increase in the expected unit profit due to the increased market share attained through the offering of an enterprise warranty program. Some results are obtained through consideration of various goal values of the chosen objectives.
{"title":"Enterprise Warranty Programs for Two-dimensional Policies with Multiple Objectives","authors":"Amit Mitra","doi":"10.1108/S0276-897620180000019014","DOIUrl":"https://doi.org/10.1108/S0276-897620180000019014","url":null,"abstract":"Abstract \u0000Two-dimensional warranty policies exist for certain consumer products, such as automobiles. Here, warranty is specified in terms of the time since the sale of the product as well as mileage incurred during that period. Thus, at the time of purchasing the product, the manufacturer may offer a warranty of three years or 30,000 miles, whichever occurs first. Failures in the product within this specified period of time or mileage will be covered by the manufacturer. \u0000 \u0000In this chapter, we consider the scenario of enterprise warranty programs, where customers are given the option of extending the original warranty. Thus, the buyer could be given an option to purchase a five year—50,000 mile warranty, whichever occurs first. Of course, the buyer will be expected to pay a premium to purchase this extended warranty. Such enterprise warranty programs are also found in other consumer durables, such as refrigerators, washers, dryers, and cooking ranges. \u0000 \u0000This chapter explores determination of the decision variables, such as product price, warranty time, and usage limit under the original conditions and further, for the enterprise warranty, that is, the extended warranty time and extended usage limit, as well as the premium to be charged to the buyer who selects the extended warranty. Mathematical models are developed based on maximizing the expected unit profit by selecting an enterprise warranty program. Additionally, some other objectives are also considered based on the proportional increase in the expected unit profit due to the increased market share attained through the offering of an enterprise warranty program. Some results are obtained through consideration of various goal values of the chosen objectives.","PeriodicalId":244858,"journal":{"name":"Applications of Management Science","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132588526","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}
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