管理规划:确定提高绩效的其他方法,实现最佳实践

IF 3.1 4区 管理学 Q2 MANAGEMENT
Juan F. Monge, José L. Ruiz
{"title":"管理规划:确定提高绩效的其他方法,实现最佳实践","authors":"Juan F. Monge, José L. Ruiz","doi":"10.1111/itor.13534","DOIUrl":null,"url":null,"abstract":"Planning is an important part of the management of organizations, which deals with the setting of targets that guide the actions needed to improve performance. Decision making is at the essence of planning, as it involves the identification of alternative directions for improvement and the selection of a future course of action. Decision makers (DMs) appreciate information on different ways of improving performance toward best practices, so they can make a choice of the plan that is more closely aligned with management in an ex post evaluation of possibilities. This paper responds to the need of providing DMs with a few (manageable) alternatives when planning improvements. The proposed approach is developed within the framework of data envelopment analysis (DEA). Although DEA has been used for planning, there is a gap in the literature in the sense that we can find only a few papers that have the explicit aim of identifying alternatives. In order to deal with this issue, we explore the whole DEA strong efficient frontier through all of its maximal efficient faces, which allows us to handle targets determined by a broad range of reference sets, and then use location theory tools, namely a <jats:italic>p</jats:italic>‐median problem, for the selection of <jats:italic>p</jats:italic> representatives that define alternative directions for improvement. Eventually, DMs are provided with a decision‐support tool for planning improvements by learning from the best practices of others, without requiring prior information on preferences.","PeriodicalId":49176,"journal":{"name":"International Transactions in Operational Research","volume":"74 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Planning in management: identifying alternative ways of improving performance toward best practices\",\"authors\":\"Juan F. Monge, José L. Ruiz\",\"doi\":\"10.1111/itor.13534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Planning is an important part of the management of organizations, which deals with the setting of targets that guide the actions needed to improve performance. Decision making is at the essence of planning, as it involves the identification of alternative directions for improvement and the selection of a future course of action. Decision makers (DMs) appreciate information on different ways of improving performance toward best practices, so they can make a choice of the plan that is more closely aligned with management in an ex post evaluation of possibilities. This paper responds to the need of providing DMs with a few (manageable) alternatives when planning improvements. The proposed approach is developed within the framework of data envelopment analysis (DEA). Although DEA has been used for planning, there is a gap in the literature in the sense that we can find only a few papers that have the explicit aim of identifying alternatives. In order to deal with this issue, we explore the whole DEA strong efficient frontier through all of its maximal efficient faces, which allows us to handle targets determined by a broad range of reference sets, and then use location theory tools, namely a <jats:italic>p</jats:italic>‐median problem, for the selection of <jats:italic>p</jats:italic> representatives that define alternative directions for improvement. Eventually, DMs are provided with a decision‐support tool for planning improvements by learning from the best practices of others, without requiring prior information on preferences.\",\"PeriodicalId\":49176,\"journal\":{\"name\":\"International Transactions in Operational Research\",\"volume\":\"74 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Transactions in Operational Research\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1111/itor.13534\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Transactions in Operational Research","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1111/itor.13534","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0

摘要

规划是组织管理的一个重要组成部分,它涉及设定目标,以指导提高绩效所需的行动。决策是规划的精髓所在,因为它涉及确定其他改进方向和选择未来的行动方案。决策制定者(DMs)需要了解有关提高绩效的最佳做法的各种方法的信息,以便在事后评估各种可能性时,选择与管理层更密切相关的计划。本文旨在满足管理者在制定改进计划时提供一些(可管理的)备选方案的需求。本文提出的方法是在数据包络分析(DEA)框架内开发的。尽管数据包络分析已被用于规划,但文献中仍存在空白,因为我们只能找到几篇明确以确定替代方案为目标的论文。为了解决这个问题,我们探索了整个 DEA 强有效边界的所有最大有效面,这使我们能够处理由广泛的参考集决定的目标,然后使用位置理论工具,即 p 中值问题,来选择 p 个代表,这些代表定义了可供选择的改进方向。最终,我们为管理者提供了一个决策支持工具,让他们通过学习他人的最佳实践来规划改进工作,而无需事先了解偏好信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Planning in management: identifying alternative ways of improving performance toward best practices
Planning is an important part of the management of organizations, which deals with the setting of targets that guide the actions needed to improve performance. Decision making is at the essence of planning, as it involves the identification of alternative directions for improvement and the selection of a future course of action. Decision makers (DMs) appreciate information on different ways of improving performance toward best practices, so they can make a choice of the plan that is more closely aligned with management in an ex post evaluation of possibilities. This paper responds to the need of providing DMs with a few (manageable) alternatives when planning improvements. The proposed approach is developed within the framework of data envelopment analysis (DEA). Although DEA has been used for planning, there is a gap in the literature in the sense that we can find only a few papers that have the explicit aim of identifying alternatives. In order to deal with this issue, we explore the whole DEA strong efficient frontier through all of its maximal efficient faces, which allows us to handle targets determined by a broad range of reference sets, and then use location theory tools, namely a p‐median problem, for the selection of p representatives that define alternative directions for improvement. Eventually, DMs are provided with a decision‐support tool for planning improvements by learning from the best practices of others, without requiring prior information on preferences.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Transactions in Operational Research
International Transactions in Operational Research OPERATIONS RESEARCH & MANAGEMENT SCIENCE-
CiteScore
7.80
自引率
12.90%
发文量
146
审稿时长
>12 weeks
期刊介绍: International Transactions in Operational Research (ITOR) aims to advance the understanding and practice of Operational Research (OR) and Management Science internationally. Its scope includes: International problems, such as those of fisheries management, environmental issues, and global competitiveness International work done by major OR figures Studies of worldwide interest from nations with emerging OR communities National or regional OR work which has the potential for application in other nations Technical developments of international interest Specific organizational examples that can be applied in other countries National and international presentations of transnational interest Broadly relevant professional issues, such as those of ethics and practice Applications relevant to global industries, such as operations management, manufacturing, and logistics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信