東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Methods in productivity and efficien...

Johnson, Andrew.

FindBook

Google Book

Amazon

博客來

Methods in productivity and efficiency analysis with applications to warehousing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Methods in productivity and efficiency analysis with applications to warehousing./
作者:	Johnson, Andrew.
面頁冊數:	185 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-03, Section: B, page: 1632.
Contained By:	Dissertation Abstracts International67-03B.
標題:	Economics, General. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3212245
ISBN:	9780542607035

Methods in productivity and efficiency analysis with applications to warehousing.
Johnson, Andrew.

Methods in productivity and efficiency analysis with applications to warehousing. - 185 p.

Source: Dissertation Abstracts International, Volume: 67-03, Section: B, page: 1632.

Thesis (Ph.D.)--Georgia Institute of Technology, 2006.

A set of technical issues are addressed related to benchmarking best practice and performance in warehouses. In order to identify best practice, first performance needs to be measured. There are a variety of tools available to measure productivity and efficiency. One of the most common tools is data envelopment analysis (DEA), which assesses individual performance relative to a peer group. For a system that consumes inputs to generate outputs, previous work in production theory can be used to develop basic postulates about the production possibility space and to construct an efficient frontier which is used to quantify efficiency. Beyond inputs and outputs warehouses typically have practices (techniques used in the warehouse) or attributes (characteristics of the environment of the warehouse including demand characteristics) which also influence efficiency. Previously in the literature, a two-stage method has been developed to investigate the impact of practices and attributes on efficiency. When applying this method to two sets of warehouse data, two issues arose: how to measure efficiency in small samples and how to identify outliers. The small sample efficiency measurement method developed in this thesis is called multi-input/multi-output quantile based approach (MQBA) and uses deleted residuals to estimate efficiency. The outlier detection method developed in this thesis introduces the inefficient frontier. Both overly efficient and overly inefficient outliers can be identified by constructing an efficient and an inefficient frontier. The outlier detection method incorporates an iterative procedure previously described, but not implemented in the literature. Further, this thesis also discusses issues related to selecting an orientation in super efficiency models. Super efficiency models are used in outlier detection, but are also commonly used in measuring technical progress via the Malmquist index. These issues are addressed using two data sets recently collected in the warehousing industry. The first data set consists of 390 observations of various types of warehouses. The other data set has 25 observations from a specific industry. For both data sets, it is shown that significantly different results are realized if the methods suggested in this document are adopted.

ISBN: 9780542607035Subjects--Topical Terms:

1017424
Economics, General.

Methods in productivity and efficiency analysis with applications to warehousing.
LDR:03211nmm 2200277 4500 001 1833051
005 20070907105127.5
008 130610s2006 eng d
020 $a 9780542607035
035 $a (UMI)AAI3212245
035 $a AAI3212245
040 $a UMI $c UMI
100 1 $a Johnson, Andrew. $3 1921765
245 1 0 $a Methods in productivity and efficiency analysis with applications to warehousing.
300 $a 185 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-03, Section: B, page: 1632.
500 $a Adviser: Leon McGinnis.
502 $a Thesis (Ph.D.)--Georgia Institute of Technology, 2006.
520 $a A set of technical issues are addressed related to benchmarking best practice and performance in warehouses. In order to identify best practice, first performance needs to be measured. There are a variety of tools available to measure productivity and efficiency. One of the most common tools is data envelopment analysis (DEA), which assesses individual performance relative to a peer group. For a system that consumes inputs to generate outputs, previous work in production theory can be used to develop basic postulates about the production possibility space and to construct an efficient frontier which is used to quantify efficiency. Beyond inputs and outputs warehouses typically have practices (techniques used in the warehouse) or attributes (characteristics of the environment of the warehouse including demand characteristics) which also influence efficiency. Previously in the literature, a two-stage method has been developed to investigate the impact of practices and attributes on efficiency. When applying this method to two sets of warehouse data, two issues arose: how to measure efficiency in small samples and how to identify outliers. The small sample efficiency measurement method developed in this thesis is called multi-input/multi-output quantile based approach (MQBA) and uses deleted residuals to estimate efficiency. The outlier detection method developed in this thesis introduces the inefficient frontier. Both overly efficient and overly inefficient outliers can be identified by constructing an efficient and an inefficient frontier. The outlier detection method incorporates an iterative procedure previously described, but not implemented in the literature. Further, this thesis also discusses issues related to selecting an orientation in super efficiency models. Super efficiency models are used in outlier detection, but are also commonly used in measuring technical progress via the Malmquist index. These issues are addressed using two data sets recently collected in the warehousing industry. The first data set consists of 390 observations of various types of warehouses. The other data set has 25 observations from a specific industry. For both data sets, it is shown that significantly different results are realized if the methods suggested in this document are adopted.
590 $a School code: 0078.
650 4 $a Economics, General. $3 1017424
650 4 $a Engineering, Industrial. $3 626639
690 $a 0501
690 $a 0546
710 2 0 $a Georgia Institute of Technology. $3 696730
773 0 $t Dissertation Abstracts International $g 67-03B.
790 1 0 $a McGinnis, Leon, $e advisor
790 $a 0078
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3212245