On the use of archetypes as benchmarks

Benchmarking plays a relevant role in performance analysis, and statistical methods can be fruitfully exploited for its aims. While clustering, regression, and frontier analysis may serve some benchmarking purposes, we propose to consider archetypal analysis as a suitable technique. Archetypes are extreme points that synthesize data and that, in our opinion, can be profitably used as benchmarks. That is, they may be viewed as key reference performers in the comparison process. We suggest a three-step data driven benchmarking procedure, which enables users: (i) to identify some reference performers, (ii) to analyze their features, (iii) to compare observed performers with them. An exploratory point of view is preferred, and graphical devices are adopted throughout the procedure. Finally, our approach is presented by means of an illustrative example based on The Times league table of the world top 200 universities. Copyright © 2008 John Wiley & Sons, Ltd. This paper is based on a presentation given at the SMAP06 Workshop, Cassino, April 2006.

[1]  Duncan Temple Lang,et al.  GGobi: evolving from XGobi into an extensible framework for interactive data visualization , 2003, Comput. Stat. Data Anal..

[2]  Edward J. Wegman,et al.  Visual clustering and classification: The Oronsay particle size data set revisited , 1999, Comput. Stat..

[3]  Hatice Camgoz-Akdag Total quality management through six sigma benchmarking , 2007 .

[4]  Forrest W. Young,et al.  27 Multivariate statistical visualization , 1993, Computational Statistics.

[5]  Giancarlo Ragozini,et al.  ARCHETYPAL ANALYSIS FOR INTERVAL DATA IN MARKETING RESEARCH 1 , 2006 .

[6]  Joe Zhu,et al.  Quantitative models for performance evaluation and benchmarking , 2003 .

[7]  A. Charnes,et al.  Data Envelopment Analysis Theory, Methodology and Applications , 1995 .

[8]  Emily Stone Exploring archetypal dynamics of pattern formation in cellular flames , 2002 .

[9]  Daniel Asimov,et al.  The grand tour: a tool for viewing multidimensional data , 1985 .

[10]  Peter C. Smith,et al.  The Use of Performance Indicators in the Public Sector , 1990 .

[11]  Regina Y. Liu,et al.  Multivariate analysis by data depth: descriptive statistics, graphics and inference, (with discussion and a rejoinder by Liu and Singh) , 1999 .

[12]  Angappa Gunasekaran,et al.  A business model for uncertainty management , 2005 .

[13]  J. Hartigan Printer graphics for clustering , 1975 .

[14]  Alfred Inselberg,et al.  The plane with parallel coordinates , 1985, The Visual Computer.

[15]  Adele Cutler,et al.  Introduction to archetypal analysis of spatio-temporal dynamics , 1996 .

[16]  Raimon Tolosana-Delgado,et al.  "compositions": A unified R package to analyze compositional data , 2008, Comput. Geosci..

[17]  E. Wegman Hyperdimensional Data Analysis Using Parallel Coordinates , 1990 .

[18]  B. Kingdom Use of performance indicators and performance benchmarking in the North American water industry—findings from studies recently completed for AWWA and WEF research foundations , 1998 .

[19]  J. Aitchison,et al.  Logratio Analysis and Compositional Distance , 2000 .

[20]  Harvey Goldstein,et al.  Multilevel Structural Equation Models for the Analysis of Comparative Data on Educational Performance , 2007 .

[21]  R. Camp Business process benchmarking : finding and implementing best practices , 1995 .

[22]  M. C. Jones,et al.  The Statistical Analysis of Compositional Data , 1986 .

[23]  A. Sohal,et al.  Performance measurement of AMT: a cross‐regional study , 2006 .

[24]  Sönke Rehder,et al.  Letter to the Editor: Comment on “Logratio Analysis and Compositional Distance” by J. Aitchison, C. Barceló-Vidal, J. A. Martín-Fernández, and V. Pawlowsky-Glahn , 2001 .

[25]  Ben Clegg,et al.  Achieving internal process benchmarking: guidance from BASF , 2006 .

[26]  J. Aitchison,et al.  Biplots of Compositional Data , 2002 .

[27]  Edward J. Wegman,et al.  High Dimensional Clustering Using Parallel Coordinates and the Grand Tour , 1997 .

[28]  Jonathan D. Pemberton,et al.  Benchmarking and the role of organizational learning in developing competitive advantage , 2001 .

[29]  S. Bird,et al.  Performance indicators: good, bad, and ugly , 2004 .

[30]  Luis Rueda,et al.  Geometric visualization of clusters obtained from fuzzy clustering algorithms , 2006, Pattern Recognit..

[31]  Srinivas Talluri,et al.  A Benchmarking Method for Business-Process Reengineering and Improvement , 2000 .

[32]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[33]  Myron A. Olstein,et al.  Metric Benchmarking (PDF) , 1998 .

[34]  B. Chan,et al.  Archetypal analysis of galaxy spectra , 2003, astro-ph/0301491.

[35]  Adele Cutler A branch and bound algorithm for constrained least squares , 1993 .

[36]  V. Barnett The Ordering of Multivariate Data , 1976 .

[37]  Richard T. Carson,et al.  Archetypal analysis: a new way to segment markets based on extreme individuals , 2003 .