DocumentCode :
1200836
Title :
Identifying high performance ERP projects
Author :
Stensrud, Erik ; Myrtveit, Ingunn
Author_Institution :
Norwegian Sch. of Manage., Sandvika, Norway
Volume :
29
Issue :
5
fYear :
2003
fDate :
5/1/2003 12:00:00 AM
Firstpage :
398
Lastpage :
416
Abstract :
Learning from high performance projects is crucial for software process improvement. Therefore, we need to identify outstanding projects that may serve as role models. It is common to measure productivity as an indicator of performance. It is vital that productivity measurements deal correctly with variable returns to scale and multivariate data. Software projects generally exhibit variable returns to scale, and the output from ERP projects is multivariate. We propose to use data envelopment analysis variable returns to scale (DEA VRS) to measure the productivity of software projects. DEA VRS fulfills the two requirements stated above. The results from this empirical study of 30 ERP projects extracted from a benchmarking database in Accenture identified six projects as potential role models. These projects deserve to be studied and probably copied as part of a software process improvement initiative. The results also suggest that there is a 50 percent potential for productivity improvement, on average. Finally, the results support the assumption of variable returns to scale in ERP projects. We recommend DEA VRS be used as the default technique for appropriate productivity comparisons of individual software projects. Used together with methods for hypothesis testing, DEA VRS is also a useful technique for assessing the effect of alleged process improvements.
Keywords :
data envelopment analysis; enterprise resource planning; management science; project management; software management; software process improvement; Accenture; DEA VRS; benchmarking database; data envelopment analysis variable returns; enterprise resource planning; high-performance ERP project identification; multivariate data; software process improvement; software project productivity measurement; software projects; Best practices; Data envelopment analysis; Data mining; Enterprise resource planning; Performance evaluation; Productivity; Project management; Software engineering; Software measurement; Software performance;
fLanguage :
English
Journal_Title :
Software Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0098-5589
Type :
jour
DOI :
10.1109/TSE.2003.1199070
Filename :
1199070
Link To Document :
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