Topic 7: Designing Adaptive Organizations Organizing Organizing is the Deployment
A Method For Designing Improvements in Organizations, Products, and Services
description
Transcript of A Method For Designing Improvements in Organizations, Products, and Services
A Method For Designing Improvements in Organizations, Products, and Services
Stuart UmplebyResearch Program in Social and
Organizational LearningThe George Washington
UniversityWashington, DC USA
E-mail: [email protected]
Dragan TevdovskiMathematics, Statistics and Informatics University Sts. Cyril and Methodius Skopje, MacedoniaE-mail: [email protected]
Second Conference of the Washington Academy of Sciences
Washington DC, March 2006
Introduction
A method for determining priorities for improvement in an organization
Priority means high importance and low performance
Quality Improvement Priority Matrix
The approach to design This approach to design is “piecemeal”
rather than “utopian” It is “bottom up” rather than “top down” It uses the judgments of employees or
customers Features to improve are ranked by
urgency Several projects can be worked on
simultaneously
Quality Improvement Priority Matrix
References
The method was first described by the specialists from GTE Directories Corporation in 1995
Armstrong Building Products Operation used the method in1996
Naoumova and Umpleby (2002) - evaluation of visiting scholar programs
Melnychenko and Umpleby (2001) and Karapetyan and Umpleby (2002) used QIPM in a university department
Prytula (2004) introduced the importance / performance ratio
Dubina (2005) used cluster analysis and proposed standard deviation as a measure of agreement or disagreement
Goals of the Paper
Understand more fully the priorities of the Department of Management Science at The George Washington University (GWU), USA, and the Department of Management at Kazan State University (KSU), Kazan, Russia
Use and develop new methods to compare QIPMs for two organizations
The Data
A questionnaire was given to management faculty members at both GWU and KSU in 2002
The questionnaire contained 51 features of their departments
Importance and performance scales, each ranging from 0 to 10
Evaluation
Range Mean Standard
Deviation
Importance (GWU)
4.80 7.5408 1.25207
Performance (GWU) 4.90 5.4890 1.18905
Importance (KSU) 6.00 7.3371 1.84934
Performance (KSU)
8.39 4.3529 2.49989
Dispersion in the responses
Coefficient of variation
Importance (GWU) 16.60%
Performance (GWU) 21.66%
Importance (KSU) 25.21%
Performance (KSU) 57.43%
Standardization of the importance and the performance scores
Range Min Max MeanStd.
Deviation
Importance Standardized (GWU)
3.84 3.35 7.19 6.0225 1.00
Performance Standardized (GWU)
4.12 2.73 6.85 4.6157 1.00
Importance Standardized (KSU)
3.25 2.16 5.41 3.9661 1.00
Performance Standardized (KSU)
3.36 0.20 3.56 1.7408 1.00
GWU QIPM
KSU QIPM
Ranking the Priorities
Standardized importance-performance ratio (SIP)
s
s
P
ISIP
Ranking GWU Priorities According to SIP Ratio
Rank GWU Priority Features SIP
1 Office security 1.977
2 Building/ physical environment 1.781
3Dept. organization to implement its strategic plan 1.756
4 Dept. strategic plan 1.729
5Help with writing research proposals 1.724
Ranking KSU Priorities According to SIP Ratio
Rank KSU Priority Features SIP
1 Funds to support research 24.197
2 Travel support 24.170
3 Office space for faculty 12.289
4 Projection equipment 9.387
5 Salaries 6.631
Clustering the Priorities
GWU Clusters Centers
Cluster
1 2 3 4 5
Importance Standardized (GWU) 7.15 4.92 5.83 4.3 4.22
Performance Standardized (GWU) 3.62 2.87 3.48 3.72 2.92
SIP 1.97 1.71 1.67 1.15 1.44
Clustering the Priorities
GWU Clusters Centers
Cluster
1 2 3 4 5
Importance Standardized (GWU) 7.15 4.92 5.83 4.22 4.3
Performance Standardized (GWU) 3.62 2.87 3.48 2.92 3.72
SIP 1.97 1.71 1.67 1.44 1.15
GWU Southeast Quadrant
KSU Clusters Centers Cluster
1 2 3 4 5 6 7
Importance Standardized (KSU)
4.79 5.29 4.89 4.24 4.90 4.60 4.87
Performance Standardized (KSU)
0.30 0.71 1.27 2.00 2.38 3.01 3.40
SIP 15.97 7.45 3.85 2.12 2.06 1.53 1.43
KSU Southeast Quadrant
Review of what we did (1) We used 2002 data from GWU and KSU We divided importance and
performance means by st. dev. in order to achieve a common level of agreement among GWU and KSU faculty members
Combining GWU and KSU data, we calculated the nearest whole integer mean for importance and performance
Review of what we did (2)
These means were used to create a common QIPM coordinate system
For each department the features in the SE quadrant were clustered by proximity
The clusters were ordered by average SIP, a measure of urgency
Conclusions (1) Standardizing importance and
performance scores to achieve a common level of agreement magnifies the differences between the two departments
At KSU the average importance of the features is lower than at GWU. This may mean that KSU is still struggling with basics such as salaries and office space. GWU has the luxury of concern with travel and research funds and the library collection
Conclusions (2)
Faculty members at KSU evaluate the performance of their department lower than do GWU faculty members
At KSU high priority features are mostly personal concerns such as salaries
At GWU high priority features are organizational issues such as planning