Apr-Jun.12 - the Nitie

3. Case study
In this case study a state road transport undertaking
(SRTU) located in south India, operating passenger
buses has been chosen. It is one of the leading public
sector bus transport corporations generating consistent
returns as well rendering excellent service over the
present case study has been conducted in three bus
depots of Villupuram division of the SRTU. There
are various divisions such as Chennai, Villupuram,
Kumbakonam, Salem, Coimbatore, Madurai, and
98, 95 and 94 respectively. These depots each employ
around 180 bus crew members, 30 maintenance staff,
10 managerial staff, and 15 -18 administrative staff.
The average number of passengers travelling every day
is approximately 1,50,000 for depot-I, around 1,25,000
for depot-II and less than 1,00,000 passengers for
depot-III.
This study on performance improvement methods in a
public sector passenger bus transport was carried out
by collecting two types of data namely; operational
characteristics data and customers’ or passengers’
characteristics data. The performance improvement
(i) fuzzy TOPSIS - fuzzy AHP-ANOVA combining
the operational characteristics data and the customer
characteristic data; (ii) multivariate analyses of
customer characteristic data, which include principal
component analysis (PCA), discriminant analysis (DA),
and multivariate analysis of variance (MANOVA);
(iii) perceptual mapping (PM) with PCA and DA,
(iv) performance importance matrix (PIM); and (v)
a composite model integrating principal component
analysis (PCA), and conjoint analysis (CA) with the
traditional QFD. Table 2 gives the summary of the
characteristics of the various models.
3.1 Operational characteristics data
The details of the sixteen sub-criteria used for data
analysis are explained in Table 3 under three main
criteria namely – safety, operation and cost/earnings.
The data had been collected for a period of nine months
from respective depot managers. Out of these data,
twelve would be quantitative and four are qualitative
in nature.
3.2 Passenger feedback or customer characteristics
data (qualitative)
During a period of one month the regular customers,
to 60 years) and senior citizens (above 60 years) of
the three depots were surveyed and their feedback is
presented in Table 4. A customer survey was done from
150 customers each for all the three depots (50 each for
all the age group).
4. Conceptual model
The conceptual model concentrates on the applications
of performance improvement tools like FTOPSIS
and FAHP; factor analysis, discriminant analysis,
MANOVA, perceptual mapping, PIM, and QFD
in public transport company to gauge the overall
performance. An overview of this study is presented as
work are:
i. To rank the bus depots based on both operational
characteristics and customer characteristics using
FTOPSIS-FAHP-ANOVA model (Phase-I).
importance rating using principal component analysis
(PCA) model; develop a model to predict performance
using discriminant analysis (DA); and identify the
customer perceptions based on grouping criterion
using multi-variate analysis of variance (MANOVA).
Identify the best performing depot using all the above
multi-variate analyses models (Phase-II).
iii. To ascertain the strengths / weakness of the depots
using perceptual mapping (Phase-III).
iv. To develop a performance importance matrix
(PIM) model by identifying difference in perceptions
attached by the service providers’ and the customers’
for the service characteristics (Phase-IV).
v. To integrate principal component analysis (PCA),
conjoint analysis (CA) and quality function deployment
Vol. 36, No. 2, April-June, 2012
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