Supply Chain Integration Performance Measurement and Improvement in HMMB
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Date
2018-06
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Journal Title
Journal ISSN
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Abstract
Ethiopian metal manufacturing and machine building industries, is characterized by very low
level of supply chain integration, poor export capacity, poor production capacity, and poor
performance.
Generally, the aim of this research study is to measure SCIP and finally to propose supply
chain integration performance measurement and Improvement approach model to HMMBI.
To accomplish the objective, the researcher conducted a literature survey review of Supply
Chain integration, the current understanding of supply chain integration, factor of SCI,
different supply chain integration performance measurement model and the study creates a
framework for supply chain integration measurement to improve its performance.
The collected data have been summarized using descriptive analysis method and analyzed by
a statistical tool. Furthermore, the relationship in the SCOR model were tested using
Spearman’s correlation coefficient, and the regression analysis was used to analyze causal
relation and to check the fitness SCOR model with data.
The majority responses in descriptive statistics results shows that average mean value 2.72
signifies agreement by the respondent this show that supply chain integration was very rarely
practice in the case company. Furthermore artificial neural network algorithm analysis results
a high number of neurons in the hidden layer indicate that SCI in HMMBI was very poor.
The correlation result shows that there is strong correlation (ρ=0.78* - 0.921*) between supply
chain integration and its performance factor metrics and the relationship is statistically
significant.
Regression analysis confirm that the relative contribution SCI metrics factors is 65.9%-100%
of variability supply chain integration performance explained by each factor metrics at
(planning100%, sourcing100%, making 89.3%, delivery 65.9%, and returning100%). From
the whole metrics remaining 8.2% change in change can be attributed to other factors.
In general, to check model fit the data different test was done on regression analysis DurbinWatson
test,
R
square
value
test,
adjusted
R
square
value
test,
tolerance
test,
variance
inflation
factor
test,
b-value
test,
and
multicollinearity
test.
The
proposed
factor
metrics
measure
90.8%
of
supply
chain
integration
performance.
Finally,
the
researcher
proposed
SCIP
Measurement
and
Improvement
Approach
model
for
HMMBI
based
on SCOR
model.
Description
Keywords
HMMBI, Supply Chain