A new approach for supply chain risk management: Mapping SCOR into Bayesian network
Abstract: Increase of costs
and complexities in organizations beside the increase of uncertainty and risks
have led the managers to use the risk management in order to decrease risk
taking and deviation from goals. SCRM has a close relationship with supply
chain performance. During the years different methods have been used by
researchers in order to manage supply chain risk but most of them are either
qualitative or quantitative. Supply chain operation reference (SCOR) is a
standard model for SCP evaluation which have uncertainty in its metrics. In
This paper by combining qualitative and quantitative metrics of SCOR, supply
chain performance will be measured by Bayesian Networks.
Design/methodology/approach: First qualitative assessment will be done by
recognizing uncertain metrics of SCOR model and then by quantifying them,
supply chain performance will be measured by Bayesian Networks (BNs) and supply
chain operations reference (SCOR) in which making decision on uncertain
variables will be done by predictive and diagnostic capabilities.
Findings: After applying the proposed method in one of the biggest
automotive companies in Iran, we identified key factors of supply chain
performance based on SCOR model through predictive and diagnostic capability of
Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’
and its criteria that include costs of labors, warranty, transportation and
inventory have the widest range and most effect on supply chain performance.
So, managers should take their importance into account for decision making. We
can make decisions simply by running model in different situations.
Research limitations/implications: A more precise model consisted of
numerous factors but it is difficult and sometimes impossible to solve big
models, if we insert all of them in a Bayesian model. We have adopted real
world characteristics with our software and method abilities. On the other
hand, fewer data exist for some of the performance metrics.
Practical implications: Mangers often use simple qualitative metrics for
SCRM. However, combining qualitative and quantitative metrics will be more
useful. Industries can recognize the important uncertain metrics by predicting
supply chain performance and diagnosing possible happenings.
Originality/value: This paper proposed a Bayesian method based on SCOR
metrics which has the ability to manage supply chain risks and improve supply
chain performance. This is the only presented case study for measuring supply
chain performance by SCOR metrics.
Author: Mahdi Abolghasemi,
Vahid Khodakarami, Hamid Tehranifard
Journal Code: jptindustrigg150021
