Supply chain risks (SCRs) have uncertainty and interdependency characteristics that must be incorporated into the risk assessment stage of the SCR management framework. This study aims to develop SCR networks and determine the major risk drivers that impact the performance of the sago starch agro-industry (SSA).
The risk and performance variables were collected from the relevant literature and expert consultations. The Bayesian network (BN) approach was used to model the uncertain and interdependent SCRs. A hybrid method was used to develop the BN structure through the expert’s knowledge acquisitions and the learning algorithm application. Sensitivity analyses were performed to examine the significant risk driver and their related paths.
The analyses of model indicated several significant risk drivers that could affect the performance of the SSA. These SCR including both operational and disruption risks across sourcing, processing and delivery stage.
The implementation of the methodology was only applied to the Indonesian small-medium size sago starch agro-industry. The generalization of findings is limited to industry characteristics. The modelled system is restricted to inbound, processing and outbound logistics with the risk perspective from the industry point of view.
The results of this study assist the related actors of the sago starch agro-industry in recognizing the major risk drivers and their related paths in impacting the performance measures.
This study proposes the use of a hybrid method in developing SCR networks. This study found the significant risk drivers that impact the performance of the sago starch agro-industry.
