Modelling supply chain risks and their impacts on the performance of the sago starch agro-industry

Abstract

PurposeSupply 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).Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsThe 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.Originality/valueThis 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.

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“…Therefore, this work focuses on identifying and quantifying the potential risks based on four parameters, i.e. probability, impact, unpredictability and urgency, to determine the critical ones for mitigation, which will help streamline the resources and improve the performance of IPI (Anwar et al. , 2022).…”

“…They can have multiple consequences for the supply chain (SC), e.g. product shortage, poor quality, delayed delivery, price fluctuations, lost efficiency and poor cooperation, which result in poor performance (Anwar et al, 2022;Falasca et al, 2021;Tarei et al, 2018). These consequences become grimmer in the case of industries directly dealing with customer health, such as the healthcare and pharmaceutical industry.…”

“…They can have multiple consequences for the supply chain (SC), e.g. product shortage, poor quality, delayed delivery, price fluctuations, lost efficiency and poor cooperation, which result in poor performance (Anwar et al. , 2022; Falasca et al.…”

Supply chain sustainability risk decision support model using integrated Preference Selection Index (PSI) method and prospect theory

“…Zhang and Chen (2015) promoted the use of artificial intelligence tools to evaluate the criticality of the financial supply chain of SMEs. And at last, Anwar et al. (2021) reported the use of the Bayesian networks to deal with determining the most impacting supply chain risks to the performance of the SMEs.…”

A weighted fuzzy Petri-net analysis for Indian foodgrains supply chain

“…Literature review 2.1 Identification of risk factors for FSC Research on risk management started at the beginning of the 21st century. Many researchers have realized the importance of risk in supply chains, and they have implemented empirical analysis (Babu et al, 2021), simulation study (Ivanov, 2017) and conceptual models (Anwar et al, 2021) for managing risk. Several researchers have made significant contributions to analyzing risks in the FSC in recent years.…”