Shallot supply chain analysis using rapid agricultural supply chain risk assessment method: case in Bantul Regency, Special Region of Yogyakarta, Indonesia

Abstract

The supply chain of shallot commodities in Bantul Regency involves several business actors including farmers, middlemen, collectors, wholesalers, retailers, and small traders. In the process of each supply chain, risks will always be encountered. The risks in each tier of shallot supply chain certainly need to be identified and analyzed. The categories of each of these risks will determine the appropriate supply chain risk management steps based on the risk categories obtained. This study was conducted to analyze the risk of shallot supply chains in the three largest shallot producing sub-districts. The Rapid Agricultural Supply Chain Risk Assessment (RapAgRisk) method is used to carry out risk analysis, and the sampling method used was purposive sampling and snowball sampling. The results show that the farmer is the tier with most risks faced compared to the others. Excessive stock is the risk that requires adequate priority to manage, and it is faced by all levels of the shallot supply chain. Meanwhile, farmers are the weakest supply chain actors in bargaining power, and the small traders are the most vulnerable actors in the face of risks associated with quality damage and slow sales rate.

Analysis of Risk Factors of Fresh Agricultural Product Supply Chain Based on Grey Correlation Degree – Take Chengdu as an Example

“…Li et al [8] used the SCOR model and bibliometrics to identify risk factors in the supply chain of fresh agricultural products. Murtono et al [9] used the Rapid Agricultural Supply Chain Risk Assessment (RapAgRisk) method to identify the risk of onion in the supply chain of fresh agricultural products; Peyman et al [10] used the Failure Mode and Effects Analysis (FMEA) method to identify risk factors in the agricultural supply chain with three types of judgment indicators: severity, incidence, and detectability; Ming et al [11] analyzed the risk factors affecting the supply chain of fresh agricultural products under "agricultural-supermarket docking" mode and "Internet +" mode. In the application of grey correlation degree to the identification of influencing factors of supply chain, Cheng et al [12] identify the main influencing factors of agricultural product logistics by using grey relational degree; Ting et al [13] used grey model to identify the risk factors of tourism supply chain; Yan et al [14] used grey correlation degree to explore the influencing factors of agricultural product logistics in Shaanxi Province; Ge et al [15] analyzed the influencing factors of the supply chain of fresh agricultural products by using gray correlation; Kun et al [16] used ANP and grey correlation to identify the main influencing factors of the prefabricated building supply chain.…”