Risk Identification in Cassava Chip Supply Chain Using SCOR (Supply Chain Operation Reference)

Tama, Ishardita Pambudi; Yuniarti, Rahmi; Eunike, Agustina; Hamdala, Ihwan; Azlia, Wifqi

doi:10.1088/1757-899x/494/1/012050

Cited by 17 publications

(14 citation statements)

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“…AgroCorp utilizes various platforms to facilitate social and economic interactions, which allows for increased circulation. This platformization strategy creates opportunities with external parties such as the surrounding community, government, academics and NGOs [32] to establish a broader ecosystem for applying the CE concept [31]. The CE adoption in AgroCrop is integrated into their management system, requiring adjustments, adaptations that impact the economic, environmental and social aspects of business.…”

Section: Agrocrop Circularity Assessmentmentioning

confidence: 99%

Economy Circular Adoption toward Sustainable Business (Study case: Agro-industry Company in Indonesia)

Marthalia,

Tumuyu,

Asteria

2024

JPSL

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Agroindustry is one of industrial sector that considered important to adopt the Circular Economy (CE) principles, considering its close relation with agriculture and food system, and its significant contribution to the transition of low-carbon economy. In principle, the CE adoption on agroindustry, mostly focus on resource efficiency, waste management, energy efficiency and their collaborative cooperation with stakeholders. Business sector in general, are facing many challenges and obstacles to overcome the complexity for integrating business processes, supply chains and collaboration between stakeholders align with corporate’s business strategy for implementing CE. The purpose of this study is to analyse the CE adoption at micro level at agroindustrybusiness refer to their own business character. This article using the two indicator reference, firstly EMF Circulytics 2.0 (TheEllen MacArthur Foundation) that explores corporate circularity and secondly the 10R CE principles that are often being used as general CE framework. The expected outcome from this article is to open wider insights regarding CE adoption in agroindustry corporate strategy as the transformative business model innovation towards sustainable development.

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Section: Agrocrop Circularity Assessmentmentioning

confidence: 99%

Economy Circular Adoption toward Sustainable Business (Study case: Agro-industry Company in Indonesia)

Marthalia,

Tumuyu,

Asteria

2024

JPSL

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“…In this paper, the index data used in the study of ESC risks at home and abroad are addressed, and the main influencing factors of risks in the ESC network are analyzed from four aspects: reliability of ESC operation process, emergency organizational supportability, completeness of emergency, and the impact degree of emergencies. Among them, the supply chain operation process is divided into five parts: planning, procurement, production and storage, distribution, and reverse logistics according to the supply chain operation reference model (SCOR) [34][35][36][37][38] and the characteristics of ESC. The specific risk factor indicators are shown in Table 2.…”

Section: Risk Factor Analysismentioning

confidence: 99%

Transmission Mechanism of Post-COVID-19 Emergency Supply Chain Based on Complex Network: An Improved SIR Model

Chen

Yin

2023

Sustainability

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Since the COVID-19 epidemic swept the world, the emergency supply chain (ESC) has faced serious uncertainty risks. To maintain the stability of the emergency supply, risk prevention and contingency measures must be prepared. In this paper, the authors first obtain the initial risk value of 0.4 using the fuzzy comprehensive evaluation approach and then build an improved SIR model based on a complex network to investigate the risk propagation law of the ESC. The simulation results show that (1) the high number of nodes becomes the initial risk source, the risk propagates faster and the peak value arrives two days earlier on average; (2) the initial infection rate gradually increases from 0.2 to 0.4, 0.6, and 0.8, and the risk spread speed also accelerates; (3) the recovery rate of network nodes increases gradually from 0.1 to 0.2, 0.3, and 0.4, and the influence range of risk propagation decreases inversely; (4) appropriately increasing the deletion rate of network nodes is conducive to improving the stability of the ESC network. Given the above ESC risk propagation law, this paper proposes relevant risk prevention measures and suggests that a risk early warning system of node enterprises should be established in combination with the target immunization strategy. For ESC risk management, the result has significant theoretical and practical implications.

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“…The potential of applying machine learning algorithms to predict supply chain risk has been recently considered. As we can see, most of the research papers has identified the risk, with only a few studies considering the supply chain operations reference (SCOR) model to identify the various risk events (Huo and Zhang, 2011), risk factors (Ríos et al, 2019), and the risk causes (Tama et al, 2019). To assess these risks, we have applied a machine learning algorithm in our research, and also, we prioritized these risks such as low, medium, high and severe to improve the supply chain performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Model for Predicting Supply Chain Risk Through Machine Learning Algorithms

Kumar

Sharma

2023

Int. j. math. eng. manag. sci.

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The machine learning model has become a critical consideration in the supply chain. Most of the companies have experienced vari-ous supply chain risks over the past three years. Earlier risk prediction has been performed by supply chain risk management. In this study, an integrated supply chain operations reference (ISCOR) model has been used to evaluate the organization's supply chain risk. Machine learning (ML) has become a hot topic in research and industry in the last few years. With this motivation, we have moved in the direction of a machine learning-based pathway to predict the supply chain risk. The great attraction of this research is that suppliers will understand the associated risk in the activity. This research includes data pre-processing, feature extraction, data transformation, and missing value replacement. The proposed integrated model involves the support vector machine (SVM), k near-est neighbor (k-NN), random forest (RF), decision tree (DT), multiple linear regression (MLR) algorithms, measured performance, and prediction of supply chain risk. Also, these algorithms have performed a comparative analysis under different aspects. Among the other algorithms, the random forest algorithm achieves an accuracy of 99% and has accomplished superior results with a maxi-mum precision of 0.99, recall of 0.99, and F-score of 0.99 with 1% error rate. The model’s prediction indicates that it can be used to find the supply chain risk. Finally, the limitation and the challenges discussed also provide an outlook for future research direction to perform effective management to mitigate the risk.

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Risk Identification in Cassava Chip Supply Chain Using SCOR (Supply Chain Operation Reference)

Cited by 17 publications

References 0 publications

Economy Circular Adoption toward Sustainable Business (Study case: Agro-industry Company in Indonesia)

Economy Circular Adoption toward Sustainable Business (Study case: Agro-industry Company in Indonesia)

Transmission Mechanism of Post-COVID-19 Emergency Supply Chain Based on Complex Network: An Improved SIR Model

Integrated Model for Predicting Supply Chain Risk Through Machine Learning Algorithms

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