Conceptual model to manage supply chain performance (case study: pangasius.sp agroindustry in indonesia)

Subject Areas : Design of Experiment

Andreas Panudju ¹ , Marimin Marimin ² , Sapta Raharja ³ , Mala Nurilmala ⁴

1 -
2 -
3 -
4 -

Received: 2023-03-05 Accepted : 2023-04-10 Published : 2023-06-01

Keywords: SCOR, conceptual model, Supply chain performance, Pangasius sp,

Abstract :

Creating clear and timely performance reports across all components of the pangasius.sp agroindustry supply chain is pressing, particularly in monitoring each stakeholder' KPIs. The information model based on Supply Chain Operation Reference (SCOR) tries to portray the needs of each stakeholder. The essential stakeholders supply chain criteria in the pangasius.sp fish agroindustry was mapped into respectable definitions. The proposed formulation generates associated features into evaluation measures to evaluate specific performance. The performance of each attribute is then compared to industry best practices. An Application Development Framework (ADF) based on Business Process Modeling and Notation (BPMN) connects the model's operations with a cloud-based database. The front-end integrated by JavaScript with database operations based on SCOR is finished and ready for mobile and desktop use. This model enables straightforward interpretation and comprehension of performance measurement through various visualizations such as spider charts, histograms, line charts, and ETL (Extract, Transform, and Load) features. Based on the findings in Figure 8, it is apparent that the fish processing sector is presently performing below expectations. The total performance score of 78.81 signifies this. The scores for reliability, responsiveness, agility, cost, and assets qualities are moderate, indicating room for improvement. The low scores for order fulfillment cycle time (63.60) and cash-to-cash cycle time (51.70) are noteworthy, and improving these performance indicators should be the primary focus to enhance overall performance. The model would efficiently illustrate evaluation functionality by leveraging real-world data obtained from Indonesia's pangasius. sp agroindustry's three main regions, namely the provinces of West Java, East Java and Lampung. Quick geographic comparisons are provided for research at several user levels in the pangasius.sp processing, retail, collector, and aquaculture industries.

References:

Akkawuttiwanich, P., & Yenradee, P. (2017). Evaluation of SCOR KPIs using a predictive MILP model under fuzzy parameters. International Journal of Supply Chain Management, 6(1), 172–185.
Alshawabkeh, R. O. K., Al-Awamleh, H. K., Alkhawaldeh, M. I. G., Kanaan, R. K., Al-Hawary, S. I. S., Mohammad, A. A. S., & Alkhawaldah, R. A. (2022). The mediating role of supply chain management on the relationship between big data and supply chain performance using SCOR model. Uncertain Supply Chain Management, 10(3), 729–736. https://doi.org/10.5267/j.uscm.2022.5.002
Asrol, M., & Syahruddin. (2022). Supply Chain Performance Measurement and Improvement for Forging Industry. International Journal of Industrial Engineering and Production Research, 33(3), 8–21. https://doi.org/10.22068/ijiepr.33.3.14
Ayyildiz, E., & Taskin, A. (2022). Humanitarian relief supply chain performance evaluation by a SCOR based Trapezoidal type-2 fuzzy multi-criteria decision making methodology: An application to Turkey. Scientia Iranica, 29(4), 2069–2083. https://doi.org/10.24200/sci.2020.52592.2786
Budiarto, D. S., Prabowo, M. A., & Herawan, T. (2017). An integrated information system to support supply chain management & Performance in SMEs. Journal of Industrial Engineering and Management, 10(2Special Issue), 373–387. https://doi.org/10.3926/jiem.2180
Chopra, A., Golwala, D., & Chopra, A. R. (2022). Scor (Supply Chain Operations Reference) Model in Textile Industry. Journal of Southwest Jiaotong University, 57(1), 368–378. https://doi.org/10.35741/issn.0258-2724.57.1.33
de Vass, T., Shee, H., & Miah, S. (2018). The effect of “Internet of Things” on supply chain integration and performance: An organisational capability perspective. Australasian Journal of Information Systems, 22, 1–29. https://doi.org/10.3127/ajis.v22i0.1734
Dias, L. S., & Ierapetritou, M. G. (2017). From process control to supply chain management: An overview of integrated decision making strategies. Computers and Chemical Engineering, 106, 826–835. https://doi.org/10.1016/j.compchemeng.2017.02.006
Dissanayake, C. K., & Cross, J. A. (2018). Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM. International Journal of Production Economics, 201, 102–115. https://doi.org/10.1016/j.ijpe.2018.04.027
Divsalar, M., Ahmadi, M., & Nemati, Y. (2022). A SCOR-Based Model to Evaluate LARG Supply Chain Performance Using a Hybrid MADM Method. IEEE Transactions on Engineering Management, 69(4), 1101–1120. https://doi.org/10.1109/TEM.2020.2974030
Jaiswal, A., & Samuel, C. (2023). A Literature Review Based Bibliometric Analysis of Supply Chain Analytics. Lecture Notes in Mechanical Engineering, 397–408. https://doi.org/10.1007/978-981-19-0561-2_35
Kamble, S. S., Mor, R. S., & Belhadi, A. (2023). Big Data Analytics for Supply Chain Transformation: A Systematic Literature Review Using SCOR Framework. EAI/Springer Innovations in Communication and Computing, 1–50. https://doi.org/10.1007/978-3-031-19711-6_1
Lhassan, E., Ali, R., & Majda, F. (2018). Combining SCOR and BPMN to support supply chain decision-making of the pharmaceutical wholesaler-distributors. In Proceedings - GOL 2018: 4th IEEE International Conference on Logistics Operations Management (pp. 1–10). IEEE. https://doi.org/10.1109/GOL.2018.8378078
Li, S., Cui, X., Huo, B., & Zhao, X. (2019). Information sharing, coordination and supply chain performance: The moderating effect of demand uncertainty. Industrial Management and Data Systems, 119(5), 1046–1071. https://doi.org/10.1108/IMDS-10-2018-0453
Lima-Junior, F. R., & Carpinetti, L. C. R. (2017). Quantitative models for supply chain performance evaluation: A literature review. Computers and Industrial Engineering, 113, 333–346. https://doi.org/10.1016/j.cie.2017.09.022
Liu, F. hwa F., & Liu, Y. cheng. (2017). A methodology to assess the supply chain performance based on gap-based measures. Computers and Industrial Engineering, 110, 550–559. https://doi.org/10.1016/j.cie.2017.06.010
Mañay, L. O. R., Guaita-Pradas, I., & Marques-Perez, I. (2022). Measuring the Supply Chain Performance of the Floricultural Sector Using the SCOR Model and a Multicriteria Decision-Making Method. Horticulturae, 8(2). https://doi.org/10.3390/horticulturae8020168
Marimin, Adhi, W., & Darmawan, M. A. (2017). Decision support system for natural rubber supply chain management performance measurement: A sustainable balanced scorecard approach. International Journal of Supply Chain Management, 6(2), 60–74.
Marimin, Djatna, T., MacHfud, Asrol, M., Papilo, P., Taufik, B., & Darmawan, M. A. (2020). Supply chain performance measurement and improvement of palm oil agroindustry: A case study at Riau and Jambi Province. IOP Conference Series: Earth and Environmental Science, 443(1), 12056. https://doi.org/10.1088/1755-1315/443/1/012056
Nathania, G., & Desrianty, A. (2023). Improved company productivities based on supply chain management performance measurement using Objective Matrix (OMAX) method. AIP Conference Proceedings, 2772. https://doi.org/10.1063/5.0115146
Oktaviani, M., & Asrol, M. (2022). Supply Chain Performance Analysis and Improvement for Electricity Industry. International Journal of Emerging Technology and Advanced Engineering, 12(7), 128–139. https://doi.org/10.46338/ijetae0722_14
Quayle, M. (2006). Purchasing and supply chain management. In Information Management (Vol. 19, Issues 1–2). Cengage Learning. https://doi.org/10.4018/978-1-59140-899-4
Ricardianto, P., Barata, F. A., Mardiyani, S., Setiawan, E. B., Subagyo, H., Saribanon, E., & Endri, E. (2022). Supply chain management evaluation in the oil and industry natural gas using SCOR model. Uncertain Supply Chain Management, 10(3), 797–806. https://doi.org/10.5267/j.uscm.2022.4.001
Saleheen, F., & Habib, M. M. (2023). Embedding attributes towards the supply chain performance measurement. Cleaner Logistics and Supply Chain, 6. https://doi.org/10.1016/j.clscn.2022.100090
Society, A. P. and I. C. (2012). SCOR Supply Chain Operation Reference Model.
Trueba-Castañeda, L., Sanz, D. S., & Trueba, A. (2022). Analysis of the supply chain in commercial ports by SCOR model. Journal of Maritime Research, 19(3), 1–8.
Tutuhatunewa, A., Ririmasse, H. C., & Noya, M. F. (2023). Measuring the performance of shipyard industry supply chain with SCOR model. The 7Th International Conference on Basic Sciences 2021 (Icbs 2021), 2588, 040013. https://doi.org/10.1063/5.0111743
Waaly, A. N., Ridwan, A. Y., & Akbar, M. D. (2018). Development of sustainable procurement monitoring system performance based on Supply Chain Reference Operation (SCOR) and Analytical Hierarchy Process (AHP) on leather tanning industry. MATEC Web of Conferences, 204, 1008. https://doi.org/10.1051/matecconf/201820401008