International Journal of Supply and Operations Management

International Journal of Supply and Operations Management

Key Actor Selection for Sustainable Supply Chain Based on Integration of SCOR DS-TOPSIS in Tapioca and Mocaf Agro-Industry

Document Type : Research Paper

Author
Faculty of Agricultural Technology, IPB University, Bogor, Indonesia
10.22034/ijsom.2026.110954.3518
Abstract
Objective: High wheat flour imports pose a high risk to the sustainability of the food industry in the event of trade wars and product boycotts by exporting countries, creating an urgent need to develop local flour as an alternative to imported wheat flour consumption. This study aims to identify and analyze sustainability related performance challenges within the tapioca and Mocaf flour supply chains in West Java.
Methods: The research applies an integrated of the SCOR DS and TOPSIS to determine priority solutions for improving the KPI performance matrix based on key actors in the supply chain. A stratified sampling method was used in West Java, specifically in Bogor, Sukabumi, Bandung, Garut, and Sumedang.
Results: The findings show that tapioca SMEs represent the ideal solution node for improving supply chain performance, with a preference value of 0.50. Based on this ideal solution, performance improvement in the tapioca and Mocaf agro‑industry supply chain in West Java should focus on coarse tapioca SMEs, prioritizing Source Availability in the KPI AM.1.1 Cash to Cash Cycle.
Conclusion: Enhancing the performance of tapioca and Mocaf flour supply chains particularly by strengthening Source Availability and shortening the AM.1.1 Cash to Cash Cycle at coarse tapioca SMEs will reinforce the economic resilience of local flour‑based agro‑industrial actors. These improvements foster inclusive and sustainable industrial development and support the achievement of Sustainable Development Goal 9 on Industry, Innovation and Infrastructure.
Keywords
Subjects

Adwiyah, R., Syaukat, Y., Mulyati, H., & Indrawan, D. (2024). Evaluating Sustainability in Palm Oil Supply Chains: a Performance Analysis With the Supply Chain Operations Reference for Digital Standard (SCOR DS). Revista de Gestao Social e Ambiental, 18(6), 1–19. https://doi.org/10.24857/rgsa.v18n6-083
Agussabti, Rahmaddiansyah, Deli, A., Arida, A., & Mahda, F. A. (2022). Factors affecting the decision of potato farmers in adopting superior seeds in Bener Meriah District. IOP Conference Series: Earth and Environmental Science, 951(1). https://doi.org/10.1088/1755-1315/951/1/012016
Al Khoiry, I., Gernowo, R., & Surarso, B. (2022). Fuzzy-ahp moora approach for vendor selection applications. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 8(1), 24–37. https://doi.org/10.26594/REGISTER.V8I1.2356
Anbarkhan, S. H. (2023). A Fuzzy-TOPSIS-Based Approach to Assessing Sustainability in Software Engineering: An Industry 5.0 Perspective. Sustainability (Switzerland), 15(18). https://doi.org/10.3390/su151813844
ASCM, M. A. F. S. (2022). Quick Reference Guide SCOR Digital Standard activities associated with all phases involved with satisfying a customer ’ s demand . Supply Chain Operations Reference Model.
Bell, S., Berg, T., & Morse, S. (2016). Rich Pictures: Sustainable Development and Stakeholders - The Benefits of Content Analysis. Sustainable Development, 24(2), 136–148. https://doi.org/10.1002/sd.1614
Chang, M. Y., & Chen, H. S. (2022). Understanding Consumers’ Intentions to Purchase Clean Label Products: Evidence from Taiwan. Nutrients, 14(18), 1–13. https://doi.org/10.3390/nu14183684
Cinar, U., & Cebi, S. (2020). A hybrid risk assessment method for mining sector based on QFD, fuzzy logic and AHP. Advances in Intelligent Systems and Computing, 1029, 1198–1207. https://doi.org/10.1007/978-3-030-23756-1_141
Dorcheh, F. R., & Rahbari, M. (2024). Identification and prioritization of factors affecting the saffron supply chain and selection strategies during the pandemic crisis based on the ANP-SWOT method. January, 1–14. https://doi.org/10.3389/fsufs.2024.1401415
Ernawati, D., Dewi, S., & Sari, N. K. (2022). Supply Chain Performance Measurement in a Refractory Brick Industry. 2022, 170–176. https://doi.org/10.11594/nstp.2022.2726
Garg, H., Agarwal, N., & Choubey, A. (2015). Entropy Based Multi-criteria Decision Making Method under Fuzzy Environment and Unknown Attribute Weights Technology & Optimization. 6(3). https://doi.org/10.4172/2229-8711.1000182
Ghadimi, F., & Aouam, T. (2021). Planning capacity and safety stocks in a serial production–distribution system with multiple products. European Journal of Operational Research, 289(2), 533–552. https://doi.org/10.1016/j.ejor.2020.07.024
Gul, M., & Yucesan, M. (2022). Performance evaluation of Turkish Universities by an integrated Bayesian BWM-TOPSIS model. Socio-Economic Planning Sciences, 80(May 2021), 101173. https://doi.org/10.1016/j.seps.2021.101173
Horticulture, F. C. and. (2022). Cassava Production by Regency/City in West Java. OpendataJabar. https://opendata.jabarprov.go.id/id/dataset/produksi-ubi-kayu-berdasarkan-kabupatenkota-di-jawa-barat
Iskandar, Y. A., Delu, K. D., Lusiani, M., & Layli, N. (n.d.). Key Performance Indicator Analysis Using Integrated SCOR- AHP : A Case Study of Indonesian ’ s Reverse Supply Chain Industry. https://doi.org/10.4108/eai.23-11-2022.2339152
Khairunnisa Naziro. (2025). Supply Chain Performance Measurement and Determination of Improvement Priorities Using the Supply Chain Operations Reference –Digital Standard (SCOR DS) and Analytic Network Process (ANP) Methods. Journal of Industrial Engineering and Halal Industries, 6(1), 81–88. https://doi.org/10.14421/jiehis.5294
Kramer, A. A., & Zimmerman, J. E. (2007). Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited. Critical Care Medicine, 35(9), 2052–2056. https://doi.org/10.0.4.73/01.CCM.0000275267.64078.B0
Kunovjanek, M., Knofius, N., & Reiner, G. (2022). Additive manufacturing and supply chains–a systematic review. Production Planning and Control, 33(13), 1231–1251. https://doi.org/10.1080/09537287.2020.1857874
Lima-Junior, F. R., & Carpinetti, L. C. R. (2016). Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management. International Journal of Production Economics, 174, 128–141. https://doi.org/10.1016/j.ijpe.2016.01.023
Masyithah, W., Onny Setiani, Yusniar Hanani Darundiati, Mursid Raharjo, & Nurjazuli. (2025). Association Between Pesticide Exposure and Type 2 Diabetes Mellitus Among Female Farmers: A Cross-Sectional Study. Jurnal Kesehatan Lingkungan, 17(3), 220–229. https://doi.org/10.20473/jkl.v17i3.2025.220-229
Meister, C., Pimentel, T., Wiher, G., & Cotterell, R. (2023). Locally Typical Sampling. Transactions of the Association for Computational Linguistics, 11, 102–121. https://doi.org/10.1162/tacl_a_00536
Ministry of Agriculture of the Republic of Indonesia. (2023). Performance Report of the Ministry of Agriculture for the Year 2023. Ministry of Agriculture of the Republic of Indonesia, 1–230.
Naing, L., Nordin, R. Bin, Abdul Rahman, H., & Naing, Y. T. (2022). Sample size calculation for prevalence studies using Scalex and ScalaR calculators. BMC Medical Research Methodology, 22(1), 209. https://doi.org/10.1186/s12874-022-01694-7
Nazim, R., Ahmad, R., & Raja, I. (2017). Criteria for Supplier Selection : An Application of AHP-SCOR Integrated Model ( ASIM ). 6(3), 284–290.
Okwu, M. O., & Tartibu, L. K. (2020). Sustainable supplier selection in the retail industry : A TOPSIS- and ANFIS-based evaluating methodology. 12, 1–14. https://doi.org/10.1177/1847979019899542
Palma-mendoza, J. A. (2014). International Journal of Information Management Analytical hierarchy process and SCOR model to support supply chain re-design. International Journal of Information Management, 34(5), 634–638. https://doi.org/10.1016/j.ijinfomgt.2014.06.002
Pathan, A. I., Girish Agnihotri, P., Said, S., & Patel, D. (2022). AHP and TOPSIS based flood risk assessment- a case study of the Navsari City, Gujarat, India. Environmental Monitoring and Assessment, 194(7). https://doi.org/10.1007/s10661-022-10111-x
Paul, P., Pennell, M. L., & Lemeshow, S. (2013). Standardizing the power of the Hosmer-Lemeshow goodness of fit test in large data sets. Statistics in Medicine, 32(1), 67–80. https://doi.org/10.1002/sim.5525
Pingmuanglek, P., Jakrawatana, N., & Gheewala, S. H. (2017). Supply chain analysis for cassava starch production: Cleaner production opportunities and benefits. Journal of Cleaner Production, 162, 1075–1084. https://doi.org/10.1016/j.jclepro.2017.06.148
Rahbari, M., Tavakkoli-Moghaddam, R., Razavi Hajiagha, S. H., & Jafari, M. J. (2025). Wheat Supply Chain Network Design: Lesson for Resilience and Sustainability in a Situation of War and Crisis. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-025-02682-0
Robertson, J. L., Smiith, K. K., Savin, N. ., & Lavigne, R. J. (1984). Effect of Dose Selection and Sample Size on The Precision of Lethal Dose Estimates in Dose-Mortality Regressuin (pp. 833–837). https://doi.org/10.1093/jee/77.4.833
Ronie, M. E., Mamat, H., Hazim, A., Aziz, A., Mohammad, N., Kobun, R., Pindi, W., Zainol, M. K., & Putra, N. R. (2025). The Potential and Current Applications of Tapioca ( Manihot esculenta Crantz ) Flour and Starch as Functional Ingredients in Food Products – a Review. 2(1), 34–64. https://doi.org/10.0.200.0/ijf.v2i1.5421
Salwin, M., Jacyna-Gołda, I., Kraslawski, A., & Waszkiewicz, A. E. (2022). The Use of Business Model Canvas in the Design and Classification of Product-Service Systems Design Methods. Sustainability (Switzerland), 14(7). https://doi.org/10.3390/su14074283
Santosa, S. H., Hidayat, A. P., & Siskandar, R. (2022). Raw material planning for tapioca flour production based on fuzzy logic approach: a case study. Jurnal Sistem Dan Manajemen Industri, 6(1), 67–76. https://doi.org/10.30656/jsmi.v6i1.4594
Sassone, G., Arbaoui, T., & Botta-Genoulaz, V. (2024). How Best Practices of SCOR DS Model Support Short Supply Chains Management: A Bibliometric Analysis. IFIP Advances in Information and Communication Technology, 726 IFIP, 259–273. https://doi.org/10.1007/978-3-031-71739-0_17
Singh, P. K., & Sarkar, P. (2019). A framework based on fuzzy AHP-TOPSIS for prioritizing solutions to overcome the barriers in the implementation of ecodesign practices in SMEs. International Journal of Sustainable Development and World Ecology, 26(6), 506–521. https://doi.org/10.1080/13504509.2019.1605547
Statistic Indonesia, B. (2023). Distribution of Wheat Flour Trade in Indonesia. 1–28.
Statistic Indonesia, B. (2024). Wheat and Meslin Imports by Main Source Countries. In Central Statistic Agency (Vol. 87, Issue 1,2, pp. 149–200). bps.go.id
Suradi, S. (2023). Waste analysis of tapioca unloading process with lean supply chain approach in Makassar Port. Acta Logistica, 10(1), 71–77. https://doi.org/10.22306/al.v10i1.353
Taipale-Erävala, K., Salmela, E., & Lampela, H. (2020). Towards a New Business Model Canvas for Platform Businesses in Two-Sided Markets. Journal of Business Models, 8(3), 107–125. https://doi.org/10.5278/jbm.v8i3.4621
Tama, I. P., Yuniarti, R., Eunike, A., Hamdala, I., & Azlia, W. (2019). Risk Identification in Cassava Chip Supply Chain Using SCOR (Supply Chain Operation Reference). IOP Conference Series: Materials Science and Engineering, 494(1). https://doi.org/10.1088/1757-899X/494/1/012050
Trisna, Marimin, Arkeman, Y., & Sunarti, T. C. (2016). Genetic algorithm based multi-objective optimization of wheat flour supply chain considering raw material substitution. ICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings, 79–84. https://doi.org/10.1109/ICACSIS.2015.7415158
Wątróbski, J., Bączkiewicz, A., Ziemba, E., & Sałabun, W. (2022). Sustainable cities and communities assessment using the DARIA-TOPSIS method. Sustainable Cities and Society, 83(February). https://doi.org/10.1016/j.scs.2022.103926
Wijaya Apip, Marimin, & Mulyati, H. (2025). Enhancing Performance and Risk Management in the Government’s Rice Supply Chain. https://doi.org/10.33964/jp.v33i3.881
Zhu, G. N., Hu, J., & Ren, H. (2020). A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments. Applied Soft Computing Journal, 91, 106228. https://doi.org/10.1016/j.asoc.2020.106228

Articles in Press, Accepted Manuscript
Available Online from 20 June 2026