Main Article Content
Agile Supply Chain Management, Palm Oil Industry, ISM, MICMAC
This paper tries to model agile supply chain management performance indicators in the palm oil industry. The interpretative Structural Modeling (ISM) method is used to find the relationship between these indicators. The ISM stages begin with identifying indicators, compiling contextual relationships, compiling reachability matrices, compiling level partitions, compiling digraphs, and compiling ISM models. Then MICMAC analysis is used to group each of these indicators into four categories based on their driving power and dependence power. In this study, 16 hands of agile supply chain management in the palm oil industry were obtained, of which the four-level ISM model could be constructed. Two indicators are at level 4, six hands are at level 3, three indicators are at level 2, and five indicators are at level 1. Meanwhile, through MICMAC analysis, five indicators are found in the independent indicators category, six hands are in the linkage indicator category, four indicators are included in the dependent indicator category, and one indicator is in the autonomous indicator category. This research can be used by managers in the palm oil industry who want to increase agility in their supply chain. In general, indicators at level 4 can affect indicators at level 3, and so on. So that management can start fixing the indicators at level 4 first. In addition, indicators that have a driving power value in MICMAC analysis can be prioritized to improve their performance.
 CNBC Indonesia, “Bunda Jangan Ngamuk, Ini 3 Penyebab Harga Minyak Goreng Mahal,” 2022. https://www.cnbcindonesia.com/news/20220106062500-4-304982/bunda-jangan-ngamuk-ini-3-penyebab-harga-minyak-goreng-mahal
 Katadata, “https://katadata.co.id/agung/berita/6233ff14d5695/kenapa-minyak-goreng-mahal-ini-tiga-alasannya,” 2022. https://katadata.co.id/agung/berita/6233ff14d5695/kenapa-minyak-goreng-mahal-ini-tiga-alasannya
 P. P. B. K. DPR RI, “Dampak Kebijakan Larangan Mudik,” 2022. [Online]. Available: https://berkas.dpr.go.id/puslit/files/info_singkat/Info Singkat-XIV-10-II-P3DI-Mei-2022-230.pdf
 R. Abdoli Bidhandi and C. Valmohammadi, “Effects of supply chain agility on profitability,” Bus. Process Manag. J., vol. 23, no. 5, pp. 1064–1082, 2017, doi: 10.1108/BPMJ-05-2016-0089.
 U. Abdul Kadar, S. R. Devadasan, and K. Balakrishnan, “Design of agile supply chain model for footwear industry,” Int. J. Bus. Excell., vol. 17, no. 2, pp. 230–244, 2019, doi: 10.1504/IJBEX.2019.097545.
 D. M. Gligor, C. L. Esmark, and M. C. Holcomb, “Performance outcomes of supply chain agility: When should you be agile?,” J. Oper. Manag., vol. 33–34, pp. 71–82, 2015, doi: 10.1016/j.jom.2014.10.008.
 P. V. Zhukov, A. A. Silvanskiy, K. Y. Mukhin, and O. L. Domnina, “Agile supply chain management in multinational corporations: Opportunities and barriers,” Int. J. Supply Chain Manag., vol. 8, no. 3, pp. 416–425, 2019.
 J. R. Jadhav, S. S. Mantha, and S. B. Rane, “Development of framework for sustainable Lean implementation : an ISM approach,” J Ind Eng Int, 2014, doi: 10.1007/s40092-014-0072-8.
 Y. Han, R. Zhou, Z. Geng, J. Bai, B. Ma, and J. Fan, “A novel data envelopment analysis cross-model integrating interpretative structural model and analytic hierarchy process for energy efficiency evaluation and optimization modeling: Application to ethylene industries,” J. Clean. Prod., vol. 246, 2020, doi: 10.1016/j.jclepro.2019.118965.
 S. Mithun, A. Hossen, Z. Mahtab, G. Kabir, S. Kumar, and H. Adnan, “Barriers to lean six sigma implementation in the supply chain : An ISM model,” Comput. Ind. Eng., vol. 149, no. January 2019, p. 106843, 2020, doi: 10.1016/j.cie.2020.106843.
 R. Primadasa and D. Tauhida, “Hubungan antar Hambatan Green Supply Chain Management (GSCM) pada Industri Kelapa Sawit di Indonesia,” J. Optimasi Sist. Ind., vol. 19, no. 1, p. 40, 2020, doi: 10.25077/josi.v19.n1.p40-49.2020.
 R. Primadasa, A. Sokhibi, and D. Tauhida, “Interrelationship of Green Supply Chain Management ( GSCM ) Performance Indicators for Palm Oil Industry in Indonesia,” 2019, doi: 10.1088/1757-899X/598/1/012034.
 Z. Yang and Y. Lin, “The effects of supply chain collaboration on green innovation performance:An interpretive structural modeling analysis,” Sustain. Prod. Consum., vol. 23, pp. 1–10, 2020, doi: 10.1016/j.spc.2020.03.010.
 S. Dhir and S. Dhir, “Modeling of strategic thinking enablers: a modified total interpretive structural modeling (TISM) and MICMAC approach,” Int. J. Syst. Assur. Eng. Manag., vol. 11, no. 1, pp. 175–188, 2020, doi: 10.1007/s13198-019-00937-z.
 X. Gan, R. Chang, J. Zuo, T. Wen, and G. Zillante, “Barriers to the transition towards off-site construction in China: An Interpretive structural modeling approach,” J. Clean. Prod., vol. 197, pp. 8–18, 2018, doi: 10.1016/j.jclepro.2018.06.184.
 A. Verma, N. Seth, and N. Singhal, “Application of Interpretive Structural Modelling to establish Interrelationships among the Enablers of Supply Chain Competitiveness,” Mater. Today Proc., vol. 5, no. 2, pp. 4818–4823, 2018, doi: 10.1016/j.matpr.2017.12.056.
 C. Singh, D. Singh, and J. S. Khamba, “Developing a conceptual model to implement green lean practices in Indian manufacturing industries using ISM-MICMAC approach,” J. Sci. Technol. Policy Manag., vol. 12, no. 4, pp. 587–608, 2020, doi: 10.1108/JSTPM-08-2019-0080.
 N. Lamba and P. Thareja, “Modelling of barriers pertaining to implementation of green supply chain management using ISM approach,” Mater. Today Proc., no. xxxx, 2020, doi: 10.1016/j.matpr.2020.09.488.
 M. N. Patel, A. A. Pujara, R. Kant, and R. K. Malviya, “Assessment of circular economy enablers: Hybrid ISM and fuzzy MICMAC approach,” J. Clean. Prod., vol. 317, no. July, p. 128387, 2021, doi: 10.1016/j.jclepro.2021.128387.
 M. Movahedipur, J. Zeng, M. Yang, and X. Wu, “An ISM approach for the barrier analysis in implementing sustainable supply chain management: An empirical study,” Manag. Decis., vol. 55, no. 8, pp. 1824–1850, 2017, doi: 10.1108/MD-12-2016-0898.
 A. H. Azadnia, G. Onofrei, and P. Ghadimi, “Electric vehicles lithium-ion batteries reverse logistics implementation barriers analysis: A TISM-MICMAC approach,” Resour. Conserv. Recycl., vol. 174, no. May, p. 105751, 2021, doi: 10.1016/j.resconrec.2021.105751.
 A. Jindal, S. K. Sharma, K. S. Sangwan, and G. Gupta, “Modelling Supply Chain Agility Antecedents Using Fuzzy DEMATEL,” Procedia CIRP, vol. 98, pp. 436–441, 2021, doi: 10.1016/j.procir.2021.01.130.
 D. M. Gligor and M. C. Holcomb, “Understanding the role of logistics capabilities in achieving supply chain agility: A systematic literature review,” Supply Chain Manag., vol. 17, no. 4, pp. 438–453, 2012, doi: 10.1108/13598541211246594.
 S. Fayezi, A. Zutshi, and A. O’Loughlin, “Understanding and Development of Supply Chain Agility and Flexibility: A Structured Literature Review,” Int. J. Manag. Rev., vol. 19, no. 4, pp. 379–407, 2017, doi: 10.1111/ijmr.12096.
 M. Balaji, V. Velmurugan, and C. Subashree, “TADS: An assessment methodology for agile supply chains,” J. Appl. Res. Technol., vol. 13, no. 5, pp. 504–509, 2015, doi: 10.1016/j.jart.2015.10.002.
 V. Jain, L. Benyoucef, and S. G. Deshmukh, “A new approach for evaluating agility in supply chains using Fuzzy Association Rules Mining,” Eng. Appl. Artif. Intell., vol. 21, no. 3, pp. 367–385, 2008, doi: 10.1016/j.engappai.2007.07.004.
 B. Talukder, G. P. Agnusdei, K. W. Hipel, and L. Dubé, “Multi-indicator supply chain management framework for food convergent innovation in the dairy business,” Sustain. Futur., vol. 3, no. April 2020, p. 100045, 2021, doi: 10.1016/j.sftr.2021.100045.
 F. E. Minguillon and G. Lanza, “Coupling of centralized and decentralized scheduling for robust production in agile production systems,” Procedia CIRP, vol. 79, no. i, pp. 385–390, 2019, doi: 10.1016/j.procir.2019.02.099.
 A. P. Chaidir, “Flexible Supply Chain Network Design For CPO Derivatives,” J. Basic Sci. Technol., vol. 9, no. 3, pp. 86–93, 2020, [Online]. Available: www.iocscience.org/ejournal/index.php/JBST
 S. Phogat and A. K. Gupta, “Development of framework for just-in-time implementation in maintenance: An ISM-MICMAC approach,” J. Qual. Maint. Eng., vol. 24, no. 4, pp. 488–510, 2018, doi: 10.1108/JQME-08-2017-0052.
 M. Ahmad, X. Tang, J. Qiu, and F. Ahmad, “Applied Sciences Interpretive Structural Modeling and MICMAC Analysis for Identifying and Benchmarking Significant Factors of Seismic Soil Liquefaction,” no. 1964, doi: 10.3390/app9020233.
 B. Ruben R, V. S., and A. P., “ISM and Fuzzy MICMAC application for analysis of Lean Six Sigma barriers with environmental considerations,” Int. J. Lean Six Sigma, vol. 9, no. 1, pp. 64–90, 2018, doi: 10.1108/IJLSS-11-2016-0071.
 W. Wang, X. Liu, Y. Qin, J. Huang, and Y. Liu, “Assessing contributory factors in potential systemic accidents using AcciMap and integrated fuzzy ISM - MICMAC approach,” Int. J. Ind. Ergon., vol. 68, no. July, pp. 311–326, 2018, doi: 10.1016/j.ergon.2018.08.011.