Demand Forecasting Capability and Inventory Flexibility in Enhancing Supply Chain Agility through Demand Information Sharing in Distribution Companies
DOI:
https://doi.org/10.61132/jukerdi.v3i3.1619Keywords:
Demand Forecasting Capability, Demand Information Sharing, Distribution Companies, Inventory Flexibility, Supply Chain AgilityAbstract
Ketidakpastian pasar dan volatilitas permintaan mengharuskan perusahaan distribusi untuk mengembangkan kemampuan rantai pasokan yang mendukung respons cepat dan efektif terhadap perubahan kebutuhan pelanggan. Studi ini meneliti pengaruh kemampuan peramalan permintaan dan fleksibilitas persediaan terhadap ketangkasan rantai pasokan melalui peran mediasi berbagi informasi permintaan. Pendekatan penjelasan kuantitatif digunakan dengan menggunakan data dari 300 manajer, supervisor, dan karyawan yang bertanggung jawab atas persediaan, perencanaan permintaan, pengadaan, logistik, dan aktivitas distribusi. Responden dipilih menggunakan pengambilan sampel bertujuan, dan data dikumpulkan melalui kuesioner skala Likert lima poin. Hubungan antar konstruk dianalisis menggunakan Pemodelan Persamaan Struktural Kuadrat Terkecil Parsial (PLS-SEM). Hasil menunjukkan bahwa kemampuan peramalan permintaan dan fleksibilitas persediaan secara signifikan meningkatkan berbagi informasi permintaan dan ketangkasan rantai pasokan. Berbagi informasi permintaan juga memiliki pengaruh positif yang signifikan terhadap ketangkasan rantai pasokan dan sebagian memediasi pengaruh kemampuan peramalan permintaan dan fleksibilitas persediaan. Temuan ini menekankan pentingnya mengintegrasikan peramalan, praktik persediaan yang fleksibel, dan pertukaran informasi permintaan untuk memperkuat ketangkasan rantai pasokan di perusahaan distribusi.
Downloads
References
Ahmed, E. H. E. H. (2024). Dynamic capabilities—Alternative perspectives. In Studies in systems, decision and control (Vol. 538, pp. 575–586). Springer. https://doi.org/10.1007/978-3-031-62102-4_48
Akhouri, D., Dastidar, N. G., Tripathy, S., Singhal, D., & Panda, D. (2024). An ISM approach among the key factors of SCM for Industry 4.0 towards sustainability in Indian manufacturing sectors. In Decision-making models and applications in manufacturing environments (pp. 445–462). CRC Press. https://doi.org/10.1201/9781003394723-20
Anitha, E., Navin Prasath, M., Sanjai, L., Andrea Shiny, J. A., & Varsini, P. (2023). Effective food demand forecasting using machine learning algorithms. In 2023 IEEE Engineering Informatics (EI 2023). IEEE. https://doi.org/10.1109/IEEECONF58110.2023.10520567
Arora, K., Abbi, P., & Gupta, P. K. (2022). Analysis of supply chain management data using machine learning algorithms. In Studies in systems, decision and control (Vol. 424, pp. 119–133). Springer. https://doi.org/10.1007/978-981-19-0240-6_8
Bai, B. (2023). Acquiring supply chain agility through information technology capability: The role of demand forecasting in retail industry. Kybernetes, 52(10), 4712–4730. https://doi.org/10.1108/K-09-2021-0853
Bais, A., & Amechnoue, K. (2025). Forecasting in supply chain using machine learning techniques. Lecture Notes in Networks and Systems, 1402, 108–116. https://doi.org/10.1007/978-3-031-91334-1_9
Dickersbach, J. T., & Passon, M. F. (2015). Service parts planning overview. In Service parts planning with SAP SCM (pp. 1–12). Springer. https://doi.org/10.1007/978-3-662-45433-6_1
Dwaikat, N. Y., Money, A. H., Behashti, H. M., & Salehi-Sangari, E. (2018). How does information sharing affect first-tier suppliers’ flexibility? Evidence from the automotive industry in Sweden. Production Planning & Control, 29(4), 289–300. https://doi.org/10.1080/09537287.2017.1420261
Goltsos, T. E., Syntetos, A. A., Glock, C. H., & Ioannou, G. (2022). Inventory–forecasting: Mind the gap. European Journal of Operational Research, 299(2), 397–419. https://doi.org/10.1016/j.ejor.2021.07.040
Huang, Y.-S., Ho, J.-W., & Kao, W.-Y. (2022). Availability and reliability of information transmission for supply chain coordination with demand information sharing. Computers & Industrial Engineering, 172, 108642. https://doi.org/10.1016/j.cie.2022.108642
Jin, H.-W. (2019). Analysis of factors affecting the benefits of demand information sharing. E&M Economics and Management, 22(3), 204–219. https://doi.org/10.15240/tul/001/2019-3-013
Kosorukov, O., Maslov, S., Sviridova, O., & Bagisbayevc, K. (2022). Models for optimizing the supply volume in conditions of uncertainty in demand, taking into account the risks of accrual of penalties, loss of customers and additional costs. Lecture Notes in Networks and Systems, 387, 249–259. https://doi.org/10.1007/978-3-030-93872-7_21
Liao, S.-H., & Widowati, R. (2021). A supply chain management study: A review of theoretical models from 2014 to 2019. Operations and Supply Chain Management: An International Journal, 14(2), 173–188. https://doi.org/10.31387/oscm0450295
Prabusankar, R. (2017). Impact of supply chain management practices on competitive advantage of small manufacturing firms in Coimbatore district. International Journal of Mechanical Engineering and Technology, 8(10), 836–843.
Rafi, D., & Mubeena, M. (2022). Role of agritech start-ups in supply chain—An organizational approach of Ninjacart. In The digital agricultural revolution: Innovations and challenges in agriculture through technology disruptions (pp. 289–299). Wiley. https://doi.org/10.1002/9781119823469.ch12
Turgut, Y., Menguc, K., Alkan, N., Kose, Y., Ata, S., Satoglu, S. I., & Kabak, O. (2022). Supply chain analytics. In Business analytics for professionals (pp. 289–334). Springer. https://doi.org/10.1007/978-3-030-93823-9_11
Udenio, M., Hoberg, K., & Fransoo, J. C. (2018). Inventory agility upon demand shocks: Empirical evidence from the financial crisis. Journal of Operations Management, 62, 16–43. https://doi.org/10.1016/j.jom.2018.08.001
Zougagh, N., Charkaoui, A., & Echchatbi, A. (2020). Prediction models of demand in supply chain. Procedia Computer Science, 177, 462–467. https://doi.org/10.1016/j.procs.2020.10.063
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Jurnal Kewirausahaan Cerdas dan Digital

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.




