Mendorong Pertumbuhan Bisnis: Memanfaatkan Pengambilan Keputusan Berbasis Data untuk Optimalisasi Sumber Daya dan Inovasi dalam Manufaktur Agil

Authors

  • Andy Rustandy STIE Manajemen Bisnis Indonesia
  • Dyah Ayu Suryaningrum Universitas Sebelas Maret
  • Dendi Zainuddin Hamidi Universitas Linggabuana PGRI Sukabumi
  • Tribowo Rachmat Fauzan Universitas Padjadjaran
  • Ivonne Ayesha Universitas Muhammadiyah Bandung

DOI:

https://doi.org/10.37385/msej.v4i6.3805

Keywords:

Pengambilan Keputusan Berbasis Data, Manufaktur Gesit, Pemanfaatan Sumber Daya, Inovasi, Efisiensi Operasional

Abstract

Abstrak ini bertujuan untuk menyoroti pentingnya Pengambilan Keputusan Berbasis Data (Data-Driven Decision Making, DDDM) dalam konteks strategi manufaktur yang gesit di Indonesia. Pendekatan kualitatif diadopsi, dengan menggunakan tinjauan literatur yang komprehensif untuk mengumpulkan data yang relevan. Jurnal akademis, artikel penelitian, laporan industri, dan studi kasus terkait tentang DDDM dalam strategi agile manufacturing dianalisis. Analisis literatur mengungkapkan bahwa penerapan DDDM dalam agile manufacturing dapat meningkatkan efisiensi operasional, kualitas produk, dan manajemen rantai pasokan. DDDM juga memungkinkan respon yang cepat terhadap perubahan permintaan pasar dan memfasilitasi pengembangan produk dan layanan inovatif yang disesuaikan dengan kebutuhan pelanggan. Mengintegrasikan DDDM dalam strategi agile manufacturing memiliki implikasi yang signifikan bagi bisnis di Indonesia. Hal ini memberdayakan perusahaan untuk mencapai keunggulan kompetitif dengan mengoptimalkan sumber daya, meminimalkan risiko, dan dengan cepat beradaptasi dengan perubahan pasar. Selain itu, DDDM menumbuhkan budaya inovasi yang berkelanjutan, menghasilkan penawaran yang berpusat pada pelanggan dan mendorong pertumbuhan bisnis jangka panjang. Memahami dan menerapkan DDDM sangat penting bagi perusahaan yang ingin tetap kompetitif dan berkelanjutan di pasar yang dinamis.

References

Alipour-Vaezi, M., … R. T.-M.-J. of I., & 2022, undefined. (2021). Scheduling the COVID-19 vaccine distribution based on data-driven decision-making methods. Jiems.Icms.Ac.Ir, 8(2), 196–206. https://doi.org/10.22116/JIEMS.2022.138130

Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technological Forecasting and Social Change, 168. https://doi.org/10.1016/j.techfore.2021.120766

Baardman, L., Cristian, R., Perakis, G., Singhvi, D., Skali Lami, O., & Thayaparan, L. (2023). The role of optimization in some recent advances in data-driven decision-making. Mathematical Programming, 200(1), 1–35. https://doi.org/10.1007/s10107-022-01874-9

Botvin, M., Hershkovitz, A., & Forkosh-Baruch, A. (2023). Data-driven decision-making in emergency remote teaching. Education and Information Technologies, 28(1), 489–506. https://doi.org/10.1007/s10639-022-11176-4

Bousdekis, A., Lepenioti, K., Apostolou, D., & Mentzas, G. (2021). A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics (Switzerland), 10(7). https://doi.org/10.3390/electronics10070828

Dash, B., & Ansari, M. F. (2022). Self-service analytics for data-driven decision making during COVID-19 pandemic: An organization’s best defense. Academia Letters, March. https://doi.org/10.20935/al4978

Farjana, M., Fahad, A. B., Alam, S. E., & Islam, M. M. (2023). An IoT- and Cloud-Based E-Waste Management System for Resource Reclamation with a Data-Driven. IoT, 4, 202–220.

Geng, X., & Xie, L. (2019a). Data-driven decision making in power systems with probabilistic guarantees: Theory and applications of chance-constrained optimization. Annual Reviews in Control, 47, 341–363. https://doi.org/10.1016/j.arcontrol.2019.05.005

Geng, X., & Xie, L. (2019b). Data-driven Decision Making with Probabilistic Guarantees (Part 1): A Schematic Overview of Chance-constrained Optimization. College Station, Part I. http://arxiv.org/abs/1903.10621

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hamoud, A. K., Hussein, M. K., Alhilfi, Z., & Sabr, R. H. (2021). Implementing data-driven decision support system based on independent educational data mart. International Journal of Electrical and Computer Engineering, 11(6), 5301–5314. https://doi.org/10.11591/ijece.v11i6.pp5301-5314

Hemalatha, C., Sankaranarayanasamy, K., & Durairaaj, N. (2021). Lean and agile manufacturing for work-in-process (WIP) control. Materials Today: Proceedings, 46, 10334–10338. https://doi.org/10.1016/j.matpr.2020.12.473

Hong, W., Wang, B., Yao, M., Callaway, D., Dale, L., & Huang, C. (2022). Data-Driven Power System Optimal Decision Making Strategy under Wildfire Events. Proceedings of the Annual Hawaii International Conference on System Sciences, 2022-January, 3570–3579. https://doi.org/10.24251/hicss.2022.436

Khalfallah, M., & Lakhal, L. (2021). The impact of lean manufacturing practices on operational and financial performance: the mediating role of agile manufacturing. International Journal of Quality and Reliability Management, 38(1), 147–168. https://doi.org/10.1108/IJQRM-07-2019-0244

Kumar, R., Singh, K., & Jain, S. K. (2019). Development of a framework for agile manufacturing. World Journal of Science, Technology and Sustainable Development, 16(4), 161–169. https://doi.org/10.1108/WJSTSD-05-2019-0022

Kumar, R., Singh, K., & Jain, S. K. (2020). Agile manufacturing: a literature review and Pareto analysis. International Journal of Quality and Reliability Management, 37(2), 207–222. https://doi.org/10.1108/IJQRM-12-2018-0349

Kumar, R., Singh, K., & Jain, S. K. (2021). An empirical investigation and prioritization of barriers toward implementation of agile manufacturing in the manufacturing industry. TQM Journal, 33(1), 183–203. https://doi.org/10.1108/TQM-04-2020-0073

Kuppler, M., Kern, C., Bach, R. L., & Kreuter, F. (2022). From fair predictions to just decisions? Conceptualizing algorithmic fairness and distributive justice in the context of data-driven decision-making. Frontiers in Sociology, 7. https://doi.org/10.3389/fsoc.2022.883999

Little, M., Cohen-Vogel, L., Sadler, J., & Merrill, B. (2019). education policy analysis archives A peer-reviewed, independent, open access, multilingual journal Data-Driven Decision Making in Early Education: Evidence From North Carolina’s Pre-K Program. EPAA AAPE, 1–27. http://epaa.asu.edu/ojs/http://dx.doi.org/10.14507/epaa.27.4198

Liu, Y., Zhang, D., & Gooi, H. B. (2021). Data-driven decision-making strategies for electricity retailers: A deep reinforcement learning approach. CSEE Journal of Power and Energy Systems, 7(2), 358–367. https://doi.org/10.17775/CSEEJPES.2019.02510

McElheran, E., & Brynjolfsson, K. (2019). Data in action:Making, data-driven decisions, predictive analytics in US manufacturing. SSRN, 1–49. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3422397

Pagan, S., Magner, K., & Thibedeau, C. (2019). Supporting Data-Driven Decision Making in a Canadian School District. International Journal for Digital Society, 10(3), 1510–1515. https://doi.org/10.20533/ijds.2040.2570.2019.0187

Qamar, A., Hall, M. A., Chicksand, D., & Collinson, S. (2020). Quality and flexibility performance trade-offs between lean and agile manufacturing firms in the automotive industry. Production Planning and Control, 31(9), 723–738. https://doi.org/10.1080/09537287.2019.1681534

Sarker, I. H. (2021). Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective. SN Computer Science, 2(5), 1–22. https://doi.org/10.1007/s42979-021-00765-8

Shahat Osman, A. M., & Elragal, A. (2021). Smart cities and big data analytics: A data-driven decision-making use case. Smart Cities, 4(1), 286–313. https://doi.org/10.3390/smartcities4010018

Sindhwani, R., Mittal, V. K., Singh, P. L., Aggarwal, A., & Gautam, N. (2019). Modelling and analysis of barriers affecting the implementation of lean green agile manufacturing system (LGAMS). Benchmarking, 26(2), 498–529. https://doi.org/10.1108/BIJ-09-2017-0245

Sugiyono. (2019). Metodologi Penelitian.

Syundyukov, E., Mednis, M., Zaharenko, L., Pildegovica, E., Danovska, I., Kistkins, S., Seidmann, A., Benis, A., Pirags, V., & Tzivian, L. (2021). Data-driven decision making and proactive citizen–scientist communication: A cross-sectional study on covid-19 vaccination adherence. Vaccines, 9(12), 1–17. https://doi.org/10.3390/vaccines9121384

Villeneuve, A., & Bouchamma, Y. (2023). Data-driven decision making using local multi-source data: Analysis of a teacher-researcher’s professional practice. Teaching and Teacher Education, 132. https://doi.org/10.1016/j.tate.2023.104198

Downloads

Published

2024-11-20

How to Cite

Rustandy, A., Suryaningrum, D. A., Hamidi, D. Z., Fauzan, T. R., & Ayesha, I. (2024). Mendorong Pertumbuhan Bisnis: Memanfaatkan Pengambilan Keputusan Berbasis Data untuk Optimalisasi Sumber Daya dan Inovasi dalam Manufaktur Agil. Management Studies and Entrepreneurship Journal (MSEJ), 4(6), 9459–9468. https://doi.org/10.37385/msej.v4i6.3805