Business Intelligence for Decision Support System for Replenishment Policy in Mining Industry

Authors

  • Franklin Chandra Pragnyono Seto Universitas Atma Jaya Yogyakarta, Indonesia
  • Yosef Daryanto Universitas Atma Jaya Yogyakarta, Indonesia
  • Ririn Diar Astanti Universitas Atma Jaya Yogyakarta, Indonesia

DOI:

https://doi.org/10.24002/ijieem.v5i1.7245

Keywords:

decising making, business intelligence, mining industry, replenishment policy, death stock

Abstract

The mining industry has unique characteristics in the sense that usually, the plant is located in a remote area while the headquarters are located in an urban area. These conditions pose challenges for the industry related to coordination within companies. This coordination is very important, especially in relation to the decision-making that must be carried out by the company. One of the important managerial decisions is related to the replenishment policy. To make replenishment decisions, companies need past data, such as biodiesel consumption rate, and current data, such as current stock and storage capacity, where the source of those data is in the plant. Often, decisions must be taken quickly because they have impacts on the continuousness of production operations at the plant. However, the remote location and shipping routes across rivers have created new challenges in the flow of goods and services supply because the shipment depends on the tides of the river. This research proposes a business intelligence system that collects, sorts, and visualizes data, then analyzes the replenishment decision to support decision-making in the mining industry. The system uses Microsoft Power BI software which is integrated with the company’s ERP system. To illustrate the applicability of the proposed system, it is applied to a coal mining company, especially in relation to the replenishment policy of biofuel. The result of this study indicates that the proposed system can work. In addition, it can reduce decision-making time by 220.65%.

 

References

Afandi, A., Farida, I.N., Mahdiyah, U. (2022, July 23). Penerapan algoritma Apriori dan model Moving Average untuk prediksi stok barang. Prosiding Seminar Nasional Inovasi Teknologi Universitas PGRI Kediri, 6(2), 421-426.

Afikah, P., Avorizano, A., Afandi, I.R., Hasan, F.N. (2022). Implementasi business intelligence untuk menganalisis data kasus virus Corona di Indonesia menggunakan platform Tableu. Jurnal Pseudocode, 9(1), 25-32.

Ahmadi, M., Jafarzadeh-Ghoushchi, S., Taghizadeh, R., Sharifi, A. (2019). Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches. Neural Computing and Application, 31, 8661–8680.

Akbar, R., Oktaviani, R., Tamimi, S., Shavira, S., Rahmadani, T.W. (2017). Implementasi business intelligence untuk menentukan tingkat kepopuleran jurusan pada universitas. Jurnal Ilmiah Informatika, 2(2), 135-138.

Albara, A., Al-Khowarizmi, A-K., Pradesyah, R. (2021). Power Business Intelligence in the data science visualization process to forecast CPO prices. International Journal of Science, Technology & Management, 2(6), 2198-2208.

Al-Khowarizmi, A-K., Nasution, I.R., Lubis, M., Lubis, A.R. (2020). The effect of a SECoS in crude palm oil forecasting to improve business intelligence. Bulletin of Electrical Engineering and Informatics, 9(4), 1604-1611.

Becker, L.T., Gould, E.M. (2019). Microsoft Power BI: Extending Excel to manipulate, analyze, and visualize diverse data. Serial Review, 1–5.

Bertsimas, D., Kallus, N., & Hussain, A. (2016). Inventory management in the era of big data. Production and Operations Management, 25(12), 2006–2009.

Bhargava, M.G., Kiran, K.T.P.S, Rao, D.R. (2018). Analysis and design of visualization of educational institution database using Power BI tool. Global Journal of Computer Science and Technology, 18(4), 1–8.

Bokde, N.D., Yaseen, Z.M., Andersen, G.B. (2020). ForecastTB - an R package as a test-bench for time series forecasting-application of wind speed and solar radiation modeling. Energies, 13(10), 2578.

da Silva, D.M.C., Pareira, P., Amaro, A.C.S. (2020, October 16-17). Logistic performance & dashboards: a flexible Power BI solution. Proceedings of the 20th Conferência da Associção Portuguesa de Sistemas de Informação 2020.

Da, Z., Engelberg, J., Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461–1499.

Fatima, A., Linnes, C. (2019). The current status of business intelligence: a systematic literature review. American Journal of Information Technology, 9(1), 1-21.

Fedushko, S., Ustyianovych, T., Gregus, M. (2020). Real-time high-load infrastructure transaction status output prediction using operational intelligence and big data technologies. Electronics, 9(4), 668.

Frazzetto, D., Nielsen, T.D., Pedersen, T.B., Šikšnys, L. (2019). Prescriptive analytics: A survey of emerging trends and technologies. The VLDB Journal, 28, 575-595.

Galli, L., Levato, T., Schoen, F. (2021). Prescriptive analytics for inventory management in health care. Journal of the Operational Research Society, 72(10), 2211–2224.

Goel, S., Hofman, J.M., Lahaie, S., Pennock, D.M., Watts, D.J. (2010). Predicting consumer behavior with web search. Proceedings of the National Academy of Sciences of the United States of America, 107(41), 17486–17490.

Gruhl, D., Chavet, L., Gibson, D., Meyer, J., Pattanayak, P., Tomkins, A., Zien, J. (2004). How to build a WebFountain: an architecture for very large-scale text analytics. IBM Systems Journal, 43(1), 64–77.

Gruhl, D., Guha, R., Kumar, R., Novak, J., Tomkins, A. (2005, August 21). The predictive power of online chatter. Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 78–87.

Heizer, J., Render, B. (2015). Manajemen operasi: Manajemen keberlangsungan dan rantai pasokan. Salemba Empat.

Holt, C.C., Modigliani, F., Simon, H. (1955). A linear decision rule for production and employment scheduling. Management Science, 2(1), 1–30.

Kallus, N. (2014, April 7). Predicting crowd behavior with big public data. Proceedings of the 23rd International Conference on World Wide Web (WWW), 625–630.

Khalwadekar, R., Gogate, U. (2022, December 2-3). Quantitative and causal analysis of techniques of Microsoft Power BI file optimisation. The 5th International Conference on Advances in Science and Technology (ICAST), Mumbai, India.

Kongprasert, N., Garrett, T., Saengphueng, S. (2021). Lean inventory management of an industrial tool distributor in Thailand using data visualization tool. Scientific Journals of Poznan University of Technology series of Organization Management, 84, 111-123.

Kurniawan, D., Saputra, A., Sanjaya, M.R., Yamani, Z. (2021). Extending the understanding of business intelligence and its applications in startups. Atlantis Highlights in Engineering, 7, 550-556.

Maricar, M.A. (2019). Analisa perbandingan nilai akurasi moving average dan exponential smoothing untuk sistem peramalan pendapatan pada perusahaan XYZ. Jurnal Sistem dan Informatika, 13(2), 36-45.

Pavkov, S., Poščić, P., Jakšić, D. (2016). Business intelligence systems yesterday, today, and tomorrow - an overview. Zbornik Veleučilišta u Rijeci, 4(1), 97-108.

Punia, S., Singh, S.P., Madaan, J.K. (2020). From predictive to prescriptive analytics: A data-driven multi-item newsvendor model. Decision Support Systems, 136, Article 113340.

Purba, M.A.B., Lase, K., Sembiring, A.I.S., Panjaitan, L.M., Putra, A.Z. (2021). Implementation of SECOS Algorithm in forecasting gold prices to improve business intelligence using MSE accuracy value measurement. Jurnal Mantik, 5(2), 1259-1265.

Salvadorinho, J., Teixeira, L., Santos, B.S. (2020). Storytelling with data in the context of Industry 4.0: a Power BI-based case study on the shop floor. Lecture Notes in Computer Science, 12427.

Soeffker, N., Ulmer, M.W., Mattfeld, D.C. (2022). Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review. European Journal of Operational Research, 298, 801-820.

Taylor, S.J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45.

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Published

2023-06-30

How to Cite

Seto, F. C. P., Daryanto, Y. ., & Diar Astanti, R. . (2023). Business Intelligence for Decision Support System for Replenishment Policy in Mining Industry. International Journal of Industrial Engineering and Engineering Management, 5(1), 51–60. https://doi.org/10.24002/ijieem.v5i1.7245

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Articles