Have a personal or library account? Click to login
Implementation of Business Intelligence for Sales Prediction and Inventory Optimization and Maintenance in SME Carton Box Company with Open-Source Application Cover

Implementation of Business Intelligence for Sales Prediction and Inventory Optimization and Maintenance in SME Carton Box Company with Open-Source Application

Management Systems in Production Engineering
Volume 34 (2026): Issue 1 (March 2026)
By: Lina Gozali,  Pricilia Micca Zulfan,  I Wayan Sukania,  Syuhaida Ismail,  Wan Hee Cheng,  Maslin Masrom and  Christhoper Robin  
Open Access
|Feb 2026

References

  1. Turban, E., Pollard, C. and Wood, G., (2021). Information Technology for Management: Driving Digital Transformation to Increase Local and Global Performance, Growth and Sustainability. John Wiley & Sons.
    Search in Google ScholarBack to article
  2. Tuli, F.A. and Kaluvakuri, S., (2022). Implementation of ERP Systems in Organizational Settings: Enhancing Operational Efficiency and Productivity. Asian Bus. Rev, 12, pp. 89-96.
    Search in Google ScholarBack to article
  3. Adewusi, A.O., Okoli, U.I., Adaga, E., Olorunsogo, T., Asuzu, O.F. and Daraojimba, D.O., (2024). Business intelligence in the era of big data: A review of analytical tools and competitive advantage. Computer Science & IT Research Journal, 5(2), pp. 415-431.
    Search in Google ScholarBack to article
  4. Niu, Y., Ying, L., Yang, J., Bao, M. and Sivaparthipan, C.B., (2021). Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), p. 102725.
    Search in Google ScholarBack to article
  5. Sanders, N.R., (2025). Supply chain management: A global perspective. John Wiley & Sons.
    Search in Google ScholarBack to article
  6. Chen, W., Yang, K., Yu, Z., Shi, Y. and Chen, C.P., (2024). A survey on imbalanced learning: latest research, applications and future directions. Artificial Intelligence Review, 57(6), p. 137.
    Search in Google ScholarBack to article
  7. Ekakitie, S.E., Kifordu, A.A. and Nwaebuni, C., (2022). Optimizing profit maximization through effective inventory control practice of manufacturing firms in Nigeria. Journal of Global Social Sciences, 3(11), pp. 89-114.
    Search in Google ScholarBack to article
  8. Demiray Kırmızı, S., Ceylan, Z. and Bulkan, S., (2024). Enhancing Inventory Management through Safety-Stock Strategies – A Case Study. Systems, 12(7), p. 260.
    Search in Google ScholarBack to article
  9. Tadayonrad, Y. and Ndiaye, A.B., (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3, p. 100026.
    Search in Google ScholarBack to article
  10. Ghaffar Nia, N., Kaplanoglu, E. and Nasab, A., (2023). Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discover Artificial Intelligence, 3(1), pp. 5.
    Search in Google ScholarBack to article
  11. Lohmer, J. and Lasch, R., (2021). Production planning and scheduling in multi-factory production networks: a systematic literature review. International Journal of Production Research, 59(7), pp. 2028-2054.
    Search in Google ScholarBack to article
  12. Safat, W., Asghar, S. and Gillani, S.A., (2021). Empirical analysis for crime prediction and forecasting using machine learning and deep learning techniques. IEEE access, 9, pp. 70080-70094.
    Search in Google ScholarBack to article
  13. Shao, C., Yang, Y., Juneja, S. and GSeetharam, T., (2022). IoT data visualization for business intelligence in corporate finance. Information Processing & Management, 59(1), p. 102736.
    Search in Google ScholarBack to article
  14. Majid, M.E., Marinova, D., Hossain, A., Chowdhury, M.E. and Rummani, F., (2024). Use of conventional business intelligence (bi) systems as the future of big data analysis. American Journal of Information Systems, 9(1), pp. 1-10.
    Search in Google ScholarBack to article
  15. Tavera Romero, C.A., Ortiz, J.H., Khalaf, O.I. and Ríos Prado, A., (2021). Business intelligence: business evolution after industry 4.0. Sustainability, 13(18), p. 10026.
    Search in Google ScholarBack to article
  16. Sanabia-Lizarraga, K.G., Carballo-Mendívil, B., Arellano-González, A. and Bueno-Solano, A., (2024). Business Intelligence for Agricultural Foreign Trade: Design and Application of Power BI Dashboard. Sustainability, 16(21), p. 9576.
    Search in Google ScholarBack to article
  17. Chandra, S., Gozali, L., Tjhia, A. and Daywin, F.J., (2021). Production Planning and Control Industry 4.0 on Plastic Bottles with the Blow Moulding Process (Case Study: PT. Peace Industrial Packaging). International Journal of Industrial Engineering and Operation Management.
    Search in Google ScholarBack to article
  18. Gozali, L., Daywin, F.J. and Wijaya, A.T., (2021). Production Planning and Control in Furniture Company at PT. Lion Metal Works. In IOP Conference Series: Materials Science and Engineering IOP Publishing.
    Search in Google ScholarBack to article
  19. Gozali, L., Marie, I.A., Hoswari, S., Christifan, A.J., Gunawan, P.A., Elliani, M.F.G.C. and Natasha, T., (2020), December. Forecasting using artificial neural networks and aggregate production planning and dynamic model of inventory control for rib and single knit fabric. In IOP Conference Series: Materials Science and Engineering (Vol. 1007, No. 1, p. 012023). IOP Publishing.
    Search in Google ScholarBack to article
  20. Richard, T., Gozali, L. and Daywin, F.J., (2023), March. Management of Raw Material Needs and Safety Stock Based on Data Forecast and System Dynamics Modeling. In 2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 7-12). IEEE.
    Search in Google ScholarBack to article
  21. Khullar, S. and Singh, N., (2022). Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation. Environmental Science and Pollution Research, 29(9), pp. 12875-12889.
    Search in Google ScholarBack to article
  22. Wijaya, A.T., Lefta, F., Gozali, L. and Daywin, F.J., (2020), December. Forecasting Analysis at PT. Lion Metal Works Using Artificial Neural Network. In IOP Conference Series: Materials Science and Engineering (Vol. 1007, No. 1, p. 012184). IOP Publishing.
    Search in Google ScholarBack to article
  23. Dastres, R. and Soori, M., (2021). Artificial neural network systems. International Journal of Imaging and Robotics (IJIR), 21(2), pp. 13-25.
    Search in Google ScholarBack to article
  24. Zhou, W., Yan, Z. and Zhang, L., (2024). A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction. Scientific Reports, 14(1), p. 5905.
    Search in Google ScholarBack to article
  25. Ahmed, S., Chakrabortty, R.K., Essam, D.L. and Ding, W., (2022). Poly-linear regression with augmented long short term memory neural network: Predicting time series data. Information Sciences, 606, pp. 573-600.
    Search in Google ScholarBack to article
  26. Kurani, A., Doshi, P., Vakharia, A., & Shah, M. (2023). A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Annals of Data Science, 10(1), pp. 183-208.
    Search in Google ScholarBack to article
  27. Singh, J., (2022). Concepts of inventory and related technical terminologies: A literature review. International Journal for Research Trends and Innovation, 7(1), pp. 1784-1796.
    Search in Google ScholarBack to article
  28. Doyer, I. and Bean, W.L., (2023). As easy as OEE: enabling productivity improvement in schools by using overall equipment effectiveness as framework for classroom data analysis. International Journal of Lean Six Sigma, 14(5), pp. 1055-1074.
    Search in Google ScholarBack to article
  29. Chandra, J.N., Gozali, L. and Jap, L., (2019). Calculation of safety stock and bottleneck minimization with theory of constraints method approach on sand coated metal roof production in XYZ Ltd. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 5-7).
    Search in Google ScholarBack to article
  30. Sheffi, Y., (2021). What everyone gets wrong about the never-ending COVID-19 supply chain crisis. MIT Sloan Management Review, 63(1), pp. 1-5.
    Search in Google ScholarBack to article
  31. Fan, L., Song, Z., Mao, W., Luo, T., Wang, W., Yang, K. and Cao, F., (2024). Change is safer: a dynamic safety stock model for inventory management of large manufacturing enterprise based on intermittent time series forecasting. Journal of Intelligent Manufacturing, pp. 1-21.
    Search in Google ScholarBack to article
  32. Malindzakova, M., Garaj, P., Trpčevská, J. and Malindzak, D., (2022). Setting MRP parameters and optimizing the production planning process. Processes, 10(4), p. 690.
    Search in Google ScholarBack to article
  33. Kalaiarasi, K., Soundaria, R., Kausar, N., Agarwal, P., Aydi, H. and Alsamir, H., (2021). Optimization of the average monthly cost of an EOQ inventory model for deteriorating items in machine learning using PYTHON. Thermal Science, 25(Spec. issue 2), pp. 347-358.
    Search in Google ScholarBack to article
  34. Amaechi, C.V., Reda, A., Kgosiemang, I.M., Ja’e, I.A., Oyetunji, A.K., Olukolajo, M.A. and Igwe, I.B., (2022). Guidelines on asset management of offshore facilities for monitoring, sustainable maintenance, and safety practices. Sensors, 22(19), p.7270.
    Search in Google ScholarBack to article
  35. Doyer, I. and Bean, W.L., (2023). As easy as OEE: enabling productivity improvement in schools by using overall equipment effectiveness as framework for classroom data analysis. International Journal of Lean Six Sigma, 14(5), pp.1055-1074.
    Search in Google ScholarBack to article
  36. Jurewicz, D., Dąbrowska, M., Burduk, A., Medyński, D., Machado, J., Motyka, P. and Kolbusz, K., (2023), September. Implementation of total productive maintenance (tpm) to improve overall equipment effectiveness (oee)-case study. In International Conference on Intelligent Systems in Production Engineering and Maintenance (pp. 543-561). Cham: Springer Nature Switzerland.
    Search in Google ScholarBack to article
  37. Mittal, S., (2024). Framework for optimized sales and inventory control: a comprehensive approach for intelligent order management application. International journal of computer trends and technology, 72(3), pp.61-65.
    Search in Google ScholarBack to article
  38. Wang, F. and Aviles, J., (2023). Enhancing operational efficiency: Integrating machine learning predictive capabilities in business intellgence for informed decision-making. Frontiers in business, economics and management, 9(1), pp. 282-286.
    Search in Google ScholarBack to article
  39. Rautaray, S.S., Pandey, M., Das, I., Sharma, B. and Mishra, S., (2022), December. An Automation Framework for Supply Chain Inventory Management using Predictive Business Analytics. In 2022 OITS International Conference on Information Technology (OCIT) (pp. 1-7). IEEE.
    Search in Google ScholarBack to article
  40. Nurdin, A.A., Salmi, G.N., Sentosa, K., Wijayanti, A.R. and Prasetya, A., (2023). Utilization of business intelligence in sales information systems. Journal of Information System Exploration and Research, 1(1), pp. 39-48.
    Search in Google ScholarBack to article
  41. Mullaikodi, C., Jagannathan, S.K., Singh, R., Tomar, Y. and Jaison, B., (2024), March. Enhancing Business Intelligence Through A Semi-Distributed Blockchain Framework for Predictive Analytics On Sales Growth. In 2024 2nd International Conference on Disruptive Technologies (ICDT) (pp. 1303-1308). IEEE.
    Search in Google ScholarBack to article
  42. Apeko, J.D., Osunmakinde, I.O., Abdulgader, M.M. and Nwosu, K.C., (2023), November. Predictive Analytics on Used Car Prices Using Business Intelligence of Bayesian Networks for Sales Risk Reduction. In 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET) (pp. 1-6). IEEE.
    Search in Google ScholarBack to article
  43. Picozzi, P., Nocco, U., Pezzillo, A., De Cosmo, A. and Cimolin, V., (2024). The Use of Business Intelligence Software to Monitor Key Performance Indicators (KPIs) for the Evaluation of a Computerized Maintenance Management System (CMMS). Electronics, 13(12), p.2286.
    Search in Google ScholarBack to article
  44. Cano Lengua, M.A., Mallma Trujillo, R.E. and Moquillaza Henríquez, S.D., (2023). Business Intelligence to Optimize Decision-Making in a Telecommunication Company.
    Search in Google ScholarBack to article
  45. Johnson, A. and Brown R., 2021. Predictive Sales Analysis Using Business Intelligence: Integrating Inventory Data. Journal of Business Analytics, 42(5), pp. 541-555.
    Search in Google ScholarBack to article
  46. Ridho, M., Gutandjala, I.I., Windart, A.T. and Pransandy, T., (2023), May. Business Intelligence Dashboard for Asset Inventory Management Monitoring at PT Indonesia Power Head Office. In 2023 8th International Conference on Business and Industrial Research (ICBIR) (pp. 728-733). IEEE.
    Search in Google ScholarBack to article
  47. Green, L. and Miller, K., (2022). Business Intelligence for Maintenance Optimization: Predictive Analysis in Manufacturing. Journal of Manufacturing Systems, 45(2), pp. 65-80.
    Search in Google ScholarBack to article
  48. Aguiar, C.G. and Alcalá, S.G.S., (2024). Business Intelligence applied to inventory management: case of a small retail company. Revista de Gest ã o e Secretariado, 15(2), pp. e3457-e3457.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/mspe-2026-0008 | Journal eISSN: 2450-5781 | Journal ISSN: 2299-0461
Journal RSS Feed
Language: English
Page range: 85 - 96
Submitted on: Mar 1, 2025
|
Accepted on: Jan 1, 2026
|
Published on: Feb 16, 2026
Published by: STE Group sp. z.o.o.
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
analytical neural network,
business intelligence,
maintenance management,
material requirement planning
Related subjects:
Business and economics,
Business management,
Management accounting, financial controlling, cost calculation, investment,
Engineering,
Mechanical engineering,
Production technology,
Tools and methods,
Process engineering and industrial engineering

© 2026 Lina Gozali, Pricilia Micca Zulfan, I Wayan Sukania, Syuhaida Ismail, Wan Hee Cheng, Maslin Masrom, Christhoper Robin, published by STE Group sp. z.o.o.
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 34 (2026): Issue 1 (March 2026)Next article