Published March 25, 2026 | Version v1
Journal article Open

A DYNAMIC STOCHASTIC APPROACH FOR PREDICTING THE PRODUCTION CAPACITY OF AN INDUSTRIAL SYSTEM

Authors/Creators

Contributors

Description

 

ABSTRACT: Time loss in the manufacturing process of a production line reduces productivity and harms the system’s brand image and long-term performance. The present research focuses on the use of scientific analysis and diagnostic techniques to correct current errors and to anticipate the future behaviour of a system. This paper proposes a stochastic approach, FORCAST-FBM, which is based on a hybridization of three methods: Failure Mode and Effects Analysis (FMEA), Bayesian Networks, and Monte Carlo Simulation. Our objective is to address a crucial issue in industrial production systems—namely, the forecasting of the quantity to be produced within a probable time frame during an upcoming production period. This approach plays a key role in production planning and management development. The proposed solution applies to any system in which production follows a chronological sequence across parallel production facilities, where components move along an automated path with no backward flow.

Files

08. Lynda B Algeria.pdf

Files (874.5 kB)

Name Size Download all
md5:7c8a8d1e67590f427fc448f74d3daeb4
874.5 kB Preview Download

Additional details

Software

Repository URL
https://www.ajme.ro
Development Status
Active

References

  • [1] C. A. Garcia et al., "Visualization of Key Performance Indicators in the Production System in the Context of Industry 4.0," in IFAC-Papers on Line, Elsevier B.V., Jul. 2023, pp. 6582–6587. doi:10.1016/j.ifacol.2023.10.310. [2] G. Levitin, L. Xing, and Y. Dai, "Standby mode transfer schedule minimizing downtime of 1-out-of-N system with storage," Reliab Eng Syst Saf, vol. 237, Sep. 2023, doi: 10.1016/j.ress.2023.109322. [3] P. Li, L. Zhai, U. Shahzad, and C. Li, "Enhancing innovation performance in renewable energy through sustainable business model innovation: The moderating role of industry 4.0 technologies," J Environ Manage, vol. 395, Dec. 2025, doi: 10.1016/j.jenvman.2025.127766. [4] P. Rezaee and S. K. Moghaddam, "Assessment of design approaches for reconfigurable manufacturing systems based on forecasted demand data," Comput Ind Eng, vol. 201, Mar. 2025, doi: 10.1016/j.cie.2025.110878. [5] S. Mittal, M. A. Khan, D. Romero, and T. Wuest, "A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs)," Oct. 01, 2018, Elsevier B.V.doi: 10.1016/j.jmsy.2018.10.005. [6] N. Grabill, S. Wang, H. A. Olayinka, T. P. De Alwis, Y. F. Khalil, and J. Zou, "AI-augmented failure modes, effects, and criticality analysis (AI-FMECA) for industrial applications," Reliab Eng Syst Saf, vol. 250, Oct. 2024, doi: 10.1016/j.ress.2024.110308. [7] C. Ogbonnaya, C. Abeykoon, A. Nasser, C. S. Ume, U. M. Damo, and A. Turan, "Engineering risk assessment of photovoltaic-thermal-fuel cell system using classical failure modes, effects and criticality analyses," Cleaner Environmental Systems, vol. 2, Jun. 2021, doi: 10.1016/j.cesys.2021.100021. [8] D. Urgun, G. Gungor, and M. Esen Gungor, "Reliability analysis for hydrogen-integrated composite renewable power systems through AI-augmented Monte Carlo simulations," Int J Hydrogen Energy, vol. 144, pp. 838–850, Jul. 2025, doi: 10.1016/j.ijhydene.2025.04.234. [9] J. Li, W. Chen, Y. Liu, J. Yang, Z. Zhou, and D. Zeng, "Diffinformer: Diffusion informer model for long sequence time-series forecasting," Expert Syst Appl, vol. 299, Mar. 2026, doi: 10.1016/j.eswa.2025.129944. [10] M. S. Roulston, D. T. Kaplan, J. Hardenberg, and L. A. Smith, "Using medium-range weather forcasts to improve the value of wind energy production," Renew Energy, vol. 28, pp. 585–602, 2003, [Online]. Available: www.elsevier.com/locate/renene [11] H. Dong, H. Zheng, and Y. P. Li, "Model predictive control for inventory management in supply chain planning," in Procedia Engineering, 2011, pp. 1154–1159. doi: 10.1016/j.proeng.2011.08.213. [12] X. Xu, Z. Wang, F. Zhou, Y. Huang, T. Zhong, and G. Trajcevski, "Dynamic transformer ODEs for large-scale reservoir inflow forecasting," Knowl Based Syst, vol. 276, Sep. 2023, doi: 10.1016/j.knosys.2023.110737. [13] X. Li, J. Williams, C. Swanson, and T. Berg, "A machine learning approach to predictive maintenance: Remaining useful life and motor fault analysis," Comput Ind Eng, vol. 206, Aug. 2025, doi: 10.1016/j.cie.2025.111222. [14] P. Castillo, I. Albizu, M. T. Bedialauneta, and E. Fernandez, "Impact analysis of DLR systems with forecasting on economic benefits and renewable generation curtailments," International Journal of Electrical Power and Energy Systems, vol. 172, Nov. 2025, doi: 10.1016/j.ijepes.2025.111246. [15] N. Dini, Y. Batmani, and M. Davoodi, "Closed-Form Robust Safe Output-Feedback Control Design for Nonlinear Systems," in IFAC-PapersOnLine, Elsevier B.V., Oct. 2024, pp. 498–503. doi: 10.1016/j.ifacol.2025.01.095. [16] A. Bracale, G. Carpinelli, P. De Falco, and T. Hong, "Short-term industrial reactive power forecasting," International Journal of Electrical Power and Energy Systems, vol. 107, pp. 177–185, May 2019, doi: 10.1016/j.ijepes.2018.11.022. [17] Y. Y. Hong and C. L. P. P. Rioflorido, "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Appl Energy, vol. 250, pp. 530–539, Sep. 2019, doi: 10.1016/j.apenergy.2019.05.044. [18] F. G. Ciampi, A. Rega, T. M. L. Diallo, F. Pelella, J. Y. Choley, and S. Patalano, "Energy consumption prediction of industrial HVAC systems using Bayesian Networks," Energy Build, vol. 309, Apr. 2024, doi: 10.1016/j.enbuild.2024.114039. [19] D. Wu et al., "Data and knowledge fusion-driven Bayesian networks for interpretable fault diagnosis of HVAC systems," International Journal of Refrigeration, vol. 161, pp. 101–112, May 2024, doi: 10.1016/j.ijrefrig.2024.02.019. [20] J. Li and J. Wang, "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, vol. 213, Dec. 2020, doi: 10.1016/j.energy.2020.118787. [21] J. Li et al., "A Transformer-based model fusing temporal dependence and variable correlation for short and medium-term electricity price forecasting," Energy, vol. 338, Nov. 2025, doi: 10.1016/j.energy.2025.138799. [22] C. Zhao, A. Ye, L. Wu, and S. Zhan, "A novel deep learning model for post-processing of short- and medium-term daily precipitation forecasts," Atmos Res, vol. 326, Nov. 2025, doi: 10.1016/j.atmosres.2025.108319. [23] F. Yang, S. Li, X. Wu, and F. Ge, "Accident causation analysis of metal processing plants based on questionnaire and Bayesian network," Journal of Safety and Sustainability, vol. 1, no. 4, pp. 247–256, Dec. 2024, doi: 10.1016/j.jsasus.2024.11.005. [24] T. M. Demke, L. Osterkamp, M. Stephan, J. Wachsmann, P. Nyhuis, and M. Schmidt, "Investigation of different forecasting methods and models using a Forecast Support System to forecast spare parts demand in the MRO industry," in Procedia CIRP, Elsevier B.V., 2025, pp. 213–220. doi: 10.1016/j.procir.2024.12.026. [25] Q. Wu, H. Zheng, X. Guo, and G. Liu, "Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks," Renew Energy, vol. 199, pp. 977–992, Nov. 2022, doi: 10.1016/j.renene.2022.09.036. [26] P. Kumari and D. Toshniwal, "Deep learning models for solar irradiance forecasting: A comprehensive review," Oct. 10, 2021, Elsevier Ltd. doi: 10.1016/j.jclepro.2021.128566. [27] J. Zhou and G. Reniers, "Emergency response actions modeling and time analysis: Considering priority of actions," Process Safety and Environmental Protection, vol. 186, pp. 1066–1075, Jun. 2024, doi: 10.1016/j.psep.2024.04.062. [28] J. Reynolds, Y. Rezgui, A. Kwan, and S. Piriou, "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, vol. 151, pp. 729–739, May 2018, doi: 10.1016/j.energy.2018.03.113. [29] N. Xin, J. Su, and M. M. Hasan, "MMformer with adaptive attention: Advancing multivariate time series forecasting for environmental applications," Appl Soft Comput, vol. 186, p. 114090, Jan. 2026, doi: 10.1016/j.asoc.2025.114090. [30] I. Karakurt and G. Aydin, "Development of regression models to forecast the CO2 emissions from fossil fuels in the BRICS and MINT countries," Energy, vol. 263, Jan. 2023, doi: 10.1016/j.energy.2022.125650. [31] M. Foumani, S. Azarakhsh, A. Dahesh, A. Tajally, R. Tavakkoli-Moghaddam, and B. Vahedi-Nouri, "A Hybrid Stacking-Bayesian Model for Defect Detection in the Lithium-Ion Battery Industry," IFAC-PapersOnLine, vol. 59, no. 10, pp. 88–93, 2025, doi: 10.1016/j.ifacol.2025.09.017. [32] A. Dahesh, R. Tavakkoli-Moghaddam, N. Wassan, A. R. Tajally, Z. Daneshi, and A. Erfani-Jazi, "A hybrid machine learning model based on ensemble methods for devices fault prediction in the wood industry," Expert Syst Appl, vol. 249, Sep. 2024, doi: 10.1016/j.eswa.2024.123820. [33] M. S. Afzal and A. W. Al-Dabbagh, "Forecasting in Industrial Process Control: A Hidden Markov Model Approach," Elsevier B.V., Jul. 2017, pp. 14770–14775. doi: 10.1016/j.ifacol.2017.08.2591. [34] J. R. Do Rego and M. A. De Mesquita, "Demand forecasting and inventory control: A simulation study on automotive spare parts," Int J Prod Econ, vol. 161, pp. 1–16, Mar. 2015, doi: 10.1016/j.ijpe.2014.11.009. [35] Q. Zhang, H. Yan, and Y. Liu, "Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information," Renewable and Sustainable Energy Reviews, vol. 202, Sep. 2024, doi: 10.1016/j.rser.2024.114691. [36] N. Khakzad, "System safety assessment under epistemic uncertainty: Using imprecise probabilities in Bayesian network," Saf Sci, vol. 116, pp. 149–160, Jul. 2019, doi: 10.1016/j.ssci.2019.03.008. [37] M. Li, F. Yang, R. Uzsoy, and J. Xu, "A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems," J ManufSyst, vol. 38, pp. 114–133, 2016, doi: 10.1016/j.jmsy.2015.11.004. [38] M. Gansterer, C. Almeder, and R. F. Hartl, "Simulation-based optimization methods for setting production planning parameters," Int J Prod Econ, vol. 151, pp. 206–213, 2014, doi: 10.1016/j.ijpe.2013.10.016. [39] B. Salah, M. Alnahhal, and M. Ali, "Risk prioritization using a modified FMEA analysis in industry 4.0," Journal of Engineering Research (Kuwait), vol. 11, no. 4, pp. 460–468, Dec. 2023, doi: 10.1016/j.jer.2023.07.001. [40] Y. Yu, Y. Yang, J. Yu, S. Wu, Q. Zeng, H. Ding, J. Ma, Q. Duan, "An improved FMEA