Abulhanova, G. A., Chumarina, G. R., Nikiforova, E. G., & Sharifullina, T. A. (2016). Economic forecasting and personnel management of small and medium enterprises. Academy of Strategic Management Journal 15(4), 67-75.
Aizenberg, I., Sheremetov, L., Villa-Vargas, L., & Martinez-Muñoz, J. (2016). Multilayer neural network with multi-valued neurons in time series forecasting of oil production. Neurocomputing 175, 980-989.
Alam, W., Sinha, K., Kumar, R. R., Ray, M., Rathod, S., Singh, K. N., & Arya, P. (2018). Hybrid linear time series approach for long term forecasting of crop yield. Indian Journal of Agricultural Sciences 88(8), 1275-1279.
Alva, I., Rojas, & J., Raymundo, C. (2020). Improving processes through the use of the 5S methodology and menu engineering to reduce production costs of a MSE in the hospitality sector in the department of Ancash. Advances in Intelligent Systems and Computing 1018, 818-824.
Artun, E., Vanderhaeghen, & M., Murray, P. (2016). A pattern-based approach to waterflood performance prediction using knowledge management tools and classical reservoir engineering forecasting methods. Gas and Coal Technology, 13(1) 19-40.
Barinova, O. I., & Shikhova, O. A. (2016). Methodological problems of milk cost forecasting in operational cost management. Innovative Way of Development of Agro-Industrial Complex: Collection of Scientific Works on Materials of XXXIX International Scientific-Practical Conference of the Faculty (pp. 156-161).
Chen, X. J., Tang, Z.-H., & Li, J. F. (2012). Preliminary study on BIPV grid-connected generation system production forecasting. Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control 40(18), 81-85.
Chunyan, L., & Jun, C. (2009). Traffic Accident Macro Forecast Based on ARIMAX Model. International Conference on Measuring Technology and Mechatronics Automation, 3, 633-636.
Cieślak, M. (Ed.). (2005). Prognozowanie gospodarcze. Metody i zastosowania [Economic forecasting. Methods and applications]. Warszawa, Poland: Wydawnic-two Naukowe PWN.
Clark, A. J., Lake, L. W., & Patzek, T. W. (2011). Production forecasting with logistic growth models. Proceedings - SPE Annual Technical Conference and Exhibition 1, 184-194.
Cortez, P., Rocha, M., Machado, J., & Neves, J. (1995). A neural network-based time series forecasting system. Proceedings of IEEE International Conference on Neural Networks
de Oliveira, R. C., Mendes-Moreira, J., & Ferreira, C. A. (2018). Agribusiness intelligence: Grape production forecast using data mining techniques. Advances in Intelligent Systems and Computing 747, 3-8.
Dupré, A., Drobinski, P. A., Alonzo, B. A., Badosa, J. A., Briard, C. C., & Plougonven, R. (2020). Sub-hourly forecasting of wind speed and wind Energy. Renewable Energy 145, 2373-2379.
Ejdys, J., Halicka, K., & Godlewska, J. (2015). Prognozowanie cen energii elektrycznej na giełdzie energii [Forecasting electricity prices on the energy exchange]. Zeszyty Naukowe. Organizacja i Zarządzanie. Politechnika Śląska, 77 53-61.
Elgharbi, S., Esghir, M., Ibrihich, O., Abarda, A., El Hajji, S., & Elbernoussi, S. (2020). Grey-Markov Model for the Prediction of the Electricity Production and Consumption. Lecture Notes in Networks and Systems 81, 206-219.
Eraslan, E. (2009). The estimation of product standard time by artificial neural networks in the molding industry. Mathematical Problems in Engineering2009, 1-12.
Eraslan, E., Farhan, A., Hassnain, S., Irum R., & Abdul, S. (2011). Forecasting milk production in Pakistan. Pakistan Journal of Agricultural Research 24(1-4), 82-85.
Gligor, A., Dumitru, C.-D., & Grif, H.-S. (2018). Artificial intelligence solution for managing a photovoltaic energy production unit. Procedia Manufacturing 22, 626-633.
Guanwu, J., Minzhou, L., Keqiang, B., & Saixuan, C. (2017). A Precise Positioning Method for a Puncture Robot Based on a PSO-Optimized BP Neural Network Algorithm. Applied Sciences 7(10), 1-13.
Gudanowska, A. E. (2017). A map of current research trends within technology management in the light of selected literature. Management and Production Engineering Review 8(1), 78-88.
Gyulai, D., Pfeiffer, A., Nick, G., Gallina, V., Sihn, W., & Monostori, L. (2018). Lead time prediction in a flow-shop environment with analytical and machine learning approaches. IFAC-PapersOnLine 51(11), 1029-1034.
Halicka, K. (2016). Prospektywna analiza technologii – metodologia i procedury badawcze [Prospective technology analysis – research methodology and procedures]. Białystok, Poland: Oficyna Wydawnicza Politechniki Białostockiej.
Jain, A., Patel, N., Hammonds, P., & Pandey, S. (2018). A smart software system for flow assurance management Society of Petroleum Engineers SPE Asia Pacific Oil and Gas Conference and Exhibition.
Kamiński, A. (1974). Metoda, technika, procedura badawcza w pedagogice empirycznej [Method, technique, research procedure in empirical pedagogy]. In R. Wroczyński, & T. Pilch (Ed.), Metodologia pedagogiki społecznej [Methodology of social pedagogy]. Wrocław, Poland: Wydawnictwo PAN.
Kikolski, M., & Ko, C. H. (2018). Facility layout design – review of current research directions. Engineering Management in Production and Services, 10(3), 70-79.
Korol, T. (2010). Systemy ostrzegania przedsiębiorstw przed ryzykiem upadłości [Systems warning companies about the risk of bankruptcy]. Warszawa, Poland: Oficyna Ekonomiczna Grupa Wolters Kluwer.
Kuladzhi, T., Babkin, I., Murtazayev, S.-A., & Golovina, T. (2017). Digital matrix micro forecast of informational and telecommunicational products cost value Proceedings of the 2017 International Conference “Quality Management, Transport and Information Security, Information Technologies”.
Kyzenko, O., Hrebeshkova, O., & Grebeshkov, O. (2017). Business intelligence in the economic management of organization Forum Scientiae Oeconomia, 5(2), 15-27.
Lai, X., Shui, H., & Ni, J. (2018). A two-layer long short-Term memory network for bottleneck prediction in multi-job manufacturing systems ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC.
Li, S., Ma, X., & Yang, C. (2018). A novel structure-adaptive intelligent grey forecasting model with full-order time power terms and its application. Computers and Industrial Engineering 120, 53-67.
Lin, B., Wong, S. F., & Ho, W. I. (2015). Study on the production forecasting based on grey neural network model in automotive industry IEEE International Conference on Industrial Engineering and Engineering Management.
Maciąg, A., Pietroń, R., & Kukla, S. (2013). Prognozowanie i symulacja w przedsiębiorstwie [Business forecasting and simulation]. Warszawa, Poland: Polskie Wydawnictwo Ekonomiczne.
Meling, L. M., Morkeseth, P. O., & Langeland, T. (1988). Production forecasting for gas fields with multiple reservoirs of limited extent Society of Petroleum Engineers of AIME, (Paper) SPE SIGMA.
Mustafa, I. K., & Jbara, O. K. (2018). Forecasting the food gap and production of wheat crop in Iraq for the period (2016-2025). Iraqi Journal of Agricultural Sciences 49(4), 560-568.
Mustafaeva, U. Z. (2007). Regression analysis of the dependence of the volume of production on the cost of it. Econ Agric Process Enterprises, 5, 46-47.
Nazarko, J. (Ed.). (2004). Prognozowanie w zarządzaniu przedsiębiorstwem, cz. 2. Prognozowanie na podstawie szeregów czasowych [Forecasting in business management, part 2. Forecasting based on time series]. Białystok, Poland: Wydawnictwo Politechniki Białostockiej.
Okubo, H., Weng, J., Kaneko, R., Simizu, T., & Onari, H. (2000). Production lead-time estimation system based on neural network Proceedings of Asia-Pacific Region of Decision Sciences Institute.
Onaran, E., & Yanık, S. (2020). Predicting cycle times in textile manufacturing using artificial neural network. Advances in Intelligent Systems and Computing, 1029, 305-312.
Qader, S. H., Dash, J., & Atkinson, P. M. (2018). Forecasting wheat and barley crop production in arid and semiarid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq. Science of the Total Environment, 613-614, 250-262.
Radziszewski, P., Nazarko, J., Vilutiene, T., Dębkowska, K., Ejdys, J., Gudanowska, A., Halicka, K., Kilon, J., Kononiuk, A., Kowalski, K. J., Król, J. B., Nazarko, Ł., & Sarnowski, M. (2016). Future Trends in Road Technologies Development in the Context of Environmental Protection. Baltic Journal of Road and Bridge Engineering, 11(2), 160-168.
Sarma, P., Lawrence, K., Zhao, Y., Kyriacou, S., & Saks, D. (2018). Implementation and assessment of production optimization in a steamflood using machine-learning assisted modeling Society of Petroleum Engineers - SPE International Heavy Oil Conference and Exhibition, HOCE 2018.
Siderska, J., & Jadaa K. S. (2018). Cloud manufacturing: a service-oriented manufacturing paradigm. A review paper Engineering Management in Production and Services 10(1), 22-31.
Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P., & Bokrantz, J. (2018). A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines. Computers and Industrial Engineering 125, 533-544.
Susanto, S., Tanaya, P. I., & Soembagijo, A. S. (2012). Formulating standard product lead time at a textile factory using artificial neural networks Proceeding of 2012 International Conference on Uncertainty Reasoning and Knowledge Engineering, URKE 2012, 6319595, 99-104.
Tariq, Z. (2018). An automated flowing bottom-hole pressure prediction for a vertical well having multiphase flow using computational intelligence techniques Society of Petroleum Engineers - SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition 2018, SATS 2018.
Tariq, Z., Mahmoud, M., & Abdulraheem, A. (2019). Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques. Journal of Petroleum Exploration and Production Technology
Theocharides, S., Makrides, G., Georghiou, G. E., & Kyprianou, A. (2018). Machine learning algorithms for photovoltaic system power output prediction. 2018 IEEE International Energy Conference, Energycon, 2018, 1-6.
Tkachev, S. I., Voloshchuk, L. A., Melnikova, Y. V., Pakhomova, T. V., & Rubtsova, S. N. (2018). Economic and mathematical modeling of quantitative assessment of financial risks of agricultural enterprises. Journal of Applied Economic Sciences 13(3), 823-829.
Trubaev, P. A., & Tarasyuk, P. N. (2017). Evaluation of energy-saving projects for generation of heat and heat supply by prime cost forecasting method. International Journal of Energy Economics and Policy 7(5), 201-208.
Wang, A., & Li, S. (2011). Prediction on the developing trend of global electric automobile based on the logistic model BMEI 2011 - Proceedings 2011 International Conference on Business Management and Electronic Information.
Wang, C., & Jiang, P. (2019). Deep neural networks based order completion time prediction by using real-time job shop RFID data. Journal of Intelligent Manufacturing, 30(3) 1303-1318.
Wasilewski, J. (2014). Application of ARIMAX models to short-term electric energy production forecasting at wind micro power plants. Przegląd Elektrotechniczny 90(7), 135-138.
Wickens, L. M., & De Jonge, G. (2006). Increasing confidence in production forecasting through risk-based integrated asset modelling, captain field case study. Society of Petroleum Engineers, 68th European Association of Geoscientists and Engineers Conference and Exhibition, incorporating SPE EUROPEC 2006, EAGE 2006: Opportunities in Mature Areas 6, 3162-3174.
Winkowska, J., Szpilko, D., & Pejić, S. (2019). Smart city concept in the light of the literature review. Engineering Management in Production and Services 11(2), 70-86.
Yang, L., Lin, H., Gong, Y., & Zhou, T. (2018). Coalbed methane production forecasting based on dynamic PSO neural network model ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery.
Yureneva, T., Barinova, O., & Golubeva, S. (2020). Forecasting the prime cost of milk production in an uncertain environment. Smart Innovation, Systems and Technologies 138, 678-693.
Yureneva, T. G., & Barinova, O. I. (2016). Cost differentiation in the dairy industry for short-term forecasting of milk cost. Management Accounting, 4, 28-37.
Zeng, B. L., Chengming L. S., Liu, S., & Li, C. (2016). A novel multi-variable grey forecasting model and its application in forecasting the amount of motor vehicles in Beijing. Computers & Industrial Engineering, 101 479-489.
Zhang, C., Orangi, A., Bakshi, A., Da Sie, W., & Prasanna, V. K. (2006). Model-based framework for oil production forecasting and optimization: A case study in integrated asset management. 2006 SPE Intelligent Energy Conference and Exhibition 2, 527-533.
Zhao, H., Huang, F., Li, L., & Zhang, C. (2018). Optimization of wastewater anaerobic digestion treatment based on ga-bp neural network. Desalination and Water Treatment 122, 30-35.
Zhou, C. L., & Liu, M. (2009). Application research on oil production forecasting based on BP neural network. Wuhan Ligong Daxue Xuebao/Journal of Wuhan University of Technology 31(3), 125-129.