Classification of forecasting methods in production engineering

• Autorzy: Winkowski Cezary

Identyfikatory

DOI

10.2478/emj-2019-0030

• Języki publikacji: EN

Abstrakty

EN

Business management is a continuous decision-making process. It is difficult to imagine a company that does not use forecasting techniques. Even small enterprises without relevant forecasting departments more or less consciously anticipate future events, forecasting the volume of production and setting directions for development. Today’s production companies must quickly adapt to changing customer requirements, implementing structural and technological changes and delivering projects related to the production of new products. Under the dynamically changing conditions, the functioning and effective management of modern enterprises depend on futureoriented information. This increases the validity of forecasting. This article aimed to identify forecasting methods and areas of their use in production engineering. The publications on this subject were reviewed in the Scopus database, using the time frame from January 1970 to June 2018. An original classification of research subareas was created using VOS viewer software, and then, a bibliometric map was developed to visualise the results of the word coexistence analysis. The analysis of the co-occurrence and co-classification of words made it possible to indicate research subareas of forecasting in production engineering and related emerging research areas and issues.

Słowa kluczowe

EN

forecasting; forecasting method; production engineering; manufacturing company

PL

prognozowanie; metoda prognozowania; inżynieria produkcji; zakład produkcyjny

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2019
• Tom: Vol. 11, no. 4
• Strony: 23--33
• Opis fizyczny: Bibliogr. 83 poz., rys., tab.

Twórcy

autor

Winkowski Cezary

• Bialystok University of Technology, Poland

Bibliografia

• 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- Munoz, 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).
• Box, G., & Jenkins, G. (1970). Time series Analysis: Forecasting and Control. San Francisco, United States: Holden-Day.
• 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: Wydawnictwo 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.
• Czerwiński, Z. (1992). Dylematy ekonomiczne [Economic dilemmas]. Warszawa, Poland: Państwowe Wydawnictwo Ekonomiczne.
• 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.
• Dupre, 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.
• Dzikevičius, A., & Šaranda, S. (2011). Smoothing techniques for market fluctuation signals. Business: Theory and Practice, 12(1), 63-74.
• 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.
• Ejdys, J., Halicka, K., & Winkowski, C. (2014). Predicting oil prices. Journal of Machine Construction and Maintenance, 92(1), 5-13.
• 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 Engineering, 2009, 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.
• Halicka, K. (2017). Main concepts of technology analysis in the light of the literature on the subject. Procedia Engineering, 182, 291-298.
• Jae, R., Shim, J.K., & Siegel, J.G. (2009). Modern Cost Management and Analysis. Barron’s Educational Series.
• 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.
• Jones, D. (2004). Estimation of power system parameters. IEEE Transactions on Power Systems, 19(4), 1980-1989.
• 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.
• Kot, S., & Grondys, K. (2011). Theory of inventory management based on demand forecasting. Polish Journal of Management Studies, 3(1), 147-155.
• 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.
• Laick, S. (2012). Using Delphi methodology in information system research. International Journal of Management Cases, 14(4), 261-268.
• 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.
• Linstone, H.A., & Turoff, M. (1975). The Delphi method: techniques and applications. Addison-Wesley Pub. Co.
• 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.
• Merchant, M. (1970). Technological forecasting and production engineering research. Ann CIRP, 18(1), 5-11.
• 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 szeregow czasowych. [Forecasting in business management, part 2. Forecasting based on time series]. Białystok, Poland: Wydawnictwo Politechniki Białostockiej.
• Ngadono, T.S., & Ikatrinasari, Z.F. (2018). Forecasting of PVB Film Using ARIMA. IOP Conference Series: Materials Science and Engineering, 453(1).
• 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., Krol, 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.
• Rahmat, R.F., Nurmawan, Sembiring, S., Syahputra, M.F., & Fadli (2018). Adaptive neuro-fuzzy inference system for forecasting rubber milk production. IOP Conference Series: Materials Science and Engineering, 308(1), 012014.
• Sagheer, A., & Kotb, M. (2018). Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing, 323, 203-213.
• 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.
• Skulmoski, G. J., Hartman, F.T., & Krahn, J. (2007). The Delphi Method for Graduate Research. Journal of Information Technology Education, 6, 1-21.
• Słownik nowy języka polskiego [New polish language dictionary]. (2002). Warszawa, Poland: Wydawnictwo Naukowe PWN.
• Sobczyk, M. (2008). Prognozowanie. Teoria, Przykłady, Zadania [Forecasting. Theory, Examples, Tasks]. Warszawa, Poland: Wydawnictwo Placet.
• Spicer, J.H. (1970). Cybernetic approach to strategic planning, marketing and production control. Rail International, 1(6), 400-404.
• 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.
• Szpilko, D. (2017). Tourism Supply Chain – overview of selected literature. Procedia Engineering, 182, 687-693.
• 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). Realtime 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.
• Witek-Crabb, A. (2016). Maturity of strategic management in organizations. Oeconomia Copernicana, 7(4), 669-682.
• Wu, Y., Hou, F., & Cheng, X. (2017). Real-time prediction of styrene production volume based on machine learning algorithms. Lecture Notes in Computer Science 10357 LNAI, 301-312.
• 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.
• Zeliaś, A. (1997). Teoria prognozy [Forecast theory]. Warszawa, Poland: Polskie Wydawnictwo Ekonomiczne.
• 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.