Affiliation:
1. Bialystok University of Technology , Białystok Poland
Abstract
Abstract
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 future-oriented 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.
Subject
Management of Technology and Innovation,Industrial and Manufacturing Engineering,Strategy and Management,Management Information Systems
Reference83 articles.
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
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