Improving control efficiency in buffering systems using anticipatory indicators for demand forecasting

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.171260

Keywords:

demand forecasting, operational forecasting, cointegrated demand series, resource usage efficiency

Abstract

Optimization of the stock management process is associated with the search for a forecasting model, a method for generation of a forecasting time series, a model of logistic operation, determining a reasonable level of reserve stocks and establishing the optimization criterion.

Successful solution to the optimization problem in general can be achieved only if the whole complex of local management problems is successfully solved. In this case, the method of generation of a cointegrated time series of demand forecasting is the central element of the technology of optimal stocks management. This relates to the fact that probabilistic nature of demand is the main factor reducing efficiency of management in systems of this class.

It was shown that the proposed method for improving management efficiency can be used in any economic system due to the possibility of construction of a single logistic operation model.

The proposed approach is based on formation of a time series specifically designed to solve the problem of forecasting the demand in stocks buffering systems. Such a series contains both information on sales volumes and data related to consumer demand.

Since consumer activity is ahead of the process of physical consumption of products, it becomes possible to use anticipatory markers in forecasting problems.

The study of operational processes using a verified indicator of efficiency has confirmed the hypothesis of presence of anticipatory markers within the framework of the formed forecast time series.

It has been established that the maximum management efficiency can be achieved in the case of a lower construction accuracy of the forecast model. This is due to the fact that the logistic operation model takes into account the costs of movement of products and their valuation at the operation input and output.

Author Biographies

Igor Lutsenko, Kremenchuk Mykhailo Ostrohradskyi National University Pershotravneva str., 20, Kremenchuk, Ukraine, 39600

Doctor of Technical Sciences, Professor

Department of Information and Control Systems

Liudmyla Mikhailova, State Agrarian and Engineering University in Podilia Shevchenka str., 13, Kamianets-Podilskyi, Ukraine, 32300

PhD, Associate Professor

Department of Energy and Electrical Systems of the Agro-industrial Complex

Hanna Kolomits, Kryvyi Rih National University Vitaliya Matusevycha str., 11, Kryvyi Rih, Ukraine, 50027

Assistant

Department of Electromechanics

Artem Kuzmenko, Kryvyi Rih National University Vitaliya Matusevycha str., 11, Kryvyi Rih, Ukraine, 50027

Senior Lecturer

Department of Electromechanics

References

  1. Chase, C. (Ed.) (2012). Demand‐Driven Forecasting: A Structured Approach to Forecasting. Wiley, 270. doi: https://doi.org/10.1002/9781119203612
  2. Carta, S., Medda, A., Pili, A., Reforgiato Recupero, D., Saia, R. (2018). Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data. Future Internet, 11 (1), 5. doi: https://doi.org/10.3390/fi11010005
  3. Martino, J. P. (1993). Technological forecasting for decision making. McGraw-Hill, 591.
  4. Biegel, J. E. (1971). Production control: a quantitative approach. Prentice-Hall, 282.
  5. Fedoseev, V. V., Garmash, A. N., Dayitbegov, D. M., Orlova, I. V., Polovnikov, V. A. (1999). Ekonomiko-matematicheskie metody i prikladnye modeli. Moscow: YuNITI, 391.
  6. Awel, Y. M. (2018). Forecasting GDP Growth: Application of Autoregressive Integrated Moving Average Model. Empirical Economic Review, 1 (2), 1–16. doi: https://doi.org/10.29145/eer/12/010201
  7. Lim, P. Y., Nayar, C. V. (2012). Solar Irradiance and Load Demand Forecasting based on Single Exponential Smoothing Method. International Journal of Engineering and Technology, 4 (4), 451–455. doi: https://doi.org/10.7763/ijet.2012.v4.408
  8. Prestwich, S. D., Tarim, S. A., Rossi, R., Hnich, B. (2014). Forecasting intermittent demand by hyperbolic-exponential smoothing. International Journal of Forecasting, 30 (4), 928–933. doi: https://doi.org/10.1016/j.ijforecast.2014.01.006
  9. Taylor, J. W. (2004). Volatility forecasting with smooth transition exponential smoothing. International Journal of Forecasting, 20 (2), 273–286. doi: https://doi.org/10.1016/j.ijforecast.2003.09.010
  10. Ravinder, H. V. (2013). Forecasting With Exponential Smoothing Whats The Right Smoothing Constant? Review of Business Information Systems (RBIS), 17 (3), 117–126. doi: https://doi.org/10.19030/rbis.v17i3.8001
  11. Alam, T. (2019). Forecasting exports and imports through artificial neural network and autoregressive integrated moving average. Decision Science Letters, 249–260. doi: https://doi.org/10.5267/j.dsl.2019.2.001
  12. Tan, L., Wang, S., Wang, K. (2017). A new adaptive network-based fuzzy inference system with adaptive adjustment rules for stock market volatility forecasting. Information Processing Letters, 127, 32–36. doi: https://doi.org/10.1016/j.ipl.2017.06.012
  13. Doszyń, M. (2019). Intermittent demand forecasting in the enterprise: Empirical verification. Journal of Forecasting. doi: https://doi.org/10.1002/for.2575
  14. Lutsenko, I. (2015). Identification of target system operations. Development of global efficiency criterion of target operations. Eastern-European Journal of Enterprise Technologies, 2 (2 (74)), 35–40. doi: https://doi.org/10.15587/1729-4061.2015.38963
  15. Lutsenko, I., Dmytriiev, I., Avanesova, N., Semenyshyna, I., Rozhnenko, Z., Danileyko, O. (2019). A method to form control over queuing systems taking into consideration the probabilistic character of demand. Eastern-European Journal of Enterprise Technologies, 1 (3 (97)), 28–36. doi: https://doi.org/10.15587/1729-4061.2019.157201
  16. Narayanan, A., Sahin, F., Robinson, E. P. (2019). Demand and order‐fulfillment planning: The impact of point‐of‐sale data, retailer orders and distribution center orders on forecast accuracy. Journal of Operations Management. doi: https://doi.org/10.1002/joom.1026
  17. Engle, R. F., Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55 (2), 251–276. doi: https://doi.org/10.2307/1913236

Downloads

Published

2019-06-24

How to Cite

Lutsenko, I., Mikhailova, L., Kolomits, H., & Kuzmenko, A. (2019). Improving control efficiency in buffering systems using anticipatory indicators for demand forecasting. Eastern-European Journal of Enterprise Technologies, 3(4 (99), 14–20. https://doi.org/10.15587/1729-4061.2019.171260

Issue

Vol. 3 No. 4 (99) (2019): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2019 Igor Lutsenko, Liudmyla Mikhailova, Hanna Kolomits, Artem Kuzmenko

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.