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

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

Igor Lutsenko, Liudmyla Mikhailova, Hanna Kolomits, Artem Kuzmenko

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.

Keywords


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

References


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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

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

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

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


GOST Style Citations


Demand‐Driven Forecasting: A Structured Approach to Forecasting / C. Chase (Ed.). Wiley, 2012. 270 p. doi: https://doi.org/10.1002/9781119203612 

Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data / Carta S., Medda A., Pili A., Reforgiato Recupero D., Saia R. // Future Internet. 2018. Vol. 11, Issue 1. P. 5. doi: https://doi.org/10.3390/fi11010005 

Martino J. P. Technological forecasting for decision making. McGraw-Hill, 1993. 591 p.

Biegel J. E. Production control: a quantitative approach. Prentice-Hall, 1971. 282 p.

Ekonomiko-matematicheskie metody i prikladnye modeli / Fedoseev V. V., Garmash A. N., Dayitbegov D. M., Orlova I. V., Polovnikov V. A. Moscow: YuNITI, 1999. 391 p.

Awel Y. M. Forecasting GDP Growth: Application of Autoregressive Integrated Moving Average Model // Empirical Economic Review. 2018. Vol. 1, Issue 2. P. 1–16. doi: https://doi.org/10.29145/eer/12/010201 

Lim P. Y., Nayar C. V. Solar Irradiance and Load Demand Forecasting based on Single Exponential Smoothing Method // International Journal of Engineering and Technology. 2012. Vol. 4, Issue 4. P. 451–455. doi: https://doi.org/10.7763/ijet.2012.v4.408 

Forecasting intermittent demand by hyperbolic-exponential smoothing / Prestwich S. D., Tarim S. A., Rossi R., Hnich B. / International Journal of Forecasting. 2014. Vol. 30, Issue 4. P. 928–933. doi: https://doi.org/10.1016/j.ijforecast.2014.01.006 

Taylor J. W. Volatility forecasting with smooth transition exponential smoothing // International Journal of Forecasting. 2004. Vol. 20, Issue 2. P. 273–286. doi: https://doi.org/10.1016/j.ijforecast.2003.09.010 

Ravinder H. V. Forecasting With Exponential Smoothing Whats The Right Smoothing Constant? // Review of Business Information Systems (RBIS). 2013. Vol. 17, Issue 3. P. 117–126. doi: https://doi.org/10.19030/rbis.v17i3.8001 

Alam T. Forecasting exports and imports through artificial neural network and autoregressive integrated moving average // Decision Science Letters. 2019. P. 249–260. doi: https://doi.org/10.5267/j.dsl.2019.2.001 

Tan L., Wang S., Wang K. A new adaptive network-based fuzzy inference system with adaptive adjustment rules for stock market volatility forecasting // Information Processing Letters. 2017. Vol. 127. P. 32–36. doi: https://doi.org/10.1016/j.ipl.2017.06.012 

Doszyń M. Intermittent demand forecasting in the enterprise: Empirical verification // Journal of Forecasting. 2019. doi: https://doi.org/10.1002/for.2575 

Lutsenko I. Identification of target system operations. Development of global efficiency criterion of target operations // Eastern-European Journal of Enterprise Technologies. 2015. Vol. 2, Issue 2 (74). P. 35–40. doi: https://doi.org/10.15587/1729-4061.2015.38963 

A method to form control over queuing systems taking into consideration the probabilistic character of demand / Lutsenko I., Dmytriiev I., Avanesova N., Semenyshyna I., Rozhnenko Z., Danileyko O. // Eastern-European Journal of Enterprise Technologies. 2019. Vol. 1, Issue 3 (97). P. 28–36. doi: https://doi.org/10.15587/1729-4061.2019.157201 

Narayanan A., Sahin F., Robinson E. P. 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. 2019. doi: https://doi.org/10.1002/joom.1026 

Engle R. F., Granger C. W. J. Co-Integration and Error Correction: Representation, Estimation, and Testing // Econometrica. 1987. Vol. 55, Issue 2. P. 251–276. doi: https://doi.org/10.2307/1913236 







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

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061