Forecast of key indicators of retail network in time

Abstract

The purpose of the study is to forecast the commodity demand, within which key indicators related to the operation of the retail network are applied. The relevance of the study is determined by the need to forecast the indicators significant for retail trade development to increase the planning efficiency of trade organization activity. The importance to forecast the future values of the indicators under consideration – the temperature regime and the number of checks – has been indicated. These indicators are significant for adequate forecasting of demand and for solving other management tasks as well. The range of the analysis methods of time series has been determined. These series have been used to solve the tasks of forecasting demand for the retail network. The approach of the state-space model is considered in almost every method. The theoretical basis of each method has been described to illuminate a sufficient variety of the applied mathematical tools. The fact that conventional (ARIMA, exponential smoothing) and modern methods used by large IT companies (Facebook and Google) are among the applied methods of forecasting has been emphasized. The choice of the prediction quality metric for the problem has been justified. The metrics are a square root of the root-mean-square error and the absolute error in percentage. A set of daily data about the amount of checks in the retail network of Izhevsk, as well as the average daily temperature conditions in the geographical area of the city have been used as the input data to make the forecast. To make short-term forecasts, the initial sample is suggested to be divided into a training sample and a test one in the proportion 9 to 1, due to the short-term of the forecast. The importance of the temperature series indicator for the activity of a retail store and for the dynamics of consumer demand has been characterized. The issue to forecast temperature accurately only on the basis of temperature time series has been discussed in the study. The models for temperature series have been calculated and the quality indicators for each model have been estimated. The value of the indicator of the number of checks to demonstrate the activity of retail trade has been described. A number of external factors affecting the dynamics of the number of checks have been listed in the present research. Among them are days of the week, whether it is a pre-holiday or a holiday. We have made conclusions about the high efficiency of the composite forecast with several methods on the basis of the predictive modeling of the check amount. Even with the help of the arithmetic mean of the forecasts for several methods, it is possible to create a more accurate prediction than for each method separately. Our further research will concern the improvement of the tool and the development of a computer-based system to forecast commodity demand.

Keywords

retail trade, forecasting of demand of goods, analysis of time series, Bayesian modeling, forecast quality metric, composition of demand forecasts

For citation

Pivkin K.S. Forecast of key indicators of retail network in time. Perm University Herald. Economy. 2017, vol. 12, no. 4, pp. 592–608. DOI 10.17072/1994-9960-2017-4-592-608

References

1. Pivkin K.S. Korrelyatsionnyi analiz faktorov vliyaniya na pokupatel'skii spros roznichnogo magazina kak etap formirovaniya modeli prognozirovaniya i upravleniya zapasami [Correlation of the factors influencing the customer demand in retail store sector as a stage of formation of a model of prediction and inventory control]. Vestnik UdGU. Ser. Ekonomika i pravo [Bulletin of Udmurt University. Series Economics and Law], 2016, iss. 3, pp. 40–50. (In Russian).
2. Hyndman R.J., Khandakar J. Automatic time series forecasting: The forecast Package for R. Journal of Statistical Software, July 2008, vol. 27, iss. 3, pp. 1–22. doi:10.18637/jss.v027.i03.
3. Svetunkov I., Kourentzes N. Complex exponential smoothing. Working Paper of Department of Management Science. Lancaster University, 2015, pp. 1–31. doi: 10.13140/RG.2.1.3757.2562. Available at: https://www.researchgate.net/publication/283488877 (accessed 12.06.2017).
4. Svetunkov I. Complex exponential smoothing. A thesis submitted for the degree of Doctor of Philosophy. Lancaster, Lancaster University, 2016. 132 p. Available at: http://eprints.lancs.ac.uk/82749/ (accessed 01.08.2017).
5. Taylor S.J., Letham B. Forecasting at scale. 2017. Available at: https://facebookin
cubator.github.io/prophet/static/prophet_paper_20170113.pdf (accessed 01.08.2017).
6. Varian H.R. Big data: New tricks for econometrics. Journal of Economic Perspectives, 2014, vol. 28, no. 2, pp. 3–28.
7. Chuchueva I.A., Pavlov Yu.N. Ekstrapolyatsiya psevdosluchainykh protsessov po maksimumu podobiya [Extrapolation of pseudorandom processes on a maximum of similarity]. Nauka i obrazovanie. El-ektronnoe nauchnoe izdanie MGTU im. N.E. Baumana [Science and Education. Scientific Edition of Bauman Moscow State Technical University], 2009, no. 7. (In Russian) Available at: http://technomag.bm
stu.ru/doc/129712.html (accessed 12.06.2017).
8. Chursin Yu.A. Mikhalevich S.S., Baidali S.A. Modelirovanie sistem avtomaticheskogo upravleniya metodom prostranstva sostoyanii [Modelling of automatic control systems with a state space method]. Pribory i sistemy. Upravlenie, kontrol', diagnostika [Tools and Systems. Management, Control, Diagnostics], 2012, no. 10, pp. 11–17. (In Russian).
9. Da Veiga C.P., Da Veiga C.R.P., Catapan A., Tortato U., Da Silva W.V. Demand forecasting in food retail: A comparison between the HoltWinters and ARIMA models. WSEAS Transactions on Business and Economics, 2014, vol. 11, pp. 608–614.
10. Gareth J., Witten D., Hastie T., Tibshirani R. Vvedenie v statisticheskoe obuchenie s primerami na yazyke R (per. s angl. S.E. Mastitskii) [An introduction to statistical learning (transl. from Engl. by S.E. Mastitskii)]. Moscow, DMK-Press Publ., 2016. 460 p. (In Russian).
11. Kemeron E.K., Trivedi P.K. Mikroekonometrika: Metody ikh primeneniya. Kniga 1. Per. s angl. Suren Avakyan i dr. Pod nauch. red. B. Demesheva [Micro-econometrics: Methods of their application. Book 1. Translated from English by Suren Avakyan, at el. Scient. edit. by B. Demesheva], Moscow, Delo Publ., 2015, 552 p. (In Russian).
12. Aivazyan S.A. Baiesovskii podkhod v ekonometricheskom analize [Bayesian method in econo-metrics]. Prikladnaya ekonometrika [Applied Econometrics], 2008, no. 1(9), pp. 93–130. (In Russian).
13. Scott S.L., Varian H.R. Predicting the present with Bayesian structural time series. 2013. Available at: https://ssrn.com/abstract=2304426 (accessed 05.08.2017).
14. Tsyplakov A. Vvedenie v modelirovanie v prostranstve sostoyanii [An introduction to state space modelling]. Kvantil' [Quantile], 2011, no. 9, pp.1–24. (In Russian).
15. Durbin J., Koopman S.J. Time series analysis by state space methods. Oxford, Oxford University Press, 2001. 273 p.
16. Vasil'eva T.V. Prognozirovanie pokazatelei nadezhnosti aviatsionnoi tekhniki s ispol'zovaniem ryadov Fur'e [Forecasting of reliability indices of aviation equipment using Fourier series]. Nauchno-metodicheskii elektronnyi zhurnal “Kontsept” [Scientific and Methodological Electronic Journal “Concept”], 2016, vol. 15, pp. 1476–1480. (In Russian) Available at: http://e-koncept.ru/2016/96214.htm (accessed 01.06.2017).
17. Belyi V.S., Adamushko N.N. Primenenie ryadov Fur'e dlya prognozirovaniya tekhnicheskogo sostoyaniya zdaniya [The application of Fourier series for the technological forecasting of the building condition]. Ekologiya i stroitel'stvo [Ecology and Construction], 2015, no. 1, pp. 11–14. (In Russian).
18. Vlasova Yu.E., Malich A.V., Zakrevskaya E.A. Prognozirovanie prodazh metodami garmonich-eskogo analiza [Forecast of sales using wave analysis method]. Mezhdunarodnyi studencheskii nauchnyi vest-nik [International Student Scientific Bulletin], 2016, no. 2. (In Russian) Available at: https://www.eduherald.ru/ru/article/view?id=15861 (accessed 18.06.2017).
19. Gorlach B.A., Shigaeva N.V. Primenenie ryadov Fur'e dlya prognozirovaniya i optimizatsii postavok predpriyatiya optovoi torgovli v aspekte upravleniya sobstvennym i arenduemym transportom [The Fourier series application for prediction and optimization of delivery costs]. Ekonomika i menedzhment inno-vatsionnykh tekhnologii [Economics and Innovations Management], 2014, no. 7 (34). (In Russian) Available at: http://ekonomika.snauka.ru/2014/07/5292 (accessed 18.06.2017).
20. Turuntseva M.Yu. Otsenka kachestva prognozov: prosteishie metody [Forecast quality assess-ment: The simpliest methods]. Rossiiskoe predprinimatel'stvo [Russian Journal of Entrepreneurship], 2011, no. 8-1 (189), pp. 50–56. (In Russian).
21. Zhulikov S.E. Matematicheskoe modelirovanie kratkosrochnogo prognoza pogody [Mathematical modelling of short-term weather forecast]. Vestnik Tambovskogo universiteta. Ser.: Estestvennye i tekhnicheskie nauki [Tambov University Reports. Series: Natural and Technical Sciences], 2009, vol. 14, no. 5-2, pp. 1021–1026. (In Russian).
22. Trokhinova A.A., Karapetyan T.A. Analiz effektivnosti deyatel'nosti predpriyatiya restoranno-gostinichnogo biznesa [Efficiency analysis of restaurant and hotel business activity]. Ekonomicheskaya nauka segodnya: Teoriya i praktika: materialy V Mezhdunarodnoi nauchno-prakticheskoi konferentsii (3 dek. 2016 g., g. Cheboksary) [Economics today: Theory and practice: Proceedings of the 5-th International scientific practical conference (December 3, 2016, Cheboksary)]. Ed. by O.N. Shirokov et al. Cheboksary, TSNS “Interaktiv plyus” Publ., 2016, pp. 95–101. (In Russian).
23. Kataeva N.N. Kharakteristika i otsenka effektivnosti merchandaizinga produktovogo magazina [Characteristics and assessment of the efficiency of grocery store merchandizing]. Nauka-rastudent.ru [Elec-tronic Scientific and Practical Journal “Nauka-rastudent.ru”], 2014, no. 12-1(12). (In Russian) Available at: http://naukarastudent.ru/12/2242 (accessed 18.06.2017).
24. Nikitin A.P. Analiz tranzaktsionnykh dannykh i opredelenie kolichestvennykh kriteriev loyal'nosti klientov [Analysis of transactional data and identification of quantitative criteria of customer loyalty]. Ekonomika. Nalogi. Pravo [Economics.Taxes. Law], 2012, no. 2, pp. 113–124. (In Russian).
25. Peresun'ko P.V., Dolzhanskaya S.A. Realizatsiya i issledovanie rezul'tatov vzveshennogo prognoza [Implementation and research of weighted prediction results]. Sovremennye informatsionnye tekhnologii [Current Information Technologies], 2016, no. 23, pp. 52–55. (In Russian).

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