Disclosing fast moving consumer goods demand forecasting predictor using multi linear regression

Main Article Content

Farizal F
Muhammad Dachyar
Zarahmaida Taurina
Yusuf Qaradhawi


Demand forecasting accuracy undoubtedly influences a company perfomance. With an accurate forecast, the company will be able to utilize its resources efficiently. In practical, most companies only utilize historical selling data as predictor to forecast their product demand either using qualitative forecasting method or time series. However, in this study on a-fast moving consumer goods (FMCG), i.e., insecticide product, these methods do not give good results as expected. The methods produce Mean Absolute Percentage Errors (MAPEs) above 20%. To provide a more accurate forecasting, this study proposes a Multi Linear Regression (MLR) model that uses predictors including climate, promotion, cannibalization, holiday, product prices, number of retail stores, population, and income. The result shows that the MLR gives the best accurate forecast compare to time series methods and simple linear regressions. Using five predictors, i.e., product price, cannibalism, price disparity, fest day and weather, the proposed MLR model gives more accurate forecast with MAPE 8.66%.


Download data is not yet available.

Article Details

How to Cite
F, F., Dachyar, M., Taurina, Z., & Qaradhawi, Y. (2021). Disclosing fast moving consumer goods demand forecasting predictor using multi linear regression. Engineering and Applied Science Research, 48(5), 627-636. Retrieved from https://ph01.tci-thaijo.org/index.php/easr/article/view/242407


[1] Evans MK. Practical business forecasting. USA: Blackwell; 2003.

[2] Chase CW. Demand-driven forecasting: a structured approach to forecasting [Internet]. 2013 [cited 2021 Jan 27]. Available from: http://ebookcentral.proquest.com.

[3] Pongiannan K, Chinnasamy J. Do advertisements for fast moving consumer goods create response among the consumers?-an analytical assessment with reference to India. Int J Innovat Tech Manag. 2014;5(4):249-54.

[4] Ramanuj M. Product management in India. 3rd ed. New Delhi: PHI Learning; 2004.

[5] Sean B. The advertising handbook. 2nd ed. UK: Routledge; 2002.

[6] Liu Y, Li M, Zhu Z. Simulated annealing sales combining forecast in FMCG. 10th International Conference on e-Business Engineering; 2013 Sep 11-13; Coventry, UK. New York: IEEE; 2013. p. 230-5.

[7] Adebanjo D, Mann R. Identifying problems in forecasting consumer demand in the fast moving consumer goods sector. Benchmark Int J. 2014;7(3):223-30.

[8] Caruana A. Steps in forecasting with seasonal regression: a case study from the carbonated soft drink market. J Prod Brand Manag. 2001;10:94-102.

[9] Montgomery DC, Jennings CL, Kulahci M. Introduction to time series analysis and forecasting. 2nd ed. New Jersey: Wiley; 2008.

[10] Mitchell A, Dodge BH, Kruzic PG, Miller DC, Schwartz P, Suta BE. Handbook of forecasting techniques. Springfield: National technical information service, USA; 1975.

[11] Chopra S, Meindl P. Supply chain management, strategy, planning and operation. 6th ed. Northwestern: Pearson; 2016.

[12] Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice [Internet]. 2019 [cited 2021 Jan 27]. Available from: https://robjhyndman.com/uwafiles/fpp-notes.pdf.

[13] Farnum NR, Stanton L-VW. Quantitative forecasting method. USA: PWS-KENT; 1989.

[14] Chatterjee S, Simonoff JS. Handbook of regression analysis. New Jersey: Wiley; 2013.

[15] Farizal, alRasyid H, Rahman A. Model peramalan konsumsi bahan bakar jenis premium di indonesia dengan regresi linier berganda. Jurnal Ilmiah Teknik Industri. 2014;13(2):166-76.

[16] Kmenta, MG. Elements of econometrics. 2nd ed. New York: Macmillan; 1980.

[17] Imdad, MU, Aslam, M, Altaf, S, Ahmed, M. Some new diagnostics of multicollinearity in linear regression model. Sains Malaysiana. 2019;48(9):2051-60.

[18] Vining GG. Statistical methods for engineers. 2nd ed. Pacific Grove: Duxbury Press; 1998.

[19] Young DS. Handbook of regression methods. Boca Raton: CRC Press; 2017.

[20] Amstrong JS, Collopy F. Error measures for generalizing about forecasting methods: empirical comparisons. Int J Forecast. 1992;8:69-80

[21] Makridakis S. Accuracy measures: theoretical and practical concerns. Int J Forecast. 1993;9(4):527-9.

[22] Joseph M. Forecast error measures: Understanding them through experiments [Internet]. 2020 [cited 2021 Jan 27]. Available from: https://towardsdatascience.com/forecast-error-measures-understanding-them-through-experiments-da7ddcb0b035.

[23] Kim S, Kim H. A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast. 2016;32:669-79.

[24] Byrne RF. Beyon traditional time-series: using demand sensing to improve foecasts in volatile times. J Bus Forecas. 2012;31(2):13-9.

[25] Makridakis SG, Wheelwright SC, McGee VE. Metode dan aplikasi peramalan. Jakarta: Erlangga; 1999.

[26] George KM, Park N, Yang Z. A reliability measure for time series forecasting predictor. IFAC-PapersOnline. 2015;48:850-5.

[27] Fotios P, Konstantinos N, Georgios PS, Vassilis A. Empirical heuristics for improving intermittent demand forecasting. Ind Manag Data Syst. 2013;113:683-96.

[28] Gilliland M. The business forecasting deal: exposing myths, eliminating bad practices, providing practical solutions. New Jersey: Wiley; 2010.

[29] Dinar M, Hasan M. Pengantar ekonomi: teori dan aplikasi. Jakarta: CV. Nur Lina; 2018.

[30] Feigin G. Supply chain planning and analytics: the right product in the right place at the right time. New York: Business Expert Press; 2011.

[31] Daniel M. Pengantar ekonomi pertanian. Jakarta: Bumi Aksara; 2004.

[32] Mankiw NG. Teori makro ekonomi (terjemahan). Jakarta: Gramedia Pustaka Utama; 2003.

[33] Rahman MA, Sarker BR, Escobar LA. Peak demand forecasting for a seasonal product using bayesian approach. J Oper Res Soc. 2011;62:1019-28.

[34] Vhatkar S, Dias J. Oral-care goods sales forecasting using artificial neural network model. Proc Comp Sci. 2016;79:238-43.

[35] Aksoy A, Ozturk N, Sucky E. Demand forecasting for apparel manufacturers by using neuro-fuzzy techniques. J Model Manag. 2014;9:18-35.

[36] Schneider MJ, Gupta S. Forecasting sales of new and existing products using consumer reviews: a random projections approach. J Forecast. 2016;32:243-56.

[37] Ma S, Fildes R, Huang T. Demand forecasting with high dimensional data: the case of SKU retail sales forecasting with intra- and inter- category promotional information. Eur J Oper Res. 2016;249:245-57.

[38] Park YS, Han, E, Kim, J, Lee EK. Factors influencing the difference between forecasted and actual drug sales volumes under the price-volume agreement in South Korea. Health Policy; 2016;120(8):867-74.

[39] Kros JF, Keller CM. Seasonal regression forecasting in the U.S. beer import market. Adv Bus Manag Forecast. 2015;7:73-96.

[40] Fan ZP, Che, YJ, Chen ZY. Product sales forecasting using online reviews and historical sales data: A method combining the bass model and sentiment analysis. J Bus Res. 2017;2:90-100.

[41] Stoops GT. A comparison of forecasting methods for estimating the sales of retail firm. USA: Bowling Green State University; 1986.

[42] Kavitha S, Varuna S, Ramya R. A comparative analysis on linear regression and support vector regression. International Conference on Green Engineering and Technologies; 2016 Nov 19; Coimbatore, India. New York: IEEE; 2017. p. 1-5.

[43] Kumar A, Adlakha A, Mukherjee K. Modelling of product sales promotion and price discounting strategies using fuzzy logic in a retail organization. Ind Manag Data Syst. 2016;116:1-35.

[44] Thiesing FM, Vornberger O. Sales forecasting using neural networks. International conference on neural networks (ICNN'97); 1997 Jun 12; Houston, USA. New York: IEEE; 1997. p. 2125-8.

[45] Guo ZX, Li M, Wong WK. Intelligent multivariate sales forecasting using wrapper approach and neural networks. 10th International Conference on Industrial Informatics; 2012 Jul 25-27; Beijing, China. New York: IEEE; 2012. p. 145-50.

[46] Dodds WB, Monroe KB, Grewal D. Effects of price, brand, and store information on buyers’ product evaluations. J Market Res. 1991;28:307-19.

[47] Menon RGV, Sigurdsson V, Larse NM, Fagerstrom A, Foxall GR. Consumer attention to price in social commerce: eye tracking patterns in retail clothing. J Bus Res. 2016;69:5008-13.

[48] Anthony SD. Combating cannibalization concerns. Harvard Business Review [Internet]. 2011 [cited 2021 Jan 27]. Available from: https://hbr.org/2011/02/combating-cannibalization-conc.

[49] Zhang Q, Zhao Q, Zhao X, Tang L. On the introduction of green product to a market with environmentally conscious consumers. Comput Ind Eng. 2020;139:1-16.

[50] Wu JH, Lin YC, Hsu FS. An empirical analysis of synthesizing the effects of service quality, perceived value, corporate image and customer satisfaction on behavioral intentions in the transport industry: a case of Taiwan high-speed rail. Innovat Market. 2011;7:83-100.

[51] Porral CC, Levy-Mangin JP. Store brands’ purchase intention: examining the role of perceived quality. Eur Res Manag Bus Econ. 2017;23:90-5.

[52] Basker E. I was four weeks before Christmas: retail sales and the length of the Christmas shopping season. Econ Lett. 2005;89:317-22.

[53] Alper CE, Aruoba SB. Moving holidays and seasonal adjustment: the case of Turkey. Rev Middle East Econ Finance. 2004;2:203-9.

[54] Stulec I, Petljak K, Naletina D. Weather impact on retail sales: how can weather derivatives help with adverse weather deviations?. J Retailing Consum Serv. 2019;49:1-10.

[55] Keleş B, Gomez-Acevedo P, Shaikh NI. The impact of systematic changes in weather on the supply and demand of beverages. J Prod Econ. 2018;195:186-97.

[56] Murray KB, Muro FD, Finn A, Leszczyc PP. The effect of weather on consumer spending. J Retailing Consum Serv. 2010;17:512-20.