Single Exponential Smoothing Method to Predict Sales Multiple Products

Rendra Gustriansyah


—Activity to predict sales multiple products intended for control of the number of existing stock, so the lack or excess stock can be minimized. When the number of sales can be accurately predicted, then the fulfilment of consumer demand can be cultivated in a timely and cooperation with suppliers maintained properly so that company can avoid losing sales and customers. This study aims to predict sales multiple products (6,877 products) using Single Exponential Smoothing (SES) approach, which is expected to improve the efficiency of the inventory system. Measurement accuracy of prediction in this study using a standard measurement Mean Absolute Percentage Error (MAPE), which is the most important criteria in analyzing the accuracy of the prediction. The results showed that the average of percentage prediction error of products using SES is high, because MAPE value obtained is 1.056% with a smoothing parameter α = 0.9

Full Text:



R. Gustriansyah, D. I. Sensuse, and A. Ramadhan, “A sales prediction model adopted the recency-frequency-monetary concept,” Indones. J. Electr. Eng. Comput. Sci., vol. 6, no. 3, pp. 711–720, 2017.

R. Gustriansyah, D. I. Sensuse, and A. Ramadhan, “Decision support system for inventory management in pharmacy using fuzzy analytic hierarchy process and sequential pattern analysis approach,” in CONMEDIA 2015 - International Conference on New Media 2015, 2016.

P. Kelle, J. Woosley, and H. Schneider, “Pharmaceutical supply chain specifics and inventory solutions for a hospital case,” Oper. Res. Heal. Care, vol. 1, no. 2–3, pp. 54–63, 2012.

S. Dwivedi, A. Kumar, and P. Kothiya, “Inventory Management: A Tool of Identifying Items That Need Greater Attention for Control,” Pharma Innov., vol. 1, no. 7, pp. 125– 29, 2012.

D. Nadyatama, Q. Aini, and M. C. Utami, “Analysis of commodity inventory with exponential smoothing and silver meal algorithm (Case study),” in 2016 4th International Conference on Cyber and IT Service Management, 2016, pp. 1– 6.

R. Anggrainingsih, Aprianto, G. Romadhon, and S. W. Sihwi, “Time Series Forecasting Using Exponential Smoothing To Predict The Number of Website Visitor of Sebelas Maret University,” in 2nd Int. Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), 2015, pp. 1–6.

Y. Ding, Q. Wen, and B. Shen, “Prediction on Diesel Price in China with an Exponential Smoothing Method,” in 2014 Seventh International Joint Conference on Computational Sciences and Optimization, 2014, pp. 593–597.

A. C. Adamuthe, R. A. Gage, and G. T. Thampi, “Forecasting Cloud Computing using Double Exponential Smoothing Methods,” in International Conference on Advanced Computing and Communication Systems (ICACCS-2015), 2015, pp. 1–5.

A. A. Hidayat, Z. Arief, and D. C. Happyanto, “Mobile Application With Simple Moving Average Filtering For Monitoring Finger Muscles Therapy Of Post-Stroke People,” in International Electronics Symposium (IES), 2015, pp. 1–6.

T. Fehlmann and E. Kranich, “Exponentially Weighted Moving Average (EWMA) Prediction in the Software Development Process,” in 2014 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement, 2014, pp. 263–270.

R. Nau, “Averaging and Exponential Smoothing Models,” 2017. [Online]. Available: [Accessed: 20-Sep2017].

K. Fu, W. Chen, L.-C. Hung, and S. Peng, “An ABC Analysis Model for the Multiple Products Inventory Control ---- A Case Study of Company X,” in the Asia Pacific Industrial Engineering & Management Systems Conference, 2012, pp. 495–503.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
View My Stats
Flag Counter