A Study of Models for Forecasting E-Commerce Sales During a Price War in the Medical Product Industry

  • Pei-Hsuan HsiehEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11588)


When faced with a price war, the accuracy of forecasting sales in e-commerce greatly influences an enterprise’s or a retailer’s merchandise inventory strategies. When faced with a price war, an enterprise might obtain certain consumption patterns by analyzing previous sales data. This case study research was conducted in collaboration with a medical product company to explore which of the various forecasting models can better inform a company’s inventory plan. The study used the company’s data from regarding sales volume, number of views, company ranking, etc. between February 7 2016 and March 28 of 2018. Three potential methods of data mining were selected from the literature: the exponential smoothing method, the linear trend method, and the seasonal variation method. Of these, the most suitable was identified for price war situations to forecast the sales volume for April 2018 and to provide concrete information for the company’s inventory plan. The results showed that the seasonal variation method is more suitable than the other two sales forecasting methods. To obtain a more accurate sales forecast during a price war, the seasonal variation method is recommended to be used in the following approaches: Adjust the seasonal index by using a simple moving average. Remove the seasonal index from the sales volume, and conduct a regression analysis using the data within the last month. The resulting predicted value (with the seasonal index removed) should be multiplied by each period’s corresponding weighted moving average to obtain a more accurate sales forecast during a price war.


E-commerce Price war Sales forecasting Inventory plan 



I would like to express my special thanks of gratitude to my four undergraduate students (Pin-Yuan Chen, Yun-Che Lin, Jun-Da Shu and Hirosato Song) who had tried so hard to manage a large dataset provided by the Case company. I would also like to thank the representative of the Case company for his clear direction in advising us to adopt certain data analysis methods. Without his help, my students and I would not be able to find a better forecasting sales model during a price war on Amazon e-commerce website for the Case company.


  1. 1.
    Avinash, B., Babu, S.: Big data technologies for e-business- Future opportunities, challenges ahead and growing trends. Int. J. Adv. Res. Comput. Sci. 9(2), 328–332 (2018)CrossRefGoogle Scholar
  2. 2.
    B2C, written by Volan, P., August 18, 2018, Price wars in the e-commerce industry: How big data helps businesses to gain market share. Accessed 22 Jan 2019
  3. 3.
    Brown, R.G.: Exponential smoothing for predicting demand 1956. Accessed 21 Jan 2016
  4. 4.
    Brynjolfsson, E., Geva, T., Reichman, S.: Crowd-squared: amplifying the predictive power of search trend data. MIS Q. 40(4), 941–961 (2016)CrossRefGoogle Scholar
  5. 5.
    Business Insider, written by Green, D., November 24, 2017, These are the most popular items sold online for Black Friday so far, according to the data. Accessed 21 Jan 2019
  6. 6.
    Chan, T.K.H., Cheung, C.M.K., Lee, Z.W.Y.: The state of online impulse-buying research: a literature analysis. Inf. Manag. 54(2), 204–217 (2017)CrossRefGoogle Scholar
  7. 7.
    Chou, Y.L.: Statistical Analysis with Business and Economic Applications, 2nd edn. Continuum International Publishing Group Ltd., New York (1975)Google Scholar
  8. 8.
    Christiaanse, W.R.: Short-term load forecasting using general exponential smoothing. IEEE Trans. Power Apparatus Syst. 90(2), 900–911 (1971)CrossRefGoogle Scholar
  9. 9.
    Cui, R., Gallino, S., Moreno, A., Zhang, D.J.: The operational value of social media information. Prod. Oper. Manag. 27(10), 1749–1769 (2017)CrossRefGoogle Scholar
  10. 10.
    Currie, C.S., Rowley, I.T.: Consumer behaviour and sales forecast accuracy: what’s going on and how should revenue managers respond? J. Revenue Pricing Manag. 9(4), 374–376 (2010)CrossRefGoogle Scholar
  11. 11.
    Everette, G.S.: Exponential smoothing: the state of the art. J. Forcasting 4(1), 1–28 (1985)CrossRefGoogle Scholar
  12. 12.
    Gahan, P., Pattnaik, M.: Optimization in fuzzy economic order quantity (FEOQ) model with promotional effort cost and units lost due to deterioration. LogForum 13(1), 61–76 (2017)CrossRefGoogle Scholar
  13. 13.
    Gallup, published by The Wall Street Journal, June 11, 2014, The myth of social media. Accessed 22 Jan 2019
  14. 14.
    Gilliland, M.: Role of the sales force in forecasting. Foresight Int. J. Appl. Forecast. 35, 8–13 (2014)Google Scholar
  15. 15.
    Gonzalez, R., Hasker, K., Sickles, R.: An analysis of strategic behavior in eBay auctions. Singap. Econ. Rev. 54(3), 441–472 (2009)CrossRefGoogle Scholar
  16. 16.
    Hirano, H., Makota, F.: Just in Time is Flow: Practice and Principles of Lean Manufacturing. PCS Press, Vancouver (2006)Google Scholar
  17. 17.
    Hyndman, R.J., Athanasopoulos, G.: Forcasting: Principles and practice, 3.1 Some simple forecasting methods 2018. Accessed 21 Nov 2016
  18. 18.
    Janssen, M., van der Voort, H., Wahyudi, A.: Factors influencing big data decision-making quality. J. Bus. Res. 70, 338–345 (2017)CrossRefGoogle Scholar
  19. 19.
    Kietzmann, J.H., Hermkens, K., McCarthy, I.P., Silvestre, B.S.: Social media? Get serious! Understanding the functional building blocks of social media. Bus. Horiz. 54(3), 241–251 (2011)CrossRefGoogle Scholar
  20. 20.
    Lapide, L.: Are you capturing enough “quick-response” revenue? Supply Chain Manag. Revi. InSights 22(2), 4–6 (2018)Google Scholar
  21. 21.
    Ma, Q., Zhang, W.: Public mood and consumption choices: evidence from sales of Sony cameras on Taobao. PLoS ONE 10(4), e0123129 (2015)CrossRefGoogle Scholar
  22. 22.
    McElroy, T.: Multivariate seasonal adjustment, economic identities, and seasonal taxonomy. J. Bus. Econ. Stat. 35, 611–625 (2016)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Moon, S., Hicks, C., Simpson, A.: The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—a case study. Int. J. Prod. Econ. 140(2), 794–802 (2012)CrossRefGoogle Scholar
  24. 24.
    Ramanathan, U., Subramanian, N., Parrott, G.: Role of social media in retail network operations and marketing to enhance customer satisfaction. Int. J. Oper. Prod. Manag. 37(1), 105–123 (2017)CrossRefGoogle Scholar
  25. 25.
    Rao, A., Bergen, M., Davis, S.: How to fight a price war. Harvard Bus. Rev. 78(2, March/April), 107–116 (2000)Google Scholar
  26. 26.
    Reinmoeller, P.: How to win a price war. MIT Sloan Manag. Rev. 55(3), 15–17 (2014)Google Scholar
  27. 27.
    Sagaert, Y.R., Aghezzaf, E.H., Kourentzes, N., Desmet, B.: Tactical sales forecasting using a very large set of macroeconomic indicators. Eur. J. Oper. Res. 264(2), 558–569 (2018)CrossRefGoogle Scholar
  28. 28.
    Seaman, B.: Considerations of a retail forecasting practitioner. Int. J. Forecast. 34(4), 822–829 (2018)CrossRefGoogle Scholar
  29. 29.
    Shah, R., Ward, P.T.: Lean manufacturing: context, practice bundles, and performance. J. Oper. Manag. 21(2), 129–149 (2003)CrossRefGoogle Scholar
  30. 30.
    Sillitoe, B.: Retailers urged to change approach to demand forecasting. Comput. Wkly., 15 June 2017. Accessed 28 May 2019
  31. 31.
    Statistics on Key Figures of E-Commerce, surveyed by ACSI, February 2018, U.S. customer satisfaction with from 2000 to 2017 (index score). Accessed 21 Jan 2019
  32. 32.
    Vahid, M., Farokhi, M., Ibrahim, O., Nilashi, M.: A user satisfaction model for e-commerce recommender systems. J. Soft Comput. Dec. Support System 3(3), 42–54 (2016)Google Scholar
  33. 33.
    Wang, G., Gunasekaran, A., Eric Ngai, W.T., Papadopoulos, T.: Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int. J. Prod. Econ. 176(C), 98–110 (2016)CrossRefGoogle Scholar
  34. 34.
    Wild, T.: Best practice in inventory management, 3rd edn. Routledge, New York, NY (2018)Google Scholar
  35. 35.
    Yousef, M.I.: Social media with its role in supporting e-commerce and its challenges. J. Fundam. Appl. Sci. 10(4S), 336–340 (2018)Google Scholar
  36. 36.
    Zhang, P.G., Qi, M.: Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 160(2), 501–514 (2005)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Cheng Kung UniversityTainanTaiwan

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