Multi-Period Optimal Capacity Strategy Based on Consumer Behavior Involved in Social Media
-
Jian Du
and
Junxiu Jia
Abstract
Social media and consumer behavior are increasingly important in business nowadays. As a new form of advertising, social media do facilitate the increase in demand and bring a challenge to manufactures. While researchers demonstrated that insufficient capacity generates the loss in the process of sales, an opposite conclusion has been obtained that the profit is larger in insufficient capacity. This study investigates this situation of a manufacturer. We develop a multi period model of insufficient capacity concerning with social media and consumer behavior. An calculation of the model indicates that a great change appears in the demand of each period. To ensure the maximum profit, the capacity of each period is computed. And the profit is almost 8 times larger than that we do not consider social media and consumer behavior. We discuss the implications of our findings for both theory and practice.
References
[1] Kaplan A M, Haenlein M. Users of the world, unite! the challenges and opportunities of social media. Business Horizons, 2010, 53(1): 59–68.10.1016/j.bushor.2009.09.003Search in Google Scholar
[2] Andzulis J M, Panagopoulos N G, Rapp A. A review of social media and implications for the sales process. Journal of Personal Selling & Sales Management, 2012, 32(3): 305–316.10.2753/PSS0885-3134320302Search in Google Scholar
[3] Paul M, Di G G, Wasko M M. Social media engagement theory: Exploring the influence of user engagement on social media usage. Journal of Organizational and End User Computing (JOEUC), 2016, 28(2): 53–73.10.4018/JOEUC.2016040104Search in Google Scholar
[4] Ngai E W T, Moon K L K, Lam S S, et al. Social media models, technologies, and applications: An academic review and case study. Industrial Management & Data Systems, 2015, 115(5): 769–802.10.1108/IMDS-03-2015-0075Search in Google Scholar
[5] Beheshti-Kashi S, Thoben K D. The usage of social media text data for the demand forecasting in the fashion industry. Dynamics in Logistics, Springer, 2016, 723–727.10.1007/978-3-319-23512-7_72Search in Google Scholar
[6] Hanimann R, Vinterbäck J, Mark-Herbert C. Consumer behavior in renewable electricity: Can branding in accordance with identity signaling increase demand for renewable electricity and strengthen supplier brands? Energy Policy, 2015, 78: 11–21.10.1016/j.enpol.2014.12.010Search in Google Scholar
[7] Goh K Y, Heng C S, Lin Z J. Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content. Information Systems Research, 2013, 24(1): 88–107.10.1287/isre.1120.0469Search in Google Scholar
[8] Fine C H, Freund R M. Optimal investment in product-flexible manufacturing capacity. Management Science, 1990, 36(4): 449–466.10.1287/mnsc.36.4.449Search in Google Scholar
[9] Lau S C, Lu M, Poon C S. Modeling the production capacity of a continuous plant using discrete event simulation. Computing in Civil Engineering, 2013: 873–880.10.1061/9780784413029.109Search in Google Scholar
[10] Avinadav T, Herbon A, Spiegel U. Optimal inventory policy for a perishable item with demand function sensitive to price and time. International Journal of Production Economics, 2013, 144(2): 497–506.10.1016/j.ijpe.2013.03.022Search in Google Scholar
[11] Azoury K S. Bayes solution to dynamic inventory models under unknown demand distribution. Management Science, 1985, 31(9): 1150–1160.10.1287/mnsc.31.9.1150Search in Google Scholar
© 2017 Walter de Gruyter GmbH, Berlin/Boston
