skip to main content
10.1145/3306500.3306523acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesic4eConference Proceedingsconference-collections
research-article

Relationship management of customer demand and production planning on e-business of Thai natural cosmetics

Published: 10 January 2019 Publication History

Abstract

The higher online customers initiate promising advantage and grasping prospective opportunity for companies in Thai ecommerce market and receive more attention to the demand of cosmetic products. It is therefore useful or even necessary to give more attention to the management of production planning. For capturing customer preferences and sales prediction purposes in large domain considerable effort has directed to construct an effective model of aggregate production planning. This research examines growth of internet Thai users in general via various forecasting techniques of demand. The available data from service customer interaction are useful to predict the monthly purchase behavior of individual users. The proposed model balancing supply with demand to minimize the total production cost or maximize profitability gives high accuracy in predictions of the aggregate number of orders placed by all users each month. However, all parts of the E-Business organization such as operations, marketing including finance departments must join planning processes to ensure that they are moving in harmony with one another. Accurate forecasting and aggregate production planning are such techniques that can move all parts of the organizations in same harmony.

References

[1]
Taweerat J., Settapong M., Navneet M., and Jesada S. 2014. The Impact of Customer Satisfaction on Online Purchasing: A Case Study Analysis in Thailand. J. Econ. Bus. and Manage. 2, 5--17.
[2]
Wattanasupachoke, T., and Tanlamai, A. 2005. E-commerce Model of Virtual Enterprises in Thailand. Bus. Rev. Cambridge, 4, 296--303.
[3]
Lalit, M.J., and Sahasakmontri, K. 1998. Green marketing of cosmetics and toiletries in Thailand, J. Consum. Mar. 15, 265--281.
[4]
Danese, P., and Kalchschmidt, M. 2011. The role of the forecasting process in improving forecast accuracy and operational performance. Int. J. Prod. Econ. 131, 204--214.
[5]
Aungkulanon, P., and Luangpaiboon, P. 2018. Evolutionary computation role in improving an accuracy of forecasting mortality data. Int. J. Adv. Soft Compu. Appl. 10(2), 71--83.
[6]
Liem, G.S., and Ria, P. 2011. China E-Commerce Market Analysis: Forecasting and Profiling Internet User. J. Arts, Sci. & Com. 2(3), 1--8.
[7]
Aungkulanon, P., Luangpaiboon, P., and Montemanni, R. 2018. An Elevator Kinematics Optimization Method for Aggregate Production Planning Based on Fuzzy MOLP Model. Int. J. Mech. Eng. and Robot. Res. 7(4), 422--427.
[8]
Yasser, A.D., César, M.O., Rogelio, S., Carlos, H., and Piero, E.R. 2015. Optimal Control Approaches to the Aggregate Production Planning Problem. Sustainability. 7, 16324--16339.
[9]
Anand, J.A., Krishnaraj, C. and Kasthuri Raj, S.R. 2016. LINGO based Revenue Maximization using Aggregate Production Planning. ARPN J. of Eng. and Appl. S. 11(9), 6075--6081.
[10]
Jain, A., and Palekar, U.S. 2005. Aggregate Production Planning for a Continuous Reconfigurable Manufacturing Process. Comp. & Oper. Res. 32(5), 1213--1236.
[11]
Wang, R.C., and Liang, T.F. (2005). Aggregate Production Planning with Multiple Fuzzy Goals. Int. J. Adv. Manuf. Tech. 25(7), 589--587.
[12]
Aungkulanon, P., Phruksaphanrat, B., and Luangpaiboon, P. 2012 Harmony Search Algorithm with Various Evolutionary Elements for Fuzzy Aggregate Production Planning. Lect. Notes in Elec. Eng. 110, 189--201.
[13]
Rafi, A., and Baghdadi, Y. 2008. E-business: Issues, challenges and architecture, Int. J. Bus. Inf. Sys. 3, 391--409.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
IC4E '19: Proceedings of the 10th International Conference on E-Education, E-Business, E-Management and E-Learning
January 2019
469 pages
ISBN:9781450366021
DOI:10.1145/3306500
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 January 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. aggregate production planning
  2. consumer behaviour
  3. e-business
  4. forecasting
  5. inventory management
  6. sales management

Qualifiers

  • Research-article

Conference

IC4E 2019

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 166
    Total Downloads
  • Downloads (Last 12 months)11
  • Downloads (Last 6 weeks)1
Reflects downloads up to 03 Mar 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media