Inventory management models and their effects on uncertain demand

N. Nemtajela, C. Mbohwa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

This paper focuses on the use of inventory models to control the material flow and purchased inventory items in manufacturing companies. The objectives of this paper are to assess the effects of demand uncertainty on inventory management and to evaluate the difference on uncertain demand subject to demand controls as determined and the models used. Three inventory management models are studied; the Economic Order Quantity (EOQ), the Activity-Based Costing (ABC), and Just-in-time (JIT). The paper was a descriptive in nature and was conducted through the use of quantitative research methods. Survey questionnaire was compiled to gather primary data from five FMCG companies in manufacturing organisations. Survey data of 255 respondents from FMCG manufacturing companies was used in the analysis.

Original languageEnglish
Title of host publication2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
PublisherIEEE Computer Society
Pages1046-1048
Number of pages3
ISBN (Electronic)9781509036653
DOIs
Publication statusPublished - 27 Dec 2016
Event2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016 - Bali, Indonesia
Duration: 4 Dec 20167 Dec 2016

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
Volume2016-December
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X

Conference

Conference2016 International Conference on Industrial Engineering and Engineering Management, IEEM 2016
Country/TerritoryIndonesia
CityBali
Period4/12/167/12/16

Keywords

  • Fast moving consumer goods (FMCG)
  • inventory management
  • inventory management models
  • uncertain demand

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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