Abstract

Supply chain management is one of the important keys to the company. Many supply chain (SC) optimization research performed deterministically, but if the model was applied in a probabilistic problem, the optimization cannot fulfill the objective. In probabilistic supply chain modeling research using statistic, it's only showed the relationship of the model to the data. The model couldn't perform the optimization phase. In this article, response surface methodology (RSM) is introduced, as a statistical and mathematical technique to model the data and do the optimization phase, that's included in a probabilistic problem. The review reveals that RSM can be an effective method to model and optimize supply chain problems, even though the research of RSM in SCM is rarely used. RSM studies in SCM usually focus on forecasting, supply chain simulation, and inventory optimization. The used of RSM is quite novel in SCM modeling and optimization research to develop a supply chain system.

Original languageEnglish
Title of host publicationAsia Pacific Conference on Research in Industrial and Systems Engineering, APCORISE 2020 - Proceedings
PublisherAssociation for Computing Machinery
Pages322-327
Number of pages6
ISBN (Electronic)9781450376006
DOIs
Publication statusPublished - 16 Jun 2020
Event3rd Asia Pacific Conference on Research in Industrial and Systems Engineering, APCORISE 2020 - Depok, Online, Indonesia
Duration: 16 Jun 2020 → …

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd Asia Pacific Conference on Research in Industrial and Systems Engineering, APCORISE 2020
Country/TerritoryIndonesia
CityDepok, Online
Period16/06/20 → …

Keywords

  • Probabilistic
  • Response Surface Methodology
  • Supply Chain Management

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