Reducing construction material cost by optimizing buy-in decision that accounts the flexibility of non-critical activities

Junna Meng*, Jinghong Yan, Bin Xue, Jing Fu, Ning He

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

7 Citations (Scopus)

Abstract

Purpose - The goal of making buy-in decisions is to purchase materials at the right time with the required quantity and a minimum material cost (MC). To help achieve this goal, the purpose of this paper is to find a way of optimizing the buy-in decision with the consideration of flexible starting date of non-critical activities which makes daily demand adjustable.

Design/methodology/approach - First, a specific algorithm is developed to calculate a series of demand combinations modeling daily material demand for all the possible start dates. Second, future material prices are predicted by applying artificial neural network. Third, the demand combinations and predicted prices are used to generate an optimal buy-in decision.

Findings - By comparing MC in situation when non-critical activities always start at the earliest date to that in situations when the starting date is flexible, it is found that making material buy-in decision with the consideration of the flexibility usually helps reduce MC.

Originality/value - In this paper, a material buy-in decision-making method that accounts non-critical activities’ flexible starting date is proposed. A ternary cycle algorithm is developed to calculate demand combinations. The results that making material buy-in decision considering non-critical activities’ flexible starting date can reduce MC in most times indicates that contractors may consider non-critical activities’ flexibility a part of the buy-in decision-making process, so as to achieve an MC decrease and profit increase.

Original languageEnglish
Pages (from-to)1092-1108
JournalEngineering, Construction and Architectural Management
Volume25
Issue number8
DOIs
Publication statusPublished - 2018

Research Keywords

  • Construction planning
  • Optimization
  • Supply chain management

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