Manufacturing enterprise’s logistics operational cost simulation and optimization from the perspective of inter-firm network

Chun Fu, Zhenzhen Shuai

Abstract


Purpose: By studying the case of a Changsha engineering machinery manufacturing firm, this paper aims to find out the optimization tactics to reduce enterprise’s logistics operational cost.

Design/methodology/approach: This paper builds the structure model of manufacturing enterprise’s logistics operational costs from the perspective of inter-firm network and simulates the model based on system dynamics.

Findings: It concludes that applying system dynamics in the research of manufacturing enterprise’s logistics cost control can better reflect the relationship of factors in the system. And the case firm can optimize the logistics costs by implement joint distribution.

Research limitations/implications: This study still lacks comprehensive consideration about the variables quantities and quantitative of the control factors. In the future, we should strengthen the collection of data and information about the engineering manufacturing firms and improve the logistics operational cost model.

Practical implications: This study puts forward some optimization tactics to reduce enterprise’s logistics operational cost. And it is of great significance for enterprise’s supply chain management optimization and logistics cost control.

Originality/value: Differing from the existing literatures, this paper builds the structure model of manufacturing enterprise’s logistics operational costs from the perspective of inter-firm network and simulates the model based on system dynamics.


Keywords


System Dynamics, Logistics Operational Cost, Supply Chain Management, Simulation

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DOI: http://dx.doi.org/10.3926/jiem.1444


Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2019

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience