Visual management condition in highways construction projects in England

Abstract

Lean construction has recently gained momentum in England's highways construction supply chain. The literature indicates that the current view to Visual Management (VM) within those lean implementations is limited to some VM tools. This paper explores the condition of VM, which is a fundamental sensory information management strategy in the lean production system, in the highways sector in England. VM in construction is a scarcely researched topic, particularly outside the building construction context. The existing VM research focuses generally on the application of a specific VM tool rather than the VM strategy itself. The paper identifies the current VM condition with its realization means (VM tools), drivers, barriers and future implementation opportunities in England's highways construction supply chain through five case studies and a focus group research effort. The main findings are; (a) the current implementation of VM, particularly on English highways construction fields, is limited, and (b) along with many drivers and implementation opportunities for VM, (c) there are also some significant barriers before VM. The main limitations of the research are that the paper discusses the issue mainly for the construction phase and limited amount of data were collected from operational site staff.

Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
Event Title: 24th Annual Conference of the International Group for Lean Construction, IGLC 2016
Event Type: Other
Event Dates: 2016-07-18 - 2016-07-24
Uncontrolled Keywords: Big room/obeya,Highways,Lean construction,Transportation sector,Visual management
Last Modified: 18 Nov 2024 08:55
Date Deposited: 27 Sep 2021 14:52
Full Text Link:
Related URLs: https://iglc.ne ... rs/Details/1357 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2016
Authors: Tezel, Algan (ORCID Profile 0000-0003-3903-6126)
Aziz, Zeeshan
Koskela, Lauri
Tzortzopoulos, Patricia

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