Title
Combining Hard And Soft System Thinking: The Development Of A Value Improvement Model For A Complex Linear Friction Welding Repetitive Process (Lfw-Vim)
Abstract
Linear Friction Welding (LFW) is a relatively new process adopted by aircraft engine manufacturers operationalizing new technologies to produce better value components. With increasing fuel prices and economical drives to reduce CO2 emissions, LFW has been a key technology in recent years for aircraft engine manufacture in both commercial and military market sectors. For joining Blades to Discus ('Blisks'), LFW is the ideal process as it is a solid state process which gives reproducibility and high quality bonds therefore improving performance. A fault detection and isolation (FDI) model of the LFW machine has been developed in [ 1] in order to detect and predict common machine faults. The purpose of this research investigation is to develop a bespoke value improvement model (VIM) for the LFW repetitive process identifying the critical influencing factors - whether human, machine system or both-to achieving the customer requirements, successful FDI model implementation and user uptake. Action research and case study intervention will be implemented at the Rolls-Royce site enabling the combination of hard systems (the FDI model) and soft systems (VIM model) to be effectively utilized to develop a holistic model (lfw-VIM). Outcomes of the research show the VIM approach can be used to aid successful change management and the implementation of a complex system. (C) 2013 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Georgia Institute of Technology. of Georgia Institute of Technology
Year
DOI
Venue
2013
10.1016/j.procs.2013.01.106
2013 CONFERENCE ON SYSTEMS ENGINEERING RESEARCH
Keywords
Field
DocType
Lean Thinking, PDCA, DMAIC, Systems Thinking, Soft Systems, Hard Systems, Process Improvement, Manufacturing Engineering, Action Research, Fault Detection, Modelling
Hard systems,Friction welding,Bespoke,Soft systems methodology,Computer science,Fault detection and isolation,Manufacturing engineering,Emerging technologies,Systems thinking,Artificial intelligence,Machine learning
Journal
Volume
ISSN
Citations 
16
1877-0509
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Darren T. Williams100.34
Richard Beasley200.34
Paul M. Gibbons300.34