Article Information
Publication date (electronic): 30 June 2017
DOI: 10.emerg/10.17357.9a9ded6ec23d2c5374f4ffcd629178ef
From ignorance to knowing and applications for improving obsolescent manufacturing systems
Bio:
Dr. C. Rose-Anderssen was involved in the project ‘Cooperation Environment for Rapid Design, Prototyping and New Integration Concept for the Factory of the Future’ at Advanced Manufacturing Research Center with Boeing, the University of Sheffield, UK. Previously, he was a Research Associate at the AMRC. He was engaged in the ESRC research project Modelling the Evolution of the Aerospace Supply Chain. Before that he worked as a Research Officer in the project New Product Development as a Complex System of Decisions at the Complex Systems Research Center, Cranfield University. He worked as a naval architect and manager in the shipbuilding industry in Northern Europe for many years. He worked as a consultant in shipbuilding in Asia and as a manager in the Norwegian offshore engineering industry.
Bio:
James S. Baldwin is a Lecturer in Manufacturing Technology at AMRC, the University of Sheffield. Research interests include the development and application of evolutionary theory and classification science in the context of engineering management, operations, production and supply chain management, strategic management, and organizational behavior.
Bio:
Keith Ridgway (CBE, Fellow of the Royal Academy of Engineering) is executive dean of the University of Sheffield AMRC, and research director and co-founder of the AMRC with Boeing. He also takes additional role as Executive Chair of the Advanced Forming Research Centre (AFRC) at the University of Strathclyde. Focusing on strategy development of three major manufacturing research centres in complementary areas to guide future manufacturing policies in the UK and support collaborating companies to gain access to the facilities and expertise in high value manufacturing.
Abstract
The aim of this paper is to present a bench marking and diagnostic tool within the hierarchical Linnaean and cladistic representation of the discrete manufacturing systems presented. This is achieved through attempts of moving away from the ignorance of the past through a knowledge creating process exploring the opportunities of the future. The paper develops a theoretical perspective facilitating a knowledge creation process for moving away from the ignorance of the past and present, an engaging in a collective inquiry for developing instruments for manufacturing change. There are two main stages for the research methods in this paper. Firstly, there is a speed-read technique of quick species identification. The Linnaean hierarchy of discrete manufacturing organization is the map into which the manufacturing organization can search out its closest present identity. Secondly, there is a practical application on fitness/performance improvement. This is characterized by a comparison of a current company species to the ideal or typical textbook species. This exercise is done within the high-resolution profiles or representations of both the current and ideal states of the species. Using the speed-read and the kiviat comparison approach, a manufacturing organization can identify where they are in evolutionary history of discrete manufacturing systems. Then it can be assisted in searching out the general improvement potential of their organization. The classifications forms the basis for a further practical stage of the research—a web-based expert system and diagnostic tool that will complement a larger software system architecture. The aim of this is to simplify, and make accessible, essential tools for the rapid design, simulation and virtual prototyping of factories. The classifications also have a novel use in an educational context as it simplifies and organizes extant knowledge and adds another layer of information in terms of the evolutionary relationships between manufacturing systems. The work presented here is the first attempt at unifying extant classifications producing complementary, comprehensive, classifications of generic production systems that spans industrial sectors of discrete manufacturing. Based on this classification it presents application for manufacturing change.
Read Full Article
Access requires a current subscription