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 organisation 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.
The paper explores the Darwinian idea of natural selection through the preservation of favorable variations and the rejection of injurious variations. This is shown through focus on the evolutionary processes of variation and selective retention. Variability is necessary is necessary for success in a rough and unpredictable environment. It is the micro-diversity that drives evolving, emerging organizational structures. The paper has tried to answer how manufacturers can make sense of variety and see opportunities for the future. Thus how can these processes be explained through the complexity of interactive entities. The methodology through which the evolutionary processes of variation and selective retention is explored is through cladistics and Linnaean classifications. The concept of evolutionary stable strategy is applied to these systems. This is demonstrated through the examples on the Varieties of Product Centered Genus. The paper then suggests a three level approach to variation, selection and retention, namely a genetic analogy where the phenotypic or interactor manifestation is taken, the concern about the fitness of the Variety within the external environment, and finally the implementation of a new manufacturing Variety through human action.
A complex adaptive systems perspective is used to examine the sustainability of the supply network in the commercial aerospace manufacturing sector. A framework for the analysis of coevolutionary dynamical change is used which examines the structure, integration methods and process dynamics within the supply network. Multiple methods are used for data collection from 8 firms in the sector. The framework identifies 14 management implications related closely to the sector’s current heterarchical supply network archetype. The management implications address known environmental factors for the sector and the broader techno-economic paradigm.
The paper investigates three levels of learning—adaptive, reactive and expansive—for the transformation of knowledge to enhance innovation and competitive advantage in commercial aerospace supply chains. A perspective of supply chains as complex Activity Networks is used for data analysis based on in-depth interviews in a global setting. Themes for the interviews were identified through rigorous literature research. The paper provides evidence of levels of learning in commercial aerospace supply chains. We found that a) adaptive learning brings a supply chain up to present industrial standards only, b) reactive learning makes the supply chain competitive, and c) expansive learning gives the supply chain potential for competitive advantage. By considering supply chains as the interaction of different work activities, the forces of change can be better understood. The findings may be useful to practitioners in understanding the importance of different levels of learning to supply chain sustainability.