A fuzzy front end model for concurrent specification in new product development

This research reports on the development of a new model for an early design stage in new product development (NPD) programmes called the Fuzzy Front End (FFE). The new FFE model aims at overcoming two kinds of limitations identified in previous FFE models. The first limitation concerns current trends in FFE model improvement including the need for a data-driven model, and to address agile development, incremental and radical NPDs, balanced explicitness and responsiveness characteristics, and balanced procedural and performative structures. The second limitation concerns deficiencies in the performance structure and operating mechanism regarding contextual performance and concurrent collaboration. This means that performances in the FFE do not systematically link with each other, either in a single functional domain or multidimensionally across diverse functional domains, but instead exist independently. A pragmatic-prescriptive model has been functionally embodied by analysing real-world FFE scenarios using inductive reasoning. The model is data-driven with a performative structure wherein parameters can interlock for contextual performance and concurrent collaboration throughout the entire FFE process. With this interlocking structure, once an initial parameter is produced, all remaining parameters considered from both perspectives can be obtained successively. This model allows performers to explicitly understand the purpose and roles of parameters and their relationships from both perspectives when processing parameters. The model thus leads to more agile FFE execution by reducing the iterative work needed to correct defective parameters which have not been handled with contextual performance and concurrent collaboration in mind but instead exist independently. A theoretical-descriptive model, produced by validating the developed pragmatic-prescriptive model, using deductive reasoning, consists of mathematical formulas, providing the underlying concept of an overall FFE as well as that of its parts. Consequently, the pragmatic-prescriptive model can serve as functional performance guidance, while the theoretical-descriptive model can serve as conceptual performance guidance when employing the pragmatic-prescriptive model.Open Acces

A knowledge sharing framework to support rapid prototyping in collaborative automotive supply chain

In today’s global economy, competition is increasingly driven by a high rate of product renewal. In this context, with market demands for the development of high quality products at lower costs, highly customisable and with short life cycles, new technologies have been adopted by the automotive manufacturers in the move away from a local economy towards the global economy. The continuous evolution of this technology often requires the updating and integration of existing systems within new environments, in order to avoid technological obsolescence. To allow companies to compete in the global market, they (the companies) can no longer be seen acting as standalone entities and are having to reconsider their organisational and operational structure. This thesis presents a Knowledge Sharing Framework Design Roadmap to support rapid prototyping in the automotive and collaborative supply chain. IranKhodro Diesel (IKD) is the automotive company and CarGlass Company (Iran) is the supplier and sponsor of this research study. These two companies will be used to develop and test the Knowledge Sharing Framework Design Roadmap (KSFDR) methodology. An industrially based case study was conducted in IKD and CarGlass to identify key elements in the Knowledge Sharing Framework and provide the focus for this study. The study itself drew on empirical sources of data, including interviews with IKD personnel via an internal company survey. The absence of mechanisms to make information accessible in a multilingual environment and its dissemination to geographically dispersed NPD project team members was identified along with the lack of explicit information about the knowledge used and generated to support first stage rapid prototyping in the product development process with respect to reduction of costs and lead times. The Knowledge Sharing Framework Design Roadmap was tested between IKD and CarGlass. The business objectives in both IKD and CarGlass are the main drivers of knowledge system development. The main novel point from this research study is that this particular framework can be used to capture and disseminate information and knowledge. This was supported by positive feedback from a series of interviews with NPD practitioners. The Knowledge Sharing Framework Design Roadmap (KSFDR) methodology, however, can also be applied in other manufacturing and business environments. Further testing of the framework is strongly advised to minimise any minor flaws, which remain

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists