Statistics for Innovation: Statistical Design of "Continuous" Product Innovation: Statistical Design of "Continuous" Product Innovation Based on Real and Simulated Experiments - Brossura

Brossura

ISBN 10: 884700814X ISBN 13: 9788847008144

Casa editrice: Springer, 2009

Riassunto

4. 1. 1 ImportanceofComputerSimulation The importance of experimenting for quality improvement and innovation of pr- ucts and processes is now very well known: “experimenting” means to implement signi?cant and intentional changes with the aim of obtaining useful information. In particular, the majority of industrial experiments have two goals: · To quantify the dependence of one or more observable response variables on a group of input factors in the design or the manufacturing of a product, in order to forecast the behavior of the system in a reliable way. · To identify the level settings for the inputs (design parameters) that are capable of optimizing the response. The set of rules that govern experiments for technological improvement in a ph- ical set-up are now comprehensively labeled “DoE. ” In recent years, the use of - perimentation in engineering design has received renewed momentum through the utilization of computer experiments (see Sacks et al. 1989, Santner et al. 2003), which has been steadily growing in the last two decades. These experimentsare run on a computer code implementing a simulation model of a physical system of int- est. This enables us to explore the complex relationships between input and output variables. Themain advantageofthis is that thesystem becomesmore“observable,” since computer runs are generally easier and cheaper than measurements taken in a physical set-up, and the exploration can be carried out more thoroughly. This is particularly attractive in industrial design applications where the goal is system - timization. 4. 1.

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Dalla quarta di copertina:

The objective of this book is to illustrate statistical methodologies that incorporate physical and numerical experiments and that allow one to schedule and plan technological innovation, similar to any other productive activity. This methodology should be implemented through a structured procedure aimed at reducing the high rate of commercial failure characterizing actual innovation processes. In fact, it is well known that:

i) The rate of commercial failure of a innovative idea is very high (90–94 out of 100 proposals for innovation undergo substantial failure in the EU and in the USA).

ii) Low reliability in the long run and sensitivity to usage conditions are the factors that determine the failure of the innovation.

The definition of an iterative design activity is an objective that can be reached by subdividing the complex innovation process into "short" steps in experimental statistics research. The approach adopted to analyze customer needs and the tools used to reduce unwanted variability form the framework for the statistical design of "continuous" product innovation.

Starting from the observation that product innovation is achieved when a "quality" that is able to satisfy a new customer need is conferred on the product and survives over real operating conditions and time, this book illustrates the operative steps required to perform the whole innovation process iteratively.

Contenuti:

Design for Innovation.- Analysis of User Needs for the Redesign of a Postural Seat System.- Statistical Design for Innovation in Virtual Reality.- Robust Ergonomic Virtual Design.- Computer Simulations for the Optimization of Technological Processes.- Technological Process Innovation.- Design for Computer Experiments: Comparing and Generating Designs in Kriging Models.- New Sampling Procedures in Coordinate Metrology Based on Kriging-Based Adaptive Designs.- Product and Process Innovation by Integrating Physical and Simulation Experiments.- Continuous Innovation of the Quality Control of Remote Sensing Data for Territory Management.- An Innovative Online Diagnostic Tool for a Distributed Spatial Coordinate Measuring System.- Technological Process Innovation via Engineering and Statistical Knowledge Integration.- Innovation of Lifecycle Management.- Bayesian Reliability Inference on Innovated Automotive Components.- Stochastic Processes for Modeling the Wear of Marine Engine Cylinder Liners.- Research and Innovation Management.- A New Control Chart Achieved via Innovation Process Approach.- A Critical Review and Further Advances in Innovation Growth Models.

Su questa edizione

• Editore: Springer
• Data di pubblicazione: 2009
• ISBN 10: 884700814X
• ISBN 13: 9788847008144
• Rilegatura: Copertina flessibile
• Numero di pagine: 284
• Redattore: Erto Pasquale