Statistics for Innovation

Transcription

1 Statistics for Innovation

2 Pasquale Erto Editor Statistics for Innovation Statistical Design of Continuous Product Innovation 123

3 Pasquale Erto Department of Aerospace Engineering University of Naples Federico II, Italy ISBN e-isbn DOI / Library of Congress Control Number: Springer-Verlag Italia This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any otherway, and storage in databanks. Duplication of this publication or parts thereof is only permitted under the provisions of the Italian Copyright Law in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the Italian Copyright Law. Cover concept: Simona Colombo, Milano Cover picture: Riccardo Dalisi Typesetting: le-tex publishing services ohg, Leipzig, Germany Printing and binding: Grafiche Porpora, Segrate, Milano Printed on acid-free paper Springer-Verlag Italia Via Decembrio Milano springer.com

4 Foreword and Acknowledgements This book collects the main contributions to the research program Statistical design of the continuous product innovation carried out by a group of five units from the Italian Universities of Naples Federico II, Bologna, Salerno, Palermo and the Polytechnic of Turin, respectively. Some contributions are mainly applicative and concern the innovation of specific products or processes; other contributions are mainly theoretical and propose new methods to support the innovation process. All ones are aimed at showing that the technological innovation can be planned and designed likely any other product feature. Until the past century, firms gained competitive advantage from reducing costs, minimizing process variability and continuously improving their products. In the current global marketplace, these initiatives are only prerequisites to survive, while innovation is the actual source of competitive advantage. Product innovation is realized when the product is provided with a new feature aimed at fulfilling a new customer need. Since customer needs change rapidly over time, product innovation must be a continuous process and, consequently, it cannot rely on a brilliant and contingent intuition as for an invention. Often some needs are latent and cannot be elicited from a traditional customer survey, so they must be identified differently, by means of more advanced statistical tools. Sometimes, the innovation is implemented by exploiting a new technology, which is not necessarily the most advanced one, since its aim is fulfilling a new customer need not showing an intrinsic novelty. The above essential considerations are only examples of those developed by the contributions contained in this book. From the whole set of contributions a new approach to the innovation as a never-ending series of manufacturing cycles arises. Each cycle requires a series of steps, each performed using a specific statistical and/or engineering tool. The contributions result from different Statistics and Engineering schools, hence their whole set is not biased by any specific point of view. Different opinions that were not able to converge were left unchanged and, when possible, all were tested facing practical applications. Nevertheless, many unsolved questions arise, v

5 vi Foreword and Acknowledgements but these certainly will boost further research to advance the statistical and engineering knowledge addressed to innovation. A glance to the table of contents is the most effective way to master the whole range of practical and theoretical topics covered by this book. It starts from product innovation attained by engineering design in virtual reality; then shows examples of process innovation obtained integrating physical and simulation experiments; proposes a Bayesian approach to the innovation of the reliability and maintenance service; ends with an advanced approach to management of the research and innovation activities themselves. This book and the whole research program Statistical design of the continuous product innovation (PRIN ) have been financially supported by the Italian Ministry of University and Scientific and Technological Research (MIUR). Naples, 24 August 2008 Pasquale Erto

6 Contents Part I Design for Innovation 1 Analysis of User Needs for the Redesign of a Postural Seat System.. 3 Stefano Barone, Alberto Lombardo, and Pietro Tarantino 1.1 Introduction Evolution in the Customers Concept of Quality Traditional Methods for Capturing Customers Needs VoiceoftheCustomer Quality Function Deployment KanoModel KanseiEngineering Advanced Methods for Capturing Customers Needs IntegratingKanseiEngineering A New Practical Way to Measure Customer Preferences for Product Attributes A Simplified Version of the Kano Questionnaire NeedsAnalysisfortheDesignofaPosturalSeatSystem Regulations and Figures on Disability ObjectivesoftheStudyandWorkPlan CustomerIdentification Benchmarking IdentificationofNeeds ImportanceofNeeds RelationModel Design Suggestions Conclusions References vii

7 viii Contents 2 Statistical Design for Innovation in Virtual Reality Antonio Lanzotti, Giovanna Matrone, Pietro Tarantino, and Amalia Vanacore 2.1 Introduction From Emotions to Innovation in Virtual Reality ThreeApplicationsofStatisticalDesignforInnovation RailwayCoachInteriorDesign RailwaySeatDesign PosturalSeatSystemInnovation Conclusions References Robust Ergonomic Virtual Design Stefano Barone and Antonio Lanzotti 3.1 Introduction Aims and Phases of the Robust Ergonomic VirtualDesign(REVD)Strategy Key Steps of the Parameter and Adjustment DesignPhases AFirstApplication:Three-WheeledVehicle Anthropometric Noise Factor for a Mixture Population A Second Application: Mini-Car User Packaging AdjustmentDesignofaNewMini-CarDrivingSeat SensitivityofComfortLosstoMixVariation AdjustmentOptimization Conclusions References Computer Simulations for the Optimization of Technological Processes Alessandro Baldi Antognini, Alessandra Giovagnoli, Daniele Romano, and Maroussa Zagoraiou 4.1 Introduction ImportanceofComputerSimulation SimulatorsandEmulators SequentialComputerExperiments StochasticSimulators ConstructionoftheEmulators AProtocolforCreatingEmulators A Special Type of Emulator: The Kriging Technique AccuracyofthePredictor ExperimentsforaKrigingModel SequentialExperimentsforKriging DesignandAnalysisofSequentialExperiments Recent Developments in Sequential Computer ExperimentsforKrigingwithApplications... 78

8 Contents ix Application-DrivenSequentialDesigns A Modified Version via Randomization Robust Parameter and Tolerance Design viacomputerexperiments Robust Parameter and Tolerance Design: CrossedArraysandCombinedArrays The Proposed Simulation Protocol and Its Application to the Integrated Design of Parameters and Tolerances AnApplicationtoComplexMeasurementSystems OtherPotentialApplications References Part II Technological Process Innovation 5 Design for Computer Experiments: Comparing and Generating Designs in Kriging Models Giovanni Pistone and Grazia Vicario 5.1 Introduction KrigingPrediction AClassofDesigns:FractionsofaRegularGrid Comparing Different Designs: Cases 2 2, 3 3, LHDesigns LHDesigns Conclusions References New Sampling Procedures in Coordinate Metrology Based on Kriging-Based Adaptive Designs Paola Pedone, Daniele Romano, and Grazia Vicario 6.1 Introduction KrigingModels Prediction Capability: Kriging vs. Regression AdaptiveCMMInspectionPlans Application to Straightness and Roundness Case1:Straightness Case 2: Roundness Conclusions References Product and Process Innovation by Integrating Physical and Simulation Experiments Daniele Romano 7.1 ExperimentsandInnovation Physicalvs.ComputerExperiments AnIntegratedApproach Applications to Product and Process Development

9 x Contents Product Innovation: The Climbing Robot ProcessInnovation:TheFlockingProcess DiscussionandFutureDevelopments References Continuous Innovation of the Quality Control of Remote Sensing Data for Territory Management Elisabetta Carfagna and Johnny Marzialetti 8.1 Land-CoverDatabases Quality of Land-Cover Databases Statistical Quality Control by Acceptance Sampling Classical Methods Sequential Acceptance Sampling Examples of Quality Control and Land-Cover Databases Validation The International Geosphere Biosphere Programme: GlobalLand-CoverDataSet The Global Land Cover Map 2000 Validation and Quality Control CORINELandCover TheISTATExperiment Unbiased Estimates of the Quality Parameters with Adaptive SequentialSampling AnAdaptiveSequentialProcedure Two-StepAdaptiveProcedure Continuous Improvement of a Database of Remote Sensing Data ConclusionsandFutureDevelopments References An Innovative Online Diagnostic Tool for a Distributed Spatial Coordinate Measuring System Fiorenzo Franceschini, Maurizio Galetto, Domenico Maisano, and Luca Mastrogiacomo 9.1 Introduction The Concept of the Reliability of a Measurement MScMS Technological and Operating Features MScMS Diagnostic System Energy Model-Based Diagnostics Setting Up the Test Parameters An Example of the Application of Energy Model-Based Diagnostics Conclusions References...175

10 Contents xi 10 Technological Process Innovation via Engineering and Statistical Knowledge Integration Biagio Palumbo, Gaetano De Chiara, and Roberto Marrone 10.1 Introduction Technological Context and Case Study Pre-experimentalPlanning ExperimentalDesignandSet-Up Analysis of Results and Technological Interpretation AnalysisofResults Technological Interpretation of Results Conclusion References Part III Innovation of Lifecycle Management 11 Bayesian Reliability Inference on Innovated Automotive Components Maurizio Guida and Gianpaolo Pulcini 11.1 Introduction Prior Inference on the Failure Probability PastData FormalizingModificationEffectiveness EffectofWorkingConditionsandCostReduction Prior Inference on the Failure Probability of the New Product PriorPredictionoftheNumberofFailedItems Field Failure Data for the New Product Posterior Inference on the New Product Posterior Inference on the Reliability PosteriorPredictionoftheNumberofFailedItems ACaseStudy Conclusions Appendix References Stochastic Processes for Modeling the Wear of Marine Engine Cylinder Liners Massimiliano Giorgio, Maurizio Guida, and Gianpaolo Pulcini 12.1 Introduction The Case Study: System Description, Technological Information andexperimentaldata FormulatingtheModels Model 1: Shock Model with a Deterministic andconstanteffect Model2:GammaWearProcess Model3:State-DependentMarkovWearProcess...220

11 xii Contents 12.4 NumericalApplicationandResults Conclusions A Appendix:EstimationProcedures A.1 PLShockModel A.2 GammaProcess A.3 PoissonMarkovProcess References Part IV Research and Innovation Management 13 A New Control Chart Achieved via Innovation Process Approach Pasquale Erto and Giuliana Pallotta 13.1 Introduction IdentifyingtheNewNeededEstimationFeatures DevelopingtheInnovativeEstimationProcedure First Innovation Step: A Shewhart-Type Control Chart of the Weibull Percentile Second Innovation Step: A Bayesian Cumulative Control Chart of the Weibull Percentile Third Innovation Step: A Bayesian Cumulative and Adaptive Control Chart of the Weibull Percentile Conclusions References A Critical Review and Further Advances in Innovation Growth Models Pasquale Erto and Amalia Vanacore 14.1 Introduction A Comparison Among Different S CurveModels CriteriaUsedtoComparetheModels Consistency of the Properties of the Model with the Dynamics of Technological PerformanceGrowth The Stability of Least Squares Estimates Versus Different Assumptions About the Error Term Sustainability of the Assumption of Linearity Seeking a Good Reparameterization for the Generalized WeibullModel The Goodness of Fit of Each Model Conclusions References Index...261

12 List of Contributors Baldi Antognini, Alessandro Department of Statistical Sciences, University of Bologna Barone, Stefano Department of Technology, Production and Managerial Engineering, University of Palermo, Viale delle Scienze, Palermo Carfagna, Elisabetta Department of Statistical Sciences, University of Bologna De Chiara, Gaetano AVIO S.p.A., Manufacturing Technologies Department, Pomigliano, Naples, Italy Erto, Pasquale Department of Aerospace Engineering, University of Naples Federico II, P. le Tecchio 80, 80125, Naples, Italy Franceschini, Fiorenzo Department of Production Systems and Business Economics (DISPEA), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy xiii

13 xiv List of Contributors Galetto, Maurizio Department of Production Systems and Business Economics (DISPEA), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy Giorgio, Massimiliano Department of Aerospatial and Mechanical Engineering, Second University of Naples, Aversa (NA), Italy Giovagnoli, Alessandra Department of Statistical Sciences, University of Bologna Guida, Maurizio Department of Information Engineering and Electrical Engineering, University of Salerno, Fisciano (SA), Italy Lanzotti, Antonio Department of Aerospace Engineering, University of Naples Federico II, P. le Tecchio 80, Naples, Italy Lombardo, Alberto Department of Technology, Production and Managerial Engineering, University of Palermo, Viale delle Scienze, Palermo Maisano, Domenico A. Department of Production Systems and Business Economics (DISPEA), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy Marrone, Roberto AVIO S.p.A., Manufacturing Technologies Department, Pomigliano, Naples, Italy Marzialetti, Johnny Department of Statistical Sciences, University of Bologna

14 List of Contributors xv Mastrogiacomo, Luca Department of Production Systems and Business Economics (DISPEA), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy Matrone, Giovanna Department of Aerospace Engineering, University of Naples Federico II, P. le Tecchio 80, Naples, Italy Pallotta, Giuliana Department of Aerospace Engineering, University of Naples Federico II, P. le Tecchio 80, 80125, Naples, Italy Palumbo, Biagio Department of Aerospace Engineering, University of Naples Federico II, P. le Tecchio 80, 80125, Naples, Italy Pedone, Paola INRIM (Italian National Research Institute of Metrology), Strada delle Cacce, 91, Torino, Italy Pistone, Giovanni Politecnico di Torino, Department of Mathematics, Corso Duca degli Abruzzi, 24, Torino, Italy Pulcini, Gianpaolo Istituto Motori, National Research Council, Napoli, Italy Romano, Daniele Department of Mechanical Engineering, University of Cagliari, Piazza d Armi, 1, Cagliari, Italy romano@dimeca.unica.it

15 xvi List of Contributors Tarantino, Pietro Department of Aerospace Engineering, University of Naples Federico II, P. le Tecchio 80, Naples, Italy Vanacore, Amalia Department of Aerospace Engineering, University of Naples Federico II, P. le Tecchio 80, Naples, Italy Vicario, Grazia Politecnico di Torino, Department of Mathematics, Corso Duca degli Abruzzi, 24, Torino, Italy Zagoraiou, Maroussa Department of Statistical Sciences, University of Bologna