Model granularity in engineering design – concepts and framework

In many engineering design contexts models are indispensable. They offer decision support and help tackle complex and interconnected design projects, capturing the underlying structure of development processes or resulting products. Because managers and engineers base many decisions on models, it is crucial to understand their properties and how these might influence their behaviour. The level of detail, or granularity, of a model is a key attribute that results from how reality is abstracted in the modelling process. Despite the direct impact granularity has on the use of a model, the general topic has so far only received limited attention and is therefore not well understood or documented. This article provides background on model theory, explores relevant terminology from a range of fields and discusses the implications for engineering design. Based on this, a classification framework is synthesised, which outlines the main manifestations of model granularity. This research contributes to theory by scrutinising the nature of model granularity. It also illustrates how this may manifest in engineering design models, using Design Structure Matrices as an example, and discusses associated challenges to provide a resource for modellers navigating decisions regarding granularity.

[1]  A. Fine,et al.  The Dappled World , 2000 .

[2]  Tyson R. Browning,et al.  Applying the design structure matrix to system decomposition and integration problems: a review and new directions , 2001, IEEE Trans. Engineering Management.

[3]  Tarja Knuuttila,et al.  Models as Epistemic Tools in Engineering Sciences: A Pragmatic Approach. , 2009 .

[4]  P. Clarkson,et al.  Predicting change propagation in complex design , 2004 .

[5]  R. Giere.  How Models Are Used to Represent Reality , 2004, Philosophy of Science.

[6]  Hoda A. ElMaraghy,et al.  Optimum granularity level of modular product design architecture , 2013 .

[7]  Gregory M. Mocko,et al.  Engineering design complexity: an investigation of methods and measures , 2008 .

[8]  Tyson R. Browning,et al.  On the alignment of the purposes and views of process models in project management , 2010 .

[9]  Marko A. Hofmann.  Ontologies in Modeling and Simulation: An Epistemological Perspective , 2013, Ontology, Epistemology, and Teleology for Modeling and Simulation.

[10]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[11]  Andreas Tolk,et al.  Conceptual modeling with processes , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[12]  Margaret Morrison,et al.  Models as Mediating Instruments , 1999 .

[13]  Frederick K. Frantz.  A taxonomy of model abstraction techniques , 1995, WSC '95.

[14]  Albert Albers,et al.  INTEGRATED PRODUCT AND PROCESS MODELS: TOWARDS AN INTEGRATED FRAMEWORK AND REVIEW , 2015 .

[15]  Hoda A. ElMaraghy,et al.  Determining Granularity of Changeable Manufacturing Systems Using Changeable Design Structure Matrix and Cladistics , 2015 .

[16]  Bruce Edmonds,et al.  Syntactic Measures of Complexity , 1999 .

[17]  Vesa Salminen,et al.  Towards a Theory of Complicatedness: Framework for Complex Systems Analysis and Design , 2001 .

[18]  K. Wiesner,et al.  What is a complex system? , 2012, European Journal for Philosophy of Science.

[19]  W. Ashby,et al.  Every Good Regulator of a System Must Be a Model of That System , 1970 .

[20]  Jami J. Shah,et al.  Mechanical Engineering Design Complexity Metrics: Size, Coupling, and Solvability , 2010 .

[21]  David C. Wynn,et al.  Model-based approaches to support process improvement in complex product development , 2007 .

[22]  Ronald J. Brachman,et al.  What IS-A Is and Isn't: An Analysis of Taxonomic Links in Semantic Networks , 1983, Computer.

[23]  Jianhua Dai,et al.  Entropy measures and granularity measures for set-valued information systems , 2013, Inf. Sci..

[24]  Steven D. Eppinger,et al.  Improving the systems engineering process with multilevel analysis of interactions , 2014, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[25]  Norman E. Fenton,et al.  Measurement : A Necessary Scientific Basis , 2004 .

[26]  Daniel S. Weld.  Reasoning about Model Accuracy , 1992, Artif. Intell..

[27]  Jakob F. Maier,et al.  Model granularity and related concepts , 2016 .

[28]  M. Kane.  Measurement theory. , 1980, NLN publications.

[29]  P. John Clarkson,et al.  Modelling the evolution of uncertainty levels during design , 2011 .

[30]  Andreas Tolk,et al.  Truth, Trust, and Turing - Implications for Modeling and Simulation , 2013, Ontology, Epistemology, and Teleology for Modeling and Simulation.

[31]  P. John Clarkson,et al.  Simulating progressive iteration, rework and change propagation to prioritise design tasks , 2014 .

[32]  P. John Clarkson,et al.  Planning development processes for complex products , 2010 .

[33]  Tarja Knuuttila,et al.  Models, Representation, and Mediation , 2005, Philosophy of Science.

[34]  Roman Frigg,et al.  Models and fiction , 2009, Synthese.

[35]  W. Hennig.  Phylogenetic Systematics , 2002 .

[36]  H. ElMaraghya,et al.  A granularity model for balancing the structural complexity of manufacturing systems equipment and layout , 2015 .

[37]  Eun Suk Suh,et al.  Level of modularity and different levels of system granularity , 2011 .

[38]  Luciano Floridi,et al.  The Method of Levels of Abstraction , 2008, Minds and Machines.

[39]  Jack P. C. Kleijnen,et al.  EUROPEAN JOURNAL OF OPERATIONAL , 1992 .

[40]  Paul A. Fishwick,et al.  The role of process abstraction in simulation , 1988, IEEE Trans. Syst. Man Cybern..

[41]  Roman Frigg,et al.  Re-presenting scientific representation , 2003 .

[42]  Michael Pidd,et al.  Just Modeling Through: A Rough Guide to Modeling , 1999, Interfaces.

[43]  Yiyu Yao,et al.  Information granulation and rough set approximation , 2001, Int. J. Intell. Syst..

[44]  C. Eckert,et al.  What is a Process Model? Reflections on the Epistemology of Design Process Models , 2010 .

[45]  Stewart Robinson,et al.  Conceptual modelling for simulation Part I: definition and requirements , 2008, J. Oper. Res. Soc..

[46]  HERBERT A. SIMON,et al.  The Architecture of Complexity , 1991 .

[47]  Andreas Kohn,et al.  Increasing the reproducibility of structural modelling , 2015 .

[48]  D. Grose,et al.  Reengineering the aircraft design process , 1994 .

[49]  Carolyn Conner Seepersad,et al.  A high-definition design structure matrix (HDDSM) for the quantitative assessment of product architecture , 2012 .

[50]  Jean-Daniel Zucker,et al.  Abstraction in Artificial Intelligence and Complex Systems , 2013, Springer New York.

[51]  Tyson R. Browning,et al.  The many views of a process: Toward a process architecture framework for product development processes , 2009, Syst. Eng..

[52]  Daniel DeLaurentis,et al.  Framework for measuring complexity of aerospace systems , 2014, Research in Engineering Design.

[53]  Tyson R. Browning,et al.  Key concepts in modeling product development processes , 2006, Syst. Eng..

[54]  Eun Suk Suh,et al.  Seeing Complex System through Different Lenses: Impact of Decomposition Perspective on System Architecture Analysis , 2015, Syst. Eng..

[55]  Viktor Mikhaĭlovich Glushkov,et al.  An Introduction to Cybernetics , 1957, The Mathematical Gazette.

[56]  Daniel Gross,et al.  Report from the fidelity implementation study group , 1999 .

[57]  Yiyu Yao,et al.  Probabilistic approaches to rough sets , 2003, Expert Syst. J. Knowl. Eng..

[58]  Madhav Erraguntla,et al.  Simulation modeling at multiple levels of abstraction , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[59]  Tyson R. Browning,et al.  A Survey of Activity Network‐Based Process Models for Managing Product Development Projects , 2007 .

[60]  D. V. Steward,et al.  The design structure system: A method for managing the design of complex systems , 1981, IEEE Transactions on Engineering Management.

[61]  Michael Weisberg.  Who is a Modeler? , 2007, The British Journal for the Philosophy of Science.

[62]  Davor Svetinovic,et al.  Hierarchical Decomposition and Multidomain Formulation for the Design of Complex Sustainable Systems , 2010 .

[63]  Yiyu Yao,et al.  A measurement theory view on the granularity of partitions , 2012, Inf. Sci..

[64]  Roger J. Brooks,et al.  Choosing the best model: Level of detail, complexity, and model performance , 1996 .

[65]  Mathias Weske,et al.  Business process model abstraction: a definition, catalog, and survey , 2012, Distributed and Parallel Databases.

[66]  Curtis E. Dyreson,et al.  A Glossary of Time Granularity Concepts , 1997, Temporal Databases, Dagstuhl.

[67]  Richard J. Mayer,et al.  Using Ontologies for Simulation Modeling , 2006, Proceedings of the 2006 Winter Simulation Conference.

[68]  Bernard P. Zeigler,et al.  Theory of modeling and simulation , 1976 .

[69]  Scott L. Rosen,et al.  A Structured Approach for Rapidly Mapping Multilevel System Measures via Simulation Metamodeling , 2014, Syst. Eng..

[70]  Oliver Holschke,et al.  Impact of Granularity on Adjustment Behavior in Adaptive Reuse of Business Process Models , 2010, BPM.

[71]  Christopher Alexander.  Notes on the Synthesis of Form , 1964 .

[72]  Steven D. Eppinger,et al.  A simulation-based process model for managing complex design projects , 2005, IEEE Transactions on Engineering Management.

[73]  N. Cartwright.  The dappled world : a study of the boundaries of science , 1999 .

[74]  Rik Eshuis,et al.  Constructing customized process views , 2008, Data Knowl. Eng..

[75]  Andreas Tolk,et al.  Exploring the components of dynamic modeling techniques , 2012 .

[76]  Tarek N Al-Geddawy,et al.  A DSM Cladistics Model for Product Family Architecture Design , 2014 .

[77]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[78]  Herbert A. Simon,et al.  Causality and Model Abstraction , 1994, Artif. Intell..

[79]  John D. C. Little,et al.  Models and Managers: The Concept of a Decision Calculus , 2016, Manag. Sci..

[80]  Ahmed Azab,et al.  Modelling evolution in manufacturing: A biological analogy , 2008 .

[81]  P. John Clarkson,et al.  DIFFERENT LEVELS OF PRODUCT MODEL GRANULARITY IN DESIGN PROCESS SIMULATION , 2015 .

[82]  Olga Levina,et al.  Granularity as a Cognitive Factor in the Effectiveness of Business Process Model Reuse , 2009, BPM.

[83]  Tyson R. Browning,et al.  A conceptual framework for tackling knowable unknown unknowns in project management , 2014 .

[84]  David E. Goldberg,et al.  An information theoretic method for developing modular architectures using genetic algorithms , 2007 .

[85]  Erik Ostermann,et al.  The Structure Of Scientific Theories , 2016 .

[86]  Andrew Kusiak,et al.  Integrated product and process design: A modularity perspective , 2002 .