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Technical University of Košice, Slovakia 37th AIM Conference in Vienna “Human Centered Production in Cyber-Physical Production Systems” Variety Induced Complexity in Mass Customization

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  • Technical University of Košice, Slovakia

    37th AIM Conference in Vienna “Human Centered Production in Cyber-Physical Production Systems”

    Variety Induced Complexity in Mass Customization

  • 1. Brief introduction of TUKE 2. Recent related research works 3. The main topic of the presentation: Variety Induced Complexity in Mass

    Customization 3.1 Human-Centered Production and Mass Customization 3.2 Introduction to Mass Customization 3.4 Complexity issues of Mass customization 3.5 Conclussions

    2

    Agenda of Presentation

  • Variety Induced Complexity in Mass Customization Brief Introduction of TUKE

    Faculty of Metallurgy Faculty of Mechanical Engineering Faculty of Electrical Engineering and Informatics Faculty of Civil Engineering Faculty of Economics, Faculty of Manufacturing Technologies Faculty of Arts Faculty of Aeronautics Faculty of Mining, Ecology, Process Control and Geo-technology

    The Technical University of Košice was founded in 1952,

  • Variety Induced Complexity in Mass Customization Brief Introduction of TUKE

    TUKE is member of the RawMatTERS – winner of EIT Raw Materials

    TUKE is research member of EFFRA -European Factories of the Future Research Association

    TUKE is the first university from Slovakia involved in Knowledge and Innovation Community (KIC)

    TUKE is the first University from Slovakia who is a research member of EFFRA

    http://eit.europa.eu/sites/default/files/EIT Raw Materials - Factsheet 2014_0.pdf

  • Variety Induced Complexity in Mass Customization

    Brief Introduction of TUKE The first fellow of CIRP in Slovakia became Prof. Ján Buda from TUKE (1973)

    Presently, he is fellow emeritus of The CIRP.

  • Variety Induced Complexity in Mass Customization Recent related research works: International book projects

  • Variety Induced Complexity in Mass Customization 3.1 Human-Centered Production and Mass Customization

    The lesson learned from the CIM crisis: Human work in factories can never be entirely replaced by computers and artificial int. (Zuehlke, 2009). Arguments: 1) full automation is inflexible, error-prone and expensive to keep

    running 2) According to concept Indutry 4.0 educated and skilled workers will integral part of the Cyber-physical production systems

    Further development of mass customized production will follow paradigm of HCP.

    Zühlke, Detlef. "SmartFactory –a Vision Becomes Reality.„ Information Control Problems in Manufacturing. Vol. 13. No. 1. 2009.

  • Variety Induced Complexity in Mass Customization

    Mass customization strategy is influencing Human-centered production.

    Gorecky, D., Mura, K., & Arlt, F. (2013, August). A Vision on Training and Knowledge Sharing Applications in Future Factories. In Analysis, Design, and Evaluation of Human-Machine Systems (Vol. 12, No. 1, pp. 90-97).

    The main impacting factors on Human-centered production (Gorecky, 2013): -Globalisation

    - Rapid technological developments

    - Changing customer demands – Firms tend to produce a wide variety of products and product variants to satisfy a broad customer spectrum. - Demographic changes

    -Knowledge-based Economy

  • Variety Induced Complexity in Mass Customization

    3.2 Introduction to Mass Customization

    Gilmore J, Pine B. The four faces of mass customization. Harv Bus Rev, 1997; 75(1):91-101.

    Anderson (2004): Mass customization means manufacturing custom products in large quantity (not just assembly modules) quickly and efficiently to achieve higher customer satisfaction.

    THE FOUR APPROACHES TO MASS CUSTOMIZED MANUFACTURING (Gilmore and Pine):

  • Variety Induced Complexity in Mass Customization

    3.3 Complexity issues of Mass customization In the context of increasing complexity of mass customized operation, product variant management gains importance.

    There are more methodological approaches to a quantification of product variety induced complexity: 1. Combinatorial Based Approach to determine all of the possible product configurations (PCs) 2. Axiomatic Design and Entropy Based Approach

    Combinatorial Based Approach / proposed methodological framework defines: 1) entry assembly components as basic elements for the calculation of PCs 2) Procedures for the calculations in individul assembly nodes 3) Procedure to calcuate PCs of entire SC. Note: A component, in this context can be understood as a part, module, and group of products, property or other characteristic of the module/product. Practically, three types of entry assembly components has to be identified: - stable components, - voluntary components, - compulsory optional components.

    Source : ElMaraghy W, ElMaraghy H, TomiyamaT, Monostori L. Complexity in engineering design and manufacturing. CIRP Ann Manuf Technol, 2012; 61(2):793–814

  • Variety Induced Complexity in Mass Customization

    3.3 Complexity issues of Mass customization Combinatorial Based Approach

    Config. Calculator

  • Variety Induced Complexity in Mass Customization

    3.3 Complexity issues of Mass customization Axiomatic Design and Entropy Based Approach

    1 Step: Transforming nodes of MCA into axiomatic design matrices

  • Variety Induced Complexity in Mass Customization

    3.3 Complexity issues of Mass customization Axiomatic Design and Entropy Based Approach

    2 step: Application the concepts of Entropy and Degree of Disorder to quantify product variety induced complexity through complexity indicator presented by Guenov

    The first one, denoted as Systems Design Complexity (SDC), SDC = ∑Nj ln Nj, where ‘Nj‘ is interpreted as number of interactions per single DP of measured designed matrix. The second measure is Degree of disorder:

    Where ‘N’ is number of interactions within a measured design matrix, and N1, N2, …, NK are number of interactions per each DP of the same matrix. Third indicator Ln Ω was proposed in order to obtain less exponential and more tangible values of Ω . Calculated values of the , Ln and SDC for all product classes and sub-classes have been determined for both, FS1 and FS2.

    Guenov, Marin D. "Complexity and cost effectiveness measures for systems design." (2002).

  • Variety Induced Complexity in Mass Customization

    3.3 Complexity issues of Mass customization Axiomatic Design and Entropy Based Approach

    Fragment values of complexity indicators for classes CL1 to CL6. Scenario #1

    Fragment values of complexity indicators for classes CL1-∞. Scenario #2

  • Variety Induced Complexity in Mass Customization

    3.3 Complexity issues of Mass customization Comparison of two different approaches

  • Variety Induced Complexity in Mass Customization 3.3 Complexity issues of Mass customization Procedure to calcuate PCs of entire SCs :

  • Variety Induced Complexity in Mass Customization

    3.4 Conclussions

    Comparison of two different approaches showed: 1) Both, PCs and SDC measures can be desribed by more or less a

    concave complexity functions.

    2) SDC indicator applies results of PCs

    3) SDC indicator brings more realistic complexity values than values

    of all of possible product configurations (PCs).

  • TECHNICAL UNIVERSITY OF KOSICE

    18

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