Complexity in engineering design and manufacturing · – Henry Ford's minimum complexity approach...
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Complexity in engineering design and manufacturing W. ElMaraghy, H. ElMaraghy, T. Tomiyama, L. Monostori Special thanks to: E. Abele, M. Abramovici, P. Butala, G. Chryssolouris, S. J. Hu, S-G. Kim, Y. Koren, S. C-Y Lu, D. Mourtzis, G. Schuh, K. Ueda, H. Van Brussel, H-P. Wiendahl 2012 STC O keynote paper International Academy for Production Engineering Paris Meeting – France 26 January 2012
Transcript of Complexity in engineering design and manufacturing · – Henry Ford's minimum complexity approach...
Complexity in engineering design and manufacturing
W. ElMaraghy, H. ElMaraghy, T. Tomiyama, L. Monostori
Special thanks to: E. Abele, M. Abramovici, P. Butala, G. Chryssolouris, S. J. Hu, S-G. Kim, Y. Koren, S. C-Y Lu, D.
Mourtzis, G. Schuh, K. Ueda, H. Van Brussel, H-P. Wiendahl
2012 STC O keynote paper
International Academy for Production Engineering
Paris Meeting – France 26 January 2012
• The changed environment – Manufacturing is facing unprecedented challenges:
o market volatility, variety in customer demands
o distributed global manufacturing, fierce competition
o faster response time, and shorter life-cycles
• The response – Agile business strategies to respond to changing market conditions
– Market demand satisfied with complex products and design methods
– Complexity of products and response time dictate IMS, FMS, RMS
– New business paradigms: competition/collaboration
– Adaptive, changeable and co-evolutionary design, manufacturing and management systems
Motivation
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• The main challenge
– Increasing complexity continues to be one of the biggest challenge facing manufacturing today.
– Increase in complexity of the manufacturing, technological, business, social systems, and the environment
• Additional challenges – Product imitations and plagiarism
– Fragile economy, etc...
Motivation - Challenges
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Soci
al
Technology
Environment
Busi
ness
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• Past practice during the development of industrialization – Henry Ford's minimum complexity approach to auto production
– Other clever engineering technological innovations
– Reductionist approaches: critically successful in stable times
• The present and future directions – In the age of uncertainty we must accept that complexity is the norm
– Past methods of eliminating complexity have reached their limit
– Useful when uncertainty was limited and complexity manageable
– Complexity of markets should be exploited as “opportunities”
– To manage complexity, need to understand types, sources and ways to manage the increasing complexity in design and manufacturing.
Motivation – The future
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Objectives • Problem statement, clarification
– Review the breadth of complexity in engineering design, manufacturing and business
– Clear statements on academic and industrial aims • Survey & results
– Investigate, summarize the types and sources of complexity – List the main complexity metrics – Review different methods to manage complexity in both the
functional and the physical domains, including operational issues – Highlight the new sources of complexity such as multidisciplinary – Give results of industrial surveys and research achievements – Examples and case studies
• Future directions – Trends and roadmap for future research – Promising directions and importance of foresight
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Sources & Drivers of complexity
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Customer Requirements
Social and Environmental Pressures: Government Legislation,
Standards
Complexity of Market Forces: Global Competition,
Turbulence, Variety, Short Delivery, Zero Defect, etc...
Sources of Complexity
Product Structure
Product Features
Product Variety & Number of Parts
Coupling (Functional & Physical)
Paradigm and Technologies
Modules & Configuration
Planning & Scheduling
Human Cognitive Ergonomics
Global Supply Chain
Prod
ucts
Proc
ess
and
Sys
tem
s
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• Functional & • Physical Domains
Static & Dynamic
Complexity
Scope
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Design & Product
Development Complexity
● Number of parts ● Multidisciplinarity ● Manufacturability ● Size, Geometry ● Variety
Mfg. & Manufacturing
Systems Complexity
Changeability ● Responsiveness ● Volume, Speed ● Operational ● Flexibility ●
Business and Market Complexity
● Supply Chain Dynamics ● Global Competition ● Market Turbulence ● Foresight
Qua
lity
Design Manufacturing Business (global supply chain)
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Scope - Perspectives
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Process Complexity
Product Complexity
Production & Operational Complexity
Adapted from: ElMaraghy, W., & Urbanic, J., Modelling of Manufacturing Systems Complexity.., CIRP Annals 2003
Strategic Functional, Logical Physical, Technological Tactical and Operational
And from different perspectives and domains: e.g. Complexity due to Multi-disciplines & Different perspectives
Logistics & Global Supply Chain
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Scope – Size / Scale
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“Nano-manufacturing complex products”
“Machines to global supply chains”
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Literature – Books ~ 20
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Literature – Dissertations ~ 50
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Abbasi, Maisam, 2008. Perspectives of Complexity and Intelligence on Logistics and Supply Chain Management. M.Sc. Thesis, University of Borås. Adamsson, Niklas, 2008. Interdisciplinary integration in complex product development - Managerial implications of embedding software in manufactured goods. Doctoral Thesis, Royal Institute of Technology (KTH). Arafa, Amir Taher Abd-Allah, 2011. Dynamic Analysis for Enterprise Strategic Flexibility using System Engineering Methodology. Ph.D. Dissertation, University of Windsor. Badrous, Sameh Nozhy Samy, 2011. Complexity of Products and their Assembly Systems. Ph.D. Dissertation, University of Windsor. D’Amelio, Valentina, 2010. Design Interference Detection for Multi-Disciplinary Product Development. Ph.D. Dissertation, Delft University. Deif, Ahmed Mahmoud, 2007. Dynamic Analysis of Agile Manufacturing Planning and Control (MPC) Systems using Control Theory. Ph.D. Dissertation, University of Windsor. Dickmann, John Q. Jr., 2009. Operational Flexibility in Complex Enterprises: Case Studies from Recent Military Operations. Ph.D. Dissertation, MIT. Kreimeyer, Matthias F., 2009. A Structural Measurement System for Engineering Design Processes. Doktor-Ingenieurs genehmigten Dissertation, Technische Universität München. Wang, Hui, 2010. Product Variety Induced Complexity and its Impact on Mixed-Model Assembly Systems and Supply Chains. Ph.D. Dissertation, the University of Michigan. Alamoudi, Rami Hussain, 2008. Interaction Based Measure of Manufacturing Systems Complexity and Supply Chain Systems Vulnerability using Information Entropy. Ph.D. Dissertation, University of Miami.
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Literature – Journals > 400
CIRP Keywords Complexity in engineering
papers – “Compendex” 26/01/2012, CIRP Paris Meeting STC O Kn:
“Complexity in engineering design and manufacturing”
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Complexity in engineering : CIRP Track 3 “Complexity” Related Topics
RMS Reconfigurable
DC Dynamics & Control
Modeling
M Multi-Disciplinarity
RE Release Engineering
DTM Design Theory & Methodology
C Changeability
ECN: Collaborative Negotiation ADC: Axiomatic Design Complexity Theory
EEC: Emergence, Evolution , Co-evolution
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Method: nature of complexity Complexity is about Relationships Systems Engineering View
Quantitative Complexity Computational Complexity
Qualitative Complexity Complexity Results from Unknowingness, In science,
and Uncertainty in Applied Science Qualitative Complexity Comes from Uncertainty If We Can Foresee and Enumerate All Possible
Situations, Uncertainty Can Be Evaluated Statistically, Probabilistically or Stochastically
If Not, Uncertainty Cannot Be Quantified that way.
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Complexity metrics • Systems complexity [Calinescu et al., 2000]
– Entropic measures of information (amount of information required to predict state of the system).
• Design, Manufacturing Process and Operational Complexity [ElMaraghy W. & Urbanic, 2003 and 2004] – Function of number of parts and their interactions. – Physical and cognitive Factors.
• Manufacturing Systems Complexity [ElMaraghy H. et al., 2005] – New Classification & Coding System “Complexity Coding System”
used to evaluate the complexity of manufacturing systems. – Beneficial property, given reduction of complicatedness.
• Axiomatic design [Nam Suh, 2005] – Time independent (imaginary and real complexity). – Time dependent (uncertainty of future events).
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Complexity in manufacturing system Complex manufacturing systems that comprise large
number of different resources, and are designed to respond dynamically to changing requirements represent an expanded space of of alternatives and choices.
The dynamic nature of the manufacturing environment greatly increases the number of decisions that need to be made.
The integration of many software and hardware functions makes it difficult to predict the effect of a decision on the system performance.
The cost of added complexity must be balanced against improved performance, Hence, the need to assess both structural and operational complexity.
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Manufacturing complexity Product Complexity Environment Volume
Process Complexity
Operational Complexity
Procedures and Tasks
Production Control
Features and Tools
Information
Effort
Physical Cognitive
Metrics
ElMaraghy, W., & Urbanic, J., Modelling of Manufacturing Systems Complexity.., CIRP Annals 2003
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IEE Spectrum: by Robert N. Charette / February 2009
Imag
e: D
aim
ler Electronics in Cars today:
50 to 100 CPUs 100 Million line of code ! The Boeing new 787 “Dreamliner”
has about 6.5 million line of software code to operate its avionics and onboard support systems.
In 2005, Toyota voluntarily recalled 160,000 of 2004 and 2005 Prius hybrids because of a software issues. More recently, more problems ! Last year alone, there were several automotive recall notices related to software problems: Chrysler (Jeep Commanders ); Volkswagen recalled about 4,000 of its 2008 Passats and Passat Wagons; and GM’s Cadillac CTS.
Really Complex Products: “This Car Runs on Code”
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Complexity resulting from multi-disciplinarity
Multi-Disciplinarity as Combinations of (Well-Known) Disciplines Multi-Disciplinarity Causes Cross-Disciplinary Problems
Example: Troubles During Integration of Subsystems
Subsystems Are Well-Understood
When Subsystems Are Integrated, Unforeseen or Unidentified Problems Are Detected: Printers (Valentina D’Amelio et al. 2010)
AGV (Tomiyama, ElMaraghy et al. CIRP Annals 2007)
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Cross-Disciplinary Problems Strategy No Uncertainty = No Complexity
Risk Analysis with Causality
If Uncertainties Unavoidable, Reduce “Unknowns” Convert It to a Probabilistic Problem by
Enumerating Possibilities Information Content/Entropy Minimum Approach Discover “Interferences”
Design Interference Detector If Impossible, Mitigation Through Being Prepared
for the Worst Cases
Risk Analysis without Causality
App
licat
ion
or C
onte
xt
Method 1 Method 2
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Multi-disciplinary complexity design:
Most products are multi-domain systems, e.g. mechatronics systems, are multi- and inter- disciplinary devices exist, but there is no unified methods, or common language among engineers from the various disciplines, to deal with that.
These unpredictable problems are the consequence of insufficient integration of domain knowledge and methods.
Building a physical prototype and “test benches”, is often essential to develop those multi-disciplinary products.
Mechatronics is not only the constructive coupling of domains that enable a system to work but also the destructive coupling of domains that generates unpredicted problems.
Products & Assembly Systems Complexity
Product Assembly Complexity Samy S.N., ElMaraghy H. (2010b)
Code-based assembly system structural complexity ElMaraghy H. (2006), Samy S.N., ElMaraghy H.(2010c)
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Structural Complexity Coding System
Tomiyama T, D'Amelio V, Urbanic J, ElMaraghy W (2007) Complexity of multi-disciplinary design. CIRP Annals-Manufacturing Technology. 56(1): 185-188.
Systems Structural Code (ElMaraghy, H. 2006)
System Capabilities
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Manufacturing Systems Structural Complexity
0.00
5.00
10.00
15.00
20.00
25.00
30.00
DedicatedMilling M/Cs
(M1+M2+M3)
Broach CNC ParallelLine
Sy
ste
m S
tru
ctu
ral C
om
ple
xit
y In
de
x
BuffersIndex
MaterialHandlingIndexEquipmentIndex
Heavy Material Removal V6 Cylinder Block Manufacturing System Complexity Breakdown
Using The SCC Code
Piston Assembly
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Models and potential complexity measures
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L. Monostori, et al. 41st CIRP ISMS, Tokyo, Japan, May 26 – 28, 2008
(1) Environment model: sequence of random variables (time series X1,
X2, …, Xt, ..., Xn) Potential complexity measure: Information complexity (entropy)
(2) Collaboration model: complex adaptive systems Potential complexity measure: computational complexity (measure of applied resources)
(3) Enterprise network model: network and graph theory Potential complexity measure: topological (graph) complexity (adjacency, components, walks)
Figure 21 Conceptual model structure (Arafa and ElMaraghy
2011b)
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Figure 29 Volume flexibility sub model
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Volume Flexibility
Fixed Cost
<Capacity>
+
<Unit Fixed Cost>
+
Unit Variable Cost <Initial Unit
Variable Cost> +
<Learning>
-
Price
<Change in Price>
+
Contribution Margin +
+
-
+
Managing the Different Aspects of Manufacturing Complexity
Goals and
Strategy
Environm
ent
Technology; Infratsructure
Operational Complexity Compliance
Product/Process/ Service Complexity
Global Competition, Complex Supply Chains
Customer Demand, Product Variety
Time-to-Market Pressures
Responsiveness Ability Turbulent Market Demands
Trained Workforce and Educated Management 30
Increased Complexity
Applications of engineering design and manufacturing to complex challenges
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Adapted from: Presidential Address by Prof. Dr. Ing. F. Klocke, Manchester, August 2008 And the US NAE: “The Grand Challenges for Engineering” – The Engineer 2020 Report
Energy Environment, Global warming, Fresh water shortages
Health, New diseases
Mobility
Communication Safety, Security Aging infrastructure
Applications of engineering design and manufacturing to complex challenges
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Acatech, oct. 2007 Presidential Address by Prof. Dr. Ing. F. Klocke, Manchester, August 2008
Environment, climate, resources
Economy growth welfare
Individual and collective needs
Overall balance
Demographics
Challenges
Economic/societal forces
SoS and Engineering Systems
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Traditional SE practices, while suitable for some situations, are not sufficient for systems of systems Need to use ES (Engineering Systems)** for socio-technical problems Research is required to evolve new methodologies and tools for performing multi-disciplinary socio-technical engineering studies
Product Systems Engineering
Global Enterprise ES
Systems of Systems
Systems Engineering Management
Com
plex
ity
1950-1960s 1970s 1990s 2010 2000s 1980s
Systems Theory
2020+
** More information about this subject can be found in a new MIT Press book: de Weck OL, Roos D, Magee LC (2011) “Engineering Systems - Meeting Human Needs in a Complex Technological World”
Managing complexity in design & manufacturing
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Managing Design & Manufacturing
Complexity in Functional & Physical domains
Reduce the (- ve) effects of complexity Control the (+ ve) aspects of complexity
Decoupling, sub-assemblies Design for Manufacturability Interference detection in multidisciplinary product development Reduce variety, reduce number of parts Apply design methodologies for robustness New product development paradigms (e.g. ECN, concurrent engineering, axiomatics) Use of engineering tools to minimize perceived (imaginary) complexity Minimize information content (entropy) Modularization, standardization & variants Minimize static and dynamic complexity Products and market intelligence and foresight
Use modularity, product platforms Process simplification, clustering Use of Intelligent sensors and control logics to mitigate against complexity Flexible and responsive manufacturing Use robust planning & control systems Apply robust scheduling strategies and team work, quality circles, lean principles, etc. Develop hard and soft technologies to deal operational issues (e.g. Cognitive training Operational processes to reduce “effort” Delayed differentiation & JIT Manufacturing Reconfigurable & changeable manufacturing Global dynamic logistics &SC management
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Mastering Innovation & Complexity
Source: Deloitte Research [2005]: http://www.deloitte.com/view/en_AU/au/article/eb00a7d2770fb110VgnVCM100000ba42f00aRCRD.htm
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Contents 1. Introduction
1.1 Sources of complexity
1.2 Perspectives on complex systems
2. The nature of complexity 2.1 Complicatedness, complexity and chaos
2.2 Complexity in engineering 2.3 Complexity of the product development process
2.4 Framework and methodologies for complex product development and architecting
2.5 Complexity in manufacturing processes and systems
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Contents (2)
3. Complexity in engineering design and its measures 3.1 Information theory/ uncertainty / entropy
3.2 Types of complexity (functional, static, and dynamic)
3.3 Heuristics measures of complexity metrics
3.4 Statistical complexity metrics
3.5 Products modularity, platforms and complexity 3.5.1 Products modularity and its effect on complexity of
the manufacturing process, the supply chain and the organization
3.5.2 Product platforms
3.6 Multi‐discipline complexity of engineered systems
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Contents (3)
4. Manufacturing systems complexity 4.1 Types of manufacturing systems complexity 4.2 Complexity of engineered products 4.3 Measuring manufacturing systems complexity 4.3.1 Entropy and information content measures 4.3.2 Measuring manufacturing systems complexity in the functional
domain 4.3.3 Measuring manufacturing systems complexity using heuristics
and indices 4.4 Integrating products and assembly systems complexity 4.4.1 Assembled products complexity 4.4.2 Assembly systems complexity 4.5 Manufacturing systems configuration and layout complexity 4.5.1 The structural complexity of systems layout 4.5.2 Layout complexity indices
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Contents (4)
5. Business / enterprise complexity 5.1 The global supply chain complexity
5.2 The socio-technical systems
5.3 Managing the dynamic business landscape
5.4 Sustainability and evolution of engineering systems
6. Current directions
6.1 Embracing complexity in engineering and business
6.2 Complex products and engineering systems
6.3 Chaordic manufacturing systems
6.4 Trends in managing the business complexity
Acknowledgements References
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Schedule Action Due by
Outline presented to STC O at the 61th GA 26/08/2011
Paper ( 24 pages) presented to STC O Chairman 25/01/2012
Presented to STC O - available to interested colleagues 26/01/2012
Final version ready – Approval by the STC Chairman 31/01/2012
Keynote paper submitted on EES for review 22/02/2012
Comments of EC to the responsible author 22/03/2012
Updated version re-submitted on EES 22/04/2012
Final checking and approval by the EC chairman 22/05/2012
CIRP Office gives Publisher the approval to print 01/06/2012
External STC O presentation at the 62nd GA 26/08/2012
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