Agile SYSTEMS ENGINEERING vs. AGILE SYSTEMS ...

Agile SYSTEMS ENGINEERING vs. AGILE SYSTEMS Engineering Prof. Reinhard Haberfellner Technical University, Graz, Austria

Prof. Olivier L. de Weck Massachusetts Inst. of Technology, Boston, USA

Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Outline 1. Introduction: What am I talking about? 2.

Agile SYSTEMS ENGINEERING 

Different existing process models: where to find agility – and where not?



Agility is not for free



Approaches to extend agility

3. AGILE SYSTEMS engineering 

Manifestations of Inflexibility: Examples



How to do better: Examples



Generalization



Theory

4. Summary Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Definitions

Agility = Property of a system that can be changed rapidly • Agile SYSTEMS ENGINEERING: agility installed in the systems engineering process

• AGILE SYSTEMS engineering: systems that can respond to changed requirements after initial fielding of the system


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Agile SYSTEMS ENGINEERING vs. AGILE SYSTEMS Engineering

Part I: Agile SYSTEMS ENGINEERING


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Descriptive Levels for the SE-Process Level

Description

Examples

1

Life Cycle Phase Concept Definition, Development, Production

2

Program Activity Mission Analysis, Prelim. Design, Detail Design

3

SE Process

4

Engineering Specialty Area

Requirements Analysis, Architecture Definition, System Design Software, Human Factors, Mechanical Design

Source: INCOSE, SE-Handbook, 2004 Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Action Model:

A.D. Hall – BWI/ETH Module 3:

PROBLEM

Module 4:

PROJECTPHASES

PROBLEM SOLVING CYCLES

Initialization Preliminary Study (D)

Variants of Solution Principles

Search for Solutions

Main Study (D)

Variants of Overall Concepts

Formulation of objectives Synthesis of solutions Analysis of solutions Evaluation

Selection

Detailed Studies (D)

Variants of Detailed Concepts Module 1:

TOP DOWN Module 2: DEVELOPMENT OF

VARIANTS

= selected variant, further worked on

Search for Objectives

Situational analysis

Decision

Establishment of s. (R)

D = Entwicklungsphase(n) R = Realisierungsphase

Introducing, handing over (R) Termination of p.

Source: Haberfellner,a.o., 2002


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Action Model acc. to A.D. Hall – BWI/ETH Module 3:

= adding flexibility PROBLEM

PROJECTPHASES

Module 4:

PROBLEM SOLVING CYCLES

Initialization Preliminary Study (D)

Variants of Solution Principles

Main Study (D)

Variants of Overall Concepts

Search for Solutions

Formulation of objectives Synthesis of solutions Analysis of solutions Evaluation

Detailed Studies (D)

Variants of Detailed Concepts Module 1:

TOP DOWN Module 2: DEVELOPMENT OF

VARIANTS

= selected variant, further worked on

Search for Objectives

Situational analysis

Establishment of s. (R)

Selection Decision

D = Development R = Realisation

Introducing, handing over (R) Termination of p.


Source: Haberfellner, a.o, 2002

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Waterfall Process and Iterative Prototyping

Waterfall Process

Iterative Prototyping Cycles Vers 1 Vers 2 Vers 3

Definition Analysis Design Detailed Requts Functions Definition

Programming Test Maintenance Acceptanc e

= bureaucracy: reducing flexibility

System Performance System FunctioConcept Reqs. nality Prototype Components Integration Design/ Develop

= iterations produce agility


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Manifesto for Agile Software Development

We are uncovering better ways of developing software by doing it and helping others do it. Through this work we have come to value:

• •

• •

Individuals and interactions over processes and tools Working software over comprehensive documentation Customer collaboration over contract negotiation Responding to change over following a plan = enhancing flexibility

Source: http://agilemanifesto.org/principles.html - April 25, 2005 Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Simultaneous (Concurrent) Engineering Linear Phase Concept Preliminary Study Product Development

Main Study

Preliminary Study

Detailed Studies

Main Study

Preliminary Study

Development of Production Facilities

Overlapping Phase Concept

Main Study

Detailed Studies

= increasing speed = killing flexibility

Procurement Installation

Detailed Studies

Production Preparation Prototype 0-Series

Procurement of Production Facilities

Procurement

Installation

. . .

Production Preparation Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Agility in the SE-process is not for free  Time-consuming activities 



Developing variants Decisions which variant is best (on different levels)

 Reducing the range of variants in early phases 



Simultaneous- or Concurrent-Engineering-concepts scale down the range of variants in early phases of a project. Unfortunately exactly this openness for variants would add flexibility

 Simultaneous-Engineering = working on different levels of detail at the same time 

 

The more intensely you are working on detailed concepts the lesser are the possibilities to change the overall concept An early design freeze may increase the speed But: try to modify or to change a frozen concept!

 Conclusion: There is a basic conflict between an agile and a fast running SE-process. Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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The dynamics of the overall concept Stage Project Phase

1

2

3

4

5

Overall Concept

*

*

*

Detailed Concept 3

Detailed Concept 4

Solution Principle

Preliminary Study

Main Study

Detailed Concept 1

Detailed Studies

Establishment of S.

Detailed Concept 2

= External Influence

*

= Check, Modification, Adaption = Different Degree of Realization Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT


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The dynamics of the overall concept = enhancing flexibility

= reducing flexibility

Stage Project Phase

1

2

3

4

5

Overall Concept

*

*

*

Detailed Concept 3

Detailed Concept 4

Solution Principle

Preliminary Study

Main Study

Detailed Concept 1

Detailed Studies

Establishment of S.

Detailed Concept 2

= External Influence

*

= Check, Modification, Adaption = Different Degree of Realization Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT


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“Set based design” Decisions for Realization-Steps, not for Variants of the Overall Concept V1

1. Develop different feasible Variants

V2

V3

V4

V5

Vm = Variants of Overall Concepts Source: Shigley 1989 Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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“Set based design” Decisions for Realization-Steps, not for Variants of the Overall Concept 1

V1 1

2

V2

V3 2

1

2

V4

1

V5

2. Plan required actions for every Variant 3. Look different plans over for same actions

Vm = Variants of Overall Concepts


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“Set based design” Decisions for Realization-Steps, not for Variants of the Overall Concept 1

V1 1

2

V2

1 RS1

RS2

1

2

V3

2 2

V4

1

V5

4. Decide for realization of those actions which are scheduled for many Variants (RS1 = Realization Step 1) etc.



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5. Eliminate Variants which are no more feasible V2

V3 RS1

RS2 RS3

V4

RSn = Realization-Step 1,2,.. V5



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Etc. etc.

V2

V3 RS1

RS2

RS4 RS3

V4

V5 RSn = Realization-Step 1,2,.. Vm = Variants of Overall Concepts


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Etc. etc.

V2

V3

RS1

RS2

RS4 RS3

V4

V5 RSn = Realization-Step 1,2,.. Vm = Variants of Overall Concepts = postponing decisions for concepts  Enhancing agility Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Agile SYSTEMS ENGINEERING vs. AGILE SYSTEMS Engineering

Part II: AGILE SYSTEMS engineering


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Manifestations of Inflexibility

Macro Systems

Dominant Uncertainties

Inflexibility Manifestations

Space Systems Communications Satellites

# of subscribers, activity level, service type readiness, govt. funding

Vehicle Systems Commercial Aircraft Cars & Trucks

travel demand, demographic Aircraft cabin size, range, shifts, regulations, fuel emissions are locked in, prices, competition CAFE-standards violated shifting customers

Infrastructures Oil & Gas Exploitation Systems Commercial Buildings

fuel and gas prices, reserves contained in reservoirs, interest rates, Utilization patterns, land prices

System capacity is inadequate, cannot switch type of service

cannot take advantage of a previously unknown oil field High vacancy rates of commercial real estate


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Manifestations of Inflexibility in Systems: Examples

 Low Earth Orbit Satellite Constellations: 

Iridium and Globalstar  financial desaster

 Automotive Manufacturing Plant: 

Daimler Chrysler’s PT Cruiser (good selling, Mexican site) could not be shifted additionally to Belvidere, Ill. (Neon, poor selling) because of several inches.


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Examples of Agile Systems

 Optimal staging of satellite constellations for growth (de Weck 2004a)

 Optimal modular facility design for divestment (Kalligeros 2004)


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Staged Cutback

 Stadium for the European Soccer Championship 2007 in Klagenfurt/Austria 



Need for a new stadium with capacity of 30.000 pers  just for a couple of weeks After championship  15.000 pers

 Solution (steel construction): 

Built a stadium for 30.000



Cut-back to 15.000



Offer a 15.000 stadium for sale by using the disassembled parts Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Staged cutback

 30.000 stadium


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Staged cutback

 30.000 stadium

 15.000 stadium (downsized)


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Staged cutback

 30.000 stadium

 15.000 stadium (downsized)

 15.000 stadium (for sale)


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Example for “Operational flexibility”: Magna Automotive, Graz/Austria  Designed for flexibility.  

Different vehicles assembled in same production line in any order. Production and product data transmitted from USA and from D

 In the assembly line until now have already been simultaneously assembled:   

Chrysler Voyager + PT Cruiser Jeep Grand Cherokee + Mercedes M Class Chrysler Voyager + Jeep Grand Cherokee + Chrysler 300C

 Bodyshell work is always done separately because of different work cycles.  Body painting is possible in one paint shop for:  

Chrysler Voyager, Jeep Grand Cherokee, Chrysler 300C, Mercedes G, Saab Cabrio and Mercedes E Class


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3 different types of cars in one MAGNA-assembly line

Jeep Grand Cherokee Chrysler Voyager Jeep Grand Cherokee

Chrysler 300C


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Agile Systems – what is needed?

 AGILE SYSTEMS are those that have flexibility embedded in them such that they can be changed in response to unfolding user demand after they are initially fielded.  Requires three elements: 





necessary flexible elements inside the system that allow it to be changed easily and quickly a set of sensors to monitor external attributes to alert decision makers when changes are warranted a decision mechanism by which the benefits and costs of system adaptation are compared and system state changes are triggered.

 Practical view: Agility = Rather a mindset than a methodology ? 

project team, party commissioning, steering committee, customer


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When is agility in systems needed?

 When the systems are  



Expensive, involving significant upfront investment Long-lived, e.g. >10 years. User requirements may change significantly during the lifecycle. If significant switching costs exist, i.e. the expense might be too large for building an entirely new system each time the requirements change.

 Designing flexibility into systems is not for free, but it may be  

an insurance against downside risks, a way to take advantage of unexpected upside opportunities. Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Approaches to Agile System Design  Capacity Adaptation (Staged Deployment) 



Build a small initial system, grow in discrete stages, but only if needed

Example: factory design, satellite constellations

 Modular and Platform-based Design 



Design products/systems from a common core platform, customize variants with modules as needed Example: car platforms, modular buildings

 Real Options 

Create right but not obligation to modify system later



Examples: Extensible car garages


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Real Options Theory Real options analysis applies the financial principles of options (Black and Scholes 1973) to “real” or physical systems such as power plants, copper mines and so forth (de Neufville 2000). A project is treated as a “black box” and the conceptual and analytic effort focuses on trying to work the available data into forms suitable for the tools of financial analysis. A case in point is the Black-Scholes (1973) formula for pricing European call options:

V  N  d1  A  N  d 2  Xe  rT



 2  d1  ln   r  T /  T X  2  d 2  d1   T

A = current value of the underlying asset X = exercise price T = Time of expiration R = Risk free interest rate  = Volatility of the underlying asset N(d) = Cumulative value of normal distr. at d

V = value of call option


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Quantifying Uncertainty: GBM

Demand [Nusers]

5 x 10

Geometric Brownian Motion Model: Monte Carlo Simulation

1.6 1.4 1.2 1 0.8 0.6

0.4 0

5

Time [years]

10

15

GBM model of uncertain demand, DT = 1 month, Do = 50,000, m = 8% p.a.,  = 40% p.a. – 3 scenarios are shown Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Is it worthwhile? Value-at-Risk Curves

 Flexibility valuation via relative comparison of E [NPV], (Hassan et al. 2005) Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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Summary

 Distinguish between agile SYSTEMS ENGINEERING and the engineering of AGILE SYSTEMS 



Agile SYSTEMS ENGINEERING: flexibility is embedded in the design process itself

AGILE SYSTEMS engineering: it is the system that exhibits the ability to be changed easily and quickly.

 Question is whether the key uncertainties can be resolved before fielding of the system Reinhard Haberfellner, TU-Graz and Olivier de Weck, MIT

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The End

 Field of Agile Systems Engineering is evolving  Academic research and practice are in flux  “Work in progress”


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