Value of Systems Engineering

Total Page:16

File Type:pdf, Size:1020Kb

Value of Systems Engineering INCOSE SECOE Systems Engineering Center of Excellence Honourcode, Inc. Honourcode, Inc. Understanding the Value of Systems Engineering How can we quantify worth of what we do? Eric Honour Honourcode, Inc. Director, INCOSE Systems Engineering Center of Excellence Value of Systems Engineering; INCOSE Symposium 6/04 2 Agenda Heuristic Claim of SE Gathered results on Value of SE NASA Tracking 1980s “Boundary Management” study “Large Engineering Projects” MIT study “Impact of SE at NASA” (SECOE 02-02) “Impact of SE on Quality & Schedule” Boeing “SE Effectiveness” IBM study “Value of SE” research (SECOE 01-03) Value of Systems Engineering; INCOSE Symposium 6/04 3 Heuristic Claim of SE Better systems engineering leads to Better system quality/value Lower cost Shorter schedule Traditional Design Risk SYSTEM DETAIL PRODUCTION Time DESIGN DESIGN INTEGRATION TEST Risk Saved Time/ Cost “System Thinking” Design Time Value of Systems Engineering; INCOSE Symposium 6/04 4 NASA Tracking 1980s Total Program Overrun 32 NASA Programs 200 GRO76 Definition $ OMV 180 Definition Percent = ---------------------------------- TDRSS Target + Definition$ 160 GALL 140 IRAS Actual + Definition$ HST Program Overrun = ---------------------------------- TETH 120 Target + Definition$ GOES I-M MARS 100 EDO CEN LAND76 ACT ERB77 MAG 80 COBE CHA.REC. STS 60 LAND78 GRO82 SEASAT ERB88 40 VOY HEAO Program Overrun Program UARS 2 EUVE/EP DE R = 0.5206 20 ULYS SMM IUE ISEE 0 PIONVEN 0 5 10 15 20 Definition Percent of Total Estimate Source Werner Gruhl NASA Comptroller’s Office Value of Systems Engineering; INCOSE Symposium 6/04 5 “Boundary Management” Study Study of 45 high-tech new product development teams ...Ancona and Caldwell, Research Technology Management, 1990 Significant portion of time is spent at team boundaries Individual Time Spent Outside Team 14%* Successful teams spent more time in boundary * Typically limited to few individuals management Within Team 38% Processes used did not correlate with success Alone 48% Value of Systems Engineering; INCOSE Symposium 6/04 6 “Large Engineering Projects” Study of 60 LEPs (power generation, transportation, oil production, technology) The Strategic Management of Large Engineering Projects, MIT Press 2000 Evaluation by interviews and by objective and subjective quality measures. Percent of Projects Meeting: Cost Targets 82% Schedule Targets 72% Objective Targets 45% 18% 37% Failed! Value of Systems Engineering; INCOSE Symposium 6/04 7 “Large Engineering Projects” Significant Findings: Most important determinant was a coherent, well-developed organizational/team structure A structure of leadership creates greater success Technical difficulties, social disturbance, size were not statistically linked to performance All projects had turbulent events Technical excellence could not save a socially unacceptable project Process definition is important but not sufficient. Value of Systems Engineering; INCOSE Symposium 6/04 8 Impact of SE at NASA (SECOE 02-02) Participating Organization Education Level NASA 136 INCOSE 243 Bachelor 34% Total 379 Master 55% Doctor 11% Work Experience Job Titles 0-10 41% 11-20 37% 21+ 22% SE 56% PM 17% Other 27% Age Groups 20-39 29% 40-59 61% 60+ 10% ...significant differences by organization Value of Systems Engineering; INCOSE Symposium 6/04 9 Key Survey Results - Cost Percent Spent on SE • Respondents marked bracket to show percent of total cost spent on SE on last project. • Mode at 6-10% of project 0-5% 6-10% 11-15% 16% + • Few projects spent 11-15% • Unexplained bimodal response >16% (perhaps interpretation of “project”) Cost Benefit of SE • Respondents believe strongly in cost benefit of SE • In secondary question, few respondents could quantify • INCOSE respondents much higher % SEs, had more positive beliefs Very Poor Poor Fair Good Excellent Value of Systems Engineering; INCOSE Symposium 6/04 10 Impact of Systems Engineering on Quality and Schedule Empirical evidence obtained from three parallel (same time) projects Each developed a complex, robotic Universal Holding Fixture (UHF) Each used a different level of SE Results are compared Trait UHF1 UHF2 UHF3 Size 10’ x 40’ 8’ x 50’ 6’ x 14’ Accuracy ±0.005” ±0.003” ±0.003” Contact Sensors None 57 108 Vacuum Sensors 1 70 108 Real-time checks No Yes Yes Probe contours No Yes Yes NC interface No Yes Yes Impact of Systems Engineering on Quality and Schedule – Empirical Evidence, W. Forrest Frantz, Boeing Corp. 1995 Value of Systems Engineering; INCOSE Symposium 6/04 11 Impacts Requirements to RFP (weeks) Design to Production (weeks) UHF3 UHF3 UHF2 UHF2 UHF1 UHF1 0102030 0204060 Overall Development Time (weeks) Use of better SE reduced UHF3 Overall cycle time UHF2 UHF1 Time to create req’s Time to design/produce 050100 Time to test ...even in the face of more complex, higher quality systems! Value of Systems Engineering; INCOSE Symposium 6/04 12 Systems Engineering Effectiveness Study of 8 software product development projects during upgrade of SE processes Determining Systems Engineering Effectiveness, Bruce Barker, IBM Commercial Products, Conference on Systems Integration, Stevens Institute 2003 Evaluation by cost and schedule against a standard estimating method. Historical Database, New Identify Convert Cost per “Point” Product affected to Concept components “points” Evaluate Estimate Product Line Impact, Cost, Architecture Complexity Schedule Costing method applies only to project management, business management, systems engineering, system integration, and delivery into production. Application development costs are not included. © Copyright IBM Corp 2003 Value of Systems Engineering; INCOSE Symposium 6/04 13 Used With Permission Systems Engineering Effectiveness Significant Findings: Impact and complexity provide an effective method to perform parametric costing. Early parametric costing works. Preliminary data indicates that the use of Systems Engineering will improve project productivity when effectively combined with the Project Management and Test Processes. Systems engineering improves productivity. $/Point Averages 2000 $1,454/pt Without SE $1,350/pt 2001 $1,142/pt With SE $944/pt 2002 $818/pt © Copyright IBM Corp 2003 Value of Systems Engineering; INCOSE Symposium 6/04 14 Used With Permission “Value of SE” (SECOE 01-03) Multi-year effort to obtain statistical data Correlate amount/quality of SE with project quality/success Development Parameterized by • Technical “Size” Quality • Technical Complexity • Risk Level (function of Technical Value, Cost, Schedule, Risk) 6-10% ? SE Effort Value of Systems Engineering; INCOSE Symposium 6/04 15 Respondent Data 10 43 respondents 8 1 project not completed 6 Values $1.1M - $5.6B 4 SE Cost 0.3% - 26% 2 Number of Projects 0 0 5 10 15 20 25 SE Cost % 10 Cost, schedule, quality 8 correlate better with “Systems 6 Engineering Effort”: 4 SEE = SE Qual * (SE Cost %) 2 Number of Projects 0 0 5 10 15 20 25 SE Effort = SE Quality * (SE Cost %) Value of Systems Engineering; INCOSE Symposium 6/04 16 Cost Overrun vs. SE Effort 3.0 90% Assurance (1.6σ) 2.6 2.2 Average Cost Overrun 1.8 1.4 Actual/Planned Cost 1.0 0% 4% 8% 12% 16% 20% 24% 28% 0.6 SE Effort = SE Quality * SE Cost/Actual Cost Honour, “Value of SE” INCOSE 2004 Value of Systems Engineering; INCOSE Symposium 6/04 17 Schedule Overrun vs. SE Effort 3.0 90% Assurance (1.6σ) 2.6 2.2 Average Schedule Overrun 1.8 1.4 Actual/Planned Schedule 1.0 0% 4% 8% 12% 16% 20% 24% 0.6 SE Effort = SE Quality * SE Cost/Actual Cost Honour, “Value of SE” INCOSE 2004 Value of Systems Engineering; INCOSE Symposium 6/04 18 Test Hypothesis: Quality 1.2 Hypothesis 1.0 Quality 0.8 SE Effort 0.6 Development Quality 10.0 (Cost/Schedule Based) 9.0 0.4 8.0 0% 4% 8% 12% 16% 20% 24% 7.0 SE Effort 6.0 5.0 4.0 3.0 Comparative Success 2.0 1.0 0.0 Honour, “Value of SE” 0% 4% 8% 12% 16% 20% 24% INCOSE 2004 SE Effort Value of Systems Engineering; INCOSE Symposium 6/04 19 Conclusions “Value of SE” SE effort improves development quality Cost, schedule, subjective Hypothesis is supported by the data Optimum SE effort is 10-15% or more Cost, schedule overruns are minimized However, note wide dispersion of data Also note few data points at this level; most projects spent far less Quality of the SE effort matters Lower quality SE reduces effectiveness Honour, “Value of SE” INCOSE 2004 Value of Systems Engineering; INCOSE Symposium 6/04 20 Honourcode, Inc. Questions? Eric Honour Honourcode, Inc. Director, INCOSE SECOE Pensacola, FL, USA +1 (850) 479-1985 [email protected] Value of Systems Engineering; INCOSE Symposium 6/04 21 Survey Data Value of Systems Engineering; INCOSE Symposium 6/04 22 Survey Definitions Systems Engineering Cost - all costs to perform traditional SE tasks, no matter who performs them. Typical: Technical management and coordination Mission and/or need analysis System architecting System-level technical analysis Requirements management Risk management ...and other tasks associated with these. Value of Systems Engineering; INCOSE Symposium 6/04 23 Survey Definitions Systems Engineering Quality - subjective evaluation of the overall quality of the systems engineering effort. Scale of 0-10 0 represents SE having no useful value, 5 represents a normally effective SE effort, 10 represents unexcelled, world class quality. Comparative Success - subjective evaluation of the overall project success against other comparable projects. Scale of 0-10 0 represents a failed project, 5 represents a project comparable in success to most other projects 10 represents unexcelled, world class success. Value of Systems Engineering; INCOSE Symposium 6/04 24 Known Limitations Self-reported, unchecked data Subjective data items No quality controls Definition of “Systems Engineering” “SE Cost” dependent on respondent perception Respondent base not controlled Respondents primarily systems engineers Perceptive influences are likely Hypotheses well known, widely accepted Respondents likely to have subconscious bias Value of Systems Engineering; INCOSE Symposium 6/04 25.
Recommended publications
  • Capabilities in Systems Engineering: an Overview
    Capabilities in Systems Engineering: An Overview Gon¸caloAntunes and Jos´eBorbinha INESC-ID, Rua Alves Redol, 9 1000-029 Lisboa Portugal {goncalo.antunes,jlb}@ist.utl.pt, WWW home page: http://web.ist.utl.pt/goncalo.antunes Abstract. The concept of capability has been deemed relevant over the years, which can be attested by its adoption in varied domains. It is an abstract concept, but simple to understand by business stakeholders and yet capable of making the bridge to technical aspects. Capabilities seem to bear similarities with services, namely their low coupling and high cohesion. However, the concepts are different since the concept of service seems to rest between that of capability and those directly related to the implementation. Nonetheless, the articulation of the concept of ca- pability with the concept of service can be used to promote business/IT alignment, since both concepts can be used to bridge different concep- tual layers of an enterprise architecture. This work offers an overview of the different uses of this concept, its usefulness, and its relation to the concept of service. Key words: capability, service, alignment, information systems, sys- tems engineering, strategic management, economics 1 Introduction The concept of capability can be defined as \the quality or state of being capable" [19] or \the power or ability to do something" [39]. Although simple, it is a powerful concept, as it can be used to provide an abstract, high-level view of a product, system, or even organizations, offering new ways of dealing with complexity. As such, it has been widely adopted in many areas.
    [Show full text]
  • Need Means Analysis
    The Systems Engineering Tool Box Dr Stuart Burge “Give us the tools and we will finish the job” Winston Churchill Needs Means Analysis (NMA) What is it and what does it do? Needs Means Analysis (NMA) is a systems thinking tool aimed at exploring alternative system solutions at different levels in order to help define the boundary of the system of interest. It is based around identifying the “need” that a system satisfies and using this to investigate alternative solutions at levels higher, the same and lower than the system of interest. Why do it? At any point in time there is usually a “current” or “preferred” solution to a particular problem. In consequence organizations will develop their operations and infrastructure to support and optimise that solution. This preferred solution is known as the Meta-Solution. For example Toyota, Ford, General Motors, Volkswagen et al all have the same meta-solution when it comes to automobile – in simple terms two-boxes with wheel at each corner. All of the automotive companies have slowly evolved in to an industry aimed at optimising this meta-solution. Systems Thinking encourages us to “step back” from the solution to consider the underlying problem or purpose. In the case of an automobile, the purpose is to “transport passengers and their baggage from one point to another” This is also the purpose of a civil aircraft, passenger train and bicycle! In other words for a given purpose there are alternative meta-solutions. The idea of a pre-eminent meta-solution has also been discussed by Pugh (1991) who introduced the idea of dynamic and static design concepts.
    [Show full text]
  • Putting Systems to Work
    i PUTTING SYSTEMS TO WORK Derek K. Hitchins Professor ii iii To my beloved wife, without whom ... very little. iv v Contents About the Book ........................................................................xi Part A—Foundation and Theory Chapter 1—Understanding Systems.......................................... 3 An Introduction to Systems.................................................... 3 Gestalt and Gestalten............................................................. 6 Hard and Soft, Open and Closed ............................................. 6 Emergence and Hierarchy ..................................................... 10 Cybernetics ........................................................................... 11 Machine Age versus Systems Age .......................................... 13 Present Limitations in Systems Engineering Methods............ 14 Enquiring Systems................................................................ 18 Chaos.................................................................................... 23 Chaos and Self-organized Criticality...................................... 24 Conclusion............................................................................ 26 Chapter 2—The Human Element in Systems ......................... 27 Human ‘Design’..................................................................... 27 Human Predictability ........................................................... 29 Personality ............................................................................ 31 Social
    [Show full text]
  • Fundamentals of Systems Engineering
    Fundamentals of Systems Engineering Prof. Olivier L. de Weck Session 12 Future of Systems Engineering 1 2 Status quo approach for managing complexity in SE MIL-STD-499A (1969) systems engineering SWaP used as a proxy metric for cost, and dis- System decomposed process: as employed today Conventional V&V techniques incentivizes abstraction based on arbitrary do not scale to highly complex in design cleavage lines . or adaptable systems–with large or infinite numbers of possible states/configurations Re-Design Cost System Functional System Verification Optimization Specification Layout & Validation SWaP Subsystem Subsystem . Resulting Optimization Design Testing architectures are fragile point designs SWaP Component Component Optimization Power Data & Control Thermal Mgmt . Design Testing . and detailed design Unmodeled and undesired occurs within these interactions lead to emergent functional stovepipes behaviors during integration SWaP = Size, Weight, and Power Desirable interactions (data, power, forces & torques) V&V = Verification & Validation Undesirable interactions (thermal, vibrations, EMI) 3 Change Request Generation Patterns Change Requests Written per Month Discovered new change “ ” 1500 pattern: late ripple system integration and test 1200 bug [Eckert, Clarkson 2004] fixes 900 subsystem Number Written Number Written design 600 major milestones or management changes component design 300 0 1 5 9 77 81 85 89 93 73 17 21 25 29 33 37 41 45 49 53 57 61 65 69 13 Month © Olivier de Weck, December 2015, 4 Historical schedule trends
    [Show full text]
  • ESD.00 Introduction to Systems Engineering, Lecture 3 Notes
    Introduction to Engineering Systems, ESD.00 System Dynamics Lecture 3 Dr. Afreen Siddiqi From Last Time: Systems Thinking • “we can’t do just one thing” – things are interconnected and our actions have Decisions numerous effects that we often do not anticipate or realize. Goals • Many times our policies and efforts aimed towards some objective fail to produce the desired outcomes, rather we often make Environment matters worse Image by MIT OpenCourseWare. Ref: Figure 1-4, J. Sterman, Business Dynamics: Systems • Systems Thinking involves holistic Thinking and Modeling for a complex world, McGraw Hill, 2000 consideration of our actions Dynamic Complexity • Dynamic (changing over time) • Governed by feedback (actions feedback on themselves) • Nonlinear (effect is rarely proportional to cause, and what happens locally often doesn’t apply in distant regions) • History‐dependent (taking one road often precludes taking others and determines your destination, you can’t unscramble an egg) • Adaptive (the capabilities and decision rules of agents in complex systems change over time) • Counterintuitive (cause and effect are distant in time and space) • Policy resistant (many seemingly obvious solutions to problems fail or actually worsen the situation) • Char acterized by trade‐offs (h(the l ong run is often differ ent f rom the short‐run response, due to time delays. High leverage policies often cause worse‐before‐better behavior while low leverage policies often generate transitory improvement before the problem grows worse. Modes of Behavior Exponential Growth Goal Seeking S-shaped Growth Time Time Time Oscillation Growth with Overshoot Overshoot and Collapse Time Time Time Image by MIT OpenCourseWare. Ref: Figure 4-1, J.
    [Show full text]
  • Lecture 9 – Modeling, Simulation, and Systems Engineering
    Lecture 9 – Modeling, Simulation, and Systems Engineering • Development steps • Model-based control engineering • Modeling and simulation • Systems platform: hardware, systems software. EE392m - Spring 2005 Control Engineering 9-1 Gorinevsky Control Engineering Technology • Science – abstraction – concepts – simplified models • Engineering – building new things – constrained resources: time, money, • Technology – repeatable processes • Control platform technology • Control engineering technology EE392m - Spring 2005 Control Engineering 9-2 Gorinevsky Controls development cycle • Analysis and modeling – Control algorithm design using a simplified model – System trade study - defines overall system design • Simulation – Detailed model: physics, or empirical, or data driven – Design validation using detailed performance model • System development – Control application software – Real-time software platform – Hardware platform • Validation and verification – Performance against initial specs – Software verification – Certification/commissioning EE392m - Spring 2005 Control Engineering 9-3 Gorinevsky Algorithms/Analysis Much more than real-time control feedback computations • modeling • identification • tuning • optimization • feedforward • feedback • estimation and navigation • user interface • diagnostics and system self-test • system level logic, mode change EE392m - Spring 2005 Control Engineering 9-4 Gorinevsky Model-based Control Development Conceptual Control design model: Conceptual control sis algorithm: y Analysis x(t+1) = x(t) +
    [Show full text]
  • Theoretical Approach to the Interaction Between the Meta-System Schemas of the Artificial (Built) Environment and Nature
    Theoretical Approach to the Interaction Between the Meta-system Schemas of the Artificial (Built) Environment and Nature José L. Fernández-Solís, Ph. D. Kent D. Palmer, Ph. D. Timothy Ferris, Ph. D. Texas A&M University, 3137 TAMU, College Station, TX 77843-3137 USA P.O. Box 1632, CA 92867 USA University of South Australia, Mawson Lakes Blvd., Mawson Lakes, 5095 AU Emails: [email protected] ; [email protected] ; [email protected] Abstract: This is an exploration of how the artificial environment links with the natural environment and requires a conceptual construct that bridges these two environments. Palmer’s General Schema Theory, a highly theoretical work, elaborated on in a dissertation by Fernández-Solís (2006), provides a theory of schemas and a theory of the schema of meta-systems which will be useful for interpreting the relationship between the artificial and natural environment. The concept of meta-system is used first to generate and then to validate the concept of meta-industry. This paper argues that the building construction industry (which produces the built or artificial environment) that is categorized as an ‘industry’ by academia, actually acts as a meta-industry. Meta-industry is a concept that encapsulates an ‘industry of industries,’ which implies that the artificial and the natural environment interact at the meta-level. Climate change is construed as the reaction of the natural to the artificial environment at the meta- level. The approach to this pre-paradigmatic work uses the tools of philosophical and critical thinking, rationality and logic. General Schemas thinking at the meta- systemic level is presented as a new approach towards the formation of a novel paradigm of the artificial environment.
    [Show full text]
  • Meta-Systems Engineering -- Kent Palmer
    Meta-Systems Engineering -- Kent Palmer world schema, etc. that help us elucidate META-SYSTEMS phenomena2. Each of these various schema calls for a different response from ENGINEERING engineering, and thus engenders new engineering disciplines, or at least new approaches to the engineering of large scale A NEW APPROACH TO SYSTEMS systems. Among them are Meta-systems ENGINEERING BASED ON Engineering3, Special Systems based Holonic EMERGENT META-SYSTEMS AND Engineering4 and Domain Engineering5, HOLONOMIC SPECIAL SYSTEMS World Engineering6 and Whole Systems THEORY Design7. What is needed is a way to understand how these various kinds of Kent D. Palmer, Ph.D. schema and their associated engineering disciplines fit together into a coherent set of P.O. Box 1632 approaches. In this paper we will develop a Orange CA 92856 USA theory of how this coherence of different 714-633-9508 [email protected] 2 The approach taken in this work is further Copyright 2000 K.D. Palmer. elucidated by several papers by the author written for All Rights Reserved. International Society for the Systems Sciences (ISSS; Draft Version 1.0; 04/23/2000 http://isss.org) 2000 conference in Toronto. These papers may be seen at Paper for http://dialog.net:85/homepage/autopoiesis.html International Council on Systems Engineering including the following titles “Defining Life And The 10th Annual Symposium Living Ontologically And Holonomically;” “New General Schemas Theory: Systems, Holons, Meta- Keywords: Holonomics, Meta-systems, Systems & Worlds;” “Intertwining Of Duality And Systems Theory, Systems Engineering, Non-Duality;” “Holonomic Human Processes;” and “Genuine Spirtuality And Special Systems Theory” Meta-systems engineering, Special 3 Systems, Emergent Meta-systems, Whole Van Gigch, John P.
    [Show full text]
  • Formal System Concepts
    Paper #106 Formal Systems Concepts Joseph J. Simpson Mary J. Simpson System Concepts System Concepts 6400 32nd Avenue N.W., #9 6400 32nd Avenue N.W., #9 Seattle, WA 98107 Seattle, WA 98107 [email protected] [email protected] Abstract A common conceptual thread that runs through a large portion of systems, systems engineering, and software engineering literature is explored by this paper. This conceptual thread is based on the formal concept of a Moore type of sequential machine (Moore 1956) as well as the definition of a system and a meta-system. Practical approaches and applications generated by this common thread are outlined and discussed in terms of complexity reduction, design efficiency and communication enhancement. Hartmanis and Stearns utilized the abstract definition of a sequential state machine as a mathematical model of discrete, deterministic computing devices with finite memory. These machines have a finite set of inputs, outputs and internal configurations. Wymore expanded on this work while developing his model-based systems engineering approach. In each case, Moore and Wymore, the system concept has the same form. The system content; specific inputs, outputs types and transition functions, are different. In the software engineering arena, “abstract state machines” and “requirements state machines” have been developed as formal approaches closely related to Moore state machines. This paper presents an expanded systems and meta-systems framework that extends the formal system concepts. System abstraction stacks are introduced in this paper. Introduction Similar to formal logic, formal system concepts are concerned with the form of the system and not the system content.
    [Show full text]
  • Leveraging System Science When Doing System Engineering
    Leveraging System Science When Doing System Engineering Richard A. Martin INCOSE System Science Working Group and Tinwisle Corporation System Science and System Engineering Synergy • Every System Engineer has a bit of the scientific experimenter in them and we apply scientific knowledge to the spectrum of engineering domains that we serve. • The INCOSE System Science Working Group is examining and promoting the advancement and understanding of Systems Science and its application to System Engineering. • This webinar will report on these efforts to: – encourage advancement of Systems Science principles and concepts as they apply to Systems Engineering; – promote awareness of Systems Science as a foundation for Systems Engineering; and – highlight linkages between Systems Science theories and empirical practices of Systems Engineering. 7/10/2013 INCOSE Enchantment Chapter Webinar Presentation 2 Engineering or Science • “Most sciences look at certain classes of systems defined by types of components in the system. Systems science looks at systems of all types of components, and emphasizes types of relations (and interactions) between components.” George Klir – past President of International Society for the System Sciences • Most engineering looks at certain classes of systems defined by types of components in the system. Systems engineering looks at systems of all types of components, and emphasizes types of relations (and interactions) between components. 7/10/2013 INCOSE Enchantment Chapter Webinar Presentation 3 Engineer or Scientist
    [Show full text]
  • Systems Engineering Overview
    Systems Engineering Overview Axel Claudio Alex Gonzalez Objectives • Provide additional insights into Systems and into Systems Engineering • Walkthrough the different phases of the product lifecycle • Discuss why it is important to have good requirements, interfaces, designs and that they are well defined • Provide a brief overview of how every engineering role is accountable and important within the System 2 Why this is important? • Modern societal and economic advancement depends on successfully designing, building, and operating efficient large scale systems • Despite rigorous processes, some systems still fail – Space shuttle accidents, NE US power grid blackouts, Gulf oil spill • Failures often traced to uncontrolled, unanticipated, and unwanted interactions between elements • More, and more detailed, processes not the answer (process still imp.) • Its about achievement of “an elegant design”; one that: – Works as intended – produces intended result – Is robust (e.g., graceful degradation to failures, changes in environments, etc.) – Is efficient, producing the desired result with lower/fewer resources than alternatives – Minimizes unintended actions, side effects, and consequences • Core concern of engineers should be ensuring these qualities 3 Driving Factors to Modern System Design • Advanced Technology and Risks • Increased Competition • Increased Specialization • Increased requirements/capability • Rapid changes in technology • Fast time-to-market most critical • Increasing pressure to lower costs • Increased presence of embedded
    [Show full text]
  • Graph-Based Digital Blueprint for Model Based Engineering of Complex Systems
    27th Annual INCOSE International Symposium (IS 2017) Jul 15-20, 2017, Adelaide, Australia Graph-Based Digital Blueprint for Model Based Engineering of Complex Systems Manas Bajaj1, PhD Jonathan Backhaus Tim Walden, LM Fellow Chief Systems Officer Staff Systems Engineer Chief Engineer Intercax Rotary & Mission Systems Advanced Systems Lockheed Martin Corporation Lockheed Martin Corporation Manoj Waikar Dirk Zwemer Chris Schreiber Senior Software Arch. President, Intercax Space Systems Company Intercax Lockheed Martin Corporation Intercax Lockheed Martin 47 Perimeter Center East, Suite 410, Corporate Engineering, Atlanta, GA 30346, USA Technology & Operations, USA www.intercax.com www.lockheedmartin.com Copyright © 2017 by Intercax LLC and Lockheed Martin. Published and used by INCOSE with permission. Abstract. Complex, cyber-physical systems must be founded on a digital blueprint that provides the most accurate representation of the system by federating information from engineering models across multiple enterprise repositories. This blueprint would serve as the digital surrogate of the system and evolve as the actual system matures across its lifecycle, from conception and design to production and operations. This paper presents a graph-based approach for realizing the digital blueprint, which we refer to as the Total System Model. The paper is divided into five parts. Part 1 provides an introduction to use cases for model-based systems engineering. Part 2 introduces graph concepts for the Total System Model. Part 3 provides a demonstration of the graph-based approach using Syndeia software as a representative application. Part 4 provides a summary of this paper, and Part 5 lays out potential directions for future work. Part 1: Model Based Systems Engineering (MBSE) The complexity in modern systems is growing at an unprecedented pace.
    [Show full text]