Eric Rebentisch

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EM.413 System Engineering meeting Eric Rebentisch

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Afreen Siddiqi

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On Optimization

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in industry

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Amazon, airlines, communication systems use critically use optimization models

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“large scale” is between thousands and millions of variables

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dodging local extrema to find the global extrema

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linear vs non-linear problems

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most interesting engineering problems are non-linear

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simplex method

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if you have a linear problem, the intersections of all your linear constraints is where the optimal solutions will exist

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types

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LP = linear programming, single objective

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NLP = non linear programming, single objective

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MOLP = multiple objective, linear programming

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MONLP = multiple objective, non linear programming

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Mean variance optimization

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EM.411 System Engineering Eric Rebentisch meeting

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Transition to manufacturing

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Tom Toner

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Concept to launch process typically 3-5 years

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Integrated Development Teams

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Project organization structure

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Design for X strategies

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Design for manufacturing, sourcing, etc

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Keeping head count minimal seems relevant to this problem

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As with all organizational problems

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Mythical man month

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Designers should be the integrators

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As opposed to the partners / suppliers handling any integration

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System engineering vs supply chain management

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Seems to be an extension of Product development process

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Or he says sticking to pdp is the best bet

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IP risk

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Ideally, you outsource the high IP risk equipment and processes less

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key role for the system engineer to make these decisions

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What percent of your effort was for protecting IP?

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collapsed:: true

For new tech, maybe 5%, up to 10%

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Possible government could have a role in this

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Is there some way to remove the need for companies to worry about protecting IP ?

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Seems like this would require a strong intellectual property conception from government

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At first right, this seems difficult and perhaps unproductive

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Maybe some new social conception of property

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Society owns everything?

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Aren’t patents supposed to manage this? Why are they insufficient?

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Maybe a new type of patent that is lower barrier but offers some protection

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An org defines what they are doing, and has some gradually expiring right to make money of anyone doing something similar

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This could even be cumulative

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Every new tech and process builds off of this history of IP and divided up revenue into a tree of technology history

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Kind of like sampling songs that sample other songs

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Everyone in the chains gets a piece of the pie

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Interesting idea, give it a name

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There should be a global graph of organizational processes and technologies

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Every organization publishes their technologies and processes and references other technologies and processes

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Revenue should flow up this tree of references, with some expiring function

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This should take care of IP risk

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Government may need to provide some processes for free

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Andy Grove INTEL

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Only the paranoid survive

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The decisions here are similar to how software companies integrate as well

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eg

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White box vs black box sdks

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Seems like everything needs to have a clear interface to lubricants this transition

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Atomicizing to

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protect IP risk,

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simplify sourcing,

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make redundant sourcing possible

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Simplify integration

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Teaching this detached from a specific context feels hard to really digest

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Good that Tom Toner is presenting from his experience, at least

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Eric Rebentisch

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EM.411 meeting Eric Rebentisch System Engineering

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Model based engineering

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Model-based systems engineering

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Common frameworks

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SysML

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OPM

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UML

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Doors NG

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Common tools

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OPCloud

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Matlab

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Modelica

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Microsoft visio

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3D experience

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Etc

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Similar to Generative design

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Airbus

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Digital design manufacturing and simulation

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Roller coaster tycoon seems like a pretty good system model

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Or simulator

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NASA JPL created a total integration environment

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OpenMBEE

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integrated things like

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Github

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Jira

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Jupyter

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Cameo simulation

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Docker

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Etc

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Turn the systems engineering V into a Diamond

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The real world V gets mirrored in a parallel digital pipeline

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Are we modeling the product or the process?

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Either, both

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Model process to model a product…?

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Often the process is the product

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In software, the gradient between model and product is more blurry

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Maybe that also makes it harder to justify modeling, it feels more productive to focus on the product directly

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But the product is essentially a robust model

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First do system engineering, then build a model

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Musk’s Principles of System Design

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Make requirements less dumb

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Delete the part or process

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Simplify or optimize the design

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Modeling is when the place you document is the same as the place you think about and examine

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“Documenting with style”

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Model based enterprise

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Digital twin

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Digital thread

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requirements are typically how engineers communicate

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Communication between organizations should be the flow of requirements

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System engineer delivers system technical solution

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System architecture delivers system concept and plan

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Program manager delivers value to the system stakeholders

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System Design/EM.412 meeting Eric Rebentisch System Engineering

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Modeling uncertainty

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EM.413 Eric Rebentisch System Engineering meeting

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Thinking about how changes propagate throughout the system

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Change Propagation Index

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Rule of thumb

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Every milestone multiplies the cost of change by 10

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System Engineering with Eric Rebentisch

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Selecting among design options

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Matrices

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Pugh concept selection matrix

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Unweighted

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Weighted matrix

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Analytic hierarchy process

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Maybe Fibonacci weighting would make sense

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Trade study

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Explores the solution space

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Process

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Study rules

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Includes choosing evaluation criteria and their relative importance

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This feels similar to setting up the matrices

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Sample

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Test

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Could be back off the envelope, or a contractor deliverable

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Tradespaces

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Pareto front is essentially about ROI

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Which is essentially about opportunity cost

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Relationship of attribute to utility

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Multi-Attribute Tradespace Exploration

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Cost is often used to represent the combined attributes

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Single attribute

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Probably best to segment by highest connectivity and highest sensitivity architectural decisions

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Feels like evaluation criteria for trade studies, attributes for tradespace and matrices are really the operative work. These different methods are essentially different ways of data visualization

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Multiple stakeholders

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Arrow’s impossibility theorem

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In general there is no optimal solution to a problem where stakeholders have conflicting needs

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Design vector

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The vector representation of the Architectural Decisions

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Made up of design variables

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Design-value matrix

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Attributes vs design variables

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Are these 1-1?

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No

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EM.413 System Engineering Eric Rebentisch meeting

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John Thomas presenting on system safety and security

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Single loop vs double loop learning

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Single Loop

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What happened?

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What did we do?

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->

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Two mistakes can appear different though the same methodology led to both

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Double Loop

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What happened?

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What did we do?

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Why did we do this?

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Reliability Block Diagram

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shows redundancies

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used in Fault Tree Analysis

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Systems Theoretic Process Analysis

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Define purpose of analysis

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Model of control structure

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Identify unsafe control actions

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Identify loss scenarios

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EM.413 Eric Rebentisch System Engineering meeting

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Complexity

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Complexity correlates with cost

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Typical measures

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parts count

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source lines of code or function calls

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information content

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->

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should actually be Technical Simplicity

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Mentions the difference between Technical Simplicity vs Interfacial Simplicity

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Dimensions of complexity in system development

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functional

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->

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Technical Simplicity

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McCabe graph theoretic structure

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Shannon Entropy

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Gutman Graph Energy

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structural

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organizational

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->

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homomorphic forces between structural and organizational?

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complex vs complicated

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funny to hear both these brought up

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I guess I would say complicated sounds like “someone added complexity, hence they complicated it”.

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is there a difference between functional complexity and structural complexity?

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We haven’t gotten this to a science yet

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Complexity vs modularity as a tradespace

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Modular Design

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Topology

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understand the role of System Architecture on overall complexity

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EM.413 System Engineering meeting Eric Rebentisch

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Systems in the service industry

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Attributes of a system

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Intagible

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non physical

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Heterogenous

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sociotechnical factors mean delivery and experience will vary

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Inseparable

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value provided at time of interaction

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Perishable

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value does not exist unless produced

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Key system concepts

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co creation of value between offeror and beneficiary

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time and place dependent

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->

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to what degree is MIX a service?

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Product-service systems as a gradient

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many systems are part product, part service

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->

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from peer who worked at Planet labs

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planet labs reframed the problem of building a satellite into serving the data from a satellite

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when you think in the solution neutral space, you can see opportunities to reframe products as services

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Design of services

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what response are you aiming for

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Disneyland is a service

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digital twin

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types

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digital twin

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data travels between digital and physical automatically

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digital shadow

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data flows automatically from physical to digital, manual in inverse direction

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digital model

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manual data flow in both directions

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examples

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google maps

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digital twin?

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uses traffic data

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->

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value seems to be determined by how the flow of data is organized from physical to digital

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EM.413 System Engineering meeting Eric Rebentisch

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Lifecycle Costs

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Tom Toner

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Semiconductor machine

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Mean Time Between Failures set as constraint

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Cymer OnPulse

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Sell a device but charge based on uptime

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Data safety is sometimes a problem

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Good margins for Cymer, customers liked the predictable pricing

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Lifecycle Assessment

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->

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Started in the environmental movement to check environmental impact

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Different time spans reflect different ROIs

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SESAME model

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Lifecycle Cost Analysis

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Discussion on electric vehicles

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https://www.theguardian.com/commentisfree/2023/jun/03/electric-vehicles-early-adopter-petrol-car-ev-environment-rowan-atkinson

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Holistic approach requires thinking about transportation

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public transportation more efficient in terms of space and energy

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cost of maintaining roads

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heavier

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Eric Rebentisch

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Requirements communicate intent across a boundary

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Hence require precision

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A Unifying Language

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Working on NLP related to requirements and processes

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EM.411 meeting Eric Rebentisch System Engineering

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I tend to ignore the note granular, complicated, step by step processes that professors outlines

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For better or worse

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eg

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The “V” model of system engineering

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Is all cost actually cost in human well-being ?

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Verification, validation, integration, testing

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Verification vs validation

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Verification

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Meeting requirements

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Validation

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Satisfying customer needs

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Integration

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Put the parts together to make sure things work together

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Includes the expected operating environment

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eg

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Autonomous vehicles testers on public streets

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Integration readiness level

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How easy is it to integrate things

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A stepping stone to System Readiness Level

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Now what the heck is system readiness level? So

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Seems important

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The story of F35 software development

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The kind of thing I think I could help do

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Went from extreme slow iteration cycles to much more rapid testing

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This seems like the process could be quite different for first time, one off systems vs repeated builds

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We’re really talking about first delivery, QA is different (if probably a subset)

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The core idea is cost of change goes up over time

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Testing

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Functional or non functional

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Models

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The cost pressure is to do model based engineering and simulation

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Models need maintenance and testing as well

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Thinking about the technological change 11.325

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Are technologies and systems isomorphic ?

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Maybe technologies are a type of system

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Both seem to be defined by fulfilling a function

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All this essentially means we need to build a system to deliver the system

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This level of detail seems excessive for such abstract out flexible guidelines

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I’m guessing this is mostly about exposure to different options for processes

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I guess I oscillate between thinking about building the product vs building the process that delivers the product at scale

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Both are systems that need to be built

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Similarly

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We seem to be talking about a system that needs to be built to deliver another system

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A system to deliver a system

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isomorphism ->

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System Engineering

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A system to design a system

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isomorphism ->

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System Architecture

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A system to build the systems to design and deliver a system

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isomorphism ->

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Process management

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Eric Rebentisch

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EM.413 System Engineering meeting Eric Rebentisch

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sociotechnical systems

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Integration practices

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How to change an organization

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EM.411 meeting Eric Rebentisch System Engineering

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Interface

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Interface design

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Speaking is an interface between two brains

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Interfaces have Layers of abstraction

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NPR radio -> listener

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Human language

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Satellite communication

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Interfaces might also contain multiple sub-interfaces

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Implies that systems are made up of sub-systems

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Is a sub system different from a module ?

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I suspect it’s only semantic

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A system can be represented as a network

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An interface is a connection in the network

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Can show connections through a DSM

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DSM

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DSM of DSMs is a multi domain matrix

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Really a data visualization tool

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dsmweb.org

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A fully populated DSM is a poorly designed system, I think

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Sparsity

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Off diagonal clusters represent connective modules between clusters / modules

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What systems are not themselves interfaces ?

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Again thinking of systems as a set of inputs and outputs

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Common interfaces enable modularity

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Modular Design

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The spectrum of consumer modularity

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Modular phone failed

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Google project Ara phone

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Too commoditized, operating too much at the margin of performance already

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Framework Modular laptop

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Might work? TBD

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Modular desktop

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Very standard

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Modularity for consumers is different than modularity for producers

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Internal API modularity can be about reducing maintenance or future iteration cost

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Interfaces are handoffs

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How does user experience plug in with all this talk of interfaces?

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Eric Rebentisch

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