From project to product mindset and onwards to product platform architectures

Auckland
From project to product
mindset
and onwards to product platform
architectures

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Introduction
Jorn Bettin
Partner – S23M

Collaboration for Life
From project to product mindset 
and onwards to product platform architectures
Jorn Bettin
ProductTank Auckland Meetup, July 2018

product
limited knowledge
many unknowns
batch size : 1 
volume : 1
this is not a product
this is a project

Is it possible to stay innovative and economically manage
many hundreds or even thousands of products or product
variants?
simple product
simple configuration
batch size : > 1,000 
volume : > 10,000
product

very complex product
simple configuration
batch size : 1 
volume : > 10,000
product line

very complex product
complex configuration
batch size : 5 … 100
volume : > 500
product line

complex product
batch size : 1 … 10 
volume : 100
the boundary case
between project and product

complicated product
complicated configuration
batch size : 1 
volume : > 10,000
ERP
this smells more like
a project than a product

complex product
batch size : 1 
volume : > 10,000
ERP
this would be
a product line
Configuration

Problems with the IT mindset
1) Purpose. In an IT mindset organization, the staff exists to service the perceived
technology needs of “the business.”  
In a technology-enabled product organization, the staff exists to service the needs of
your customers, within the constraints of the business. This is a profound and far-
reaching difference. Most of what is below stems from this difference.
2) Passion. In an IT mindset organization, product and tech are mercenaries.
There is little to no product passion. They are there to build whatever.  
In a product organization, product and tech are missionaries. They have joined the
organization because they care about the mission and helping customers solve real
problems.
3) Requirements. In an IT mindset organization, requirements are “gathered” from
stakeholders, prioritized in the form of roadmaps, and implemented.  
In a product organization, we must discover the necessary product to be built.
Moreover, we know that most ideas will not work with customers the way we might
hope, and we also know that those that do work will require several iterations to achieve
the necessary business results. IT mindset methods are simply too slow, too expensive
and generate far too much waste.
… https://svpg.com/product-vs-it-mindset/

Problems with the project management mindset
I am so concerned if a company makes an explicit choice to move to something
like SAFe. This is a very clear and loud statement about culture to the company.
It tells me the company does not understand or appreciate the role of
engineers or designers in innovation. It tells me they are all about top-
down control. Most importantly, it tells me the leaders don’t understand
what they need to do to compete against the likes of Amazon.
Amazon and Google both also provide services to businesses, some of which
are in regulated industries, so this introduces constraints on them too.
Now each of these companies has their own favorite techniques, and each has a
different company culture, but all three have built their businesses on the
concept of hiring skilled people – especially engineers, designers and product
managers – and then empowering them to solve hard problems.
That is why I can’t conceive of any of these companies ever moving to a process
anywhere even close to SAFe. Maybe more importantly, they are an existence
proof that you don’t need to. https://svpg.com/scaling-agile-faq/

From project to product mindset
The steps towards a product mindset:
1. Realisation that there is potential demand from 3 or more
customers for a given solution
2. Confirmation of the opportunity to generate significant profit by reusing
prior work
3. Any variabilities are initially approached pragmatically, via copy, paste,
and adaptation where needed
4. Adaptations quickly lead to variant / version hell
5. Variant / version hell is successfully addressed via configuration files
and tools operated by the product development team
6. If there is demand, the solution can now be sold to thousands of
customers with minimal adaptation effort

Guess which product requires an “implementation project”

From product to product line mindset
The steps towards a product line mindset:
1. Implementation projects (= product configuration) become a
bottleneck due to (b) complexity and (b) often a limited number of
people with critical configuration domain knowledge
2. Product demand outstrips the ability to implement on time
3. Product configuration costs become a pain point for customers
4. Inadequate product configurability leads to a demand for customisation
5. Customisations quickly leads to variant / version hell
6. Variant / version hell is successfully addressed via model oriented
commonality and variability analysis and the development of
configuration tools that can be operated by customers
7. If there is demand, the product can now be sold to customers
with minimal need for “implementation projects”

… is the method of effectively pre-tailoring the product to the idiosyncrasies of every user
… postpones the task of differentiating a product  
until the latest possible point in the supply network
… is about achieving
a tremendous increase in variety and customisation
without a corresponding increase in costs
Mass customisation

Mass customisation

Model Oriented Domain Analysis & Engineering
… offers a systematic approach for
conducting commonality and variability analysis across the needs of
all customers and potential prospects,
and for sourcing and surfacing the domain knowledge needed to
hide all implementation details from the users of configuration tools,
so that most of these tools can be operated by customers
… is the method of effectively minimising the complexity of configuration  
by pre-tailoring the product to the idiosyncrasies of every category of user
… hands the task of differentiating a product  
for a specific category of user to power users of that category

Product line management and engineering

Model Oriented Domain Analysis & Engineering

Product line work streams & feedback loops
Design Principles and thinking tools
Platform Engineering, domain specific reuse and simplification
Product Engineering, configuration, customisation, testing
Product Line Operations, monitoring, incident management
Experimentation, problem resolution, ideation
w
ork
stream
s
&
feedbacks
feedbacks

Product line artefacts
product/service designs
results of product/service usage
product/service platform (engineering tools and methods)
thinking tools and design principles
knowledge

Frequency of events & product line team structures
weekly / monthly events and feedbacks
daily events and operational decision making
monthly / quarterly events and feedbacks
stable / timeless design principles and thinking tools
weekly / monthly events and feedbacks
stream
team
s

Product line value creation
maximisation of automation (3 to 100 times faster)
maximisation of automation (fewer errors)
knowledge distillation & systematic reuse
application of thinking tools and design principles
exploration of new domains (broader customer base)

Product line implications
shifts towards conﬁguration by power users
shifts towards monitoring
focus on architecture and better conﬁguration tools
focus on teaching thinking tools and design principles
availability of spare time for experimentation

Product line engineering with MODA + MODE

Context : Exponential change in communication
- 1,800,000 years: Cumulative cultural transmission, teaching, imitation, experimentation
- 200,000 years : Spoken human languages — local communication of tacit knowledge
- 5,400 years : Written human languages — communication across time, explicit knowledge
- 600 years : Printing press — 1-to-many communication across space, scale
- 180 years : Electrical telegraph & telephone — global peer-to-peer communication, on demand
- 15 years : Internet — global 1-to-many communication, zero marginal cost, dirt cheap
- Now : Internet of things – machine-to-machine communication, new technologies every month 
apps
time

Tapping into the visual processing capacity  
of the human brain
The brain’s capacity for processing visual data is
around 20 times higher than the brain’s capacity for
processing audio data.
Even with simple technologies such as whiteboards and
markers it is possible to design and use highly expressive
and unambiguous visual languages that are much easier
for humans to parse and understand than information in a
linear format (audio or text).
MODA+MODE therefore makes extensive use of visual
languages and provides guidance for developing
further domain specific visual languages.

Viewpoints and perspectives

Human scale visual language design can be
understood as the elaboration of the role of cognitive
characteristics of humans within ergonomics.
Systems, models and technologies are only
understandable as long as they do not generate
cognitive loads that exceed human cognitive
limits.
Developing visual languages and interaction styles  
that are better than English or any other linear language

Naive product specification
focus on user experience
and interactions between
users and the product
collaboration
product
user

The objective of designing  
human scale visual languages and interaction styles
… is to improve filtering, collaboration,
thinking, and learning:
1. between humans,
2. between humans and software systems,
3. and between software systems.
collaboration
collaboration
collaboration
collaboration

Categories of languages and interaction styles
ﬁltering, collaboration, thinking, learning
collaboration
collaboration
1. verbal, non-verbal, tool-mediated
(textual, mathematical, diagrammatic,
artistic, etc.)
2. speech2text, text2speech, textual,
mathematical, diagrammatic, artistic,
etc.
3. textual protocols

Language and interaction system design activities
collaboration
collaboration
1. Culture design
2. User experience design
3. Interoperability standard design

Creating a learning organisation / system

Creating a learning organisation with MODA + MODE
formalising
agent and
perspective-
based
modular
models
connecting
formal models
between agents
updating of
individual
mental models
and related
examples
interactive
sharing
of knowledge
MODA + MODE extends the concepts of
continuous improvement and the SECI
cycle into the realm of digital business
and data intensive supply chains

6 Questions
Investigating decision making processes:
1. Identification of relevant agents: Who?
2. Identification of agent motivations: Why?
3. Identification of relevant events: When and how often?
4. Identification of relevant valuable resource flows: What? and Where?
5. Identification of limits of understanding: What do we (not) know?
6. Identification of heuristics: How?
=> obligations
=> value chain
=> logistics
=> value creation
=> risks, opportunities
=> principles, system speciﬁcations
to surface and validate tacit knowledge about socio-technological systems

play, learn, observe, question, imagine
a language for
describing
value creation
a language for
describing 
motivations
a language for
describing
interactions
The human lens
to make sense of the world and the natural environment from a human perspective, 
to evolve our value systems, and to structure and optimise human activities
system
lens
semantic
lens
logistic
lens

design / engineering
transportation /
communication
quality / maintenance
energy / food 
production
culture
value creation
human artefactshuman symbols
critical self-reﬂection
nature
human societies
motivations
resourcesevents
agents
interactions
The human lens defines 
categories that are invariant across cultures, space, and time
learn
play
observe
question
imagine
system
lens
semantic
lens
logistic
lens

Joe wants
to buy
a blue car
Your Organisation/Service
Example of domain analysis
Explanatory video: https://youtu.be/if-0kSIyJHI
agent
event
resource
motivation
fast and convenient  
personal transport

Understanding events : business as usual
Joe wants to buy a blue car

Understanding events : changes in context
increased
demand
defective tyres received from supplier
supply chain
management
t
demand

product line
engineering
supply chain
management
sales
application
engineering
customer
supplier
Visualising the
domain supply
chain

REA commonality and variability analysis
events
operational
fruit ready to harvest
fruit ready to ship
fruit arrival in market
fruit in retail store
order received
customer payment received
grower payment made
invoice issued
strategic
new grower
new retailer
new regulator
new logistic service
new market
external
disease
currency crash
oil price shock
signiﬁcant change in demand
agents
Zespri
Z EU
Z Japan
Z Korea
Z China
Z US
retailersregulators
consumers
logistics services
packhouses growerscoolstores
resources
ofﬁces
trusted relationshipsquality assurance systems
orchards
tacit knowledge
logistics automation systems
information assetsmoney
Kiwifruit
business intelligence systems

Organisational learning over time
product line
product
project
platform & experimental  
advanced power tools
prototype & product  
rough power tools
3 customers
product support 
life cycle management
product line operations 
product family management
solution 
off-the-shelf tools
solution delivery 
project management
Mindset Design focus Management focus
cost & time 
bottlenecks
opportunistic 
reuse
strategic 
reuse
fewer 
unknowns
known 
variabilities
Depthofknowledge

Agile :  
unknown variability,
tech experiments
platform engineering
customer needs
knowledge
product platform
products
feedback
feedback
experimentation
product engineering
customer needs
ideas
technologies
Lean :  
known variability,
statistical  
quality control
product line operations
Business Agility :  
changing context,
business experiments

Organisational design patterns

The organisational pattern for digital service delivery

Business architectures for the digital service economy
Mapping a business operating model onto an enterprise and domain architecture

Business architecture is the non-IT part of enterprise and domain architecture

Rule of thumb: buy Enterprise Architecture and build Domain Architecture

Thinking tools

MODA + MODE backbone principles for  
creating learning organisations and understandable systems
26 principles that provide  
a meta paradigm to avoid  
getting entrapped in a paradigm
MODA + MODE thinking tools for interdisciplinary research, design, and engineering:  
https://coininco.files.wordpress.com/2017/08/moda-and-mode-lenses-and-principles.pdf

Techniques and tools for context specific methodology
construction, sourced from a range of domains
Source domains (sciences)
• Scientific method
• Theory of knowledge
• Cognitive science
• Creativity, neurodiversity
movement
• Disability studies
• Critical psychology
• Sociology
• Social psychology
• Organisational psychology
• Evolutionary biology
• Cell biology
• Medicine
Source domains (engineering)
• Probabilistic reasoning
• Product line engineering
• Domain engineering
• Model driven engineering
• Domain specific modelling
• Continuous improvement
• Kaizen
• Agile software development
• Management
• Risk management
Mathematical foundations
• Category theory
• Model theory
• Denotational semantics
• Multi-state logics

The 26 backbone principles
1. Understand that minorities
and outsiders are well
positioned for uncovering
attempts of deception
2. Give minorities and
outsiders access to private
means of communication
3. Operate transparent
governance
4. Adapt the cognitive load
generated by technology to
human cognitive limits
5. Recognise neurological
differences as authentic
and valuable sources of
innovative potential
6. Value metrics from the
physical and biological
world more than human
opinions
7. Value local perspectives more than
widely-held popular beliefs
8. Value the strength of shared beliefs
and corresponding evidence more
than the number of shared beliefs
9. Use 4-state information quality
logic to minimise ambiguity
10.Use probabilistic reasoning to
acknowledge uncertainty
11.Conduct commonality and
variability analysis
12.Formalise the results of
commonality and variability
analysis
13.Develop visual domain specific
languages to describe familiar
domains in unambiguous terms
14.Understand that all information is
dependent on perspective and
viewpoint
15.Understand that a multitude of
perspectives generates new
insights
16.Validate shared understanding
by sharing of models and
corresponding instances
17.Understand that power
gradients stand in the way of
transformation
18.Aim for optimal conflict in a
supportive and trusting team
environment
19.Use agile experiments when
venturing into unfamiliar
domains to learn from
mistakes
20.Conduct an adequate number
of experiments in different
contexts to minimise risk
before global application of
major changes
21.Understand that
collaboration occurs to
the extent that there is
shared understanding
22.Recognise paradoxes
and disagreements as
the essence of
continuous improvement
23.Practice everyday
improvement, everybody
improvement,
everywhere
improvement
24.Engage in niche
construction
25.Use feedback loops to
create learning systems
26.Use modular
decentralised design to
promote reuse without
compromising resilience

A specific culture may have further bones, but one or more 
missing vertebrae lead to an organisational learning disability
Our experience

Creativity

Collaboration for Life © 2016 S23M
Conceptual blending is at the core of creativity
Diverse knowledge is necessary to solve various problems in the world and
to create value in the future, and overcome challenges that go beyond the
framework of research in industry, government and academia.
 
[from the mission of the Honda Research Institute]

Creativity = Having a “less well functioning mental bureaucrat”
Neurodivergent people:
• Adhere to idiosyncratic moral value
systems rather than social norms
• Are okay with exploring ideas that upset
the “social order”
• Spend much more time experimenting
and implementing ideas that others
would consider crazy or a waste of time
• Have untypical life goals: new forms of
understanding, making a positive impact,
translating ideas into artistic expression
Autists in particular tend to:
• Easily suffer from sensory and social
overload
• Have unusually developed pattern
recognition abilities
• Have an unusual ability to persevere
Deep innovation:
• https://autcollab.org/deep-innovation/
autistic
perseverance
autistic
hypersensitivity
autistic pattern
recognition
neurodivergent
creativity
autistic
authenticity
individual
autistic rituals
invention
innovation
neurological traits
derived/aggregate behaviours

The Two Traits of the Best Problem-Solving Teams:
Cognitive Diversity and Psychological Safety
HBR
autistic 
collaboration
absence
of
neurodiversity

play, learn, observe, question, imagine
Scientists, Engineers, Entrepreneurs, Artists & Mathematicians
CIIC brings together academic researchers and practitioners every 3 months  
to tackle wicked problems that don’t have an obvious solution.
Challenges that Go Beyond the
Established Framework of Research in
Industry, Government and Academia
Conference on Interdisciplinary
Innovation and Collaboration
https://ciic.s23m.com/

CIIC workshop results (https://ciic.s23m.com/expected-results/):
• June 2018 – Human, non-human and ecosystem health
• March 2018 – Topics at the intersection of agriculture and healthcare
• December 2017 – Design of interaction patterns for knowledge validation and trust building
• September 2017 – Interaction and collaboration of humans and intelligent machines
• June 2017 – Human scale computing
• March 2017 – Neurodiversity
• December 2016 – Making information interactive
• September 2016 – The potential and limits of clinical decision support systems
• March 2016 – Design and development of tools for effective self-care
• December 2015 – Is there a place for barter?
• September 2015 – How do we need to redefine economic progress? What is value?
• June 2015 – Growing New Zealand’s contribution to a sustainable world

• Allows knowledge to flourish in the open creative spaces between disciplines and organisational silos
• Complements the typical yearly cycle of domain-specific conferences
• Has a quarterly cycle and feedback loop between teams that supports  
on-going and longer-term collaborations more effectively than yearly events
• Captures knowledge flows and transdisciplinary insights in reusable semantic models and patterns
• Currently runs in Auckland at AUT and in Melbourne at RMIT,  
and the CIIC community is available to assist with replicating the concept in other locations
• Can help coordinate collaboration between locations via the CIIC Web site and related online tools
• Invites further co-sponsors from industry, academia, and government
• Is an antidote to bureaucratic limitations; large organisation are now inquiring about establishing
regular in-house CIIC style events
• Provides a safe environment for neurodivergent / autistic people to connect and collaborate
CIIC invites communities and economic ecosystems  
to sharpen their collaborative edge by embracing open innovation.
https://ciic.s23m.com/about/

AUT – Auckland University of Technology
MODA + MODE is being integrated into the  
curriculum on entrepreneurial strategies, creative technologies,  
and methodologies for trans-disciplinary research and collaboration

Book on Domain Engineering
Domain Engineering is of considerable practical
significance, as it provides methods and techniques that
help reduce time-to-market, development costs, and
project risks on one hand, and helps improve system
quality and performance on a consistent basis on the other.
• The most comprehensive and up-to-date work on
domain engineering
• Covers all important technological aspects, including
software product lines, domain-specific languages, and
conceptual modeling
• Introduces novel approaches and techniques, and
includes a wealth of pointers for further research
• ISBN 978-3-642-36653-6, published 2013
http://www.springer.com/computer/swe/book/978-3-642-36653-6

Case studies

The MODA + MODE approach has a fractal characteristic that enables
it to operate at all levels of scale, with explicit support for feedback
loops between different levels of scale:
• Development of collaboration platforms that improve the resilience
and performance of economic ecosystems.
• Development of technology platforms that harness deep domain
expertise to streamline the development of new products.
• Improvements in quality, reliability, and productivity of specific
teams or technological systems.
• Integrating the knowledge of multiple domain experts in a cross-
disciplinary context to co-create innovative solution designs.
• Translating tacit knowledge into explicit knowledge that does not
decay over time.
Typical use cases in industry, academia, and government

Case Studies - New product and platform development
Company Main Context Description Results
From prototype
to product 
 
Agile product
development
New methodology and knowledge
transfer into an ongoing project.
Introduction of best practices for agile
software development
The professionalism and skills that
S23M brought were exceptional, top rate.
I was surprised at how well the teams
engaged with you guys.
Core team now proficient in the use of new
techniques
Product line
management
& engineering
Legacy software re-engineering relating
to product definition and premium
calculation
Simplification of artefacts & workflows
Product specification artefacts are  
20 times smaller
New premium calculation engine runs  
100 times faster
Product line
engineering
Simplification of application development
on a new technology platform
Automation and improvement of quality
assurance
Domain experts with no software
engineering background are able to
develop complete applications
4-fold productivity increase

Case Studies - Collaboration and interoperability
Product line
engineering
Visual design tools for  
postal automation systems
Modular visual language specifications
for mail piece coding strategies
2 to 3-fold productivity improvement
Medium & long-term quality gains
Statistical report specifications now  
6 times smaller
Supply chain
optimisation and
risk management
Development of a QA and test
management framework for  
supply chain optimisation
Risk management of SAP Cloud migration
The framework was fundamental for the
success of the projects.
The risk profile was managed very
effectively with the methodology from S23M
ERP optimisation
Simplification of application development
on a new technology platform
Automation and improvement of quality
assurance
Real-Time inventory system, error rates
in warehouse management are close to
zero
 
7 people maintain web applications
across 170 locations & 23 languages  
(1,500 database tables)

Case Studies - Industry standard design & development
Health
informatics
industry standard
development
Improving speed and accuracy of a
requirements to health informatics
standard and reference software
implementation publication process
Many critical ﬂaws in open source
tooling were corrected and contributed
back to the international health informatics
standards community.
S23M was able to reveal the power of
automatically generated traces between
models using formal model
transformations, merged with human-
documented correspondences.
State Government Design
Agency, National
Construction Speciﬁcation
Agency, Universities and
Cooperative Research
Centres, Australia
Developing a
national BIM
building product
library
Replacement of generic design objects
in Computer Aided Design and
Manufacturing (CAD/CAM) software with
actual properties and 3D geometry of
building products
A viable standards-based BIM building
product library awaiting deployment
investment, licensed for public-good
purposes.  
 
The solution is currently used as an
academic and professional teaching tool
for the construction industry.

Thank you!
Jorn Bettin 
jorn.bettin@s23m.com
Nothing beats capturing the knowledge flow
of leading domain experts to co-create
organisations & systems that are
understandable by future generations of
humans & software tools.

Appendix

Thinking in systems, designs, patterns
the team the tools
We distil knowledge and enable knowledge to flow to all
the places where it can be put to good use for the world

• from an exploratory start-up culture to mature new product
development, product management, sales, and product support
• when to tweak and adapt specific practices
• when to replace specific practices and habits
• when to introduce product platform development as a work stream
• from a simple product development mindset  
to a product line engineering culture
• when to outsource or insource non-core activities
• when to grow by acquisition
• when to cannibalise, divest, or sun-set specific product lines
Advising clients on when to shift gears

We work with the tacit knowledge within your team to achieve severalfold
improvements in terms of better quality, lower costs and productivity
benefits by
• managing the risks of business transformation,
• transforming information into valuable domain knowledge,
• addressing sustainability and compliance requirements,
• making product lines configurable and customisable,
• developing new products and markets,
• optimising supply chains.
Reducing complexity and catalysing cultural transformation

We create enterprise grade SaaS solutions without proprietary lock-in
• Collaboration, trustworthiness and reliability are amongst the
biggest challenges in a digitally networked world
• Transparency, including open science, open data, and open
source software are emerging as essential tools
Addressing software needs beyond standard solutions

@pmauckland
https://www.linkedin.com/company/producttank-auckland/
https://www.meetup.com/ProductTank-Auckland/

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