This text intends to introduce the reader to algorithmic, or automated design tools: survey their current uses in footwear, and speculate on how these transformative technologies will transcend being supplemental to the design process and will radically impact footwear, and broadly, product creation. This research was compiled for my undergraduate Industrial + Interaction Design thesis project at the Syracuse University School of Design. Development of the concepts proposed in this text will continue over the spring of 2018.

1. AutomatedCreativity
+
FootwearFutures
Ryan Polgar

2. Introduction
Case Studies
Interviews
The Inspiration Industry
System Overview
Schematic
Data-Scraping
Collection Streams
Protocols
Journey map
Target Market
Proposed Approaches
Opportunites
Works Cited
6
10
18
20
24
26
28
30
34
36
40
42
44
47
Idea
Methods
Testing
Utilization
The struggles of the retail market and increasing
need for exclusive product can be solved through
the integration of advanced technologies into the
footwear design process. Brand-focused and
unique products result from company specific
genetic algorithms that scrape and analyze the
digitalfootprintofidealconsumerstoinformfuture
brand progression. Ultimately, these generative
design tools provide designers with automated
product iterations for optimized inspiration.

3. The IDEA

4. How can the footwear industry secure
growth- amidst a failing retail sector,
while designing for the evermore
informed and discerning customer?
6.
Image courtesy of Megadem via flickr
7.
According to an industry report from IBISWorld,
from 2011 to 2016, online shoe retail revenue
increased by an annualized rate of
16%
As retail moves online, massive information can be gathered from
consumers. Data from these digital sales platforms, when coupled with
other user-specific information, yields robust insights into trends and the
future success of products. The integration of this data into generative
design processes, poses potential for better brand strategy.
According to real-estate research firm, Cushman and
Wake, between 2010 and 2013 alone, mall visits declined
50%

5. Generative design can be viewed as
a form of meta-design: where the
creator does not design the object
itself, rather the parameters or system
that generate the design.
Algorithm-optimized engineering is standard and architecture has
long been enamoured of parametricism, but the touches of generative
design’s potential are only now being felt in product design.
Why?
Meta-designisinherentlymoredifficultthandesigningitself,because
it necessitates comprehensive understanding and communication of
the development process down to actionable decisions. (Knuth)
The footwear design timeline moves too fast for the development
of a robust generative design tool. “The industry simply does not want
to invest in developing premature technology, and would rather hire
another designer to help the design process.”(Nordin) In the eighteen
months from concepting to sales floor, there is no time nor resources
to be diverted. Although in-house innovation teams routinely create
generative design scripts with the Grasshopper plug-in for the CAD
design program Rhinoceros, these projects are often confined to
performance footwear where the key competitive edge is optimization.
8. 9.

6. The Two Utilizations of
Structure Synthesis
These techniques produce support in response to biometric input.
The integration of athlete data with this type of system is a logical use,
and the performance footwear industry is the most common space for
Structure Synthesis systems to be employed. Generative design has
the potential to automate bespoke footwear production, as anyone’s
biometrics can be translated into personalized support. Currently, the
biggest barrier for this system has been the distillation of widely varied
biometrics to universally supportive designs, as a result of under-
developed digital infrastructure to allow for one-to-one production.
Generative Design Systems
Form Synthesis
Designed to produce complex and often organic (alien even)
geometries, Form Synthesis systems are utilized primarily in fashion
footwear. The lions-share of Form Synthesis systems are used to
generate Haute Couture designs, as their wild shapes often cannot be
produced with traditional shoe-making or manufacturing processes.
Many footwear designs of this method are 3D printed, immediately
exempting them from the shelves of ready-to-wear. As with Structure
Synthesis methods, the biggest question is “How to scale idiosyncratic
design and radical personlization to a mass-market?”
10. 11.

7. Partnering with world sprinting champion Shelly-Ann Fraser-Pryce
for the 2016 Rio Olympics, Nike designed the Zoom Superfly Elite.
Extensive study and analysis of Fraser-Pryce’s running style revealed a
need for a stiff running plate to compensate energy loss at the end or
sprints.
Ocean organisms inspired the plate structure that was then optimized
through a generative design system custom tailored to Fraser-Pryce’s
needs. Though prototypes were 3d printed, Nike’s final design made
concessions in manufacturing- using traditional injection molding.
Nike Zoom Superfly EliteCase 1:
ThecollaborationbetweenarchitectZahaHadidandthefashionfootwear
company, United Nude, embraced generative design principles for the
purpose of creating innovative new forms.
Parametricism was central to Zaha Hadid’s architectural practice, and
thesamesweepinglinesmanifestedthemselvesinthisdesign.Countless
iterations of these previously incalculable forms were leveraged while
still cantilevering a six and quarter inch heel.
Zaha Hadid x United NudeCase 2:
12. 13.

8. Adidas Futurecraft 4DCase 3:
Adidas built upon previous algorithmic applications: translating athletic
performance into complex, yet lightweight out-sole structures.
The analysis of biometric data coupled with revolutionarily quick 3d
printing (30 minutes per individual outsole) opened the conversation
about custom shoes, which can be algorithmically optimized for each
consumer’s respective running style and anatomy.
Adidas developed the Future-craft 4D shoe using a structure synthesis
system that prioritized performance vis-à-vis support and lightness.
Designed by Francis Bitonti Studio in collaboration with Adobe, the
Molecule Shoe employs a cell-based generative design system.
Each voxel, small, cuboid, and visually associative to pixels, is placed
according to the algorithm. The growing pattern within the specified
framework is diffrent shoe to shoe: delivering novelty to the wearer.
The voxels also operate similarly to display pixels- carrying specified
color information that corresponds to the design entirety. The molecule
shoe was 3d FDM printed using only two colors of filament, yet a
gradient was accomplished through the cell-based generative system.
Francis Bitonti x AdobeCase 4:
14. 15.

9. Imagine the use of both Structure and
Form Synthesis. How might footwear
genres change: does the distinction
between athletic and dress disappear?
Holistic CreationProposed Case 5:
+
Holistic Footwear Synthesis
Blending Structure Synthesis and Form Synthesis methods yields
the most comprehensive use of generative design tools. Holistic
Footwear Synthesis, imagined in an industry not bound to traditional
manufacturing, provides an avenue for true one to one, personalized
production. A consumer inputs their biometrics and information about
their desired aesthetic: out of the algorithm appears a unique design.
But is it necessary for a consumer to instruct the system about style?
Methods exist to collect and analyze non-binary information about
an individual’s personal style, buying habits, preferred brands, etc.
16. 17.

10. Industry Personnel Interviews:
Key Takeaways
“When it comes to version two of a
product,thedesignroomisanopinions
game. Everybody has their own idea
about what made V1 successful.”
Freelance Footwear Designer who has worked for Adidas, Yeezy,
Android Homme, and other brands.1.
Design teams could benefit from streamlined inspiration
specific to their brand or product line, rather than pulling from
disparate sources or services.
A.
Generative Design tools are widely and increasing being
used along Structure Synthesis or Form Synthesis laterals,
but have not integrated consumer data meaningfully.
B.
Identifying target consumers for scraping should revolve
around their expressed interest in the given brand, suggested
to use previous purchases as the barometer for inclusion.
C.
The more focused the inspiration, the stronger the consumer
story, and the ensuing saleability of the product.
D.
Tech Designer at New Balance. Works cross-functionally with the
development, innovation, design, and manufacturing teams.2.
Athletic Training Product Entrepreneur, and former Brand
Developer of an star NFL athlete’s fitness product line.3.
This designer actively embraces algorithmic tools in his personal design work: building expertise
using the Grasshopper plugin for Rhinoceros 5. When asked about the education of generative
design tools in university, he responed that all design programs should implement an algorithmic
CAD class. When initially pitching the idea of generative design programs being integrated with
scraped consumer data, this designer acknowledged the value but was hesitant to assume that
it would supplant the need for human designers. His opinion, which shaped this research, was
that an advanced automated trend research and design system would be of great support to
design teams to align their vision and remain cohesive, because each designer has their own
idiosyncratic inspiration process.
The conversation with this Tech Designer was the most formative for the research direction and
final concept. His role includes executing Grasshopper scripts written by the innovation team to
increase performance of on-field sports footwear such as lacrosse cleats. He and his coworkers
seek to use generative tools for “Data Design”, as opposed to “Data Decoration.” This terminology
belies the Structure Generation slant of his work, however indicates an important concern- how
to avoid applying generative design tools for purely promotional purposes, and instead focusing
on their profound potential for impacting the design process. See opposite page for quote on the
difficulty of developing on existing success. This conversation also directed the research towards
applying data scraping to consumers who have already bought brand product, as they are verified
targets.
This entrepreneur’s product applies an individual’s biometric data to develop optimized exercise
gear. He was exceptionally interested in the integration tools I was proposing, specifically how
they could impact his optimization schemes. As a former Product Line Manager, he percieved
how the value of generating focused inspiration for design teams would lead to better sales and
stronger margins. His thoughts were that scraping current consumer social media impressions
for inspiration would only benefit brands that are second to market, because the consumers
are digitally interacting with product that was conceptualized over 18 months ago. However, he
suggested that if the right consumers were scraped, this system would have immense impact for
brands to become hyper-focused on their vision and value.
18. 19.

11. Trend-spotting is the aggregation and
curation of content that is important
to an identified consumer: from the
clutter emerges a vision and story.
“Shoppers complain that everything
on the high street looks the same, but
is it any wonder? Instead of looking
for inspiration, brands are relying on
templates, and because everyone
uses the same templates, there’s no
competitive edge.”
Marc Worth the disenfrachised founder of WGSN
“Gucci Spring 2018 Ready-To-Wear Fashion Show.” Vogue. N. p., 2017. Web. 10 Dec. 2017.
The highly skilled position of trend forecaster is more art than science.
Because of the volatility involved with with trend prediction, companies
often rely heavily upon sources like WGSN, the most influential trend
forecasting service. With over 6,500 businesses subscribing (including
Nike, Coach, Adidas, and H&M) and 74,000 active users, WGSN has
far-reaching sway over creative industries. However two issues exist
that lead to homogeniety: reliance upon condensed streams that might
not align with brand perspectives, and the utilization of royalty-free
templates present on platforms like WGSN.(Truffleman)
With so much information expelled digitally by consumers, it is
competitively negligent for brands to avoid developing artificial
intelligence tools that can process data and prioritize information more
extensively, and with greater focus, than incidental inspiration or use of
current non-analytic systems.
There has been some movement in the trend forecasting industry
towards artificial intelligence tools.(Knight) Amazon has begun testing
their own AI designer, and startup Tilofy (founded in 2013) is the first
automated trend forecasting platform.(Barry)
20.

12. The METHODS

13. How can the footwear industry adapt to
advanced consumer data analysis and
meta-design principles?
Company specific genetic algorithms
that scrape and analyze the digital
footprint of ideal consumers to inform
future brand progression. These meta-
design tools balance user input, global
trends, manufacturing constraints,
and other factors to provide iterations
for designers based on saleability
predictions.
Brands can specify their own constraints and objectives by
following the flow of information diagram for generic generative design
systems by Axel Nordin, engineer and researcher on the integration
of generative design into product design. An adapted version of
this chart for footwear design and production is presented on the
following pages.
Within the objectives, brands can specify different influencers or
consumers for data collection. Further, brands can develop different
genetic algorithms for product categories. For instance, prioritizing
performance for an on-field division, or margins for off-price.
24. 25.

14. HOLISTIC FOOTWEAR SYNTHESIS:
1. Geometry Generation
2. Production Evaluation
3. Optimization Algorithm
The 3D model of a Version 1 style, off which the Version 2 will be
optimized, defines the initial editable geometry. Key features can
be chosen as prioritized traits in the genetic algorithm allowing
for propagation: creating line cohesion between v1 and v2.
The meta-designer then constrains the generative design
system to production needs and preferred processes available
to the respective brand. Color and materials can also be
preselected to remain consistent with the design language of
Version 1.
The scraped consumer data, environmental trends, and
business metrics are then input, and constitute the optimization
algorithm. Here the brand can identify and prioritize which data
streams and methods best apply to their target market. After
initial iterations are generated, the optimization algorithm can
be altered against sales projections and design team
perpective.
cts
Geometry Generation
GENERATIVE DESIGN SCHEMATIC
Production Evaluation
Optimization Algorithm
VERSION 2 ITERATIONS
Shoe Last
Version 1 pattern
Key features
Percentage change allowed
Business Metrics
Sales success of comparable / inspirational products
Margin
Production Timeline
Weekly planning report from retailers
Preferred Vendor List
Square footage and price per foot
Materials
Cost vs. impact analysis of new outsole
Percentage decrease in volume
Molding
Style Environment
High fashion
Influencers
Tribe pulse
Scraped User Information
Social Media impressions
Digital sales history
Online item interaction (regardless of purchase)
Cookies
26. 27.

15. Data Scraping
“Scraping is the set of techniques used
to automatically get some information
from a website instead of manually
copying it.” (Urru)
Data-scraping is the collection of robust amounts of information
from publically available digital platforms. Perhaps the most ubiquitous
example of data-scraping is targeted advertsing. By reviewing your
recently visited products websites, retailers such as Amazon push
equitable products on the ad space.
As of publication of this book, talent management firm hiQ
sued LinkedIn over the measures that LinkedIn had put into place to
prevent hiQ from scraping public profiles to algorithmically determine
individual’s likelyhood to leave their current job. A North Carolina court
issued LinkedIn a preliminary injunction to refrain from installing any
more barriers to hiQ’s bots and remove all barriers currently in place.
(Narula)
“Professor Orin Kerr (a professor at George Washington University
and nationally recognized scholar of criminal procedure and computer
crime law) persuasively argues that where an individual employs an
automated program that bypasses a CAPTCHA - a program designed
to allow humans but to block “bots” from accessing a site - he has
still not entered the website “without authorization.” Unlike a password
gate, a CAPTCHA does not limit access to certain individuals; it is
instead intended “as a way to slow a user’s access rather than as a
way to deny authorization to access.” Kerr, supra, at 1170.”
The above quote from the hiQ Labs, Inc. v. Linkedin Corporation
court transcript on behalf of hiQ indicates that breaching preventative
technologies to scraping is not illegal. Though a verdict has not been
issued by the court, the initial ruling belies regulatory opinion that public
information on the web is not proprietary data to the platform on which
it was posted.
28. 29.

16. Business Metrics
KEY DATA STREAMS
Style Environment
Scraped User Information
Business Metrics
Sales success of comparable / inspirational products
Profit Margin
Production Timeline
Weekly planning report from retailers
Preferred Vendor List
Square footage and price per foot
Materials
Cost analysis of new outsole
Percentage decrease in volume
Molding
Style Environment
High fashion
Influencers
Tribe pulse
Instagram
Likes
Follows
Comments and Comment Tags
Locations tagged
Digital Sales History
Brand / Item / Price
Retailer
Path to Purchase
Online item interaction (regardless of purchase)
Brand / Item / Price
Other items viewed
Mobile vs. Desktop
COLLECTION METHODS
High fashion information extracted from trends.google via Python script
Influencers are associated with selected consumers
Tribe pulse is determined viascraped consumer locations and hashtagging
Obtained via Instagram’s API Platform, dependent on ruling for or against HiQ
Alternatively, compiled into tables via Python script
Cost analysis function of milling time amortized over x pairs
Volume analysis performed via Rhinoceros Volume command
Preferred materials selected as function of desired margin and cost
Square footage determined via Rhinoceros UnrollSRF or Smash Commands
Success of inspirational products determined via trends.google and social reach
Profit margin estimated from automated production costs and inspiration product reach
Production Timeline automatically informs production needs like molding
Weekly planning report from retailers used as preliminary style selection
Cookies compiled into tables via Python script
Path to purchase are previous sites or platforms accessed to prior to view/purchase
30. 31.

17. TESTING

18. Product Under Test: The generation and automated selection of
inspirational 3D footwear iterations.
Business Case: This test will determine the success and functional
creativity of the generative CAD program. This will aid design teams in
continuously focusing their specific algorithm closer to brand identity.
Test Objectives: Does the generative CAD program output applicable
representations? How closely do the generated designs reflect the
brand direction? Are the data streams prioritized adequately?
Tools Used: Prototype generative design program per brand needs.
Human designer for comparison.
Test Tasks: Provide the same compiled data to a human designer and
the generative program. Compare the algorithmic output against the
human-designed iterations.
Product Under Test: The scraping methods and organization of
compiled data.
Business Case: This test will determine efficiency of scraping and
usability of resulting insights through manual data processing. This will
inform brand meta-designers of bottlenecks within their generative
design system, prior to expending resources on development.
Test Objectives: Does the data-processing flow output data in a
processable format for CAD integration? Is the data focused adequately
for design generation? Are the streams balanced per brand needs?
Tools Used: Flowchart based on provided holistic synthesis genetive
design schematic- detailing the specific scraping processes for each
data stream, and prioritization against other streams. Preselected
consumer profiles for manual scraping.
Test Tasks: Leverage Amazon’s Mechanical Turk (MTurk) to manually
execute the perscribed scraping methods and compilation of data.
34. 35.
Protocol A: Data-Processing Flow Protocol B: Generative CAD Program
1. Create Human Intelligence Task (HIT) on MTurk
2. Supply preselected consumer profiles and flowchart
3. Publish HIT
4. Review and approve HIT results
5. Tweak scraping methods or stream prioritization
1. Recruit human designer who reflects desired brand process
2. Supply human and generative program with compiled data
3. Instruct human to design product around provided information
4. Compare iterations
5. Adapt generative program to emulate human-produced design

19. Scripting Flowchart Coding
Brand
Introspection
Testing Schedule
Meta-Design
Product
Development
Determining Data Streams
Brand-Attentive Meta-Designer
Insular Meta-Designer
+
-
Referencingbrandneeds
Prioritizing Streams
A1
Consumer Determination
Successful applications of a specific generative design system necessitates the
consistent alignment of methods with brand needs and periodic testing for
refinement. Remaining insular to brand needs will lead to system abandonment
on behalf of poor algorithmic performance.
System Execution
Reviewal
Data-Scraping
These diagrams explain the meta-design process of
creating a generative design system. This should be used
as a process guide and testing timeline for brands. The
testing protocols are explained on follwing pages.
Implementation and Refinement
A2
B1
A3
B2
B3
Abandonment
Adoption
36. 37.
Meta-Designer Journeymap

20. UTILIZATION

21. Generation Z is the first generation of
digital natives: spending four hours a
day online.(Pew)
Estimated to represent 40% of the US
population by 2020, Gen Z holds
$44 BILLION
of current buying power,
Reaching Gen Z is not important
but imperative for brands, and the
immense social and commercial data
of Gen Z can optimize design strategy.
40.

22. In 2015, the Pew Research Center for Internet and Technology
interviewed teens aged 13-17 about their internet usage: 71% of teens
use more than one social network site.(Lenhart)
“Key to success with younger consumers have been efforts by brand
to develop engaging content for digital platforms like Instagram and
Snapchat, so they and their creative directors can connect with fans
multiple times a day.”(Paton)
To reflect the different needs of each
brand, three different schemes for the
proposed generative design system
will be pursued:
A. Performance Optimization
B. Styling Optimization
C. Saleability Optimization
52% of all US teens
use Instagram.
92% of luxury brand
social interactions occur
on Instagram.
Using a widely adopted visual medium as a main data stream, when
paired with opportunity for frequent brand interaction, provides ample
input for a potential generative design system. Brands need only define
their priorities for the system.
42. 43.

23. 1. Conceptual refinement of the data-processing flow for the three
proposed optimization schemes. Testing as previously defined.
2. Design and development of the generative CAD program using
the Grasshopper plug-in for Rhinoceros 5. Possible exploration of the
Mosquito media plug-in for mock social integration.
3. Digital representation and physical production of shoe styles
though the generative design program defined using data mock-
sourced in simulation of the conceptual optimization schemes.
Development of the Generative Design
System will proceed through three
distinct steps.
45.

24. Idea
Thompson, Derek. “What In The World Is Causing The Retail Meltdown Of 2017?.” The
Atlantic. N. p., 2017. Web. 12 Dec. 2017.
Hurley, Madeline “OD5093: Online Shoe Sales”. IBISWorld. September. 2016. Web. 12
Dec. 2017.
Nordin, Axel. “Challenges in the industrial implementation of generative design systems: An
exploratory study.” Artificial Intelligence for Engineering Design, Analysis and Manufacturing,
30 Jan. 2017. Accessed 24 Sept. 2017.
Rhodes, Margaret et al. “Check Out Nike’s Crazy New Machine-Designed Track Shoe.”
WIRED. N. p., 2017. Web. 18 Sept. 2017.
Knight, Will. “Amazon Has Developed An AI Fashion Designer.” MIT Technology Review. N.
p., 2017. Web. 18 Sept. 2017.
Levine, Barry, ““First Automated Trend Forecasting Platform” Predicted The Rainbow Bagel.”
MarTech Today. N. p., 2016. Web. 13 Dec. 2017.
Trufelman, Avery. “The Trend Forecast - 99% Invisible.” 99% Invisible. N. p., 2016. Web. 11
Sept. 2017.
Methods
Vargiu, Eloisa, and Mirko Urru. “Exploiting Web Scraping In A Collaborative Filtering- Based
Approach To Web Advertising.” Artificial Intelligence Research 2.1 (2012): n. pag. Web. 27
Nov. 2017.
Narula, Prayag. “LinkedIn Vs. hiQ Ruling Casts A Long Shadow Over The Tech Industry.”
Forbes Tech, Forbes, 20 Sept. 2017. Accessed 30 Nov. 2017.
hiQ Labs, Inc. v. Linkedin Corporation, F. Supp. 3d. (N.D. Cal. 2017).
Renner, Gábor, and Anikó Ekárt. “Genetic Algorithms In Computer Aided Design.” Computer-
Aided Design 35.8 (2003): 709-726. Web. 27 Nov. 2017.
Works Cited
Testing
Travis, David. “The 1-Page Usability Test Plan.” Userfocus.co.uk. N. p., 2017. Web. 13 Dec.
2017.
Utilization
Shay, Matthew. “Ibmvoice: Move Over Millennials: Generation Z Is The Retail Industry’s Next
Big Buying Group.” Forbes. N. p., 2017. Web. 13 Dec. 2017.
Steiner, Hallie. “Gen Z Rising.” JWT Intelligence. N. p., 2015. Web. 13 Dec. 2017.
Finch, Jeremy. “What Is Generation Z, And What Does It Want?.” Fast Company. N. p.,
2015. Web. 13 Dec. 2017.
Lenhart, Amanda. “Teens, Social Media & Technology Overview 2015.” Pew Research
Center: Internet, Science & Tech. N. p., 2015. Web. 13 Dec. 2017.
Paton, Elizabeth. “Gens Y And Z Are Buying Lots Of Luxury Stuff After All.” Nytimes.com. N.
p., 2017. Web. 14 Dec. 2017.
Dingwall, Kate, “New Study Finds Instagram Accounts For 92% Of Brand Social Interactions.”
FashionNetwork.com. N. p., 2017. Web. 14 Dec. 2017.
47.