UX STRAT Online 2021 Presentation by Mike Kuniavsky, Accenture

1. COMPUTATIONAL DESIGN Niche manufacturing, AI and A research framework from Accenture Labs Mike Kuniavsky UX STRAT 2021

2. Copyright © 2021 Accenture. All rights reserved. 2 EXECUTIVE SUMMARY • We propose enabling niche manufacturing using computational design, so manufacturers and retailers can reach small markets faster, more accurately, and with less waste. • Niche manufacturing takes audiences identified by digital marketing and makes unique products for each market. • Computational design is a software-first approach that maximizes designer productivity using data and AI assistants throughout the design process. • Designers and AI assistants collaborate to interpret live data streams to create product design spaces with infinite variations, instead of unique product designs. • This approach requires new design practices and AI design assistants, which Digital Experiences in Accenture Labs is developing.

5. Copyright © 2021 Accenture. All rights reserved. 5 …throughIoT(2004-2012ish) 4 5 Living Cube · Living products Living Cube · Living products each of these ever-changing needs. Specifi- cally, the Living Cube introduces a built-in food cooling and heating system to the living room without adding additional furniture to an already busy environment. form The Living Cube evokes a traditional coffee table, but with a distinct difference: when the leather ‘saddle’ slides to the side, the Cube’s design ceases to be furniture and becomes instead a technology, complete with displays, controls, functional surfaces, and storage compartments. The cooling compartment includes a bottle carrier for four to eight bottles, reflecting the times when the living room serves more as a drinking area than an eating area.And for the times when snack food is the focus, the heating surface has two different compartments underneath: one for the storage of small induction heating cookware and another for silverware. interaction When closed, the Living Cube emits only a slight glow of diffused white LEDs to indi- cate the temperature of the cooling compartment, communicate that the Cube is on, and help identify it in the dark. In recognition of the task-specific way in which people will use the product, the sliding motion of the saddle has been designed to create a small transformation ritual that signals the transition from one kind of activity to another, while revealing only the controls for either the cooling side or the heating side. ← → dimensions Overall 50 by 90 by 50cm Cooling unit 32 by 46 by 30cm Warming zone 36 by 17cm Saddlebag 24 by 22 by 6cm materials Leather, Satinized glass, Stainless steel, MDF • •• • • •• •• •••• •• •• ••••••••••• • ••• ••••• •• •• ••• • • •••••••••• •••• •••••••• ••••••••••• ••••••• • ••• •••• ••••••• • ••• ••• •••••••••• ••• •••••••••••• •• •••• •••••• ••••••••••••• • ••••••• ••• ••• •• ••• •• ••••••••• ••• •••• •• •••• •• •• ••• •• •••• •• ••• •• •••••••••••••••••••••••• •••• •••••• • •••••••••••• ••• ••••• ••••••• •• •••••• •••••••• •••••••• • ••••• • ••• •• •• •• ••• ••• •••• •• • ••• • ••••••••••••• ••• • ••••••• •••• ••••

11. Copyright © 2021 Accenture. All rights reserved. 11 Howproductsaremadeandsoldischanging Markets are smaller and move more quickly • By geography: maximize regional focus • By interest: address needs of medium-sized sub mass-market interest groups. • By time: capitalize on momentary markets Retailers want to reduce the risk of entering these smaller markets • Reduce the cost to enter small markets, and enter multiple markets simultaneously • Reduces the complexity of competing in a new market Manufacturers want to design and engineer more efficiently • Reconfigure manufacturing lines quickly • Reinvent product introduction processes • Reduce work required in design changes • Respond to regulatory changes

15. Copyright © 2021 Accenture. All rights reserved. 15 Aslotsizegoesdown,productvariationgoesup (ifyouwanttocapturethesamesizedmarket) • Today: processes are optimized almost exclusively for mass manufacturing • Tomorrow (2022-2025): moving towards niche manufacturing with smaller lot sizes and more product variations serving more diverse users • Future (2025+): personalized manufacturing where products are designed for individuals and produced in ultra-small lot sizes • Niche manufacturing is the bridge between lot size 1M and lot size 1. P E R S O N A L I Z E D M A N U F A C T U R I N G NICHE MANUFACTURING MASS MANUFACTURING Today Tomorrow Future Zara Apple Dell 1 10 6 10 4 102 LOT SIZE PRODUCT VARIATION 1 10 102 10 3 Invisalign

16. Copyright © 2021 Accenture. All rights reserved. 16 Butwedon’tyethavenichemanufacturing. Why?(atheory) Many of the pieces are already in place • CRM gives us customer interest data • IoT systems give us product usage data • CNC manufacturing enables automatically-reconfigurable tools • CAD and PLM systems allow high precision digital product definition • There are clear market advantages: capture more total market share and less total risk by making small bets Why don’t we have widespread niche manufacturing?

17. Copyright © 2021 Accenture. All rights reserved. 17 Traditionaldesigneffortquicklyincreases beyonddesignercapacitybecauseofthetools • Design teams have limited resources of: • Time • Money • Knowledge • Team members • Designing one product is hard enough. Designing 10, 100, 1000 unique variations becomes untenable because design tools scale linearly to the number of designers. 10x the designs = 10x the designers • Increasing team size does not enable teams to make better use of knowledge or manufacturing flexibility because the tools are still the same. 1 10 Design Team Capacity Number of product variants 100 1000 Zara* (300 designers on staff) Currently unfeasible https://www.businessinsider.com/zara-design-process-beats-trends-2018-11

19. Copyright © 2021 Accenture. All rights reserved. 19 Human+AI+DATA=IncreasedDesignCapacity =ComputationalDesign • Change design effort from scaling linearly to scaling exponentially • Augmenting human teams with AI-based tools has a multiplicative factor on team effort – this collaboration changes the math on design capacity • Designers are supported by AI design assistants • AI assistants are supported by data via a digital thread across the design process including manufacturers and customers 1 10 Number of product variations 100 1000 AI DATA DATA AI DATA AI DATA Design Team Capacity HUM AN +M ACHINE+DATA

20. Copyright © 2021 Accenture. All rights reserved. 20 Computationaldesignvscomputerizeddesign Computationbakedinateverylevelredefinesdesignand engineeringpractice Computerized design (CAD) tools replicate pre-digital design practices with digital tools. Super paper, meet super pencil! Computational design creates design practices that can only be done with a computer. • Data-driven • Parametric • Automatically evaluated • Simulated • Instead of one-off detailed representations, computational design tools create product possibility spaces. Scriptable CAD is just the start of computationally defined metaproducts.

21. Copyright © 2021 Accenture. All rights reserved. 21 Computational design: Many names for the same idea Computational Design Parametric Design Generative Design Algorithmic Design Performance-based Design Evolutionary Design

23. Copyright © 2021 Accenture. All rights reserved. 23 1.Software-definedeverything Everyaspectisencodedassoftware,notasvaluesinsoftware • Physical appearance - keep a product on brand even when its design is completely generated by software • Functionality – configuration of options into an included bill of materials creation • Firmware – software that enables all the options to work together • Digital twin – provision the cloud service that enables specific functionality, collects usage information • Every possible digital product from industrial equipment-as-a-service to consumer products to interactive environments

24. Copyright © 2021 Accenture. All rights reserved. 24 2.Metaproducts Interlinkeddesignsthatdefinegenerativeproductspaces • Software-defined parametric metamodels describe an infinite range of potential product designs, rather than a single product design. • Changes cascade through the whole design. • Difficult for products made up of many materials and manufacturing methods today, but we’ll get there and we’ll start with simple designs.

25. Copyright © 2021 Accenture. All rights reserved. 25 3.Agiledesignpractices Nothingiseverfinished Abandon waterfall design: design isn’t a step in the product development process. It is the process. Bring rapid iteration/high-data design pioneered in online design to physical products. Expand: Augment design capabilities with AI assistants • AI design assistants (requirements gathering, constraint definition, simulation, customer/user data analysis) • Feedback-driven design, assisted design and manufacturing (generative design) Enable: continuous data-driven design + manufacturing • Digital twins collect data about how products are actually used. This data is fed back to designers as they design the next generation of a product • CRM systems allow for customer niche identification by behavior/values/desires/associated purchases Software is a tool for the mind. - Reas & Fry, 2014

26. Copyright © 2021 Accenture. All rights reserved. 26 Process Iteration Data Feedback Design Subprocess Problem Formation Preliminary Requirements Conceptual Design HW & SW Detailed Design Testing Sourcing Manufacturing Quality Verification Product Launch Sales After Sales DIGITAL THREAD Simulation Validation Simulation and validation become parts of every design subprocess. Each of these is powered by data on the digital thread. 4.DigitalthreadforProductDesign Connectdatasourcesdirectlytoresponsivetools • As design and manufacturing processes become increasingly agile and iterative, teams need to move fluidly between design phases. • Iterations will be powered be new sources of product and customer, and manufacturing data. • Digital threads connect digital twins of products, customers, and processes so relevant information is always available to designers at every step.

28. Copyright © 2021 Accenture. All rights reserved. 28 Computationaldesignprinciples 1. Designer + AI work as a team Designers and engineers collaborate with AI assistants on teams. Each team works together on a representation of the design. 2. Shared representations At each stage of the computational design journey the designer and AI take on different roles as they work together on a shared representation of the design challenge. 3. Everyone does what they’re best at AI assistants take on tasks that only computers can do, such as analyzing large amounts of data and evaluating thousands of variants of an idea. Designers take on tasks that only people can do, such as defining goals, evaluating simulations for unquantifiable criteria, and making tradeoffs. Snap-to-grid is a primitive assistant Snap-to-product will offer autocomplete assistance for finished designs

29. Copyright © 2021 Accenture. All rights reserved. 29 Computationaldesignprinciples DESIGNER + AI CO-ANALYZE AND CO-CREATE DESIGNER • Creating constraints and relationships. • Define a general style • Sets functional goals AI ASSISTANT(S) • Analyzing data • Modifies parameters, generates and • Evaluates designs SHARED REPRESENTATION

31. Copyright © 2021 Accenture. All rights reserved. 31 Doublediamondphasesforcomputationaldesign aproposal Discover • The team captures business, user, and customer needs and wants. • Merge purchase behavior, IoT sensor, CRM, qualitative research, and business discovery data. • Defines goals for a parameter space definition of product value and effectiveness. Define • The team parametrically describes the product space • Creates a metamodel of all potential product designs • Encodes product goals as fitness functions Explore • The team explores the product space by creating and evaluating hundreds to millions of design variants. • Fitness functions help prioritize among design variants. Fitness functions can be functional (such as mechanical performance), cost (such as estimated manufacturing costs), or esthetic (whether a design fits brand style guidelines). Evaluate • The team evaluates design variants using simulation. Simulations can be algorithmic (crowd flow simulations in an architectural space) or sensory (AR simulation). • The team uses results of the simulations to adjust parameter space definition, metamodel descriptions and fitness functions. Designers and AI assistants work as a team and take on different roles to jointly produce different representations of the product idea as they interpret data and respond to it with new design constraints and settings. There are many different design and engineering roles throughout product development, and there are many kinds of AI assistants.

32. Copyright © 2021 Accenture. All rights reserved. 32 Computationaldesignjourney Design Goal AI Assistant Role Activity Simulation Engineer Evaluate subjective design factors Provide suggestions / make adjustments Select solutions to move forward with Designer Role Curator Generative Designer Compose Generative Design System Select fitness functions Design Director Constraint Manager Define problem Prioritize requirements Define style grammar Systems analyst Quantitative Research Document business challenges Ask research questions Generate insights Researcher Director Activity Provide objective comparisons Prioritize designs based on multiple factors Capture feedback from humans Generate solutions Present solutions Refine solutions based on feedback Document constraints and requirements Debate priorities Define parameters Collect quantitative field data Find patterns in data Present summaries Raise questions Shared Reprensatation Intelligent research interface Requirements prioritization Parametric metamodel Data-driven need list Customer genome Outputs Generative Design System Design options on fitness curves Hi-Fi simulation results Refined design option Intelligent Requirement System Style Grammar System Generative Design System Design space fitness map High-Fidelity Simulators Design feedback environment DISCOVER DEFINE DEFINE EXPLORE EVALUATE Capture business, user, and customer needs and wants Specify problem, functions, requirements and initial parameters Generate possible solutions Evaluate solutions, refine requirements and parameters, select designs to more forward

35. Copyright © 2021 Accenture. All rights reserved. 35 Explore:GenerativeDesignDemo • A digital twin of a customer’s driving style. • Prototype uses the digital twin to generate a unique wheel design that meets the driver’s performance style and esthetic preferences, while still meeting all functional requirements.

36. Copyright © 2021 Accenture. All rights reserved. 36 Explore:ThemeInspiredStyleTransfer • A product design space of many potential designs. • Prototype generates many potential 3D designs based on product manager/designer interest/inspirations or market performance data.

37. Copyright © 2021 Accenture. All rights reserved. 37 Evaluate:IntelligentPlannerToolDemo • A digital twin of a FEMA emergency facility location, based on photogrammetry of the actual site. • Simulated, learning agents explore potential layout designs to explore impact of various facility layout decisions. Agents have individual different goals (get supplies, give out supplies, etc), and the capability to learn from their mistakes.

38. Copyright © 2021 Accenture. All rights reserved. 38 “Ideas flow from [a design material] to us and though we feel to be the creator we are involved in a dialogue with [it]. The more subtly we are tuned to our medium, the more inventive our actions will become. Not listening to it ends in failure.” - Anni Albers, Bauhaus professor, 1982 Computational Design is a new design material with which we are just beginning a dialogue.

39. Copyright © 2021 Accenture. All rights reserved. 39 MikeKuniavsky Technology R&D Sr Principal Copyright © 2021 Accenture. All rights reserved. Incollaborationwith NikolasA.Martelaro Assistant Professor, Carnegie Mellon

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