User experience and design
Bernard Yannou, Professor
[email protected]
How to know if goods and services are well adapted?
Using a home theatre Using a jigsaw for cutting wood Using a refrigerator
Energy and water consuming activities Providing efficient solutions against falls of elderly
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What to observe? What to conclude?
Huge amount of data on consumer habits, choices and preferences can/could be accessible Capturing data for learning of people motivations, usage and purchasing contexts, perceptions, preferences and satisfactions Which data to measure? How to get innovation insights/leads out of that and use it in the design process?
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How to mix information streams for specifying and innovating?
Which design platform for gathering information for innovation and redesign? 1) Experiments in labs of prototypes or existing solutions
Azqueta, I. 2014. Global platform for future experimentations and developments of intelligent refrigerators, MSc in IE bibliographical study. Ecole Centrale Paris - Laboratoire Génie Industriel.
2) Surveys and observation of people
3) Innovation processes (need expression, creativity workshops, internal research, usercentred design, crowdsourcing)
4) Design analytics from online reviews, tweets…
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How to experiment in labs?
Experiment protocol
Results
user can access refrigerator several times per day, place or remove shelves, add, move, expire food. Simulator measures shelves positions, at which shelf food is placed, height of goods. Most of customers use no more than 4 shelves Selve #2 rarely used Front area of shelves is used more actively Bottom and upper shelves are the most populated
Redesign
Lewis K. and Horn D. Design analytics in consumer product design: a simulated study. ASME International Design Engineering Technical Conferences, 2013
4 shelves only Increase the vertical space of the top slot in order to stock higher objects
Redesign with similar architecture
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How to relate observable indicators to KPI/Functions/Values assessment? 3) Performance modelling by data fitting or expertise (fuzzy rules)
4) Assessing performances from measures
Intermediate Indicators
2) Observation and data recording
Number of door openings Duration of door opening Ambient temperature Cabinet Load Time examining refrigerator's interior contents Time loading the refrigerator Time spent searching an item Time spent on grocery shopping Time spent cleaning refrigerator Time thinking recipes Quantity of food wasted Usage of shelf space Shelf configuration used by consumers User-friendly interface Size of food items Configuration possibilities Item distribution by size Average cost of electricity Noise
Lushnikova, A. 2014. Hybridizing design analytics techniques on fridges, MSc in IE bibliographical study. Ecole Centrale Paris Laboratoire Génie Industriel.
KPIs Energy Consumption Time
Food Waste
Refrigerator
Comfort of use
6) Changing architecture
Economic Cost
Freezer/Cooler position Volume of Freezer section Volume of Col-section Shelf layout Compartment layout Refrigerant used … … Temp required in F-section Temp required in C-section
Design parameters
1) Functional analysis
5) Adapting design parameters 7
Design analytics from online reviews Data extraction
Preprocessing
Text processing
Sentiment analysis
Sentiment rating
5 4 3
Took ROOT
IT
Average
2
Longer ADVMOD
NSUBJ
Model's Score
1 0 1 3 5 7 9 11 13 15
run DEP
to
wires
AUX
DOBJ
the DET
across room POBJ
the DET
did NSUBJ
PREP
than
it
MARK
hook
NSUBJ
to AUX
XCOMP
it DOBJ
up PRT
Amazon Product Code: B003B8VBJ2 Product name: Sony BRAVIA DAV-DZ170 Home Theatre System (Electronics) Review: Well, Sony definitely let me down on this one. First off this unit was easy to set up. It took longer to run the wires across the room than it did to actually hook it up. But the volume on this was sub-par. Even on the max level volume (35) it still wasn't that loud. The main problem was the amount of bass that it produces. The bass is so overpowering that you can barely even hear people talking in the movie, and there is no way to adjust the levels at all.
actually ADVMOD
Automatic sentiment rating, heuristics validated by experimentation Also delivering of good/bad functions, and solutions Next step: using function glossary to synthesize insights
Raghupathi D., Yannou B., Farel R., Poirson E. 2014. Learning from product users, a sentiment rating algorithm, In International Conference on Design Computing and Cognition, 23-25 June, London, UK.
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What to observe on users / customers / purchasers?
Usage modelling is needed, design by usage is useful for creating values Data on usage patterns are needed
Design Optimization framework Candidate product
Performance evaluation Objective function
Functional analysis (simplified and averaged description of expected functions and performances in lifecycle situations)
Representation of averaged preferences (assessed by polls, marketing experts, design workshops…)
Preference aggregation model
Multiple objectives
Pareto optimal solutions set
Competing products
Degree of absolute satisfaction
Averaging customer preferences is awful! No consideration of competing offers
Degree of performance dominance (distance to Pareto frontier)
Performance evaluation
Yannou B., Yvars P.-A., Hoyle C., Chen W., (2013), Set-based design by simulation of usage scenario coverage. Journal of Engineering Design, vol. 24(8), p. 575-603, doi. 10.1080/09544828.2013.780201.
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Choice and Market Share modeling framework
Market segmentation
Primary selection of attributes influencing the choice
Questionnaires
Choice model (e.g.: EUVT Conjoint analysis Discrete choice analysis)
Competing products
Market share model Market part
A market segmentation and survey is possible only if the product class exists
Candidate product
Not adapted to an innovative offer
Yannou B., Yvars P.-A., Hoyle C., Chen W., (2013), Set-based design by simulation of usage scenario coverage. Journal of Engineering Design, vol. 24(8), p. 575-603, doi. 10.1080/09544828.2013.780201.
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Design by usage coverage simulation framework Collecting usage situations is more independent of existing classes of solutions Candidate (parameterized) product Space of usage scenarios Collection of typical usage contexts (or lifecycle situations)
Customer demographics, User skills, Performance bounds, Preference profiles
Simulated customer desired attributes/performances (Physics-based models and/or Human appraisal experiments)
Mapping models between simulated performances and usage scenarios
A product is considered as a mean to deliver a service. The service quality depends on the user skill
Feasible usage scenarios at an expected quality level
Absolute usage coverage indicators
Competing products
Relative usage coverage indicators
Yannou B., Yvars P.-A., Hoyle C., Chen W., (2013), Set-based design by simulation of usage scenario coverage. Journal of Engineering Design, vol. 24(8), p. 575-603, doi. 10.1080/09544828.2013.780201.
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A design by usage approach based on physical models of performances and constraint programming
Which existing solution is the best for my needs?
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A design by usage approach based on physical models of performances and constraint programming
Bosch family of jigsaws
50 equations of physics (efforts, velocities, cutting law…) Usage coverage simulation
Ergonomics user-related data Survey on usage scenarios sets of a set of representative users
The service delivered depends on the user capabilities as well as 14 the products and usage scenarios !!!
Simulation of market share and optimization of a scale-based product family
Wang J., Yannou B., Alizon F., Yvars P.-A., 2013. A Usage Coverage-Based Approach for Assessing Product Family Design. Engineering With Computers, 29 (4), 449-465.
Development of Usage Coverage Indicators for a product family Simulation of market share in different market contexts
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How to get information in case of diffuse situations?
Falls among the elderly (65 years and older)
A painful situation, in France per year (in 2010):
3.5 million falls/year 5,000 deaths/year directly caused by falls As a comparison: 1,800 deaths by car accidents 80,000 hospitalizations/year because of falls 2 Billions € of financial costs of hospitalization
A diffuse situation
Multiple fall (usage) contexts Multiple causes No commercial interest to organize data collection
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A design by usage approach based on learning of usages scenarios segments and usage coverage simulation Bekhradi A., Yannou B., Farel R., Zimmer B., 2013. Building of usage scenarios space for investigating the fall situations of the elderly people, IDETC Conferences, Portland, Oregon. Male: 1
60 medical publications
Female: 1,7
Probability of falling
More than 30 contextual variables e.g. Gender={Male, Female}
Male 42% Female 58%
Modality (segment) size
Experts' heuristic-rules declaratives
Usage scenario space (Oracle)
Performance vector of pain points
Bekhradi A., Yannou B., Farel R., Jena S., Zimmer B., 2014. Simulating global utility of design solutions to elderly falls by building relevant usage segmentation, In International Design Conference, Dubrovnik, Croatia.
Simulation of usage segment coverage and global perforrmances
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A new innovation method and tool for simulating in advance the utility to create in targetting painfull situations Bekhradi A., Yannou B., Farel R., Jena S., Zimmer B., 2014. Simulating global utility of design solutions to elderly falls by building relevant usage segmentation, In International Design Conference, Dubrovnik, Croatia.
Value buckets
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How to organize feedback to people and incent them to well behave?
The case of energy and water consumption of households
Eco-designing buildings
Zaraket T., March 31 2014. Stochastic activity-based approach of occupantrelated energy consumption in residential buildings. PhD dissertation. Ecole Centrale Paris, Laboratoire Génie Industriel.
Presence at home Issue: Energy performance of a residential building
Location Altitude Surface area Orientation Energy-consuming actions
HVAC
Temperature
Lighting Hot water
Humidity
Fabi et al. (2012), Yun et al. (2011), McLoughlin et al. (2012)
Occupant behavior (activities, usages) is the most complex phenomenon (Robinson 2006, Page et al.2008)
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Data model for a systemic view of energy consumption in residential building
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SABEC simulation scheme (Stochastic Activity-Based approach of occupant-related Energy Consumption ) Activity Patterns For each activity
Diversity of user profiles Income Number of occupants
Family types
Activity quantity per individual
Socioprofessional class
Aggregation
Education level
Activity quantity per household Individuals attributes
Age
Appliance(s) use pattern
A specific household Household attributes
Influencing parameters
Energy consumption per activity
Appliance Appliance(s) ownership Appliance(s) characteristics
Interesting notion of cascading of service units Overall energy consumption for all activities 23
Simulating “Watching TV” consumption TV’s technology TV ownership
2
1
Given Household
TV’s energy efficiency
2,3,4
CRT
LCD
Plasma
Stochastic
Stochastic
Stochastic
Energy consumption (KwH) Statistical data 1
2
3
1 4
Individual TV watching duration
REDOMECE
Activity sharing heurisrics Stochastic
Model fitting
Simulating “Washing clothes” consumption Given Household
WM’s ownership
WM’s characteristics
WM: washing machine/
Energy efficiency
Capacity Clothes weight worn/individual/day 1,2
3
Dirty clothes weight /month
Number of cycles /month
Consumption Kwh/month
White clothes
White clothes
White clothes
Colored clothes
Colored clothes
Colored clothes
3
3
Clothes changing rate/month/individual
White/colored %
4
WM filling %
Statistical data 1
2
3
4
5
Energy consumption (KwH/month)
6
Survey Stochastic
Wahsing temperature (white/colored)
Stochastic
Static or dynamic analysis of residential energy consumption? Dwelling
Household variables
Domestic appliances
Smart-metering National statistical data SABEC-Prediction (Static Model)
Parametric predictive modeling
SABECmonitoring (Dynamic Model)
Dwelling
Clustering of households’ profiles Dwelling
Regional & Building National network
Regional Building & National network
SABEC-Prediction Database Energy consumption allocation per activity
Real Consumption Database
Specific household ?
Human-Machine Interface Learning Oracle
Static or dynamic analysis of residential energy consumption? Which HMI for managing residential energy consumption and incenting positively occupants to save energy and water? Human-Machine Interface Recommendations and Action Plans Diagnostics, Recommendations according to Occupants’ Profile
Data Cloud Consumption analysis
Reference points
Household Consult
Inform
Specific Consumption
Visualization Diagrams, Datasheets, Audio-Visual Alerts
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20 20 17 14
15 12,16 10
9 8
7,29
5 4
5
2 0 Laundry
Entertainment
Real consump on of HH1
Food
House-caring
Self-care
Na onal average consump ons
Prediction database
Building Consumption Regional Consumption
National Consumption
Monitoring database
