complex systems analytics: a promising enabler for design ......complex systems analytics: a...

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Complex Systems Analytics: a Promising Enabler for Sustainable Design and Manufacturing

Harrison M. [email protected]

Associate ProfessorDepartment of Industrial & Enterprise Systems EngineeringUniversity of Illinois at Urbana‐Champaign

Overview

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User‐Generated Contents Product Life CycleFlu Spread Pattern

Predictive Systems Design Analytics

100

50

02008 2009 2010 2011 2012

(Present)

FeatureCount

Time

CameraAppsMP3

Prediction

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Let’s envision ….

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Manufacturers can predict when a product will reach its end of life and the condition of the returned product based on the product attributes and customer demographic data at the moment a consumer purchases a product.

Is it better to keep my phone as long as it lasts? “I am a green farmer. I would like to keep my harvester as long as I can.”


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Even at the early stages of product design, the manufacturer can predict which component will be reused, recycled, remanufactured, or replaced with a new component; and, in turn, modularize platforms (i.e., better design) for easy, profitable end‐of‐life recovery operations.

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Manufacturers can design a variety of products that are targeted for the new product market, as well as the remanufactured product market in a simultaneous manner for a better market coverage and higher profit.

Research Contributions – Predictive Design and Analytics

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Wind Farm Layout Design

Energy DistributionHybrid, Renewable Energy Generation

Green Profit DesignMultidisciplinary Design Optimization – Analytical Target Cascading (ATC)

Trend Mining Design

Kim, et al. 2003 Trans. ASME

Tucker & Kim 2008, 2009 Trans. ASMEMa & Kim 2014 Trans. ASME

Kwak & Kim 2010, 2012 Trans. ASMEKwak, Ma & Kim 2014 J Cleaner Prod.

Lu & Kim 2010 Trans. ASME Lu & Kim 2014 Eng. Opt. J.

Kannan, Shanbhag & Kim 2012 Opt. Mtd. & Software

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How does CDC detect influenza epidemic?

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Visit

Patients with Influenza‐Like Illness (ILI) visit physicians.

Report

Physician visits are reported to CDC on regular basis.

CDC analyzes the collected data and publishes a report on ILI.

It takes _________ to detect influenza epidemics.It takes _________ to detect influenza epidemics.one week

How does Google detect influenza epidemics?

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[Source] Ginsberg et al., 2009, “Detecting influenza epidemics using search engine query data,” Nature 457(19).

Search Submit

Can estimate current flu activity around the world in near real‐time. Can estimate current flu activity around the world in near real‐time.

For product design, can we utilize user‐generated data like this?For product design, can we utilize user‐generated data like this?

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Are these waste (or opportunities)?

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Management of Used and End‐Of‐Life Electronics in 2009 (Source: EPA)

Ready for End‐of‐Life Management(million of units)

Disposed(million of units)

Collected for Recycling(million of units)

Rate of Collection for Recycling(by weight)

Computers 47.4 29.4 18 38%

Televisions 27.2 22.7 4.6 17%

Mobile Devices 141 129 11.7 8%

A product lives a life and causes environmental issues throughout the life (especially in usage phase).

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Material Extraction

ManufacturingSales &

Customer UseProduct Design

Reverse Logistics

End‐of‐Life

Manufacturing Maintenance Usage (Fuel)Usage (Emission) End-of-Life0

50000

100000

150000

200000

250000

300000

10312.11(2.19%)

258182.5(54.78%)

94364.36(20.02%)

29838.24(6.33%)

GW

P 1

00

a [

kg C

O2

-eq

]

78637.09(16.68%)

• Natural resource depletion• Climate change• Toxic chemical releases• Waste and land management

⁞

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For example, a machine generates…

Kwak et al. (2012) “Life Cycle Assessment of Complex Heavy Duty Off-Road Equipment,” ASME ISFA.

Global Warming Potential (kg CO2e)

11,800x 9.4x 0.5x824 Tons CO2e

Manufacturing Maintenance Usage End-of-Life Total0

20

40

60

80

100E

I-99

sco

re [

%]

Fossil fuels Minerals Land use Acidification Ecotoxicity Ozone layer Radiation Climate change Resp. inorganics Resp. organics Carcinogens

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A major obstacle is that there exists a time gap between design and end‐of‐life.

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Age Distribution of EOL Products

Kwak, M., Behdad, S., Zhao, Y., Kim, H. M. and Thurston, D. "E‐waste Stream Analysis and Design Implications,“ Journal of Mechanical Design, Vol. 133, No. 10, 2011.

18 months

4 years

14 years

20 years (20,000 hrs)

Remember one week delay for flu case?

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A major obstacle is that there exists a time gap between design and end‐of‐life.

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Kwak, M., Kim, H. M. and Thurston, D. "Formulating Second‐Hand Market Value as a Function of Product Specifications, Age, and Condition," Journal of Mechanical Design, 2011.

Market Value of EOL Products

Can we make recovery profitable?How to overcome the obsolescence ?Can we make recovery profitable?

How to overcome the obsolescence ?

Product Specification Trends (Laptop)

Green profit of reman changes over time!

A model for assessing the time‐varying advantages of remanufacturing can

• Compare the remanufactured and brand‐new versions of a product,• Clarify how the nature of the product influences the time‐varying value,• Provide a multi‐dimensional assessment tool (cost, impact, and net profit).

1 2 3 4 5 6 7 8 9 10-10

0

10

20

30

40

50

60

70

80

Tot

al S

avin

g O

ver

Bra

nd-N

ew (

%)

Product Age (year)

Manufacturing Cost Environmental Impact Net Profit

Physical DeteriorationTechnological Obsolescence

Val

ue o

f Rem

anuf

actu

ring

Kwak, M. and Kim, H. M., "Assessing Time‐Varying Advantages of Remanufacturing: A Model for Products with Physical and Technological Obsolescence," ICED15, Milan, Italy, 2015.

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For original equipment manufacturers (OEMs), producing both new and remanufactured products can be an effective strategy to achieve “green profit” (i.e., profits generated by an environmentally sustainable business).

Reman is composed of three sequential activities.

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There is strong interdependence among the activities.

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Let’s see how these can be integrated for analytics.

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Design Analytics

Life Cycle Design


50000

100000

150000

200000

250000

300000

10312.11(2.19%)

258182.5(54.78%)

94364.36(20.02%)

29838.24(6.33%)

GW

P 1

00a

[kg

CO

2-eq

]

78637.09(16.68%)

Predictive Design Analytics

Amount of diesel fuel (l)

Available historical data timepresent future

Build usage model Predict usage

real value

constant rate

PUMS

(e.g., 7‐year data) (e.g., 9.5‐year data)

User‐generated contents and advanced telematics help establish the link from design to predictive analytics.

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Type Doors Cylinders HP RPM MPG Price Income Age Gender Choice

Standard 2 4 288 4900 31 45400 46816 40 Female Toyota

Standard 4 4 262 5100 30 41315 66590 54 Male Chevrolet

Standard 4 4 207 5000 35 37028 97504 39 Male Honda

Customer Choice & Review

[Source] http://deere.com

Customer Usage Pattern

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Mining customer trends

Tucker, C. and Kim, H. M. "Trend Mining for Predictive Product Design,” Journal of Mechanical Design, Vol. 133, No. 11, 2011.Tucker, C. and Kim, H.M., "Predicting Emerging Product Design Trend by Mining Publicly Available Customer Review Data,” ICED, 2011.Ma, J. and Kim, H. M. ”Predictive Usage Mining for Life Cycle Assessment" Transportation Research ‐ Part D, Vol. 38, pp. 125‐143, July 2015.

Customer review mining

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Counting and Prediction

Review Text Retrieval

Product Feature Text Mining

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Eric Brill. A simple rule‐based part of speech tagger. In Proceedings of the Third Conference on Applied Natural Language Processing, pages 152–155, 1992.

Included memory is stingy.

The review is turned into a sequence with Part Of Speech* tags.

{included, VB}{memory, NN}{is, VB}{stingy, JJ}

{included, VB}{$feature, NN}{is, VB}{stingy, JJ}



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Unstructured Data

Text Tagging

Date Feature Count Type

Jan 31 Screen 5 Cons

Feb1 Camera 9 Pros

Feb1 Apps 15 Pros

Feb1 Keypad 4 Pros

Feb1 Weight 7 Pros

Feb1 MP3 3 Pros

Feb1 WiFi 8 Cons

Feb1 Color 4 Cons

Feb1 Screen 7 Cons

Feb2 Camera 8 Pros

Feb2 Apps 14 Pros

Feb2 Keypad 2 Pros

Structured Data


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100

50

0

FeatureCount

Time

CameraAppsMP3

Prediction

e.g., Holt‐Winters Forecasting

Level:

Trend:

Season:

1 1( ) (1 )( )t t t s t tL y I L T

1 1( ) (1 )t t t tT L L T

( ) (1 )t t t t sI y L I

Emerging

Static

Obsolete

Large‐scale sensor data from telematics system

• Companies such as Caterpillar (PRODUCT LinkTM) and John Deere (JD LinkTM) have developed telematics systems for their machinery.

• Operational data can be gathered in real time for various purposes: asset utilization monitoring, location tracking, fleet management, machine health prognostics, etc.

source : http://www.equipmentworld.com/24

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Engineering design scenario: combine harvesters

Estimate environmental impacts of product life cycle (e.g., 10 years or 20 years) with a usage model from sensor data




real value

constant rate

PUMS


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Ma, J. and Kim, H.M. “Predictive Usage Mining for Life Cycle Assessment," Transportation Research – Part D, Vol. 38, pp. 125‐143, 2015.

Predictive Usage Mining for Sustainability (PUMS)

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(Infrequent) Usage modeling

• Automatic segmentation‐ Detect periodic low‐activity periods and group them separately

‐Magnify important patterns and make predictions

‐ Combine the results and maintain the time stamp

Periodic very low‐activity periods

Original data Segmented data

predicted values

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Usage modeling

• Time series models‐ PUMS‐ets

‐ PUMS‐arima

[Ref.] Hyndman et al. (2008)

autoregressive model, AR(p) integration, I(d)

forecast error

observed time series

Smoothing factor, 0 <α <1

moving average model, MA(q)observed time series

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Being green can be profitable?

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Obstacles to making green profit

• OEM remanufacturer’s concerns:• Unbalance b/w supply of cores and demand for reman products

• Unproven environmental sustainability of remanufacturing

• Cannibalization of new product sales

Relevant literature:• Pricing: Focused on pricing from the economic perspective w/ little design

and process consideration. Guide et al. (2003), Ferrer et al. (2010), Vadde et al. (2011)

• Production planning: Pricing was separated from production planning. Mangun and Thurston (2002), Kwak and Kim (2010), Jayaraman (2006), Franke et al. (2006)

• Environmental assessment of remanufacturing: Mostly Life Cycle Assessments (LCAs) comparing new and reman products for the sake of product evaluation not decision‐making. Smith and Keoleian (2004), Goldey et al. (2010)

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Let’s consider economics together with environment.

For OEM remanufacturers, the new model considers joint pricing and production planning to develop an optimal portfolio of new and remanufactured products.

Two objectives:

Decision variables: • : buy‐back price and take‐back quantity of the end‐of‐life product

• : selling price and the production quantity of the new product

• : selling price and the production quantity of the reman product

,u up x

,n np x

,r rp x

1 2Maximize [total profit , total environmental saving ]f f

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Transition matrix

Transition matrix represents the relationship between product design and remanufacturing operations in a matrix form, such that the impact of product design can be mathematically reflected in the production planning.

Input Outputi ij jj J iT Y

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Mathematical model (1/2)

The objective is to maximize the total profit from the sales of new and remanufactured products, while achieving environmental‐impact saving δ.

1

2 ,

3 ,

4

5

: ( )

: ( , )

: ( , )

g :

: ( ) { ( )}

k k k k

n l n l n rl L

r l r l n rl L

r k

M Nw k k n r i i j j i i d rk K i I j J i I

g X A s P k K

g Z Q d P P

g Z Q d P P

Z X

g e e X E Z e M e Y e N e Z

max. ( ) ( )M Nn n n r r i i k k j j i i d ri I k K j J i I

P C Z P Z c M P X c Y c N c Z

Profit from the new product Profit from the remanufactured product

Cost of recycling

Cost of takeback

Cost of production

Cost of spare parts

Cost of reassy

and sales

Take‐back availability of end‐of‐life products, given buy‐back price

Production quantity not exceeding the demand, given selling price

Upper limit for remanufacturing

Environmental‐impact saving exceeding the target δ

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Mathematical model (2/2)

Constraints for the Input‐output flow balance

Variable conditions

1

2

3

: 0 corresponding to the end-of-life product ( )

: 0 corresponding to a part with external purchase availability

: 0 corres

k ij j ij J

i ij j ij J

ij j ij J

h X T Y M i k k K

h N T Y M i

h T Y M i

4

5

6

ponding to a part without external purchase availability

: 0 corresponding to the remanufactured product

: 0 part with external purchase availability

: 0 corre

ij j rj J

i

i

h T Y Z i

h N i

h M i

sponding to the remanufactured product

, , , , 0 and integer , ,

, , , 0 ,

k j n r i

k n r i

X Y Z Z N i I j J k K

P P P M i I k K

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Example: smartphone

Baseline case: New only. No takeback is conducted.

Target case: Both new and remanufactured products are offered.

Example: engine water pump

Suppose that an OEM manufacturer of engine water pumps is considering starting their own remanufacturing business.

Attribute New pump Competitor

Price $100 $50

Brand OEM Third‐party

Condition New Reman

Market share 45% 55%

Current market status (baseline)

Parameter Value

Available cores in the market 5,000 units

Attainable cores at pu=0 500 units

Buy‐back price for 100% take‐back $30

Assumptions on market conditions

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Customer preference in the market

Total market size: 3,000 units

Customers prefer a new product w/ lower price and OEM brand.

, , ,

, ,

Utility of product : [1 (1 )] ( )

where

0 remanufactured product 0 remanufactured by third-party

1 (new product) 1 (OEM)

j cond j cond price j price brand j brand

j cond j brand

j U y y y

if ify y

else else

, 1 criticalj price j jy p p

Attribute Utility factor (β) Ideal Critical

Price 0.8 $0 $100

Brand 0.2 OEM Third‐party

Condition 0.45 New Reman

Customer preference assumption

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Bi‐objective problem: ε‐constraint approach

Using the ε‐constraint approach, the model can incorporate the trade‐offs between profit and environmental saving.

Lower bound for f2(f2 under max f1)

max f2

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Case study: Scenario 1 (new production only)

The company optimizes the selling price and quantity of the new pump. No remanufacturing is conducted.

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Case study: Scenario 2 (remanufacturing only)

For this period, closed‐loop production is conducted. The company produces only remanufactured pumps. The selling price and production quantity for the remanufactured pump are optimized.

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Case study: Scenario 3 (new and reman together)

The company optimizes and sells both the new and remanufactured products.

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The results shows an opportunity to increase profit and environmental saving at the same time.

Efficient frontier: Pareto‐optimal solutions

Profitable Green, Profitable

Green

Inferior

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Reman product should be cheaper.

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Green profit of reman changes over time!

A model for assessing the time‐varying advantages of remanufacturing is developed. The model:

• Compares the remanufactured and brand‐new versions of a product,• Clarifies how the nature of the product influences the time‐varying value,• Provides a multi‐dimensional assessment tool (cost, impact, and net profit).

1 2 3 4 5 6 7 8 9 10-10

0

10

20

30

40

50

60

70

80

Tot

al S

avin

g O

ver

Bra

nd-N

ew (

%)

Product Age (year)

Manufacturing Cost Environmental Impact Net Profit

Physical DeteriorationTechnological Obsolescence

Val

ue o

f Rem

anuf

actu

ring

44

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Products w/ physical and technological obsolescence

Each part has its own lifecycle characteristics, in terms of cost, technological obsolescence, and physical deterioration.

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Products w/ physical and technological obsolescence

Each part has its own lifecycle characteristics, in terms of cost, technological obsolescence, and physical deterioration.

Probability that a part is reusable both physically and technologicallycan be modeled as:

where

target (0)

0

( ) ( , )i i

i in

w t f n t

‐ w(t) : reusability of part i after t years of use‐ n : change in the part generation over t years‐ fi(n,t) : the probability that a total of n generations of part i will appear in [0, t]

the maximum change in part generation allowed for t years to satisfy customer requirement

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Cost advantage of a remanufactured product

Proposition 1. The cost advantage of remanufacturing over producing the equivalent brand‐new product is formulated as CNR‐RR(t).

• NR: Production of brand-new products w/ responsible take-back and recycling• RR: Remanufacturing w/ recycling; RS: Remanufacturing w/ part resale and recycling

Probability that a part is reusable both physically and technologically

Cost saving from reusing a part

target (0)

,target0

( ) ( ) ( , ) ( ) C ( ) ( )i i

new recond matlNR RR i i i i i

i I n

C t w t f n t C t t V t

1 2 3 4 5 6 7 8 9 100

50

100

150

200

250

300

350

400

450

Cos

t ($,

in p

rese

nt v

alue

at t

=0)

Timing of remanufacturing (product age, in year)

CNR(t) CRR(t) CRS(t) CNR-RR(t) CNR-RS(t)

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Environmental advantage of a reman product

Proposition 2. The environmental advantage of a remanufactured product over its equivalent brand‐new is formulated as ENR‐RR(t).

Probability that a part is reusable both physically and technologically

Avoided impact by reusing a part

target (0)

,target0

( ) ( ) ( , ) ( ) ( ) ( )i i

new recond matlNR RR i i i i i

i I n

E t w t f n t E t E t E t


1 2 3 4 5 6 7 8 9 10-50

050

100150200250300350400450500

Env

iron

men

tal i

mpa

ct (

kg C

O2e

)


ENR(t) ERR(t) ERS(t) ENR-RR(t) ENR-RS(t)

48

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Net profit advantage of a reman product

Proposition 3. Let β be the price ratio of the remanufactured product to the equivalent brand‐new. Then, the advantage of remanufacturing from the net‐profit perspective is given as ΠRR‐NR(t).

target (0)

,target0

( ) ( ) ( )

( 1) ( ) ( , ) ( ) C ( ) ( )i i

RR NR R RR N NR

new recond matlN i i i i i

i I n

t P C P C

P w t f n t C t t V t


1 2 3 4 5 6 7 8 9 10-150-100-50

050

100150200250300350

Net

pro

fit (

$, in

pre

sent

val

ue a

t t=

0)


NR(t) RR(t) RS(t) RR-NR(t) RS-NR(t)

(β=0.7)

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1 2 3 4 5 6 7 8 9 10-20%

0%

20%

40%

60%

80%

100% ENR-RR(t) ENR-RS(t) RR RS

Timing of remanufacturing (product age, in year)Env

iron

men

tal-

impa

ct r

atio

ove

r br

and-

new

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Net profit advantage of a reman product

Corollary 1. The range of β where the remanufacture product becomes more profitable than the brand‐new is β≥β*, where β* is:

target (0)

*,target

0

1 (1/ ) ( ) ( ) C ( ) ( ) ( , )ii

new recond matlRR NR N i i i i i

i I n

P w t C t t V t f n t

Let t=4. Remanufacturing (RR) makes sense if consumers are willing to pay more than 81% of new price on a REMAN product.

Remanufacturing is more profitable only if consumers are willing to pay more than β*of new price.

If beta is known as 0.7, a reasonable strategy is to remanufacture (RR) until t=2.5 and to choose new production afterwards.

• NR: Production of brand-new products w/ responsible take-back and recycling• RR: Remanufacturing w/ recycling; RS: Remanufacturing w/ part resale and recycling 50

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CONCLUSION

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Where is this research heading?

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Consider cell phones’ fate when designing them.

Acknowledgement of support: NSF CAREER award, Engineering Design and Innovation Program grant, GOALI grant, CAT, Deere.

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Summary

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Design Analytics

Life Cycle Design


50000

100000

150000

200000

250000

300000

10312.11(2.19%)

258182.5(54.78%)

94364.36(20.02%)

29838.24(6.33%)

GW

P 1

00a

[kg

CO

2-eq

]

78637.09(16.68%)

Predictive Design Analytics




real value

constant rate

PUMS


Being green can be sustainable by carefully linking predictive analytics with design and manufacturing.

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Well‐designed products enable more profitable, sustainable end‐of‐life product take‐back and recovery.

A quantitative model can close the loop of pre‐life, usage life and end‐of‐life of products for sustainability.

Predictive design analytics enables integrated approach to design, manufacturing, and sustainability.

complex systems analytics: a promising enabler for design ......complex systems analytics: a...

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