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15 871 Recitation #4 15.871 Recitation #4 Md Modeli ling Product Ad t Adoption P d ti & Diffusion Fall 2013

15 871Recitation #4 - MIT OpenCourseWare · Bass Diffusion Model Rate ARRate AR B . Potential Adopters P Adoption + + R. Market . Saturation Word of Mouth + + Probability of Adoption

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Page 1: 15 871Recitation #4 - MIT OpenCourseWare · Bass Diffusion Model Rate ARRate AR B . Potential Adopters P Adoption + + R. Market . Saturation Word of Mouth + + Probability of Adoption

         

 

15 871 Recitation #415.871 Recitation #4 M dModeli ling Product Ad t AdoptionP d ti

& Diffusion

Fall 2013

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AgendaAgenda • Adoption/Diffusion ModelsAdoption/Diffusion Models

– Modified Bass Diffusion Model (starting Vensim model)

• Delayys – Response of a Delay (Ch 11.1‐11.2, more in H2)

• Stock Managgement – Beer Game Stock Management Example

• Learning Curve (Ch 10, BD pg 337‐8)

• Partial Model Tests • Robustness under Extreme Conditions

10/4/2013 2

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Historical Data: Upload vdf

VCR S l US VCR Sales, US

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+ ++

Bass Diffusion Model

Rate ARRate ARB

Potential Adopters P

Adoption

+ R+ Market +Saturation Word of Mouth

Probability of + Contact with Adoption from Adoption from

Adopters PCA

-Advertising AA Word of Mouth AW

+ ++ Advertising Adoption

Effectiveness a Fraction i

++ BB Social Contacts

Contacts SC Market +with Saturation Total Population N Adopters CA + +

Contact Rate c

Adopters A

Page 5: 15 871Recitation #4 - MIT OpenCourseWare · Bass Diffusion Model Rate ARRate AR B . Potential Adopters P Adoption + + R. Market . Saturation Word of Mouth + + Probability of Adoption

Modified Bass Diffusion Model

Page 6: 15 871Recitation #4 - MIT OpenCourseWare · Bass Diffusion Model Rate ARRate AR B . Potential Adopters P Adoption + + R. Market . Saturation Word of Mouth + + Probability of Adoption

   

          

 

     

         

         

                 

Total Population, N

Population Not Willing Potential Adopters Adopters Population Not Willing to Adopt, N – PWA

Potential Adopters P

Adopters A

=PNWA =PNWA

Population Willing to Adopt PWA =PWA

N INTEGRAL(Net Population Increase N = INTEGRAL(Net Population Increase, NN0))

Adopters = INTEGRAL(Adoption Rate, Initial Adopters)

Potential Adopters = MAX(0, Population Willing to Adopt – Adopters)

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   Stock Management StructureStock Management Structure

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PART A INTERACTIVE : BEER GAME STOCK MANAGEMENT EXAMPLE

Draw the causal loop diagram (CLD) and write the equation for the “order rate” according to assumptions that will be presented.

Please note that your CLD and equation for the “order rate” should be “dimensionally consistent”.

Inventory shipments delivery

BB order rate= ?

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PART A INTERACTIVE : MAPPING OF A SIMPLE STOCK‐MANAGEMENT STRUCTURE

Draw the causal loop diagram (CLD) and write the equation for the “orders” according to the assumptions below. Please note that your CLD and equation for the “orders” should be “dimensionally consistent”.dimensionally consistent

Inventory shipments deliveryy

ASSUMPTIONS:

• No delays in deliveries • No returns • Shipments are exogenous • Firm adjusts orders to bring inventory in line with “desired inventory” • Seeks to close any inventory gaps over a 2-week period.

HINT Wh t h ld i t th “ d ” i bl ? • HINT: What should go into the “orders” variable?

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Response of a DelayResponse of a Delay

Product Retirement= DELAY N(Purchase Rate, Useful Life of Product, 0, 6)

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PART B INTERACTIVE : PULSE RESPONSE OF A DELAY

Please sketch your best estimate of “retirements” after a pulse of purchases:

Installed Base retirements purchases

How does the Retirements flow look like? Purchase Rate

Units/YearUnits/Year

10/4/2013 0 0 Time 11

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Price & LearningPrice & Learning Curve

Ch 10, BD pg 337‐8

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f

Basic Learning Curve Formulation

• c0, E0 = initial unit costs and cumulative experience

c f ((E )) t

t tt Et esdst E e ds

E 0

t • = learning curve strength c c t 0 EE 00 (1 f )c c (2E / E ) • Example: For a learning curve0 0 0 0

with f=0.3, =0.5146oror

ln(1 f ) / ln(2) log2 (1 f ) • f= 0.3 implies a 30% cost

reduction for every doubling ofreduction for every doubling of cumulative experience ( ≈ 0.51)

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MIT OpenCourseWarehttp://ocw.mit.edu 15.871 Introduction to System DynamicsFall 2013 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.