A System Dynamics Approach for Robust Product Planning and Strategy
Based on Simulated Market Performance
By
Thomas K. Mathai
Submitted to the System Design and Management Program in Partial Fulfillment of
Requirements for the Degree of
Masters of Science in Engineering and Business Management
at the
Massachusetts Institute of Technology
February 2002
@ Thomas K. Mathai, All rights reserved.
The author hereby grants to MIT permission to reproduce and distribute publicly paper and
electronic copies of this thesis document in whole or in part.
Signature of Author
Thomas K. Mathai
System Design and Management Program
February 2002
Certified by
Dr. James M. Lyneis
Thesis Supervisor
MIT
Certified by
Dr. Mike Renucci
Corporate Advisor
Engineering Director, Lincoln-Mercury
Accepted by
GM LFPr
Steven D. Eppinger
Co-Director, LFM/SDM
sor of Management Science and Engineering Systems
Accepted by
4
MASSACHUSETTS INSTITUTE
OF TECHNOLOGY
Paul A. Lagace
Co-Director, LFM/SDM
Professor of Aeronautics & Astronautics and Engineering System
JUL 1 9 2002
LIBRARIES
BARKER
MITLibraries
Document Services
Room 14-0551
77 Massachusetts Avenue
Cambridge, MA 02139
Ph: 617.2532800
Email: docs@mit.edu
http:/llibraries.mit.edu/docs
DISCLAIMER OF QUALITY
Due to the condition of the original material, there are unavoidable
flaws in this reproduction. We have made every effort possible to
provide you with the best copy available. If you are dissatisfied with
this product and find it unusable, please contact Document Services as
soon as possible.
Thank you.
The images contained in this document are of
the best quality available.
2
A System Dynamics Approach for Robust Product Planning and Strategy
Based on Simulated Market Performance
By
Thomas K. Mathai
Submitted to the System Design and Management Program in Partial Fulfillment of
Requirements for the Degree of Master of Science in Engineering and Business Management
ABSTRACT
Robust decisions on product strategy require an integrated view of upstream and downstream
influences- company wants, product attributes, customer wants, product development
constraints, and market dynamics. The main focus of this thesis was to explore a systemic view
for decision-making in product strategy. By visualizing the potential effects of upfront decisions
on the downstream market, the robustness of the decisions can be tested, given the competitive
offerings in the market, customer wants, and product development constraints in tackling system
interaction issues/emergent behavior. An overall system dynamics framework was developed
linking product actions of competing companies to customer wants, buying and service
experience, and usage experience. Elements of brand value, awareness, and pricing were
included in determining the overall attractiveness of products in the market. The parallel universe
of used vehicles was included to quantify the effect of used vehicles on the new product market.
The model was applied to a universe of three SUVs. Relevant data concerning brand opinions
and ratings, and market segmentation was collected from customer surveys.
Overall, it was found that the fleet dynamics that result from product decisions, especially
interactions between the new and used markets, were critical in the success of a product strategy.
In particular, quality was found to be the single most important driver in determining the
eventual success of the product. A shorter development cycle ensured success only if quality
degradation was minimal. Quality effects are amplified because of the used vehicle market. This
is due to the fact that usage experience of the new car buyers is reinforced by that of the used car
buyers with a phase lag. Another intriguing result from the model is that, in a mature market with
little growth, continued quality improvements eventually lead to sales decline. With regard to
longer delays between product upgrades to accommodate a platform product strategy, the
adverse market effects due to a longer cadence is more than made up by quality enhancements
due to lower re-engineering and increased part reusability. It was also seen that matching
competitive product offerings that entails quick comprehensive redesigns, affects vehicle sales
adversely in the long run if surprising system interactions compromise quality, even if the dip in
quality is temporary. Finally, the effect of recent 0% financing on future sales was studied. Even
though a prolonged dip was seen in new vehicle sales, model results suggest that the effect may
have more to do with the glut of cheaper used vehicles than with "pull ahead" sales. The effect of
used vehicle market on the new vehicle market is significant, and companies will have to be
proactive in managing the used market.
Systems Dynamics was found to be a good tool in studying the relevant market dynamics
associated with product decisions and the resultant consequences under different scenarios.
Although a rudimentary model was made for this study, additional structure and validation are
required to improve the analysis capability.
3
4
TABLE OF CONTENTS
Page Number
1. Problem Statem ent ...............................................................
6
2. B ackground ......................................................................
6
3. Applications of Systems Dynamics ...........................................
11
4. System Dynamics Model Used in this Study .............................
14
4.1 Initial system and boundary diagram ....................................
14
4.2 Refined System Diagram and Simplifying Assumptions ...............
17
4.3 M odel Details ..
19
.........................................
4.4 Equilibrium state ..............
..........................................
5. Discussions of Scenarios, Results, and Conclusions ..................
49
50
5.1 Scenario 1: Platform Strategy and Frequency of Product Upgrades ... 50
5.2 Scenario 2: System Interactions and Incremental Innovation ............ 64
5.3 Scenario 3: Continuous Quality Improvements.............................
78
5.4 Follow-up Discussion on Scenarios 1 & 3: Sensitivity to Quality ..... 83
5.5 Scenario 4: Zero Percent Financing.........................
87
6. General Comments and Next Steps .................................
94
7. References ................................................
97
8. Appendix: Model Equations ................................
100
5
1. Problem Statement
Systems architecture stresses the need to capture both upstream and downstream influences
on the product while framing product strategy and architecture [1]. In the automotive sector,
in many instances, these influences are conflicting. In the US auto market, growth in the
number of models and market segments have increased the number of product development
projects four-fold over the last 25 years [2]. In this context, companies can reduce their costs
and improve quality by the platform-based development approach. Platform-based product
strategy maximizes part commonality and part reusability across vehicle lines. However,
products developed under the platform strategy may not match the specific requirements of
the market. The timing and some attributes of the product should match with other vehicles
in your portfolio that have part commonality. This may constrain the company's ability to
redesign the product as often as the market dictates. Additionally, a bigger scope of product
changes, even though dictated by the market, may present insurmountable challenges in
unexpected system interactions and adverse emergent system behavior affecting the quality
of the product. Hence, robust decisions on functional product strategy require an integrated
view of upstream and downstream influences- company wants, product attributes, customer
wants, product development constraints, and market dynamics. This study attempts to
address this issue by applying a microworld simulation approach [3] to product planning and
strategy. Specifically, the study develops a system dynamic model to assess the impact of
alternative product strategies on new vehicle sales.
2. Background
According to Rechtin and Maier [4], the primary function of an architect is to translate
between the problem domain concepts of the customers and the solution domain concepts of
the manufacturer. In this translation lies the tension and trade-offs between constraints
imposed by the manufacturer's corporate/business unit goals on the one side, and the market
requirements on the other. At a high level, this translation is captured in product planning and
strategy.
6
A product plan identifies the number and types (portfolio) of products to be developed by the
organization and the timing of their introductions [5]. In generating a product plan and
strategy, one has to think holistically. The definition for holistic thinking presented in
Crawley [1] is as follows:
To think holistically is to encompass all aspects of the task at hand, taking into
account the influences and consequences of anything that might interactwith the
task.
The interesting aspect of this definition is the stress on consequences. This involves feedback
and has to be viewed as a system. The plans and strategies of your solution concepts have
consequences in the market, which in-turn should affect your planned solutions. Envisioning
the consequences is non-trivial, and it involves learning the underlying structures that exhibit
system behavior.
PEOPLE
ECONOMIC
SYSTEM
ENTERPRISE
GLOBAL
ECONOMY
ECONOMIC
IMPACT
CAPITAL
PROFITS
IIJMAN
CORPORATE
RL
E
MANAGEMENT
SHARE HOLDER
GOALS
HUMAN
RESOURCES
SOCIAL
IMPACT
&
SYSTEMS
PRODUCT DEVELOPMENT
REGULATIONS
MARKET NEEDS
Cnsrit
SolutionH Concepts
CORPORATE
IMAGE
,ut
INTEL ECTUAL
COMPETITION
PROPERTY
TECHNOLOGY
PRODUCTS
SERVICES
SERVICES
MANUFACTURED
INFORMATION
SYSTEMdS
ENGINEERING
TOOLS
TECHNOLOGY
GOODS
POLLUTION
RAW MATERIAL
&
NATURAL
SYSTEM
KNOWLEDGE
ARTIFACT SYSTEM
Fig. 2.1: The enterprise and societal contexts under which an architect operates [1].
7
The enterprise and societal contexts under which an architect operates is given in Fig. 2.1.
This is a minor adaptation of the presentation given in Crawley [1]. As is evident in Fig. 2.1,
the relevant upstream and downstream influences that affect product planning and strategy
are many, but the key representations to note are the feedback lines coming from the
downstream influences.
The products that are manufactured in many organizations are themselves complex, but the
influences of the contextual variables and their interactions on the product increases
complexity exponentially. An automobile for example has over 20,000 parts. The complexity
due to the sheer number of parts and the extant significant interactions between them is
enormous. Indeed, each new element added to an existing pool of elements roughly doubles
the potential number of interactions [6,7]. Furthermore, the number of products that have to
be introduced to maintain market share of firms has increased tremendously. In Wheelright
and Clark [2], the example of a manufacturer of heart monitors (Physio Control) is presented
where, due to competitive pressures, the number of models jumped from eight to sixteen and
the product production life shrank from fourteen to five years in a span of five years from
1985 to 1990. In addition to that, the complexity of the heart monitors increased more than
ever before due to increasing customer demands and technological advances. The situation
in the automobile market is very similar. Competitive pressures fueled growth in models and
market segments that has increased the number of product development projects four-fold in
25 years. Consequently, there are smaller volumes per model and shorter product lives,
leading to a forced reduction in resource requirements per model for efficiency [2].
The situations presented above contain two types of complexity: detail complexity and
dynamic complexity [3,8]. The increasing number of interactions associated with increasing
number of parts is a detail complexity commonly dealt in systems engineering [6]. Sterman
[8] also calls it combinatorial complexity. A snapshot of detail complexity describes it.
Dynamic complexity, on the other hand, arises from interactions among elements (of a
system like in Fig. 2.1) over time [8]. Some of the general instances of dynamic complexity
that Senge [3] lists, that are relevant to the discussion here, are as follows:
8
"
Situations where cause and effect are subtle.
" The effects over time of the decisions are not obvious.
" The effects of decisions are different in the short run and the long.
" The effects of decisions on one part of the system are different from the effects on
another part.
" Obvious interventions produce non-obvious consequences over time.
There are many systems engineering tools like Quality Function Deployment (QFD),
analytical and experimental Design of Experiments (DOE), Failure Modes and Effects
Analysis (FMEA), etc., that address combinatorial complexity. Design Structure Matrix
(DSM) methods are also gaining popularity in addressing design and process complexity
[9,10,11]. These tools do provide the capability to fine-tune and streamline existing
processes. However, in the situations presented earlier, fundamental changes in product and
market strategy may be needed. The implementation of these changes in strategy has
-
implications not only in combinatorial complexity but also in dynamic complexity
interactions of the system elements over time. If a suggested solution to sagging sales is to
bring out quicker product upgrades, the new vehicle market may respond positively initially
but the used vehicle market may have a bloated inventory. This may result in lowered
residual value which will in-turn increase cost of ownership. Indeed, regarding product
planning and strategy, Senge [3] states, "... developing a profitable mix of price, product (or
service) quality, design, and availability that make a strong market position is a dynamic
problem".
In the case presented in Wheelright and Clark [2], Physio Control called for the creation of
platform products that would serve as the bases for derivative products for the various market
segments. Platform strategy addresses the scope of product changes, the frequency of
changes, product timing, product quality, etc. (from a development viewpoint, i.e., not a
market viewpoint). Implementing this strategy, for example, will force some products to have
longer gap between product upgrades to ensure reusability of parts across products that share
the platform. Reusability of parts prevents re-engineering, and hence should result in higher
quality, reduced variability in timing and reduced development costs [12]. However, the
9
ability to redesign the product as often and as specific as the market dictates, is
compromised. A pictorial representation of this dilemma is given in Fig. 2.2. The quality and
cost improvements have a positive effect on success metrics like sales, but the disadvantages
due to withholding the product from the market counter balance that effect. The robustness of
this strategy can be assessed only when the dynamic interplay between these opposing forces
is clearly understood (Scenario 1, discussed in Section 5, addresses the dynamics associated
with this aspect of platform strategy).
Effect due to withholding
product from the market
Effect due to part
commonality (quality/cost)
+*etffect
Gap between Product Upgrades
Fig. 2.2: A schematic representation of competing forces against frequency of product
upgrades (some products are assumed to have longer gaps to fit a platform
strategy).
As was stated earlier, increasing customer demands make the product itself more complex.
Changes are often made due to competitive pressures that will increase the number of
unknown interactions that results in unintended emergent behavior [6]. With shorter cycle
time, such interactions may become intractable. Reinertsen [7] suggests what is described as
'incremental innovation' - smaller changes in sequence over a larger time scale rather than a
big change in compressed time. Implementation of this strategy also has dynamic
implications similar to the interaction of the forces represented in Fig. 2.2. (Scenario 2,
described in Section 5, focuses on such a situation).
10
The key to robust product plans and development strategies is then to clearly understand the
potential effects of upfront decisions in the market downstream, given the context of
competitive offerings in the market, customer wants, and product development constraints.
System models - models that provide interaction and feedback over time - are vehicles to
attain this capability. Real-world successes of the use of modeling to improve products as
well as processes are well in evidence [3,13,14]. Rechtin and Maier [4] calls modeling the
"centerpiece of systems architecting". They define modeling as "the creation of abstraction or
representation of the system to predict and analyze performance". This was indeed the
approach taken in this study. A system dynamics model-based framework was developed
linking product actions of an automotive firm as well as those of competing companies to
customer wants, buying/service experience, and usage experience. The structures that feed
back the consequences of the actions of the firms were integral to the automotive market
system being studied.
3. Applications of Systems Dynamics
Jay Forrester originated system dynamics in 1956, a result of his prior experience in feedback
control systems, digital computers, simulation, and management [15]. The emergence of
systems dynamics as a viable tool started with the successful explanation of fluctuations in
capacity utilization of General Electric's household appliance division. Initial understanding
or existing mental models attributed these fluctuations to business cycles - variations in
economic activity brought about by over-production of consumer goods followed by
cutbacks and layoffs that peak 3 to 10 years apart. Forrester [15] showed that policies being
followed in GE would produce instability in production even if orders from customers
arrived at a constant rate. His work in inventory, production, and distribution culminated in a
book on the structures typically found in manufacturing industry dynamics [16]. Once the
behavior of a structure is known in one setting, its behavior could be understood in all other
contexts where it occurs. This "transferability of structure" expanded systems dynamics's
applicability into diverse areas such as in urban housing dynamics [17] and in national
economies examining the forces underlying inflation, unemployment, etc. (for example,
System Dynamics National Model [18]). Many other examples of applications in banking,
11
paper industry, plywood industry, information technology, and in societal problems like drug
abuse, are given in Ref. [19].
More recent applications of system dynamics, while expanding the traditional areas of
strategic management in diverse industries (see for example real-world applications
described in references [3], [8], [20], and [21]), has branched into non-traditional areas like
the global climate-economy studies [22] and the human immune system modeling to benefit
pharmaceutical research [23].
In the area of system dynamics simulation methodology, there is research in the area of
combining system dynamics with the strengths other emerging simulation methods. Prasad
and Chartier [24] identifies a modeling difficulty in system dynamics in relating global
parameters to local parameters - like the effect of organizational culture on an individual
employee, or like in this project, the effect of brand on an individual's buying habit. In Ref.
[24], agent-based modeling (discrete rule-based) techniques are combined with system
dynamics modeling to generate a new tool called TalentSim.
There has been a growing interest in system dynamics in the automotive industry over the
past five years, particularly in the area of program management. Ford Motor Company has
developed a system dynamics tool set called the Program Management Modeling System
(PMMS) [25]. PMMS has two models, one at each individual vehicle program level and the
other at the aggregate product portfolio level. The vehicle program-level model captures the
interdependencies between program timing, resources, content, and quality of execution.
Product portfolio-level model is used to support cycle plan development by assessing various
corporate strategies such as workload smoothing and resource allocation. System dynamics
has also been applied recently to emissions technology strategy [26].
Very few applications of system dynamics to automotive product and market strategies have
been published to date. The work done on automobile leasing strategy [8] is the most
illuminating application presented to date in literature. Decision Support Center, a group
within General Motors formed to help business units develop and implement strategy,
12
developed a sophisticated approach called the 'dialogue decision process'. The dialogue
decision process involves a series of dialogues between two groups: first group consisting of
decision-makers and the second group consisting of a core team charged with
implementation. For the leasing strategy study, system dynamists, who were part of the
second team, developed a model that captured the interaction between production, vehicle
inventory, and new and used vehicle markets. The prevailing mental model considered
leasing a boon as it stimulated sales. Also, if lease terms were shortened, trade-in-times
would be shorter, leading to higher sales. Simulation results using system dynamics model
however showed that shorter trade-in-times flooded the used vehicle markets causing the
used vehicle price (as well as the residual value) to plummet. High quality used vehicles then
started taking the market away from new vehicles. This feedback effect was poorly
understood because of the long delays involved [8].
The current study focuses on the effect of product planning and strategy decisions on the
performance of the product in the market downstream. An overall system dynamics
framework is developed linking the product actions of automobile manufacturers to customer
wants, buying and service experience, and usage experience. Brand-related structures were
also included in determining the overall attractiveness of the products in the market. Four
scenarios relating to product and market strategies were considered for simulation and the
results are discussed.
13
4. System Dynamics Model Used in this Study
4.1 Initial system and boundary diagram
Initial system structure and boundary
Brand
Opinion for each
attrbuteMARKET
Product Actions
t >= 0
tesantabbutes&
"Le Ito
Prodct 1Manuactue1
Production
Actions Capacity)
t>=0
Sales Volume
- - - - - - - - - - DYNAMICS (Product -Attractiveness)
*Product (options, attrib utes)/experience(Quality)
*Purchase exprience
*S ervice E xperience (post purchase)
nConsum erprception (brand opinion)
r
e
Brand Opinion fo~r eachjr>
Attribute
Value Equations for th e customersEa
in the segment (Acting on product
attributes and brand opinion)
attributeies/tait
Marketing
Actions (pricing)
t >= 0
Customer Buying Behavior?
# of vehicles/household,
vehicle replacement frequency
Profit
Prod uct 2Man ufacturer 2
-
Foreawh producfmantfacbxerir
he segm rt
Cost
-
Pe.Ceived
E
Product attracheness is diffewetfor differentsegmerts
0
-a
-I-.Productn /Man ufacturern
Distribn. Actions
(sale&Service)
t>= 0
(Advertising)
BEranmd
BronddpininsfrFuel
Prrtivtes
. . -.Productn./Manufacturern---.-.-.-.
GpinonmmentcRegu-ltis
.
- .
-. -.-
fohra1
segrnt
. ..-.-.-..
. . ortiseme
frig
'
Image Actions
. . . ..
t>=0
Con ditions/F uel Prices
over time
Fig. 4.1: Initial boundary diagram of the "system" under consideration
The study began with a crude definition of the system to be studied and modeled. The outer box
with the dotted line was considered as the boundary of the system (even though this was revised
to reduce scope). Lines with double arrows show potential feedback effects and interactions. For
example, the performance of the automobile industry affects the overall economy as it accounts
for approximately 5 to 6% of the GDP of the United Sates [27]. Ford Motor Company and
General Motors are still the only companies that each account for approximately 1% of the
nation's economic output and each is eight times the size of Microsoft. The auto industry is the
biggest user of steel, the second-biggest user of glass and the third-biggest buyer of textiles [28].
14
Hence, the profitability of the automobile manufacturers affects the buying capacity of the
customers which in-turn affects the profitability of the companies. On similar lines, regulations
in fuel economy will have such feedback effects. For example, the latest trends in the Sport
Utility Vehicle (SUV) market show large growth in car-based crossover utilities as the industry
responds to SUV fuel economy concerns. However, according to J. D. Power survey [29], most
of the vehicles traded-in by Ford Escape buyers (Ford's crossover SUV, the segment leader) are
cars (see Fig. 4.2 - The y-axis gives the percentage of total vehicles traded-in while a new Ford
Escape is purchased).
7060Z
50
4l Cars
3 Vans
3D Pickups
20
D SUs
10
0-
Trade-in Categories of Ford Escape Buyers
Fig. 4.2: Trade-in pattern for buyers of Ford's crossover utility
Thus, even though the intended effect of the offering was to improve overall fuel economy by
attracting existing SUV owners, the feedback from the market had the exact opposite effect.
Majority (62.4%) of new buyers were car owners and hence had lower emissions than the new
Escape. This may induce more stringent government regulations that will force further
investments by the manufacturers for increased fuel economy.
In order to narrow the scope of this thesis to a manageable level, the focus of the study was
limited to the impact of product decisions on market dynamics. Therefore, product actions,
production actions, marketing actions, etc. of the manufacturers were introduced into the model
15
as exogenous variables (More details will be discussed in the following sections). Thus, upfront
decisions are expressed in the model as exogenous factors that disturb the equilibrium of the
market forces. The system dynamics model of the market includes the perceived attributes of the
manufacturers's product offerings, value equation of customer segments, customer's buying and
service (dealership) experience, and customer's usage experience (quality), as well as the
interactions between the used and new markets
The high-level structure inside the smaller dotted line box in Fig. 4.1 could be described as the
interactions in the market place that determined the sales of the competing manufacturers. The
criterion for determining sales or market share was based on overall product attractiveness. A
pictorial representation of this is given in Fig. 4.3.
Product
Market Segment Value Equation
New Vehicle
Perceived Attribute
Rating
Used Vehicle
Perceived Attribute
Rating
Product
Value
4Attribute
Importance
Brand
Opinion
Dynamics
Product Attractiveness]
Fig. 4.3: High-level structure for product attractiveness.
The customers, on the market side, were binned into multiple segments and their sensitivities to
various product attributes were quantified based on customer survey data [30]. The value
equation for each customer segment was thus captured. The products, on the manufacturer side,
were described using survey-based ratings for the salient customer-driven attributes (A sample of
potential emotional and functional attributes of interest is given in Fig. 4.4). For each product,
the ratings as perceived by the customers were matched with the value equations of each
segment to determine the product's value to the customer. The product value was combined with
brand effects as shown in Fig. 4.3 in determining product attractiveness. The market share for
each product, like the market share molecule [31], was determined in proportion to their
16
respective product attractiveness. A more detailed explanation of the model will be given in the
following sections.
Functional Attributes
Safety
Towing Capability
Off-Road Capability
Sporty Performance
Cargo Carrying Capacity
Luxury
Size/People Carrying Capacity
Comfort
Technically Advanced Engg
Cost of Ownership
Quality
Emotional Attributes
Sporty/Athletic
Youthful
Expressive/distinctive
Family Safe/Secure
Conservative
Basic
Substantial/Functional
Tough
Elegant
Luxurious
Versatile
Fig. 4.4: An example of functional and emotional attributes.
4.2 Refined System Diagram and Simplifying Assumptions
The scope of the problem as defined above was large in terms of the number of subsystems as
well as the amount of detail involved. For example, seventeen market segments were identified
by the survey [30] as being relevant. If ten attributes were considered, then stocks representing
the market perception of the product attributes for each product would have been 170. To reduce
the scope and size of the model for the study, certain effects were ignored and simplifying
assumptions were made. Furthermore, it was felt that a more detailed definition of the structure
was needed to identify the important stocks and flows. The result of these efforts is represented
in Fig. 4.5.
The system can be considered to be in two domains: the domain of the products and the domain
of the customers. In the product domain, the focus is on a single product segment (mid-size
SUVs in the simulations in this study). There are multiple manufacturers, each vying for
attention from the customer domain. (Even though each manufacturer can have multiple products
in a given segment, only one vehicle per manufacturer was considered in the simulation runs).
17
Each product goes through an aging cycle [8] that tracks vehicles from production to exit from
the system through attrition. After production, the vehicles accumulate in the new vehicle
inventory. The outflow from the stock of new vehicle inventory is controlled by new vehicle
sales, which then accumulate as on-road vehicles. Through accidents and aging, some vehicles
exit through attrition. After a delay based on trade-in-time, vehicle trade-ins transfer the vehicles
from the on-road stock of vehicles to the used vehicle inventory. From the used inventory, some
exit the system at the end of useful vehicle life, while others go back into circulation as on-road
vehicles through used vehicle sales.
CUSTOMERS
Eco no mYr
nt
orings
DeographiPowner
Potenial
Bues
Ne wEntrants
Buy Rate
Dropouts
Trade-ins
Owners
Fuel Prices
Governmeni Regulations
Bra nd
;rice
Relative
Reiativ;
Attractiveness4-'
COMPETITION
lue
4
1~
FORD
Used Veh Sale
Production
New Veh
Inv
Nwe cle
Ne
he
Sales
rd n
Trade
V
rv
Used veh
ttrition Rate
Junked Ve
.0/
/
IAt trition
Fig. 4.5: Overall model structure showing important stocks and Flows.
The grayed elements in the model show some of the effects that are excluded in this study.
Dynamics due to government regulations, fuel prices, economic conditions, and other segment
offerings are ignored. The primary flow and other variables in the model are represented in bold
in Fig. 4.5. These variables depend on other variables that describe the dynamics of dealership
and usage experience of the customers as well as the brand effects. Details of the model along
with relevant inputs are given in the following section.
18
4.3 Model Details
The following discussion of the system dynamics model will begin with the stocks and flows at
the overall level mirroring the system shown in Fig. 4.5. As stated earlier, each flow variable in
Fig. 4.6 in-turn depends on the other variables. The succeeding discussions will delve into the
relevant details of the model associated with each flow variable. A complete model listing is
contained in the appendix.
The Overall Model:
Customers-Vehicles
Potential
Aging Chain
Dropouts
New Entrants
CutAer
<AIrition'1
OnRoadt.Jed>-
Used Vebh Ma-k etPonta
Share
:Init
By Prod>
Buyers-
On
Comer>
Road Vei By
N ewfProd>
<1nit
Ve)
<Production
Capacity>rde
Used Veh Sales
OnRoad
BTted
<Tradeln
TimeNew>
<Avg Veh
Prod
Lif">
ew
OnRoad Veh
Production
a
New Veh Inv
New Veh Sales
UsdAe
.--
Attrition
Trade"InUsed
raden
Vehsller
Custmer>
ITradeln
TimeNew
<'Potetfiaj
C"Ne w Vehi
Buyel s'-
Markeo Share By
Prod>,
Attrition
OnRoadUsed
d
Veh.
Fraction BY
TirUe>segmentoi
<rdl
Tim-"Faenw
-Avg Veh) Lif->
Fig. 4.6: Overall system dynamics model structure
The salient stocks and flows of the overall model are given in Fig. 4.6 (The grayed variables in
brackets have related model structure not shown in the figure). Total market size is increased by
19
new entrants and reduced by dropouts. For simplicity, tests described in Chapter 5 assume that
aggregate demand is constant. This implies that, in the customer domain, the flow rate of 'New
Entrants' is the same as the flow rate of 'Dropouts' and thus has no effect on the stock of
'Potential Buyers'. Changes in 'Potential Buyers' are only brought about by the differences
between the flow rates of 'Buys' and 'Tradelns'. The stock of 'Customers' represents the level of
current customers. 'Potential buyers' is a single-dimensional array of the various customer
segments. Out of the seventeen segments extracted from the results of the customer survey [30]
only five segments significant to the mid-size SUV market were chosen. 'Customers' is a twodimensional array of the products in the market as well as that of customer segments. The flow
rates 'Buys' and 'Tradelns' are also two-dimensional arrays of 'Products' and 'CustSegments'.
The flow rates on the customer side are an aggregation of the relevant flow rates from the vehicle
side. Thus, 'Buys' is the sum of 'New Veh Sales' and 'Used Veh Sales', while 'Tradelns' is the
sum of used and new vehicle trade-ins and the attrition from the used on-road vehicles
('AttritionOnRoadUsed'). 'TradeInNew' is for the trade-in of first-owner vehicles and
'TradelnUsed' stands for the trade-in of vehicles by second-owner or above. The linking of the
flow rates makes sure that the flow of vehicles and flow of customers are synchronized. As there
is no flow of customers between the stocks of 'Customers' and 'Potential Buyers' associated with
attrition from used vehicle inventory, 'AttritionOnRoadUsed' flow is not linked to the flows on
the customer side.
The description of the stocks and flows in the vehicle aging chain is as follows:
''New Veh Inv' - (Stock) New vehicle inventory, a single-dimensional array of products.
*
'OnRoad Veh' - (Stock) On-road vehicles, a two-dimensional array: (Products, New) and
(Products, Used). 'New' and 'Used' are specific values of the second array 'NewOrUsed'.
* 'Used Veh Inv' - (Stock) Used vehicle inventory, a single-dimensional array of products.
*
'New Veh sales' - (Flow) New vehicle sales, a two-dimensional array of products and
customer segments.
*
'Used Veh Sales'- (Flow) Used vehicle sales, a two-dimensional array of products and
customer segments.
20
*
'Attrition OnRoadUsed' - (Flow) Attrition of used on-road vehicles, a two-dimensional
array of products and customer segments. Information about customers (as signified by
the array on customer segments) is needed to link the flow of vehicles and the flow of
customers between the relevant stocks.
" 'Attrition' - (Flow) Attrition from used vehicle inventory, a single-dimensional array of
products. Please note that no information about customers are needed, and hence is a
single dimensional array unlike 'Attrition OnRoadUsed'.
Calculations for some flow rates are described below.
'New Veh Sales':
The vehicle sales are determined as a product of market share and the level of potential buyers.
'New Veh Sales' is calculated in the model as:
New Veh Sales[Products,CustSegments]=
New Veh Market Share By Prod[Products,CustSegments] *VehsPerCustomer[CustSegments]*
PotentialBuyers[CustSegments]
'VehsPerCustomer' is considered to be 1.0 in all simulations. 'New Veh Market Share By Prod' is
primarily a function of relative product attractiveness. The equations dealing with the calculation
of 'New Veh Market Share By Prod' is shown below:
New Veh Market Share By Prod[Products,CustSegments] =
[New ProductAttractiveness[Products,CustSegments]*Veh Availability[Products]*
/
New Market Share Weighting[Products,CustSegments]
SUM' (New ProductAttractiveness[Products!,CustSegments]*
Veh Availability[Products!]*NewMarket Share Weighting[Products!,CustSegments])]*
New Veh Market Share[CustSegments]
1Please note that "Products!" means that the SUM function is over 'Products' array, i.e., summed over all
the products
21
The {first term} is the calculation of a fraction based on the product of 'New Product
Attractiveness', 'Veh Availability', and 'New Market Share Weighting'. 'Veh Availability' among
them is a variable that checks the level of the vehicles in the new vehicle inventory. 'Veh
Availability' is zero if inventory is at or near zero. When 'Veh Availability' is zero, the market
share of that product is shifted to those of its competitors. 'New Product Attractiveness' is a
function of brand, price, and relative product value as denoted in Fig. 4.5. The details of the
model dealing with product attractiveness will be discussed later in a separate section. 'New
Market Share Weighting' is a variable added to emphasize a shift in loyalty of the customer.
When a new attractive product is introduced in the market, a temporary hike in the number of
potential buyers results as customers trade-in their vehicles. This increase in potential buyers
should translate towards an increase in sales of the newly introduced product that initiated the
imbalance from equilibrium. 'New Product Attractiveness' does not change fast enough to reflect
this. 'New Market Share Weighting' was introduced in the equation to enable this (Alternatively,
New Market Share Weighting' can be considered as a variable that goes into the calculation of
'New Product Attractiveness' and its separate treatment is somewhat arbitrary). The equations
related to the calculation of 'New Market Share Weighting' is as follows:
New Market Share Weighting[Products,CustSegments] =
1 +PositiveValue Ratio Changes[Products,CustSegments]
Positive Value Ratio Changes[Products,CustSegments] =
IF THEN ELSE(Overall Value Change[Products,CustSegments] >1,
Overall Value Change[ProductsCustSegments]-1, 0)
Overall Value Change[Products,CustSegments]=
NewVeh2OnRoadNew Value Ratio[Products,CustSegments]*
New Veh2OnRoadNew Rel Value Ratio[Products,CustSegments]
NewVeh2OnRoadNew Value Ratio[Products,CustSegments]=
New Product Value[Products,CustSegments]/
22
Avg New OnRoadProd Value[Products,CustSegments]
NewVeh2OnRoadNew Rel Value Ratio[Products,CustSegments] =
Rel NewVeh Value Ratio[Products,CustSegments]!
Rel OnRoadNewValueRatio[Products,CustSegments]
Rel OnRoadNewValueRatio[Products,CustSegments] =
Avg New OnRoad Prod Value[ProductsCustSegments]!
VMAX(Avg New OnRoad Prod Value[Products!,CustSegments])
Rel NewVeh Value Ratio[Products,CustSegments] =
New Product Value[Products,CustSegments]!
VMAX(New ProductValue[Products!,CustSegments])
The underlying logic behind the equations above is to give a transient higher weighting to those
products that increase value to the customers by comparing it to the value of the vehicles already
on the road. For example, 'NewVeh2OnRoadNew Value Ratio' compares the absolute value of
the new vehicle to the average absolute value of the on-road vehicles of the same product. As the
average value increases with more vehicles entering on-road stock, the ratio approaches one and
the transient advantage to the product is negated. Similarly, 'NewVeh2OnRoadNew Rel Value
Ratio' compares the value relative to the best (as opposed the absolute) among the new products
to those at the on-road level.
The last term 'New Veh Market Share' in the equation for 'New Veh Market Share By Prod' is a
variable that accounts for the aggregate share of new vehicles as opposed to the used. The sum
for 'New Veh Market Share' and 'Used Veh Market Share' should add up to 100%. The equation
for 'New Veh Market Share' is:
New Veh Market Share[CustSegments] =
Total New Veh Attractiveness[CustSegments]/
(Total New Veh Attractiveness[CustSegments] + Total Used Veh Attractiveness[CustSegments])
23
Within each customer segment, the share of each product is calculated above. The variables
'Total New Veh Attractiveness' and 'Total Used Veh Attractiveness' are the sums of 'New Veh
Attractiveness' and 'Used Veh Attractiveness' respectively, summed across all products. In the
model, the initial used vehicle market share was assumed to be twice that of the initial new
vehicle market share. From the equations, it follows that at equilibrium, the used product
attractiveness is twice that of the new product. Real-world data on the share of new and used
vehicle sales support thus assumption and will be presented in the results section.
The flow for used vehicle sales ('Used Veh Sales' ) in Fig. 4.6 is also calculated as the product of
used vehicle market share and potential buyers. The equations are similar to the ones discussed
above.
'Attrition OnRoadUsed':
The attrition rate depends on the time of residence of the vehicle in the stocks. It determining the
residence time, it is useful to look at the structure given in Fig. 4.7 where the arrays for 'New'
and 'Used' are split for easier understanding.
Residence time is
TradeInTimeNew
OnRoad
New Vehicle Sale
New
z
Usd eVehicleI
Trade InN'ew
IenoyAttrition
Trade Tradln~se
UseSales
jUsed Vehicle
=0.
OnRoadUsed
Attrition
Residence time is
(Average Life- Trade IntimeNew)
Fig. 4.7: Schematic representation of structure showing the tail end of the vehicle aging chain.
24
The residence time in OnRoadNew stock in Fig. 4.7 is the trade-in time for new vehicle owners,
i.e., TradelnTimeNew. Then the average residence time in the following stocks would be the
remaining average useful life of the vehicles, i.e., (Average Life-TradeInTimeNew). Thus, the
attrition rate 'Attrition OnRoadUsed' in Fig. 4.6 is calculated as:
Attrition OnRoadUsed[Products,CustSegments]=
(OnRoad Veh[Products, Used] *Veh FractionBy Segment[Products,CustSegments])/
(Avg Veh Life[Products]-TradeInTimeNew[Products,
CustSegments])
The rate of attrition is the level of the stock divided by the residence time. Since the attrition rate
from on-road vehicles is linked to the customer flow and hence also an array of customer
segments, the used vehicle stock, which is an aggregate of all customer segments, is multiplied
by the vehicle fraction in each customer segment. This provides the customer segment
information and computes the right attrition rate within each segment. This level of refinement is
meaningful when trade-in-time is different for different customer segments.
The attrition rate ('Attrition' in Fig. 4.6) from used vehicle inventory is computed in a similar
manner.
'TradelnNew':
'TradeInNew' is calculated similar to how attrition rate is calculated - level of stock divided by
the residence time. The equation for 'TradeInNew' is:
TradeInNew[Products,CustSegments] =
(OnRoad Veh[Products,New]*Veh FractionBy Segment[Products,CustSegments])/
TradeInTimeNew[Products,CustSegments]
This equation is very similar to 'Attrition OnRoadUsed' calculation. The trade-in-time depends
on the value of the product offerings, quality, etc. and will be discussed later. 'TradelnUsed' is
calculated in a similar fashion.
25
Having discussed the flow rates involved in the overall model, some of the model structure
related to important variables present in the overall model is discussed below.
'New Product Attractiveness':
As discussed earlier, 'New Product Attractiveness' was used in determining the new vehicle
market share for each product. It is a two-dimensional array of products and customer segments.
The model structure associated with evaluating 'New Product Attractiveness' will be discussed in
this section at varying levels of details. Only a piece-by-piece presentation of important structure
is given for reducing clutter.
<1nit
NewProdValue>
<New Product
Value>
NewVehValue
Ratio
<Rel NewVehValue
Ratio.>
Customer Value
Change
1ndied New
<Effect of Price
on Product
Attractiveness>
Product
Attractiveness
New Product
Att ractivenes
Rate of Change in
NewProdAttr
<Brand
Consideration
Index>
Time to Change
NewProdAttr
Fig. 4.8: Model structure relating to New Product Attractiveness'
'New Product Attractiveness' is calculated as a first-order smooth [31] shown in Fig. 4.8.
'Indicated New Product Attractiveness' is the goal, the gap between the goal and the current
value 'New Product Attractiveness' being closed in the duration specified by 'Time to Change
NewProdAttr'. 'Indicated New Product Attractiveness' in Fig. 4.8 is calculated as a product of
'Customer Value Change', 'Effect of Price on Product Attractivness', and 'Brand Consideration
Index', a variable capturing associated brand inertias. 'Customer Value Change' is product of
26
'NewVehValue Ratio' and 'Rel NewVehVal Ratio'. The former is a comparison of the new
product value to that of its equilibrium value and the latter is relative value compared to
competition. This is different from the calculations in 'New Market Share Weighting' where the
comparisons were made to the value of the on-road vehicles. The initial value of the new product
attributes ('Init NewProdAttr') for the products used in the scenario studies were obtained from
Ref. [30]. A brief discussion of the models relating to product value, price, and brand variables
of Fig. 4.8 is given below.
'New Product Value' and 'Customer Value Change':
As shown in Fig. 4.3, product value is obtained by matching customer perceived attribute ratings
with the importance of attributes for each customer segment. The model structure related to
computing the customer perceived attributes of the new product is given in Fig. 4.9.
Perceived
New Product
Attributes
NewProduct
AttributeFactor
/
Change inAttribute
Perception
NewProduct
Attributes
Time to Chanoe
Attr ibute Perception
Init Avg ProdAttribute
rofINew OnRoad ven
<Tradeln
Used>
Change in
ProdAttnbute ofNew
OnRoad veh
-radeln
N
Avg ProdAttribute
New OnRoad VehVe
tion
re
Ivv>
Avg ProdAttribute of
Veh flowing in to Used
Veh Inv
<Dilution Timeof New
OnRoad Veh>
Avg
ProdAtribute of
Avg ProdAttribute
of Used OnRoad
ProdAut ofsed
Veh Inv
U
Change inve
ProdAttribute of
Used OnRoad Veh
<Dilutio
of Used
OntRoad Veh>
<init NewProduct
Attributes>
Fig. 4.9: Model structure for 'Perceived New Product Attributes' and the related co-flow.
27
'Perceived New Product Attribute' in Fig. 4.9, a two-dimensional array of products and attributes,
is modeled as a first-order smooth. The target value for the smooth is obtained using external
inputs. 'New Product Attribute Factors' are exogenous inputs giving percentage changes from
equilibrium value of relevant product attributes that vary as a function of time. These factors are
read as time domain inputs from an Excel file. Based on customer survey data [30], six attributes
most significant to SUV buyers were chosen:
" Power and Performance
" Quality
*
Safety
*
Comfort
*
Styling
*
Handling
The customer perceived ratings of these attributes were also extracted from the survey data and
are input as 'Init New Product Attributes' in the model. These initial values are multiplied by the
percentage changes over time encoded in 'New Product Attribute Factors' to calculate 'New
Product Attributes'. If a product is introduced ten months from the start of simulation with a 20%
jump in power and performance, then the attribute factor will be as represented in Fig. 4.10.
PowerPerf
1.3
1.2
M 1.1
14
0.9
0
10
20
30
40
50
60
Months
Fig. 4.10: Representation of the 'New Product Attribute Factor' for a 20% increase in power and
Performance.
The rest of the structure in Fig. 4.9 is modeled based on Hines's Co-flow [31]. The co-flow
structure traces the flow of the changes in attributes through vehicle population as it travels
28
through the aging chain. The average product attributes of new on-road vehicles, used on-road
vehicles, and used vehicle inventory are the stocks 'Avg ProdAttribute New OnRoad Veh', 'Avg
ProdAttribute of Used OnRoad Veh', and 'Avg ProdAttribute of Used Veh Inv' respectively.
Each of them is a two-dimensional array of products and attributes.
The product value is then obtained by taking the sum of the products of the attribute ratings and
the attribute weights as given below.
New Product Value[Products,CustSegments]
=
*
SUM(Perceived New ProductAttributes[Products,Attributes!]
AttributeCustSegWghts[Attributes!,CustSegments])/
SUM(AttributeCustSegWghts[Attributes!,CustSegments])
'Customer Value Change', the variable used in calculating 'New Product Attractiveness' shown in
Fig. 4.8, is computed from ratios of the product value computed above. The complete set of
equations is given in the appendix.
'Effect of Price on Product Attractiveness':
The second variable in the calculation of 'New Product Attractiveness' is the 'Effect of Price on
Product Attractiveness'. The structure associated with this is given in Fig. 4.11, and can be
lumped into three components as shown in the figure.
The structure associated with production (all variables are single-dimensional arrays of products)
determines the rate of production, the first flow rate in the vehicle aging chain given in Fig. 4.6.
Production is varied to maintain inventory around desired inventory. Desired new vehicle
inventory was assumed to be 45 days worth of sales (Please note that the stock related to new
vehicle inventory has lumped vehicles stored in factory, in transit, as well as the ones at the
dealerships). The new vehicle inventory was based on the expected new vehicle sales. 'Expected
New Vehicle sales' is an average of 'New Veh sales' smoothed over a period of four months.
Production rate changes were based on the inventory ratio ('Inv Ratio'), the actual to desired
inventory. There are two options of production rate changes - discrete and continuous. Discrete
29
changes are made when the inventory ratio goes beyond a min-max band. Continuous production
changes were adopted in all simulations presented in the results section. The details of the
equations are not presented here, but are included in the appendix with the rest of the model.
New vehicle pricing
<Tbne
Rebaate
Rebate Initiatim
-10n
xpr
t
Trc
Effect of Price on
Prtduct
Attractiveness
.
\atie
Rebate Decisan
PrunctEqForPrio
De aler Margin
Veh Price'.
~Present Vlaue o f
line:>
Rel New Veh
Price
Ca ac Prodapacey
-:Ne
di
M_________e
r
oute
Expected New argi
Mnho rec 'erat
Veh Sales
.
Based on Sales
.
Irnv Gap
Max Prodn Cap
_
iei
i r
Total Cost
M onth C ost for
TrdeSRP.
m
Ne w Purchase
r
TradelnVal
i
':efrnce Res~erent Vs.
Avg Age LOOK UP:>
ol Used InV (11)oma
Res Va: w
Disc otut Factor
N ew
-:Eff
<Newl Veh
IRatio.N,,m_:
Desired Productim
Capacity
CaebestsNew
rade In
--
T d a
Neve
M nhyItr
De sire d Invenlory
Rate New
Production
Cac
Chag
ae
Chang Dedsio
Monthly cost of new purchase
Production control
Fig. 4. 11: Model Structure for the calculation of 'Effect of Price on Product Attractiveness'.
The vehicle price is dependent on the 'Dealer Price', the price that the dealer pays the
manufacturer. The 'Dealer Price' increased by the 'Dealer Margin' rate gives the 'New Veh Price'.
The dealer margin depends on the inventory ratio. If 'Inv Ratio' increases above the desired level,
the dealer adjusts him margin to reduce the price. This is done only up to a point as specified by
'Acceptable Dealer Margin'. When 'Acceptable Dealer Margin' is reached while 'Inv Ratio' is still
30
above the desired levels, manufacturer rebate kicks in. The calculated 'New Veh Price' of
products is then used in determining the monthly cost of purchase for a new vehicle.
To calculate the monthly cost of new purchase, one has to know the vehicle price and the tradein value at the end of normal trade-in-time. Trade-in value computation is facilitated by the
"residual percentage value Vs. age" look-up table. This table was constructed from prices of used
vehicle models [32]. Using the residual percentage from the look-up table, the Manufacturer
Suggested Retail Price, and the trade-in-time, a residual value is arrived at. However, this value
is then modified by the amount of used vehicles in the used vehicle inventory. Used vehicles in
inventory that are above equilibrium levels lower residual value and vice-versa. Thus, the
equation for the trade-in-value is:
TradeInValue[Products,CustSegments]=
Eff of Used Inv on Res Value[Products]*
"Reference ResPercent Vs. Avg Age LOOKUP"[Products](Normal
TradeInTimeNew[Products,CustSegments])*
MSRP[Products]
Please note that 'TradeInValue' is a two-dimensional array of products and customer segments
because the normal trade-in-time could be different for different segments.
Having known the new vehicle price and the trade-in value at the trade-in-time, the monthly cost
over the trade-in-time duration is computed using the interest rate (APR) provided to the
customer. This was done using standard discount factors found in Ref. [33]. 'Monthly Cost for
New Purchase' is then used to calculate the 'Effect of Price on Product Attractiveness' through
the customer's value equation.
The customer preference function for monthly cost of purchase was modeled using hyperbolic
tangent functions. Given the customers ideal payment amount and the maximum amount of
acceptability, a preference function can be constructed where customer satisfaction is near 100%
31
for the ideal amount and near 0% for the maximum acceptable amount. The equation used in the
model for customer's value for price is:
CustValueEqForPriceNew[Products,CustSegments] =
0.5-0.5 *TANH((Monthly Costfor New Purchase[Products,CustSegments](Cust Acceptable Amt[CustSegments] +CustIdeal Amt[CustSegments])/2)
*CalibConst[CustSegments])
CalibConst[CustSegments]=
-
2*0.5 *LN((J + (2 *IdealAmtValue[CustSegments]-1))/(]
(2 *IdealAmtValue[CustSegments]-4)))/
(Cust Acceptable Amt[CustSegments]-Cust IdealAmt[CustSegments])
The calibration constant 'CalibConst' is calculated with 'Cust Acceptable Amt', 'Cust Ideal Amt'
and the value (percentage satisfaction) associated by the customer to the ideal amount, namely
'IdealAmtValue'. Please note that these variables are all arrays of customer segments since
different segments have different ranges of acceptable and ideal cost numbers. An example of a
customer value equation for price is given in Fig. 4.12 for the following parameter values:
'Cust Acceptable Amt'= $450; 'Cust Ideal Amt'= $320; 'IdealAmtValue'= 90%
Cust Preference Vs. Monthly Payment
1.20
-
S1.00
2 0.80
J 0.60
CL 0.40
$ 0.20
0.00
0
200
400
600
800
1000
Fig. 4.12: An example of the customer sensitivity to price.
32
'Brand Consideration Index':
The third variable in the calculation of 'New Product Attractiveness' is 'Brand Consideration
Index'. Ref. [34] gives the Market Science Institute definition of brand equity as "The set of
associations and behaviors on the part of the brand's customers, channel members, and parent
corporations that permit the brand to earn greater volume or greater margins than it would
without the brand name ... ". The part of the model associated with 'Brand Consideration Index'
aims to capture the brand effects that increase or decrease vehicle sales. Ref. [34] identifies ten
qualitative measures for brand - customer satisfaction, perceived quality, perceived value, brand
awareness, and market share being among them. In the system dynamics model used in this
study, these measures are included in some form. 'Brand Consideration Index', a measure of the
consideration that customers give any brand while on the market for a vehicle, is defined as the
product of 'Brand Opinion Index' and 'Brand Awareness Index'. As shown in Fig. 4.13, 'Brand
Opinion Index' has in-turn two components -'Brand Customer Satisfaction' and 'Perceived
Brand Value Index'.
<mnit
<Indicated Brand
Brandvauelndex>
Value Index>
<Brand Customer
Satisfaction>-
Perceived
Brand value
Index
Brand
Brand Opinion
Index
nes
Rate of Change ofPeB
Brand Awareness
Time to Change
PercBrandValue
Time to Change
Brand Awareness
<I nit
(7tI~tOmer~ls>
Indicated Brand
Awareness Index
Rate of Vhange
PercBrndXalue
vae
Brand Consideration
Index
Change in
Customers
CustSharesBrandAwareness
LOOKUP
<Custoners>
Fig. 4.13: Model structure for 'Brand Consideration Index'.
33
'Brand Awareness Index':
'Brand Awareness Index' is a measure of the area under the customers-versus-time curve, the
rationale being that awareness is proportional to the number of customers that the brand has and
for how long the brand has had them. The change in customers is quantified as the ratio of the
current customers to the customers at equilibrium. 'Indicated Brand Awareness Index' is then
calculated through a look-up function, and 'Brand Awareness Index' through a first-order
smooth. The details of the equations are given in the appendix.
'Brand Opinion Index':
Brand opinion is based on the perceived value offered by the brand to the customers and the
level of customer satisfaction based on dealer and usage experience. The equation for 'Brand
Opinion Index' is given below.
Brand Opinion Index[Manufacturer,CustSegments] =
PerceivedBrand Value Index[Manufacturer,CustSegments]*
Brand Customer Satisfaction[Manufacturer]
All brand related calculations are done at the manufacturer level as seen in the equation above.
The products of the manufacturer are mapped to it. If multiple products of a manufacturer are
involved in the simulation, data at the product level will be aggregated for the brand-related
calculations.
'Perceived Brand Value Index':
'Perceived Brand Value Index' is a first-order smooth smoothed over a period of 12 months
whose target is defined by 'Indicated Brand Value Index'. The portion of the model related to
'Indicated Brand Value Index' is given in Fig. 4.14.
34
<Perceived New
Product Attributes>
<New Product
Share of Brand>
b New Product
Value
Used Product
Value
<Avg ProdAttribute of
Used Veh Inv>
0Z
Indicated Brand
Value Index
NI
Vale"Used Product
Share of Brand>
Max Value
Fig. 4.14: Model structure for 'Indicated Brand Value Index'.
'Indicated Brand Value Index' is calculated as the sum of the 'New Product Value' and 'Used
Product Value' weighted by the share of new vehicles and used vehicles for a given
manufacturer. The equation used for computing Ford's 'Indicated Brand Value Index' is given
below.
IndicatedBrand Value Index[FORD,CustSegments] =
+
SUM(New Product Value[Fords!,CustSegments]*New ProductShare of Brand[Fords!]
Used Product Value[Fords!,CustSegments] *Used ProductShare of Brand[Fords!])/
Max Value
The array 'Fords' is a subscript range that includes all relevant products of the manufacturer
'FORD'. As was described earlier, matching 'Perceived New Product Attributes' and the attribute
weights 'Attribute CustSegWghts' assigned by each segment, 'New Product Value' is calculated.
'Used Product Value' is calculated in a similar way except that the product attributes used are
from those tracked by the co-flows described in Fig. 4.9. The equation used is given below
Used Product Value[Products,CustSegments] =
SUM(Avg ProdAttributeof Used Veh Inv[Products,Attributes!]*
AttributeCustSegWghts[Attributes!,CustSegments])!
SUM(AttributeCustSegWghts[Attributes!,CustSegments])
35
'Brand Customer Satisfaction':
Customer Satisfaction is based on the buying, service, and usage experience of the customers.
Buying /service is the experience of the customer at the dealership, whereas the usage experience
is primarily based on the actual quality of the vehicle. The model structure related to 'Brand
Customer Satisfaction' is given in Fig. 4.15. It is modeled as a first-order smooth with the gap
being set by 'Indicated Brand Customer Satisfaction'. 'Indicated Brand Customer Satisfaction' is
the product of dealer customer satisfaction rating and customer satisfaction based on quality
represented by the variables 'Customer Satisfaction Dealer' and 'Customer Satisfaction Quality'
respectively. The calculation of 'Actual Brand Quality' used in the smooth for 'Customer
Satisfaction Quality' and the related customer satisfaction related to dealership experience are
described separately in the later sections. All variables are single-dimensional arrays of
manufacturer. The details of the equations are given in the appendix.
<Customer
Satisfaction Dealer>
Indicated Brand
Customer
Satisfaction
Customer
Satisfaction Quality
Time to
Change
CustSat Rate o
Change
Brand
Perceived
Customer
Brand Quality
Satisfaction
Rea Perceived
Brand Quality
Int BIC
BrandQuality
Rate of Change of
Perceived Quality
mnit Perc
<mnit Overall
BrandQuality>
CustSat>
Tim to Change
PercQuality
Actual Brand
Quality
Fig. 4.15: Model related to 'Brand Customer Satisfaction'.
'Customer Satisfaction Dealer':
36
The part of the model dealing with dealerships is given in Fig. 4.16.
vDDealer Volume
----
Relative Dealer
Volume
Effof ReDealerVol on
Service Price
A
Avain
Industry Avg
Number of Dealers
Industry Avg
Dealer Volume
A
RelDealerVolOnService
Price LOOKUP
ServPriceOnCust
Sat LOOKUP
Sales
Manufacturer Sales
Time to Get
Service
Relative Number of
Dealers
'New Vch
Sales>
Dealer Service
Price
'j r,% vehl
SaLs>
Relative Service
Time
-W
EffofRelServPrice
On CustSat
Effof RelServTime
On CustSat
>Nw4/h<Ued/e
Industry Avg
Service Time
RelServTimeOnServCustSat
LOOKUP
Target CustSat
VehAndServAvailability
ReNumOfDealers
LOOKUP
EffofVehAndService
WAvailability on CustSat
--_Dealer
Customer
Satisfaction Dealer
Fig. 4.16: Structure related to dealer customer satisfaction.
'Dealer Volume' in Fig. 4.16 depends on the amount of new and used vehicle sales. As vehicle
sales increases, the 'Dealer Service Price' goes down, thus increasing 'Customer Satisfaction
Dealer'. 'Dealer Volume' also increases 'Time to Get Service' which in-turn decreases customer
satisfaction. Similarly, as the number of dealerships increases, the 'Dealer Volume' decreases,
eventually decreasing 'Customer Satisfaction Dealer' as 'Dealer Service Price' increases. On the
other hand, as number of dealers increases, vehicle and service availability increases, thereby
increasing customer satisfaction. These effects are captured in the model shown in Fig. 4.16. The
details of the equations are given in the appendix.
'Actual Brand Quality':
Quality of vehicles are represented in the model as Things Gone Wrong (TGW) per vehicle
(lower is better). 'Actual Brand Quality' is a single-dimensional array of manufacturers. A
TGW/vehicle versus age of vehicle curve represents each manufacturer's quality. An example of
such a curve is given in Fig. 4.17.
37
TGW Vs Age of Vehicle
8
4
2-
0
0
20
40
80
60
Months
Fig. 4.17: An example of TGW Vs. Age of Vehicle curve representing manufacturer quality.
The TGW versus age of vehicle works in conjunction with exogenously applied quality factor. If
there is a 20% improvement in quality at time zero, then the TGW versus age of vehicle curve 20
months hence will be as given in Fig. 4.18. Please note that the curve up till 20 months is
modified to reflect the 20% improvement in quality.
TGW Vs Age of Vehicle
7
6
--
5
2
0
0
20
40
60
80
Age of Vehicle
Fig. 4.18: TGW Vs. Age of vehicle curve with a 20% improvement is quality initiated 20 months
earlier.
38
These look-up curves are used in the model for brand quality given in Fig. 4.19. Brand quality
involves the combination of the quality of new and used vehicles. To define the brand quality of
used vehicles, TGW/vehicle at a fixed duration of 60 months was considered ('High Mileage
Service Time').
<OnRoad
~Used Product Share
Veh>
<NewProdLct
AttributeFactors>
of Brand
Actual Brand
Quaewdy
High Mileage Qity
Factor
High Mileage
Product Quality
NewPrd~vght
~--b.New Product
Quality
4ew Product Share
of Brand
Initial Qity Factor
<NewProduct<0o
Attribute Factors>
'~
ell>
Fig. 4.19: Model structure associated with 'Actual Brand Quality'.
The exogenous quality factor input is then delayed by the fixed duration at which quality
evaluation is made, namely, 'High Mileage Time in Service'.
High Mileage Qlty Factor[Products]=
DELAY FIXED(NewProductAttributeFactors[Products,
Quality], High Mileage Time in
Service, NewProductAttributeFactors[Products,
Quality])
'High Mileage Qlty Factor' is then used to modify TGW vs. time curves as:
High Mileage Product Quality[Products]=
Quality TGW Vs Time LOOKUP[Products](High Mileage Time in Service)!
High Mileage Qlty Factor[Products]
39
This gives the effect of modifying the TGW curve as shown in Fig. 4.18. The initial quality (as
represented by 'New Product Quality' in Fig. 4.19) is evaluated at 'Initial Time in Service'. 'Initial
Time in Service' was assumed as three months in service in the simulations. The same fixed
delay function used high mileage quality is used here also.
'Actual Brand Quality' in Fig. 4.19 is then calculated as weighted combination of new and high
mileage quality. The weights are applied with the assumption that between a new and used
vehicle, 80% of the brand quality is determined by the new ('NewPrdWght'). However, please
note that the number of used on-road vehicles is twice the number of the new on-road vehicles in
the model. The 'Actual Brand Quality' calculation for a manufacturer (FORD in this case) is
given as:
Actual Brand Quality[FORD] =
SUM(New Product Quality[Fords!]*New ProductShare of Brand[Fords!]*NewPrdWght
+ High Mileage ProductQuality[Fords!]*Used ProductShare of Brand[Fords!]*
(1-NewPrdWght))
The subscript 'Fords' refer to all the Ford vehicles in the subscript range. A similar equation is
applied to all manufacturers considered.
Effect of Quality on Vehicle Life:
As quality increases, customers use the vehicles longer due to trouble-free operation.
Consequently, vehicles take longer to exit the system as multiple ownerships becomes more
common. This is captured in the model by an increase in average vehicle life. The structure
related to average vehicle life is given in Fig. 4.20.
40
Avg Veh Life
Eff ofQity on
Veh Life
TGW at Veh Life
Init TGW at Veh
Life
""4
<Quality TGW Vs
Time LOOKUP>
<Used )nRoad
Qlty F actor>
Fig. 4.20: Structure associated with average vehicle life.
The change in 'Avg Veh Life' is computed as,
Avg Veh Life[Products] = Eff of Qlty on Veh Life[Products]*NormalAvg Veh Life[Products]
All variables are arrays of products since average life is different for different products. 'Eff of
Qity on Veh Life' that modifies 'Normal Avg Veh Life' is calculated using a look-up function
that compares the change in vehicle life (ratio) with the change in TGW (ratio) calculated at
normal vehicle life. Both ratios are computed with respect to their values at equilibrium. Thus,
for a quality ratio of 1.0, the corresponding ratio of vehicle life is 1.0, implying no change in
vehicle life. The look-up function used is given in Fig. 4.21.
41
Vehicle Life Vs. Quality Ratio Look-up
2
1.5
.e
0
0.5
0
0
0.5
1
1.5
2
Quality Ratio
Fig. 4.21: Change in Vehicle life (ratio) Vs. Quality Ratio of TGW at vehicle life.
Trade-in-time:
The overall model structure shown in Fig. 4.6 has two flows of trade-ins, 'TradeInNew' and
'TradelnUsed' and is reproduced in Fig. 4.22.
<Tradeln
'ime'New>
TradeInNew
OnRoad Veh
Used Veh Inv
x
'-----WTradeInUsed
<Tradeln
TineUsed>
Fig. 4.22: Flows 'TradeInNew' and 'TradelnUsed' from on-road vehicles to the used vehicle
inventory.
Both the flows are dependant on trade-in-times - 'TradeInNew' on 'TradeInTimeNew' and
'TradelnUsed' on 'TradeInTimeUsed'. The model structure related to 'TradeInTimeNew' is given
in Fig. 4.23.
42
Effect of qual~ty- experience/time-based
d
<Av
PrdAtrb
e
On RoadNVeuh>Fc
E
falue Change
on TiTN
'
CNew ProducO
' AValue>,
Avg New OnRoad
On oad Qlty
TdeniewsuaiyNew
Value Ratio
<g NQ! w On Road
lue>
<-A vt, Ag e
o f Ne w
LOOKUP
NewVeh2OnRoadNew
Prod
(if
New
>w
h1iit A v- Prod Attribue
AANg
ProdAttriboute
New F0nRozid. Veh>,
Actual New
Actual2Initial.*-OnRoad Prod QIty
Effof Qlty on,-- QltyRatioNew
et
Rel
OnRoadNewValueRatio
iTNew
Eff of RelalRatio
onTN
p
NwcvalueInit NewProdQlty
Effecto
Ratio
Eff of APRRatio
on
TiTN
TradenTieNewVs
Effect of product value (attribute
Target TiTN
PR LOOKUP
ratings-based) on trade-in-time
V'
Effect of incentives - 0% APR
TrdIn
TimeNew
Fig. 4.23: Model for trade-in-time of new (first owner) on-road vehicles ('TradeInTimeNew').
'TradeInTimeNew' is a first-order smooth of 'Target TiTN' shown in Fig. 4.23. 'Target TiTN' is
the normal trade-in-time ('Normal TradeInTimeNew') modified by pressure to change from three
areas. First, new product introductions reduce trade-in-time as the perceived value of newly
introduced products provide a higher value than the vehicles currently in use on-road. 'New
Product Value', calculated by matching product attribute ratings to attribute weightings of
different customer segments, is compared to the product value of first-owner on-road vehicles
('Avg New OnRoad Prod Value'). As was described earlier, the product attributes are tracked
using co-flows as vehicles travel through the stocks in the vehicle aging chain.
'NewVeh2OnRoadNew Value Ratio' represents the ratio of the attributes of the new product to
that of the on-road product in the model. This ratio is used in 'Change in TradeInTime Vs Value
RatioLOOKUP' function to evaluate the effect on trade-in-time due to upgrades in a given
product. The look-up function used is given in Fig. 4.24.
43
Trade-In-Time Vs. Value Ratio
o 1.2
? 0.8
I-
~ 0.4
0
1
3
2
Value change ratio
Fig. 4.24: 'Change in TradelnTime Vs ValueRatio LOOKUP' look-up function
Only if the value ratio is 1.0 or above that there is a change in trade-in-time. Please note that this
ratio is calculated between the values of the new and on-road vehicle of a given product - the
comparison is within itself. However, if a competitor's product is introduced that fits the value
equations better, customer trade-ins will increase because of a switch in loyalty among the
customers. This is captured by the comparison of the new and on-road vehicles using the relative
value metric - that is the ratio of the value of a given vehicle to that of one with the best value in
the market. The modification of trade-in-time due to the relative value ratio change is
accomplished through the variable 'Eff of RelValRatio on TiTN'. The look-up table used in
evaluating this variable is given in Fig. 4.25.
Trade-In-Time Vs. Relative Value Ratio
o
2
1.5
1
0.5
0
0
1
2
3
4
5
Relative Value Ratio
Fig. 4.25: 'Change in TradeInTimeVsRelValRatio LOOKUP' look-up function.
44
The second effect on trade-in-time is that of the quality of the vehicles as experienced by the
customers. This calculation of quality is slightly different from the quality discussion involved
with the brand calculations. The actual age of the vehicles is first calculated. This is implemented
like the co-flows mentioned earlier with some modifications. The structure associated with age
calculations is presented in Fig. 4.26.
<Tfradeln<fadl
<dOnRoad
New OnRoad
:sd>
Kiraadcii
New>
<Used Veh
Veh Agingv>
-Traden
Dilution Tne of New
OnRoad Veh
Avg Age of New
Change in Age of
ew OnRoad
OnRoad veh
Dilution Time of
Avg Age of Veh flowing
ein
Sales>
Used OnRUooad
Veh Aging
UJsed Veh
Fig.
> 4.6:MdesrctrfroAvg Age of Useduhu e
veh>
<1radein
Change in Age of
V~p Used OnRoad Veh
<7
Avg Age of Used
Veh Inv
Change in Avg Age of-d
v
OnRoad
Dilution Tim of Used
OnRoad Veh
OnRoad
Used Veh Inv
to Used Veh Inv
Veh
Used
vehIn>
Used Veh Inv
Aging
<Init Avg Age of
Used Veh Inv>
Fig. 4.26: Model structure for computing the age of vehicles throughout the vehicle aging chain.
The primary difference of this co-flow from the ones presented earlier is that there is an
additional flow into all average age stocks. 'New OnRoad Veh Aging', 'Used Veh Inv Aging', and
'Used OnRoad Veh Aging' are all flow rates of magnitude 1.0. In the model, they capture the fact
that the average age of the stocks goes up by one month with the passage of one month in time.
All other flow rates depend on the dilution times, namely, 'Dilution Time of New OnRoad Veh',
'Dilution Time of Used OnRoad Veh', and 'Dilution Time of Used Veh Inv'. All three flows are
in-turn dependant on the relevant vehicle flow rates in the vehicle aging chain.
The quality of the first-owner vehicle, represented by 'Actual New OnRoad Prod Qlty' in the
model shown in Fig. 4.23, is calculated from 'Quality TGW Vs TimeLOOKUP' look-up
function along with the 'Avg Age of New OnRoad Veh' computed as described above. The
45
quality factor, computed through the co-flow of product attributes as described in earlier
sections, also enters into the equation.
The third area included in the model that has an effect on the trade-in-time is the impact of
incentives. In this study, the effect of 0% financing is studied due to the timeliness of the topic.
The model associated with interest rate changes is modeled as a co-flow and is shown in Fig.
4.27.
<APR rate
New>
APRRatioNew
Init Avg APR of New
OnRoad veh
1radeln
New>
.sed>
eho--11 Onv
Used
Avg APR N ew
<APR rate
New>a~~te
<Tradein
O____d Veh_
Change in APR for
OnRoadVehAvg APR ofVeh
New On~oalowing
in to Used Veh
<Dilution Tine
Inv
of New
OtnRoad Veh>
Avg APR
UedOnRoadVe
Used Oneoa VehIn
Change inused
On~~~oada
e Ut
an
Ve>
of
oad vehIe
APR
Pert
rrsedie
New>-
<APR rate
New>
Ne'PsRinetioteARtiehsorsasored in anoExelie
h
ifso
fti
Fig. 4.27: Calculation of the flow of incentives (0% APR) through the vehicle aging chain.
The variation in APR is fed into the model exogenously as a time history. The variable 'APRrate
New' is linked to the APR time history data stored in an Excel file. The diffusion of this
incentive into this market is tracked through the co-flow structure shown in Fig. 4.27. The ratio
of the exogenous input 'APRrate New' to the "average APR" of new and used on-road vehicles
are computed and represented in the variables 'APRRatio New' and 'APRRatio Used'
respectively. Thus, the saturation or stoppage of the incentive will be characterized by high (near
1.0 and above) values of 'APRRatio New' and 'APRRatio Used'. The effect of the incentives is
then computed by 'TradeInTimeNewVsAPR LOOKUP' look-up function (see Fig. 4.28). Lower
46
values of the ratio represent sudden lowering of the rates (0% incentive) where the effect on
trade-in-time is the maximum.
Trade-In-Time Vs. APR Ratio
0
1.1
0.8-
- 0.7
- 0.6
0.5
0
0.5
1
APR Ratio
1.5
2
Fig. 4.28: 'TradeInTimeNewVsAPR LOOKUP' look-up function.
The effects of value, quality, and incentives on 'TradeInTimeUsed' are treated in a similar
manner as in the case of 'TradeInTimeNew'. Variables associated with used on-road vehicles are
used instead of that associated with the new. The complete set of equations is given in the
appendix.
'Used Product Attractiveness':
The model structure for 'Used Product Attractiveness' is given in Fig. 4.29. Unlike 'New Product
Attractiveness' that was described earlier, 'Used Product Attractiveness" has a distinct quality
dimension. Price and brand effects are treated similar to 'New Product Attractiveness'. The
quality calculations are similar to the ones described earlier. The TGWs are calculated used the
average age of the vehicles in the used vehicle inventory ('Avg Age of Used Veh Inv') and the
TGW versus time look-up function for every product ('Quality TGW Vs Time LOOKUP').
Based on the evaluated quality, relative used vehicle quality ('Rel UVQlty') ratios are computed,
which are then used to calculate the effect on used product attractiveness ('Eff of Quality on
Used Prod Attractiveness') through a look-up function ('Quality Vs Used Prod Attr LOOKUP').
47
The brand effects are computed the same way as was discussed in the case of 'New Product
Attractiveness'.
Effect of Qualit
ffect of Brand
geUlev
le
QualiyVsUsed Pr
Bnd C1os eration
e
te
RelV
E:Vulto~
a
:ty
datdU
PnBestrivenr
Qualiy
s
dE
_*
Nomim Resy
V :e>
Rit
CO
Effof~ri
Reso
ResVahreModFactor Vs
Used2NewRatb
LOOKUP
itine to Change
Attractimeness
n
R
sdrdt
CtstValueEqForPrice Used
Uev
ate of an in
UsedProdAttr
Ancvei
'line to Change
Re
Used Podc
C
Res Value
RatioOAUsed2
~IndncUsed
Prod Attractn~eness
Vatachees
Target ResluaLa. Chnin
Value
Ressisd Value
on
EffofUsed
Prod
UsedNew~tiD
mina R esiaPelty
Monthly Cost For
i
iMaintenance
hang in
RfeUidualAPR
LOOKUPA
Value UsedI
nitRes~aluD
'
<Csue
UsedPd Id>
Rsdd
d Usd~Usedt
Aarge
Rate Usedan
iscount Factor
i'aent
Scrap Resilual
Value
t
i
Effect of Price
Morebly Rate
Refernce Rs~rcern
Fig. 4.29: Model structure for 'Used Product Attractiveness'
The evaluation of the effect of price involves the calculation of monthly cost of purchase. To
estimate the monthly cost of a used vehicle, the residual value of the used vehicle has to be
arrived at. This is done based on the average age of the used vehicle inventory (that is calculated
using co-flow structure). Based on a reference residual percentage look-up function for each
product ('Reference ResPercent Vs. Avg Age LOOKUP'), a nominal residual value is estimated.
However, when the used vehicle inventory increases from the reference equilibrium level, there
is downward pressure on the residual value, and vice-versa. This effect is captured through 'Eff
of Used nv on Res Value' variable. A ratio of the current used vehicle inventory level to the
48
reference equilibrium level ('Ratio Of Used2Reflnv') is first computed, which is then used with a
look-up function to calculate 'Eff of Used Inv on Res Value'. The look-up function is presented
in Fig. 4.30. Please note that if the used inventory is at the equilibrium level, 'Used2Reflnv' ratio
has a value of 1.0, and the modification factor also has a value of 1.0 leading to no change in the
estimated nominal residual value.
Residual Value Modification factor Vs.
Used-to-Reference Invermtory Ratio
1.5
0
0
LLU.
0.5
0
0
5
10
15
Used Inv Ratio
Fig. 4.30: 'ResValueModFactor Vs Used2RefRatio LOOKUP' look-up function.
Once the residual value is known, the effect of price ('Eff of Price on Used Prod Attractiveness')
is computed in a similar fashion to 'New Product Attractiveness'. It is important to note that a
lower residual value is good from a used buyer's perspective, but is detrimental from a new
vehicle buyer's point-of-view as the monthly cost of purchase increases.
4.4 Equilibrium state
Before simulation runs were made, the model was set up to be in equilibrium. This was
accomplished by equating the inflows and outflows of each stock. The process also involved
doing simulation iterations until equilibrium was achieved. The part of the model relating to
constraints enforcing equilibrium is not presented here. The complete set of equations is
presented in the appendix.
49
5. Discussions of Scenarios, Results, and Conclusions
5.1 Scenario 1: Platform Strategy and Frequency of Product Upgrades
Background:
Automotive manufacturers want to keep their hot-selling products "updated" for the changing
requirements of the market. It is in the manufacturer's interest to constantly upgrade their best
sellers; the only constraint considered being cost associated with development and
manufacturing. Product strategy, as currently practiced, is then a balance between frequency of
product changes as required by the market against manufacturer's ability to afford them. The
focus of this scenario is to look at other factors such as effect of used vehicle market and quality
that should be considered while product strategy decisions are made.
Vehicle campaigns/recalls have shown an upward trend for American automotive manufacturers
with more vehicles recalled in a year than vehicles sold in recent years. The y-axis in Fig. 5.1
shows the percentages of categories of possible causes associated with Ford product recalls. Data
over the past three years suggests that over 35% of the recalls/campaigns are associated with
design changes. New and late design changes were involved in over 50% of the company
recalls/campaigns.
It is apparent from the chart in Fig. 5.1 that there is a high correlation of quality degradation with
the "newness" or scope of design changes as well as the time scale during which design changes
occur (as implied by late changes). On the other hand, there are "cost" penalties associated with
reduced frequency and scope of product upgrades since more frequent and newer designs keep
the product up-to-date in the market place and allow it to maintain customer interest.
50
TOP 5 PRODUCT CHANGES ASSOCIATED WITH CAMPAIGNS/RECALLS
35%
-
40%-
30%
25%
I
20%
-
15%
-I-
10%
0%
-
-
5%
New Design
Cost Reduction
Late Change
Product
Enhancement
Reliability
Improvement
Fig. 5.1: Percentage of campaigns/recalls within Ford associated with product design changes
(binned by the rationale for design changes).
The tension between market needs and quality/cost payoffs is quite evident in platform-based
product development. Platform-based product strategy maximizes part commonality and part
reusability across vehicle lines. Re-engineering of parts can thus be minimized. Companies can
reduce their costs and improve quality by utilizing this approach. However, products developed
using the platform strategy may not match the specific requirements of their respective target
markets. Utilizing platform commonization strategy requires the timing and some attributes of
products to match other vehicles in the portfolio that have part commonality. This may constrain
the company's ability to redesign products as often and as specifically as the market dictates.
The scenario that is being considered looks at how the balance between these two competing
forces plays out in the market place. The data and the context are artificial but realistic. The
product names are merely surrogates.
51
Scenario description:
Product plans for Jeep Grand Cherokee and Toyota 4-Runner are assumed to have major product
upgrades every five and six years respectively. Additionally, it is assumed that their execution is
perfect without any quality degradation. In this context, multiple rates of product upgrades are
considered for Explorer - four-, five-, and six-year upgrades. For simplicity and ease of
comparison, only the Styling attribute is changed at every product upgrade. These conditions are
represented as exogenous inputs to the model and are described below. Only a four-year upgrade
is shown for Explorer in Fig. 5.2. Five- and six-year upgrades are represented in a similar
fashion.
Explorer: Styling Factor
L-
01.6
U- 1.4
1.2
(0 1
0
50
100
Time
Fig. 5.2: Styling Factor for Explorer showing upgrade every 48 months
Grand Cherokee: Styling Factor
1.6
=1.2
0
50
100
Time
Fig 5.3: Styling Factor for Grand Cherokee showing upgrade every 60 months
52
4-Runner: Styling Factor
0 1.6-
LL
i
CO
50
100
Time
Fig. 5.4: Styling Factor for 4-Runner showing upgrade every 72 months
When the product is introduced, there is 40% increase in the styling rating for the first 12
months, followed by a diminished rating of 20% increase for the next 12 months, and
culminating in the equilibrium rating at the beginning of the Pd
year onwards until the next
3
upgrade. There is arbitrariness in the shape of the exogenous inputs showing decline of the
styling factor, but is intended to represent the presumed market need for quick changes. The rate
of upgrades is fixed at 60 months and 72 months for Grand Cherokee and 4-Runner respectively.
(The exogenous inputs were arbitrarily chosen but could be made more meaningful if product
development models are coupled to the current model). As mentioned earlier, the rate of the
upgrades for Explorer is however considered for four years (48 months), five years (60 months)
and six years (72 months).
To investigate the balance between faster introductions of the product and the resulting
degradations in quality due to reduced part re-usability and platform compliance, the quality
factor for the Explorer is varied with higher quality factors for longer product upgrades, while
the Grand Cherokee and 4-Runner values are held constant at the equilibrium value.
53
Explorer: Qlty Vs Time
1.2
0 9
y 0.8
0.7
0
50
100
150
Time
Fig. 5.5: Quality Factor for Explorer for 4-year upgrades
A 15% degradation in quality is assumed for the first 12 months after a 4-year upgrade. The
following year only a 10% degradation reflecting the debugging of the design, and then back to
equilibrium values till the next upgrade.
Explorer: Qlty V& Time
1.2
1- 1. 1
0.
U- 0.9
J-J-
S0.8
0.7
0
50
100
150
Time
Fig. 5.6: Quality Factor for Explorer for 5-year upgrades
In Fig. 5.6, the quality factor for a 5-year upgrade is shown. Only 10% degradation is assumed
for the first year, followed by 5% degradation for the next 12 months, and then back to
equilibrium until the next upgrade.
54
Explorer: Qity Vs. Time
1.2
" 1 . 1 -_
_
____
-
_
__ - _ _
0 9
y0.8
0.7
0
50
100
150
Time
Fig. 5.7: Quality Factor for Explorer for 6-year upgrades
For a six-year upgrade, only a first year 5% degradation is assumed. These reduced quality hits
for longer product upgrades reflect the utilization of a platform commonization strategy. In such
a strategy, a lower number of parts will only be re-engineered, with a high content of shared
parts. The impact on quality is thus assumed to be minimal.
For constructing a realistic worst-case scenario, the quality factors for Jeep and Toyota are held
at the equilibrium value of one implying that there was no appreciable degradation in quality.
These inputs were used to perturb the model which produced three sets of results corresponding
to four, five, and six-year product upgrades for the Explorer. These results and the dynamics
related to it are discussed below.
Discussion of Results:
The results of the simulation showing Explorer new vehicle sales for the three cases are given in
Fig. 5.8. The new vehicle sales for four-year product introductions is considerably worse than the
five and six-year introductions. Even though sharp peaks in sales associated with every product
upgrade reinforces the belief in more frequent product upgrades, the overall long-term trend is
headed downwards (The dotted lines in Fig. 5.8 is to clarify the trends in the curve and is not a
part of the simulated results). The primary reason for the degradation in sales is the associated
quality hits, the negative effects of which dominate the positive effects of more frequent
upgrades. However, as will be discussed later, the differences between the curves in Fig. 5.8 are
55
much more than those implied by the differences in the exogenous quality inputs given Figs. 5.5,
5.6, and 5.7.
In the model, New Vehicle Sales for each product is computed by distributing the available
monthly potential buyers in the market based on the market share of the respective products. The
overall vehicle sales can go up if there is an increase in the number of potential buyers. The
primary parameter that drives a differential change in the share of sales between the products is
New Product Attractiveness (NPA). Hence, explanations to the dynamics associated with new
vehicle sales can be constructed by studying those associated with NPA.
19,000
17,200
15,400
Ia"o.
Ma90AS8Ki.
13,600
11, 800
0
16
32
48
64
80
Time (Month)
96
112
128
144
NVS[Explorer] : 4yrCadence
NVS[Explorer] : 5yrCadence
NVS[Explorer] : 6yrCadence
Fig. 5.8: Comparison of Explorer New Vehicle Sales for 4, 5, and 6-year product upgrades.
The Explorer NPA for the four, five, and six-year product upgrades are given in Fig. 5.9. There
is a decline in NPA for all three rates of product upgrades since all three cases had an associated
quality decline when compared with competition. However, the decline in NPA is steeper for the
four-year product changes than five and 6-year upgrades. One obvious reason for this is the
higher exogenous quality penalties that are associated with more frequent product changes given
in Fig. 5.5. However, as will be presented below, exogenous inputs alone do not explain the
56
magnitude of decline in NPA. There are interesting dynamics that amplify the effect of the
degradation in quality.
1
0.875
0.75
0.625
0.5
0
16
32
48
64
80
96
112
128
144
Time (Month)
NPA[Explorer] : 4yrCadence
NPA[Explorer] :6yrCadence
NPA[Explorer]N6yrCadence
Fig. 5.9: Explorer New Product Attractiveness for 4, 5, and 6-year product upgrades.
0.75 I
0.6625
0.575
Amplification of quality dips
0.4875
0.4
0
16
32
48
64
80
96
112
128
144
Time (Month)
Customer Satisfaction Quality[FORD] :4yrCadence
Customer Satisfaction Qualityf[FORD] :5yrCadence
Customer Satisfaction Quality[FORD] :6yrCadence
Index
Index
Index
Fig. 5.10: Explorer customer satisfaction based on quality for 4, 5, and 6-year product change.
57
Referring back to Fig. 5.5, the exogenous inputs showing the dips in quality due to four-year
product changes are of the same magnitude and frequency. However, in Fig. 5.10, the effect of
quality is amplified with time. Perceived quality of a nameplate in the market place is a
composite of the new vehicles as well as high mileage vehicles. Hence, the quality hits among
the older vehicles are perceived with a phase lag. The delayed effect among the used vehicles
reinforces the exogenous inputs corresponding to the following upgrades and thereby amplifies
the quality dips. It is important to note that the number of used vehicles sales is two to three
times the new vehicle sales (actual data presented in Fig. 5.11), and hence the quality of the used
vehicles will indeed affect the overall perceived quality of the brand.
New Vs Used Vehicles
5040-
0
-
New Vehicles
m Used Vehicles
2010-
0
1999
2000
Fig. 5.11: Actual New and Used Vehicle Sales for 1999 and 2000.
In the model, at equilibrium, Used Vehicle Sales was considered to be twice that of New Vehicle
Sales. Hence the quality hits, as it reaches the older vehicles have a considerable effect on the
perceived quality.
Comparing NPA in Fig. 5.9 and Customer Satisfaction based on Quality in Fig. 5.10, it is clear
that the latter alone does not explain the degradation seen in NPA. The degradation in customer
satisfaction based on quality is more or less linear whereas NPA degradation is of a higher order.
Investigation of the Used Vehicle Inventory (UVI) gives some clues.
58
200,000
185,000
170,000
155,000
140,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
Used Veh Inv[Explorer] 4yrCadence
Used Veh Inv[Explorer] 5yrCadence
Used Veh Inv[Explorer] 6yrCadence
Vehicles
Vehicles
Vehicles
Fig. 5.12: Explorer Used Vehicle Inventory for 4, 5, and 6-year product changes.
The UVI for the 4-year product change goes up considerably with time. Initially, the UVI is
slightly lower (difficult to distinguish from Fig. 5.12 without magnification of time scale) for the
four-year product change than the 5-year and 6-year product changes. This is because the current
customers will initially trade-in relatively less often for four-year products, as it is less attractive
when compared with the five and six-year products based on quality. With time, inferior quality
causes customers to switch loyalty thereby bringing down trade-in-time for the Explorer and
bringing up UVI for Explorer. The trend in trade-in-time, given in Fig. 5.13 bears this out. Even
though there are dips associated with product introductions (as is to be expected) for 4, 5, and 6year product changes, the overall trend for the 4-year product introduction is distinctly lower
than the other two. Hence, the reason for the glut in used vehicles is two-fold: 1) frequent
product introductions causing more trade-ins, and 2) more trade-ins due to the lowering of tradein time caused by decreasing quality.
The natural consequence of a bloated UVI is the lowering of the residual value. With lowered
residual value, some of the new vehicle sales are lost to the used market. Additionally, the
monthly cost of ownership for the new vehicle buyer increases due to the depressed residual
value. The downward trend of the Effect of Price on Product Attractiveness (Fig. 5.14) is clearly
59
more pronounced for the four-year rate of upgrade than the other two cases. (Please note that the
increasing trends of UVI in Fig. 5.12 are inverted in the trends of price given in Fig. 5.14).
This reduction in NPA lowers new vehicle sales. The combined effect of used vehicles and the
loss of customer satisfaction based on quality (Fig. 5.10) adequately explains the trend of NPA
and new vehicle sales in Figs. 5.9 and 5.8.
50
45
40
35
30
0
16
32
48
64
80
Tine (Month)
96
112
128
144
TiT N[Explorer]: 4yrCadence
TiT N[Explorer] : 5yrCadence
TiT N[Explorer]: 6yrCadence
Fig. 5.13: Explorer Trade-in-Time for new on-road vehicles for 4, 5, and 6-year product changes.
0.65
0.6124
0.575
0.5375
0.5
0
16
32
48
64
80
96
112
128
144
Time (Month)
Effect of Price on Product Attractiveness[Explorer,FunctionalTechnology] :4yrCaDmnl
Effect of Price on Product Attractiveness[Explorer,FunctionalTechnology] :5yrCaDmnl
Effect of Price on Product Attractiveness[Explorer,FunctionalTechnology] : 6yrCaDmnI
Fig. 5.14: Effect of Price on Product Attractiveness on a segment for varying product changes.
60
The adverse effect of quality is felt in the used vehicle market also. Even though the inventory is
going up and the price of used vehicles is coming down, the used vehicle sales show a downward
trend (see Fig. 5.15 and Fig. 5.16).
40,000
35,000
30,000
25,000
20,000
0
16
32
48
64
80
Time (Month)
96
112
128
144
UVS[Explorer] : 4yrCadence
UVS[Explorer] : 5yrCadence
UVS[Explorer] :6yrCadence
Fig. 5.15: Explorer Used Vehicle Sales for 4, 5, and 6-year product changes
9,500
8,625
7,750
6,875
6,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
Residual Value[Explorer] : 4yrCadence
Residual Value[Explorer] 5yrCadence
Residual Value[Explorer]: 6yrCadence
Dollars
Dollars
Dollars
Fig. 5.16: Residual value for Explorer for 4, 5, and 6-year product changes
61
The effect of quality, however, drags down the Used Product Attractiveness, thereby depressing
the Used Vehicle Sales. The curves for Effect of Quality on Used Product Attractiveness and
Used product attractiveness are shown in Figs. 5.17 and 5.18 respectively.
0.75
0.725
0.7'
0.675
I
0.65
0
16
32
48
64
80
96
112
128
144
Time (Month)
Eff of Quality on Used Prod Attractiveness[Explorer] : 4yrCadence
Eff of Quality on Used Prod Attractiveness[Explorer] : 5yrCadence
Eff of Quality on Used Prod Attractiveness[Explorer] : 6yrCadence
Dmnl
Dmnl
Dmnl
Fig. 5.17: Effect of quality on Used Product Attractiveness Explorer for 4, 5, and 6-year product
changes
2
1.75
Nil
1.5
1.25
1
0
16
32
48
64
80
96
112
128
144
Time (Month)
UPA[Explorer] : 4yrCadenc
UPA[Explorer] : 5yrCadence
UPA[Explorer] : 6yrCadence
Fig. 5.18: Explorer Used Product Attractiveness for 4, 5, and 6-year product changes
62
Conclusions:
Quality stands out as an important driver for vehicle sales. If there are significant quality hits due
to inherent product development constraints within an organization, it is better to go for longer
intervals between major product upgrades. The advantages of longer intervals are two-fold:
1) Quality degradation is reduced due to increased reusability of parts and fewer redesigns. As
evident from the simulations results, dips in quality of the vehicles amplify with time due to
the delayed perception of quality among the older vehicles (Fig. 5.10). Since the used market
is two to three times the size of the new market, the impact of quality is amplified. As the
quality effects become evident in the used market, used inventory increases as a result of
depressed used vehicle sales. This in-turn puts downward pressure on new vehicle sales.
2) Used vehicle inventory levels are lower (Fig. 5.12). When product introductions are less
frequent, the trade-ins also are less frequent. As a result, the level of used inventory is less.
This increases the residual value and consequently decreases the cost of ownership for new
vehicle buyers.
Since real world data shows degradation in quality, longer intervals are clearly better for
robustness of sales in the long run. Despite frequent peaks in vehicle sales corresponding to
vehicle introductions, a long-term comparison of sales clearly shows long-term downward trend
for 4-year product upgrades.
It is clear from the simulation results that the restraining force on increased new vehicle sales
brought about by more frequent product changes is not cost alone but also quality and the effect
of used vehicle market on the new. Please note that even though cost was not incorporated in the
model, cost impacts would only make the situation worse.
63
5.2 Scenario 2: System Interactions and Incremental Innovation
Background:
Changes in product cycle plans are commonly made in situations where competition introduces
products with significant technology and styling upgrades. Matching competitors' offerings in
the market place results in quality, cost, and time constraints depending on the scope of the
changes. If multiple subsystems have to be changed to match competition, past experience has
shown that complexity due to system interactions becomes intractable. A recent study inside
Ford looked at identifying key factors that adversely affect the reliability of vehicles. Based on
data for the past three years, the total number of reliability concerns was binned against
identified noise factors. The graph in Fig. 5.19 shows the percentage of the total concerns on the
y-axis against the noise factors plotted on the x-axis. As evident in Fig. 5.19, unexpected system
interactions are the number one cause of reliability concerns in Ford products.
RELIABILITY NOISE FACTORS
30%
25% -I-
15%
-
10%
-
20%-
5%
0%
System
Interactio ns
Deterioration
Environment
Mfg Variation
Customer
Usage
Fig. 5.19: Effect of noise factors on Ford vehicle reliability.
64
System interactions account for more than 25% of the reliability concerns. Contrary to common
association of quality with manufacturing, the effect of system interactions is more than double
that of manufacturing variation. Given this constraint, the pertinent questions are whether a
manufacturer should lead or follow the market, or whether the scope of changes should span
multiple subsystems to match competition, or whether the changes should be phased in
("incremental innovation" as suggested by Reinertsen [7]) taking into account the learning curves
associated with system interactions. Studying the implications of these choices in terms of longterm vehicle sales and market share will help in making a robust product decision.
Scenario description:
Consider a scenario where Explorer is well into a scheduled minor styling change according to
the planned product cycle. Furthermore, Ford Motor Company then discovers through
competitive intelligence that one of the manufacturer's products, say Jeep Grand Cherokee, is
being introduced with major technology and product upgrades. In this example, we assume
major body structure changes (Styling) and Powertrain changes (Power/Performance). These
changes to the Grand Cherokee are assumed without substantial quality penalties (robust
planning and disciplined execution is assumed). The exogenous variables describing these
changes for Grand Cherokee are given in Figs. 5.20 and 5.21.
Grand Cherokee: Power/Perf Factor
0
L-
~1.6
t
1.4
L
1.2
IL
0
50
100
Time
Fig. 5.20: Exogenous input for Powertrain upgrades for Grand Cherokee at 18 months.
65
Grand Cherokee: Styling Factor
-
0 1.6
-
U14
1.2
1
0o
-L
0
50
100
Time
Fig. 5.21: Exogenous input for body structure upgrades for Grand Cherokee at 18 months.
The powertrain and body structure changes are both represented by a 30% change in the ratings
at 18 months. The quality is assumed to be unaffected. Assuming that Ford Motor Company has
recently come to know this through competitive intelligence, four options are considered by the
company:
1) Accelerate product development to bring an Explorer upgrade 12 months in advance of
Grand Cherokee with both body structure and Powertrain changes to match Grand
Cherokee. The exogenous variables representing this product change are given in Figs.
5.22 and 5.23.
Explorer: Power/Perf Factor
0
c 1.6
LL-
1.4
0
o
Z 1.2
1-
0
50
100
Time
Fig. 5.22: Exogenous variable showing Explorer Powertrain upgrade 12 months before
Grand Cherokee introduction.
66
Explorer: Styling Factor
I-
0
1.6
U0)
1.4
C')
0
50
100
Time
Fig. 5.23: Exogenous variable showing Explorer styling upgrade 12 months before Grand
Cherokee introduction.
The quality penalties associated with the Explorer upgrade due to system interactions and
quick design changes are represented in Fig. 5.24.
Explorer: Qity Vs. Time
1.4
0
Z 1.2
0.8
0
50
100
150
Time
Fig. 5.24: Quality penalties due to system interactions and fast design changes for the
Explorer upgrade.
2) Perform both body structure and Powertrain changes but 12 months after Grand Cherokee
introduction.
67
The exogenous inputs for upgrades (Figs. 5.25) are similar to the previous option except
that they are shifted by 24 months. However, only a 12-month 5% penalty in quality
rating is applied signifying a more thorough design process and verification (Fig. 5.26).
Explorer: Power/Perf & Styling Factor
0)
-
-W
1.6
U- 1.4
1
0
CL0
50
100
Time
Fig. 5.25: Explorer Powertrain and Styling upgrades 12 months after Grand Cherokee
upgrades.
Explorer: Qlty Vs. Time
1.4
0
1.2
U.
No-
0.8
0
50
100
150
Time
Fig. 5.26: Explorer quality penalty for simultaneous Powertrain and body structure
upgrade 12 months after Grand Cherokee upgrades.
3) Phase in the changes; first with body structure (styling) changes 12 months before
competitor's introduction, followed by Powertrain changes 12 months after Grand
Cherokee's upgrade.
68
By making changes in sequence, Explorer will not be matching competition
immediately,
but will be mitigating the effect of intractable system interactions. Since the scope of
change is limited first to body structure, and then to Powertrain after 24 months, the
quality degradation is minimal. Degradation of 5% was assumed in the
first 12 months,
followed by 3% degradation in the next 12 months. The exogenous inputs signifying the
effect of the product introductions in this option is shown in Fig. 5.27 through Fig. 5.29.
Explorer: Styling Factor
I-
0 1.6
(U
1.4
L0)
:
1.2
c
1
0
50
100
Time
Fig. 5.27: Explorer body structure changes 12 months before Grand Cherokee
introduction
Explorer: Power/Perf Factor
L-
0
1.6
LL
'~1.4
1.2
0
1
0
50
100
Time
Fig. 5. 28: Explorer Powertrain changes 12 months after Grand Cherokee introduction.
69
Explorer: Qity Vs. Time
1.4
0
t 1.2
0.8
0
100
50
150
Time
Fig. 5.29: Explorer quality hits for sequential changes
4) Stick to the planned product development cycle.
According to the current plan, only a minor styling change is envisaged, but 12 months
ahead the Grand Cherokee introduction. This is represented by a 10% change in rating
and is shown in Fig. 5.30. Since this is planned introduction with no late changes,
schedule pressure is minimal and only 3% degradation in quality was assumed. The shape
and timing of this exogenous input is shown in Fig. 5.31.
Explorer: Styling Factor
0 1.6
U1.4
S1.2
0
50
100
Time
Fig. 5.30: Explorer styling update according to the current product development plan.
70
Explorer: Qlty Vs. Time
1.4
0
461.2U-
0.8
0
50
100
150
Time
Fig. 5.31: Explorer quality degradation for the current product development plan.
Results and Discussion:
The new vehicle sales for the Explorer for all four options are given in Fig. 5.32. When the
Explorer is introduced 12 months ahead of the competition (shown in Fig. 5.32 as "12mAhead"),
customers trade-in their vehicles for the newly introduced model and initial sales go up.
However, with time, the trend in new vehicle sales is reversed. This is due to: 1) Further tradeins with time resulting from adverse quality perceptions causing an increase in used vehicle
inventory and decrease in residual value, 2) Quality degradation, causing adverse brand inertia.
These effects are shown in Figs. 5.33 through 5.36. Much like the trend in scenario 1, the quality
ratings bring sales down with a double dip, the perception of quality being affected by the new
and used vehicle customers with a phase lag (See Fig. 5.34). The brand effects shown in Fig.
5.36 is a composite of customer satisfaction (includes the effect of quality), Brand Awareness
which is an index of market share effect, and a cumulative effect of product value as perceived
by the market. Sequenced or delayed introductions show better long-term brand effects. The
current product plan fares slightly better than "1 2mAhead" downstream but is not a better option
in the five to six-year horizon.
71
20,000
17,500
15,000
12,500
10,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
NVS[Explorer]: Planned PD Cycle
NVS[Explorer]: 12mAhead
NVS[Explorer]: 12mAfter
NVS[Explorer]: Sequenced
Fig. 5.32: Explorer New Vehicle Sales for the four product introduction options.
If Explorer is introduced 12 months after the Grand Cherokee introduction, there is an initial dip
in explorer sales. The dip is due to the sales shifting to newly introduced Grand Cherokee. As
Explorer is introduced, it captures market interest and the trend in vehicle sales is reversed. The
Explorer vehicle sales peak around 36 months before coming down (due to the balancing effects
caused by increased used vehicle inventory). The relatively robust sales soon after the
introduction could be attributed to fewer number used vehicles in the inventory initially.
Clearly, a sequenced introduction of the changes is better judging by the overall area under the
curve as well as taking into account the time value of revenues. Through a sequenced
introduction, the company not only gains from reduced quality degradation but also from
diminished number of vehicles in the used vehicle inventory. As stated earlier, a lower used
vehicle inventory helps in higher residual value and lower monthly cost of ownership.
72
250,000
212,500
175,000
137,500
100,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
Used Veh Inv[Explorer]: Planned PD Cycle
Used Veh Inv[Explorer]: 12mAhead
Used Veh Inv[Explorer]: l2mAfter
ORWONWAMM
Used Veh Inv[Explorer]: Sequenced
Vehicles
Vehicles
Vehicles
Vehicles
Fig. 5.33: Explorer Used Vehicle Inventory for the four options.
0.75
0.6875
0.625
0.5625
0.5
0
Customer
Customer
Customer
Customer
16
Satisfaction
Satisfaction
Satisfaction
Satisfaction
32
48
64
80
Time (Month)
96
112
Quality[FORD]: Planned PI ) ycle
Quality[FORD] l2mAhead
Quality[FORD]
e2mAfer
Quality [FORD] Sequencec
128
144
Index
Index
Index
Index
Fig. 5.34: Explorer quality degradation for the four options.
73
0.8
0.7
0.6
0.5
0.4
0
16
32
48
64
80
96
112
128
144
Time (Month)
EPPA [Explorer]: Planned PD Cycle
EPPA[Explorer]: 12mAhead
EPPA[Explorer]: 12mAfter
EPPA[Explorer] : Sequenced
S
S
Fig. 5.35: Effect of Price on Explorer attractiveness for the four options.
0.6
0.55
0.5
0.45
0.4
0
16
32
48
64
80
96
112
128
144
Time (Month)
BCI[FORD] Do Nothing
BCI[FORD] 12mAhead
BCI[FORD] 12mAfter
BCI[FORD] Sequenced
Fig. 5.36: Overall brand effects as described by Brand Consideration Index for the four options.
An interesting result of this analysis is the effect that changes to the Explorer has on Grand
Cherokee sales shown in Fig. 5.37. Even though nothing is changed in the Jeep strategy, robust
sales occur when Explorer is introduced twelve months in advance to the Grand Cherokee
74
introduction. The dynamics that govern this behavior is explained below; it again points to the
effect of the used vehicles and importance of managing the used vehicle market.
20,000
17,500
15,000
12,500
10,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
NVS[GrandCherokee]: Planned PD Cycle
NVS[GrandCherokee]: 12mAhead
NVS[GrandCherokee]: 12mAfter
NVS[GrandCherokee]: Sequenced
Fig. 5.37: Grand Cherokee New Vehicle Sales for the four Explorer product introduction options.
The graph shown in Fig. 5.38 indicates higher used vehicle sales for Grand Cherokee with the
advanced introduction of the Explorer. Due to quality degradation associated with faster time-tomarket, the Grand Cherokee gains the upper hand in used product sales. This in turn keeps the
used Grand Cherokee inventory low (Fig. 5.39) when Explorer has the advanced introduction.
Lower used inventory gives higher residual value and hence a cost advantage for Grand
Cherokee buyers. The effect of price on Grand Cherokee product attractiveness (Fig. 5.40)
translates to the relatively higher sales shown in Fig. 5.37 for Explorer's "l2mAhead" option.
75
40,000
35,000
30,000
25,000
20,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
UVS[GrandCherokee]:
UVS[GrandCherokee]:
UVS[GrandCherokee]:
UVS[GrandCherokee]:
Planned PD Cycle
l2mAhead
l2mAnfter
Sequenced
Fig. 5.38: Grand Cherokee Used Vehicle Sales for the four Explorer introduction options.
200,000
175,000
150,000
125,000
....
... .....
.
...
.......
100,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
Used Veh
Used Veh
Used Veh
Used Veh
Inv [GrandCherokee] Planned PD Cycle
Inv[GrandCherokee]: l2mAhead
Inv[GrandCherokee]: 12mAfter
Inv [GrandCherokee]: Sequenced
-------------
Vehicles
Vehicles
Vehicles
Vehicles
Fig. 5.39: Grand Cherokee Used Vehicle Inventory for the four Explorer introduction options.
76
0.7
0.65
0.6
0.55
0.5
0
16
32
48
64
80
96
112
128
144
Time (Month)
EPPA[GrandCherokee] Planned PD Cycle
EPPA[GrandCherokee]: 12mAhead
EPPA[GrandCherokee]: 12mAfter
EPPA[GrandCherokee]: Sequenced
Fig. 5.40: Effect of Price on new Product Attractiveness of Grand Cherokee for the four Explorer
introduction options.
Conclusions:
Based on engineering knowledge and experience, product changes involving multiple
subsystems result in unexpected and undesirable emergent behavior. The market simulation of
the eventual performance of the product indicates that the penalty due to perceived degradation
in quality overwhelms the advantage of fast introduction of a product that matches competition.
Temporary jumps in sales mask the overall downward trend in the performance. Additionally,
the importance of managing the sizeable used vehicle market is evident from the simulation
results. Actions that enhance the residual value, including production cut backs as well as
manufacturer-initiated used product upgrades, should be looked into.
77
5.3 Scenario 3: Continuous Quality Improvements
Background:
In scenarios 1 and 2, quality was seen as one of the major drivers of vehicle sales. Studies have
indicated that the overall vehicle quality has continued to improve in the last two decades.
According to the data in Fig. 5.41, defects per 100 averaged 460 with a standard deviation of 300
in 1981 for all vehicle manufacturers. Within a decade, the average came down to 150 with a
standard deviation of 25. The trends in Fig. 5.41 suggest that vehicle quality will soon converge
among manufacturers even as overall quality is continuing to improve.
Vehicle quality convergence (United States)
Defects per 100 vehicles
+I standard deviation
Mean
U -1 standard deviatior
800
700
600
500
400
300
200
IG
1
1981
18II I I I 11I 5
1989 1990 1891 1982
1987 1988
1993 1994
1995
Source: . D, Power, FCC; press clppings; McKinsey analysis
Fig. 5.41: Vehicle quality history (Source: "Are Automobiles the next commodity?", McKinsey
Report, 1996)
Even though auto market in developing nations is projected to grow at the rate of 5% to 10%
annually, mature markets like US, Europe and Japan are expected to grow only about 1%. In a
mature market, where little or no growth is expected, it is interesting to study the implications of
continuous quality improvements by vehicle manufacturers on vehicle sales.
Scenario Description:
78
The dominant strategy for every manufacturer, in the "game theory" context, is to improve
vehicle quality thereby making their vehicles more durable and more appealing to consumers. To
simulate the scenario of vehicle improvements for all manufacturers, quality ratings were
improved by 30% for Explorer, Grand Cherokee, and 4-Runner from equilibrium values after 12
months as shown in Fig. 5.42.
All manufacturers: Qlty Vs. Time
1.4
0
*S
1.2
0.8
0
50
100
150
Time
Fig. 5.42: Exogenous input for quality improvements for all manufacturers.
Results and Discussion:
The new vehicle sales initially go up as new products are introduced with higher quality. The
effect on 4-Runner in Fig. 5.42 is not as pronounced as Explorer and Grand Cherokee because
the quality ratings are relatively lower for Explorer and Grand Cherokee at the initial
equilibrium. Hence there is more to be gained with quality improvements for Explorer and Grand
Cherokee than 4-Runner.
79
20,000
15,000
10,000
5,000
0
0
16
32
48
64
80
96
112
128
144
Thime (Month)
NVS[Explorer]
NVS[GrandCherokee]
NVS[FourRunner]
Fig. 5.43: New Vehicle Sales with 30% improvement in quality ratings.
The long-term trend however is downward. This is a case where everyone loses if everyone
succeeds in improving quality. The question to be answered is whether this is typical for a
market with little growth. The structure in the model assumes no growth in the number of total
customers and in that respect, is similar to a market with no growth in overall demand.
1.5
1.125
0.75
0.375
0
0
16
32
48
64
80
Teim (Month)
96
11 2
128
144
NPA[Explorer]
NPA[GrandCherokee]
NPA[FourRunner]
Fig. 5.44: Increasing new product attractiveness
80
Unlike vehicle sales in Fig. 5.43, trends in New Product Attractiveness shown in Fig. 5.44 shows
increasing trends which should in turn produce increasing sales. The adverse effect of used
vehicle inventory seen in the earlier scenarios is absent here. In fact, used vehicle inventory (Fig.
5.45) and used vehicle sales (Fig. 5.46) both show downward trends.
200,000
150,000
100,000
50,000
0
0
16
32
48
64
80
96
112
128
144
Time (Month)
Used Veh Inv[Explorer]
Used Veh Inv[GrandCherokee]
Used Veh Inv[FourRunner]
Vehicles
Vehicles
Vehicles
Fig. 5.45: Used Vehicle Inventory trends with improved quality
40,000
30,000
20,000
10,000
0
0
16
32
48
64
80
96
112
128
144
Time (Month)
UVS[Explorer]
UVS[GrandCherokee]
UVS[FourRunner]
Fig. 5.46: Used Vehicle Sales trends with improved quality.
81
The primary reason for declining new and used vehicle sales is that with increasing quality,
customers tend to use the vehicles longer. Trade-in-time and average vehicle life increases as a
result (see Fig. 5.47). This leads to decreased attrition of vehicles from the system.
Consequently, the number of current customers increases and the number of potential buyers
correspondingly decreases (see Fig. 5.48 and Fig. 5.49).
200
175
150
125
100
0
16
32
48
64
80
96
112
128
144
Time (Month)
Avg Veh Life[Explorer]
Avg Veh Life[GrandCherokee]
Avg Veh Life[FourRunner]
m
Month
Month
Month
Fig. 5.47: Increasing vehicle life with improved quality
1.6 M
1.587 M
1.575 M
1.562 M
1.55 M
0
16
32
48
64
80
96
112
128
144
Time (Month)
Total current customers
Fig. 5.48: Trend for total number of customers with improved quality.
82
The initial dip in total customers corresponds to trade-ins resulting from the introduction of the
new vehicles. With time, the effect of improved quality dominates, resulting in more customers
holding on to their vehicles. As a result, the total number of current customers moves up to a
higher equilibrium point. Since the sum of potential buyers and customers is assumed to be
constant in our model, the reverse trend is seen in the level of Total Potential Buyers (Fig. 5.49).
140,000
125,000
110,000
95,000
80,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
Potential Buyers
Fig. 5.49: Decreasing potential buyers with improved quality.
Conclusions:
The simulation results state that convergence of vehicle quality to a higher level by all
manufacturers leads to decrease in sales in markets where there is little or no growth in demand.
This is due to the fact that, with improved quality, the attrition rate of the vehicles decreases. As
a result the pool of potential buyers decreases to a lower level of equilibrium.
5.4 Follow-up Discussion on Scenarios 1 & 3: Sensitivity to Quality
Analysis of the results in scenario 1 showed the effects of quality and used vehicle inventory on
new vehicle sales. Perceived quality effects as well as the frequency of the product upgrades
drove the level of used vehicle inventory. Quality of the vehicle and the frequency of product
83
upgrades are linked especially when the duration between upgrades approaches the product
development time. If we assume them to be independent for the purpose of this discussion, it
would be interesting to study the sensitivity of new vehicle sales to quality ratings.
Furthermore,
analysis of scenario 3 suggests that the level of used vehicle inventory will be reduced with
improvements in quality. This suggests that the effect of more frequent upgrades - the mere fact
that more vehicles are traded in to make way for the new - on used vehicle inventory can be
controlled with improvements in quality.
To simulate the conditions described above, scenario 1 was modified to have a constant fouryear product upgrade for Explorer (the reader may recall that Grand Cherokee and 4-Runner had
five-year and six-year product upgrades with no quality penalties in this scenario), but three
varying cases of quality inputs were considered:
1) Exogenous quality inputs assume same degradation as in Fig. 5.5 - a twelve-month 15%
degradation followed by a twelve-month 10% degradation
2) Quality rating is maintained at the equilibrium rating like the competitors
3) Quality is increased linearly by 50% over a ten-year period as shown in Fig. 5.50.
Explorer: Qity Vs. Time
1.50 1.3
u 1.1
~0.9
0.7
0
50
100
150
Time
Fig. 5.50: Assumed quality improvement for Explorer in case 3.
The model was used to simulate the three cases and the results are given below. For the sake of
brevity, only the new and used vehicle sales, and the used vehicle inventory are discussed.
84
200,000
172,500
145,000
117,500
90,000
_
0
_
16
__
32
48
64
80
96
112
128
144
Time (Month)
4yrQltylmprovmnt
4yrNoQltyHits
yrCadence (QtyHits)
Vehicles
Vehicles
Vehicles
Fig. 5.51: New Vehicle Sales for the 3 cases.
25,000
21,700
18,400
15,100
11,800
0
16
32
48
64
80
96
112
128
144
Time (Month)
4yrQltymprovmnt
4yrNoQltyHits
4yrCadence (QtyHits)
Fig. 5.52: Used Vehicle Inventory for the 3 cases.
The first case involving quality degradation was discussed at length in scenario 1 and it clearly
shows decreasing trend in sales coupled with an increasing trend used vehicle inventory.
If no quality degradation is present as assumed in case 2, the corresponding new vehicle sales
curve (shown in Fig. 5.51) shows a slight downward trend on the average initially before going
back up again. A slight increasing trend can be noticed overall. The corresponding used
85
inventory curve in Fig. 5.52 clearly shows an average inventory increase judging by the area
under the curve, but the trend, after the initial increase, is downward (even though it will be
converging to a average level higher than that at equilibrium). It is clear from these curves that a
four-year upgrade will give an increasing sales trend, albeit small, provided zero or near-zero
quality degradation is guaranteed. Product attractiveness in the market brought about by more
frequent product upgrades can overcome the increase in used inventory with no quality penalties.
Please note that cost impacts were not included in the model.
As seen from the curves in Figs. 5.51 and 5.52, any quality improvements over competition, if
achievable with more frequent product upgrades, provide a winning combination. The upward
trend in sales is noticed even at 32 months, where, the quality improvements are modest by
assumption. The declining trends in used vehicle inventory seen in scenario 3 are also repeated
here. The decline in used inventory is however a result of increased used vehicle sales (rather
than an overall declining attrition rate as in scenario 3) brought about by high quality used
Explorers being traded in, and is at the expense of competition's market share. This is evident
from the used vehicle sales presented in Fig. 5.53.
40,000
35,000
30,000
25,000
20,000
0
16
32
48
64
80
Time (Month)
96
112
128
144
4yrQltylmprovmnt
4yrNoQltyHits
yrCadence (QltyHits)
Fig. 5.53: Used Vehicle Sales for the three cases.
86
In the cases involving no quality degradation and quality improvements, the attractiveness of the
used vehicles coming into the used market is enhanced - improved styling changes in the first
case, and improved styling with enhanced quality in the second. These result in higher used sales
seen in Fig. 5.53 and lower used vehicle inventory seen in Fig. 5.52.
5.5 Scenario 4: Zero Percent Financing
Background:
Pricing has considerable effect on the buying habits and value equations of customers. The
automotive industry, which accounts for 6% of the GDP of the United Sates, slowed with the rest
of the economy in the summer of 2001. General Motors, which had a market share above 40% in
the early 80's, saw its share shrink below 30% in 2000. With the extraordinary events of
September 11, 2001, GM saw a drop in sales of 40% from normal. Rather than cut production
like what all manufacturers did in late 2000 (that shaved off nearly 1% off GDP for the fourth
quarter), GM came out with the "Let's Keep America Rolling" program that offered 0%
financing for all their vehicles. Most other manufacturers matched the offer leading to record
sales for the month of October 2001. Even though sales increased roughly by 30% overall,
questions were raised as to the long-term effects on the market. Differing opinions exist on
whether there is a "pull ahead" effect leading to shrinking future sales or not. The scenario
studied here ignores the cost constraints of the manufacturers and focuses primarily on the
effects in the market.
Scenario Description:
The model in equilibrium assumed an interest rate (APR) of 7.9%. To simulate the lowered APR
incentives, exogenous inputs with a temporary 3 or 6-month drop to 0% APR for all three
manufacturers were used. The exogenous input is given in Fig. 5.54. The simulation runs were
made for a 3-month and a 6-month incentive program.
87
APR Vs Time
0.1
0.08
e 0.06
0.
< 0.04
0.02
0
0
50
150
100
Time
Fig. 5.54: Exogenous input representing lowered APR for three or six months.
Results and Discussion:
The new vehicle sales for a 3-month program are given in Fig. 5.55. Since the focus of the
enquiry is on the overall effect on the market, the vehicle sales shown are the aggregate new
vehicle sales of all three manufacturers.
42,500
40,625
Approxim itely the same areas
38,750
36,875
35,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
Total New Vehicle Sales - 3mAPR
Fig. 5.55: Total new vehicle sales for a 3-month incentive program.
88
With a 3-month drop in the APR, the vehicle sales shoot up temporarily before going below the
equilibrium level. The area of the sales curve above the equilibrium level is approximately the
same as the area below. In this sense, there has been a pulling ahead of sales. It is interesting to
note that the effect of a large spike in sales over a short period is felt in the market over a much
longer period. In fact, the decreased demand for new vehicles lasts upwards of 2 years in this
case. However, this pattern in sales is not due to the market running out of customers because of
the "pull-ahead" effect (It should be noted that the assumptions in the model preclude an increase
in demand due to new customers coming into this market of three vehicles, i.e. people previously
unable to drive or afford vehicles, new or used. The pull-ahead applies to only existing
customers - one vehicle per customer - in the market universe considered in the model. Another
way to state this is that the sum of potential buyers and the customers at any given instant is a
constant). Rather, as discussed below, it is due to the increase in used vehicle inventory.
We saw earlier that increasing quality extended vehicle life resulting in lesser attrition of
vehicles from the system. Consequently, there is an increase in potential buyers followed by a
decline (see Fig. 5.49). However, no such declining trend is reflected in the current scenario. The
curve in Fig. 5.56 shows a jump in potential buyers when the price incentive comes into effect.
However, the level of potential buyers never goes below the equilibrium level like the vehicle
sales in Fig. 5.55. Hence, the dip seen in the new vehicle sales is not caused by the depletion of
potential buyers. As will be discussed later, such a trend in potential buyers would only have
been possible if the event that caused the spike in vehicle sales fundamentally increased the
trade-in-time of the products in market. Since price incentives do not change vehicle
performance/quality, it was assumed in the model that any changes in the trade-in-time is
temporary and that the effect on trade-in-time disappears once the incentives are taken away or
when the incentives reach saturation in the market.
89
125,000
121,250
117,500
113,750
110,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
PB: 3mAPR
Fig. 5.56: Spike in potential buyers due to APR incentives (but no dips below equilibrium levels
following the spike).
The spike in potential buyers corresponds to a sharp decrease in trade-in-time due to the
incentives (see Fig. 5.57). This could happen in the environment considered in the model, if the
monthly payment for current customers is substantially lower under the new terms.
60
50
40
30
20
0
16
32
48
64
80
96
112
128
144
Time (Month)
TiT N[Explorer] : 3mAPR
TiT N[GrandCherokee] : 3mAPR
TiT N[FourRunner] : 3mAPR
Fig. 5.57: Temporary drop in Trade-In-Time due to APR incentives.
90
The drop in trade-in-time disappears as soon as the duration of the incentives expires. As was
mentioned earlier, if there were any mechanism by which the trade-in-time increased above
equilibrium levels after the initiation of the incentives, we would have seen a corresponding dip
in the level of potential buyers.
The explanation for the "pull-ahead" effect seen in the new vehicle sales can be seen from the
interaction of the used and the new vehicle market.
Beginning of the increase in new
vehicle sales from equilibrium
Beginning of the dip in new
ehicle sales below equilibrium
82,500
80,000
77,500
75,000
72,500
0
4
8
12
16
20
24
Tinr (Mo rth)
28
32
36
40
Total Used Vehicle Sabs - 3mAPR
Fig. 5.58: Total used vehicle sales for APR incentives (time scale expanded in comparison to
Fig. 5.55 for clarity)
The used vehicle sales curve shown in Fig. 5.58 has an expanded time scale when compared to
Fig. 5.55 that shows the new vehicle sales. This was done to illustrate the increase in used
vehicle sales that soon followed the rise in new vehicle sales, but with a slight lag as seen in Fig.
5.58. When the APR incentives were introduced at 12 months, the new vehicle sales soon
increased dramatically. The used sales dipped slightly initially before following the upward trend
seem in the new sales. This is because sooner than usual trade-ins facilitated by APR incentives
91
increased the used vehicle inventory at a fast pace (see Fig. 5.59). This made the used vehicle
market, like in the other scenarios discussed earlier, more attractive. The shift from the new
vehicle market to the used is the primary mechanism in the model that explains the "pull-ahead"
effect seen in the new vehicle sales trend (Fig. 5.55).
If the duration of the incentives is doubled from three to six months, the dynamics remain the
same, but the effects are more pronounced. Even though the short-term benefits are greater for
larger duration of the incentive, the adverse effects on the post-incentive periods are equally
dramatic. The new vehicle sale curves for the three and six-month incentive schemes are
compared in Fig. 5.60.
400,000
375,000
350,000
325,000
300,000
0
16
32
48
64
80
96
112
128
144
Time (Month)
UVI: 3mAPR
Fig. 5.59: Increase in Used Vehicle Inventory due to 3-month APR incentive.
92
45,000
42,500
40,000
37,500
35,000
0
16
32
48
64
80
Time (Month)
96
112
128
144
All New Sales: 3mAPR
All New Sales: 6mAPR
Fig. 5.60: Comparison of new vehicle sales for three and six-month APR incentive schemes.
Conclusions:
Within the constraints and assumptions of the model, the mechanism for the "pull-ahead" effect
seen in new vehicle sales is not in the sudden depletion of potential customers but rather in the
shift in sales from the new to the used market. Sooner than usual trade-ins result in a sudden
increase in used vehicle inventory leading to lower residual values and high cost of ownership
for new vehicle buyers. Simulation results once again show the significant effect of the increase
in the used vehicle inventory on new vehicle sales. Additionally, results show that, even though
an increase in duration of the incentives produces significant short-term sales increase, the postincentive sales depress more due to a more bloated used vehicle inventory.
93
6. General Comments and Next Steps
In all the scenarios considered here, the simulation results stresses the need to consider the
impact of the used vehicle inventory when product plans are developed. The restraining force
on new vehicle sales brought about by more frequent product changes is not cost alone but
also quality and the ensuing effects of used vehicle market on the new. Temporary jumps in
sales mask the overall downward trend due to degraded quality and used vehicle market
effects.
As reported in system dynamics literature, system dynamics modeling provided good insights
into the automotive systems studied in this project. Some general comments regarding the
method are given below.
*
Typical human learning happens through hypothesis, tests, observation of results, and
adjustment of understanding or learning. However, this process becomes difficult when
the feedback from our actions is far removed in time and is often ambiguous [3]. In the
scenarios 1 and 2, even though short-term sales were increasing, the overall sales were
going downward due to quality hits. A policy to compress development cycle times for
quicker product introductions is bound to fail in the long run if there is a degradation in
quality. The results show that quicker introductions increase used vehicle inventory in
such a case, thereby decreasing residual value and increasing cost of ownership.
" The concept of "microworlds" [3] - system dynamics models of full systems - will enable
company management to study systemic issues in compressed time and space through
simulation. Through microworlds, complex interactions between product plans and
strategies, product development, and market forces can be learned, and management's
mental models updated. Internal contradictions of strategies and the consequent costs of
failure could thus be minimized. Linking models of market behavior to product
development models will thus provide such a microworld for the auto industry.
94
"
System dynamics models are not forecasting tools based on historical data, but rather it is
built on the understanding of the dynamics involved in the system. The structure of the
model, however, should be able to generate the historical data from simulation results.
The model in the current study is not correlated in that sense primarily because the
''system" is incomplete.
" System dynamics was found to be a good tool in studying the efficacy of strategies,
especially when the effects of the actions has a delayed feedback and the when the effects
are felt in a different part of the system
The model discussed in this study had many simplifying assumptions. Depending on the
focus of future development, many of the missing pieces may need inclusion.
*
The effects of the economic conditions were ignored in this model. Furthermore, since
the automobile industry accounts for 6% of the United States's Gross Domestic Product
and employs 7 million people [35], the success of automobile companies have profound
effect on the economy itself. This feedback is ignored in the model and may need
inclusion.
" The change in population and demographics will change the market segments as well as
their value equations. Structure representing demographics is not included in the model.
These effects may need to be included for the analysis of the system.
*
Government regulations on emissions and their impact in not included in the model. The
effect of fuel prices, ignored in the current model, may need inclusion.
"
A standard "market share" system dynamics molecule was used to determine the share of
sales. When dealing with finer levels of refinement (like multiple market segments), other
methods like probabilistic or agent-based modeling could be used to enhance the model.
95
* In the simulations, only a three-product SUV market is considered. In reality, there are
upwards of 50 products competing in this segment. At least the dominant products will
have to be included to make the calculated numbers more realistic.
*
Customer loyalty was not explicitly modeled in this study. This could be included in the
structure associated with 'Brand Opinion' in the current model.
96
7. References
1.
Crawley, E., "System Architecture", SDM Core Course, 2000.
2.
Wheelwright, S. C., and Clark, K. B., "Revolutionizing Product Development", Page
5, Pages 313-316, Free Press, 1999.
3.
Senge, P. M., "The Fifth Discipline - The Art and Practice of The Learning
Organization", Pages 71-73, Pages 313-335, Currency Paperback Edition, 1994.
4.
Rechtin, E, and Maier, M. W., "The Art of Architecting", Page 24, Page 13, CRC
Press, 1997.
5.
Ulrich, K. T., and Eppinger, S. D., "Product Design and Development", Page 37,
McGraw-Hill, 2000.
6.
Boppe, C., "Systems Engineering", SDM Core Course, 2000.
7.
Reinertsen, D., and Smith, P. G., "Developing Products in Half the Time", Page 71,
John Wiley & Sons, 1998.
8.
Sterman, J. D., "Business Dynamics: Systems Thinking and Modeling for a Complex
World", Pages 21-23, Pages 42-55, McGraw-Hill, 2000.
9.
Eppinger, S., "A Planning Method for Integration of Large Scale Engineering
System", ICED 97, August 19-21, 1997.
10.
Eppinger, S., and Whitney, D., "A Model-Based Method for Organizing Tasks in
Product Development", Research in Engineering Design, 6:1-13, Springer-Verlag,
1994.
11.
Zambito, A. P., "Using Design Structure Matrix to Streamline Automotive Hood
System Development", SDM M.S. thesis, MIT, 2000.
12.
Reinertsen, D., "Managing the Design Factory", Pages 45, 59, 89, and 127, The Free
Press, 1997.
13.
Thomke, S.H., "Simulation, Learning, and R&D Performance: Evidence from
Automotive Development", Research Policy 27, 55-74, Elsevier, 1998.
14.
Thomke, S. H., and Fujimoto, T., "The Effect of 'Front-Loading' Problem-Solving on
Product Development Performance", The Journal of Product Innovation
Management, Vol. 17, No. 2, March 2000.
97
15.
Keough, M., and Doman, A., "The CEO as Organization Designer: An Interview with
Professor Jay Forrester, the Founder of System Dynamics", The McKinsey
Quarterly, No. 2, pp 03-30, 1992.
16.
Forrester, J. W., "Industrial Dynamics", Productivity Press, Cambridge MA, 1961.
17.
Forrester, J. W., "Urban Dynamics", Productivity Press, Cambridge MA, 1969.
18.
Senge, P. M., "The System Dynamics National Model Investment Function", PhD
Dissertation, MIT, 1978.
19.
Roberts, E. B., "Managerial Applications of System Dynamics", Productivity Press,
Cambridge MA, 1978.
20.
Mayo, D., D., Pott, J. D., Dalton, W. J., "A System Dynamics Perspective on the
Risks and Benefits of Public Transport Restructure: Case Study of London
Underground", The
UITP International Congress, Toronto, Canada, May,
1999.System Dynamics Review, Vol. 16, No. 1, 2000.
5 3 rd
21.
Lyneis, J. M., "System Dynamics for Market Forecasting and Structural Analysis",
System Dynamics Review, Vol. 16, No. 1, 2000.
22.
Fiddaman, T., "Feedback Complexity in Integrated Climate-Economy Models", PhD
Dissertation, MIT, 1990.
23.
Systems Dynamics Society, President's Newsletter, Vol. 13, No. 1, May, 2000.
24.
Prasad, M. V. N., and Chartier, D. A, "Modeling Organizations Using Agent-Based
Simulations', presented at 'A Workshop on Agent Simulation: Applications, Models
& Tools', Chicago, October 1999.
25.
Ford's Program Management Modeling System, FPDS Digest, Vol. 3, No.4, April
1998.
26.
Gerwin, H. R., "A System Dynamics Study of Technology Strategy Implementation",
M. S. Thesis, MIT, 2000.
27.
The New York Times, "Vehicle Sales Remain Robust This Month", November 28,
2001.
28.
The New York Times, "Ford to Curtail Auto Production and Cut 5000 Jobs", August
18, 2001.
29.
The New York Times, "Car-based SUVs are Unlikely to Stem Slide in Fuel
Economy", May 19, 2001.
98
30.
Global Portfolio Structure Research: USA - 17 Segment Solutions, Ford Motor
Company, March 2001.
31.
Hines, J., "Molecules of Structure", Version 1.4, 1997.
32.
Used vehicle prices of older models from www.edmunds.com.
33.
Class Notes, Engineering Risk Benefit Analysis, SDM course, 2000.
34.
"Mysteries of Brand Equity Revealed", http://universe.indiana.edu/clp/be/brandl .htm,
2001.
35.
"GM's 0% Finance Plan is Good for Economy, Risky for the company", Wall Street
Journal, October 30, 2001.
99
8. Avvendix: Model Equations
New Market Share Weighting [Products, CustSegments]=
1+Positive Value Ratio Changes[Products,CustSegments]
New Veh Market Share By Prod[Products,CustSegments]=
(New Product Attractiveness[Products,CustSegments]*New Market Share
Weighting[Products,CustSegments]*Veh Availability[Products]/
SUM(New Product Attractiveness[Products!,CustSegments]*Veh
Availability[Products!]*New Market Share Weighting[Products!,CustSegments]))*
New Veh Market Share[CustSegments]
~
Dmnl
Indicated New Product Attractiveness[Fords,CustSegments]=
((1-OnOff EoP)+OnOff EoP*Effect of Price on Product
Attractiveness[Fords,CustSegments])*
Calibration for Eff of Price on NewProdAttr[Fords,CustSegments]*
((1-OnOff BCI)+OnOff BCI*Brand Consideration Index[FORD,CustSegments]*
Customer Value Change[Fords,CustSegments]) ~~I
Indicated New Product Attractiveness[Chryslers,CustSegments]=
((1-OnOff EoP)+OnOff EoP*Effect of Price on Product
Attractiveness[Chryslers,CustSegments])*
Calibration for Eff of Price on NewProdAttr[Chryslers,CustSegments]*
((1-OnOff BCI)+OnOff BCI*Brand Consideration
Index[CHRYSLER,CustSegments]*
Customer Value Change[Chryslers,CustSegments])
Indicated New Product Attractiveness[Toyotas,CustSegments]=
((1-OnOff EoP)+OnOff EoP*Effect of Price on Product
Attractiveness[Toyotas,CustSegments])*
Calibration for Eff of Price on NewProdAttr[Toyotas,CustSegments]*
((1-OnOff BCI)+OnOff BCI*Brand Consideration
Index[TOYOTA,CustSegments]*
Customer Value Change[Toyotas,CustSegments])
~
Init
Index
NewProdValue[Products,CustSegments]=
INITIAL(
New Product Value [Products,CustSegments])
Customer Value Change [Products, CustSegments]=
Rel Value Ratio [Products, CustSegments]*
(New Product Value[Products,CustSegments]/Init
NewProdValue[Products,CustSegments])
Overall Value Change[Products,CustSegments]=
Value Ratio New OnRoad[Products,CustSegments]*
(Rel Value Ratio[Products,CustSegments]/Rel
NewOnRoadValueRatio [Products, CustSegments])
100
Positive Value Ratio Changes [Products,CustSegments]=
IF THEN ELSE(Overall Value Change[Products,CustSegments]>1,
Value Change[Products,CustSegments]-l , 0
Overall
New Veh Sales[Products,CustSegments]=
NonZeroProtect
LOOKUP (NVIRelLevel [Products])*
NonZeroProtect LOOKUP(PotBuyersRelLevel[CustSegments])*
New Veh Market Share By Prod[Products,CustSegments]
*VehsPerCustomer[CustSegments] *Potential Buyers [CustSegments]
~
Vehicles/Month
~
This is an array and will have to be modified
Customer Satisfaction Dealer [Manufacturer]=
SMOOTH(Target CustSat Dealer[Manufacturer],3)
~~
Dmnl
EPPA[Products]=
SUM(Effect of Price on Product Attractiveness[Products,CustSegments!])
Target CustSat Dealer[Manufacturer]=
1*Eff of RelServPrice On CustSat[Manufacturer]*Eff of RelServTime On
CustSat[Manufacturer]*Eff of VehAndService Availability on
CustSat[Manufacturer]
Dmnl
C=
SUM (Customers [Products!, CustSegments!])
Target TiTU[Products,CustSegments]=
Normal TradeInTimeUsed[Products,CustSegments]*
Eff of APRRatio on TiTU[Products]*
Eff of Qlty on TiTUsed[Products,CustSegments]*
Eff of RelValueRatio on TiTU[Products,CustSegments]*
Eff of Value Change on TiTU[Products,CustSegments]
Month
TradeInTimeUsed[Products,CustSegments]=
SMOOTH (Target TiTU[Products,CustSegments]
,2)
Month
Target TiTN[Products,CustSegments]=
Normal
Eff of
Eff of
Eff of
TradeInTimeNew [Products, CustSegments]*
RelValRatio on TiTN[Products,CustSegments]*
Value Change on TiTN[Products,CustSegments]*
Qlty on TiTNew[Products,CustSegments]*
101
Eff of APRRatio on TiTN[Products]
~
Month
TradeInTimeNew[Products,CustSegments]=
SMOOTH(Target TiTN[Products,CustSegments],2)
~
Init
Month
The LOOKUP Table Could be made for each CustSegment
Avg APR of Used Veh Inv
[Products]=
INITIAL(
APR rate New[Products])
~
Index
Avg APR New OnRoad Veh [Products]= INTEG
Change in APR for New OnRoad[Products],
Init Avg APR of New OnRoad veh[Products])
Index
Avg APR of Used OnRoad Veh [Products]= INTEG
Change in APR of Used OnRoad Veh[Products],
Init Avg APR of Used OnRoad Veh[Products])
~
Index
Avg APR of Used Veh Inv
[Products]= INTEG
Change in Avg APR of Used Veh Inv[Products],
~
Init
Init Avg APR of Used Veh Inv[Products])
Index
Avg APR of New OnRoad veh[Products]=
APR rate New[Products])
INITIAL(
Change in APR for New OnRoad [Products]=
(APR rate New[Products]-Avg APR New OnRoad Veh[Products])/Dilution Time
of New OnRoad Veh[Products]
Index/Month
All New Sales=
SUM(New Veh Sales[Products!,CustSegments!])
All Used Sales=
SUM(Used Veh Sales[Products!,CustSegments!])
Eff of APRRatio on TiTU[Products]=
TradeInTimeNewVsAPR LOOKUP(APRRatioUsed[Products])
102
Change in Avg APR of Used Veh Inv [Products]=
(Avg APR of Veh flowing in to Used Veh Inv[Products]-Avg APR of Used
Veh Inv[Products])/
Dilution Time of Used Veh Inv[Products]
~
Index/Month
Avg APR of Veh flowing in to Used Veh Inv [Products]=
ZIDZ(
Avg APR New OnRoad
Veh[Products] *SUM(TradeInNew[ProductsCustSegments!])
+Avg APR of Used OnRoad Veh[Products]*
SUM (TradeInUsed [Products, CustSegments!]) ,SUM (TradeInNew [Products
,CustSegments!] )+SUM(TradeInUsed[Products,CustSegments!])
~
Index
APRRatioNew[Products]=
APR rate New[Products]/Avg APR New OnRoad Veh[Products]
APRRatioUsed[Products]=
APR rate New[Products]/Avg APR of Used OnRoad Veh[Products]
TradeInTimeNewVsAPR LOOKUP(
[(0,0.6)(2,2)], (0.00611621,0.75),
(0.0795107,0.768421), (0 .171254,
,0.868421) , (0.373089,0.902632),
875,0.989474),(1,1),(2,1))
Init
Avg APR of Used OnRoad Veh
APR rate New[Products])
Index
(0.525994,0.960526),
[Products]=
0. 807895), (0. 287462
(0 .648318,
0. 981579),
(0. 782
INITIAL(
Eff of APRRatio on TiTN[Products]=
Trade InTimeNewVsAPR LOOKUP (APRRatioNew [Products])
Change in APR of Used OnRoad Veh [Products]=
(Avg APR of Used Veh Inv[Products]-Avg APR of Used OnRoad
Veh[Products])/
Dilution Time of Used OnRoad Veh[Products]
~
Index/Month
Rel UsedOnRoadValueRatio[Products,CustSegments]=
Avg Used OnRoad Prod Value [Products, CustSegments]/
103
VMAX(Avg Used OnRoad Prod Value[Products!,CustSegments])
Dmnl
Eff of RelValRatio on TiTN[Products,CustSegments]=
Change in TradeInTimeVsRelValRatio LOOKUP(Rel Value
Ratio[Products,CustSegments]/Rel NewOnRoadValueRatio
[Products,CustSegments])
~Dmnl
Rel NewOnRoadValueRatio[Products,CustSegments]=
Avg New OnRoad Prod Value[Products,CustSegments]/
VMAX(Avg New OnRoad Prod Value[Products!,CustSegments])
Dmnl
Eff of RelValueRatio on TiTU[Products,CustSegments]=
Change in TradeInTimeVsRelValRatio LOOKUP(Rel Value
Ratio[Products,CustSegments]/Rel UsedOnRoadValueRatio
[Products,CustSegments])
~
Dmnl
Rel Value Ratio[Products,CustSegments]=
New Product Value[Products,CustSegments]/VMAX(New Product
Value[Products!,CustSegments])
Eff of Value Change on TiTN[Products,CustSegments]=
Change in TradeInTime Vs ValueRatio LOOKUP(Value Ratio New
OnRoad[Products,CustSegments])
Avg Used OnRoad Prod Value [Products,CustSegments]=
SUM(AttributeCustSegWghts[Attributes!,CustSegments]*Avg ProdAttribute
of Used OnRoad Veh[Products,Attributes!])/
SUM(AttributeCustSegWghts[Attributes!,CustSegments])
Value Ratio New OnRoad[Products,CustSegments]=
New Product Value[Products,CustSegments]/
Avg New OnRoad Prod Value[Products,CustSegments]
Dmnl
Value Ratio Used OnRoad[Products,CustSegments]=
New Product Value[Products,CustSegments]/
Avg Used OnRoad Prod Value[Products,CustSegments]
~
Dmnl
Avg New OnRoad Prod Value[Products,CustSegments]=
SUM(AttributeCustSegWghts[Attributes!,CustSegments]*
104
Avg ProdAttribute New OnRoad Veh[Products,Attributes!])/
SUM(AttributeCustSegWghts[Attributes!,CustSegments])
Eff of Value Change on TiTU[Products,CustSegments]=
Change in TradeInTime Vs ValueRatio LOOKUP(Value Ratio Used
OnRoad[Products,CustSegments])
Brand Build Ratio[Manufacturer,CustSegments]=
Brand Consideration Index[Manufacturer,CustSegments]/
SMOOTH(Brand Consideration Index[Manufacturer,CustSegments],36)
~
Dmnl
PB=
SUM(Potential Buyers[CustSegments!])
Init
Discount Factor [Products, CustSegments]=
Init Monthly Interest Rate New[Products]*(1+Init Monthly Interest Rate
New [Products] ) ^Normal TradeInTimeNew [Products, CustSegments]/
((l+Init Monthly Interest Rate New[Products])^Normal
TradeInTimeNew[Products,CustSegments]-1)
Init Monthly Interest Rate New[Products]=
(1+Init APR[Products] )A (1/12)-l
INPA[Products]=
SUM(Indicated New Product
Attractiveness [Products, CustSegments!] )/ELMCOUNT(CustSegments)
Init APR[Products]= INITIAL(
APR rate New[Products])
Change in Customers[FORD,CustSegments]=
SUM (Customers [Fords!, CustSegments] )/SUM (Init
Customers[Fords!,CustSegments])
--
I
Change in Customers [CHRYSLER, CustSegments]=
SUM (Customers [Chryslers!, CustSegments] ) /SUM (Init
Customers[Chryslers!,CustSegments])
--
I
Change in Customers[TOYOTA,CustSegments]=
SUM(Customers[Toyotas!,CustSegments])/SUM(Init
Customers[Toyotas!,CustSegments])
~
Dmnl
105
Indicated Brand Awareness Index [Manufacturer, CustSegments] =
Init BrandAwarenessRating[Manufacturer,CustSegments]*
CustShareVsBrandAwareness LOOKUP(SMOOTH(Change in
Customers[Manufacturer,CustSegments],12))
Index
Rel UVQlty[Products]=
Used Veh Inv Quality[Products]/Init Best UVQlty
Init Used Veh Qlty[Products]=
Quality TGW Vs Time LOOKUP[Products] (Init Avg Age of Used Veh
Inv[Products])
Eff of Quality on Used Prod Attractiveness[Products]=
Quality Vs Used Prod Attr LOOKUP(Rel UVQlty[Products])
Dmnl
Init Eff of Qlty on UsedProdAttr[Products]=
Quality Vs Used Prod Attr LOOKUP(Init Used Veh Qlty[Products]/VMIN(Init
Used Veh Qlty[Products!]))
Init Best UVQlty= INITIAL(
VMIN(Used Veh Inv Quality[Products!]))
TiT U[Products]=
SUM(TradeInTimeUsed[Products,CustSegments!])/ELMCOUNT(CustSegments)
TiT N[Products]=
SUM(TradeInTimeNew[Products,CustSegments!])/ELMCOUNT(CustSegments)
Eff of Qty on TiT N[Products]=
SUM(Eff of Qlty on
TiTNew[Products,CustSegments!])/ELMCOUNT(CustSegments)
TiU C[CustSegments]=
SUM (TradeInUsed [Products!, CustSegments])
106
TiN C[CustSegments]=
SUM(TradeInNew[Products!,CustSegments])
VehLife Vs Qlty LOOKUP(
[(-0.008,0)-(2,2)],(0.00185933,1.70175),
(1.33067,0.763158),
(0.366581,1.61404),
(1.66226,0.622807),
(0.735021,1.30702),
(2,0.535088))
(1,1)
Avg Veh Life[Products]=
Eff of Qlty on Veh Life[Products]*Normal Avg Veh Life[Products]
Month
Init AAVtoUVI[Products]= INITIAL(
Avg Age of Veh flowing in to Used Veh Inv[Products])
Init DTofUVI[Products]= INITIAL(
Dilution Time of Used Veh Inv[Products])
Change in Demand:=
GET XLS DATA('Fl.XLS','Change in Demand'
,
'A'
,
'B2'
Normal Avg Veh Life[Products]=
120,120,132
-
Month
Init Avg Age of Used Veh Inv[Products]=
Init
~
AAVtoUVI [Products] +Init
Month
DTofUVI [Products]
Eff of Qlty on Veh Life[Products]=
VehLife Vs Qlty LOOKUP(TGW at Veh Life[Products]/Init TGW at Veh
Life[Products])
~
Dmnl
Init TGW at Veh Life[Products]= INITIAL(
TGW at Veh Life[Products])
~
TGW
TGW at Veh Life[Products]=
Quality TGW Vs Time LOOKUP[Products] (Normal Avg Veh Life[Products])/
Used OnRoad Qlty Factor[Products]
~
TGW
107
New Entrants[CustSegments]=
5000*Change in Demand
customers/Month
Init
Monthly Cost Used[Products,CustSegments]=
((1+Maintenance Penalty)*Init ResValue[Products]-Init
ScrapResValue [Products,CustSegments] ) *Init
Discount Factor
Used[Products,CustSegments]
~
Dollars
Init
Discount Factor Used[Products,CustSegments]
Monthly Rate Used[Products]*(1+Monthly Rate Used[Products])^Normal
TradeInTimeUsed[Products,CustSegments]/
((1+Monthly Rate Used[Products])^Normal
TradelnTimeUsed[Products,CustSegments]-1)
Init Eff of Price on UsedProdAttr[Products,CustSegments]=
Init CustValueForPriceUsed[Products,CustSegments]
~
Index
Init
ScrapResValue [Products,CustSegments]=
"Reference ResPercent Vs. Avg Age LOOKUP"[Products] (Init Avg Age of
Used Veh Inv[Products]+Normal TradeInTimeUsed[Products,CustSegments])
Init
CustValueForPriceUsed[Products,CustSegments]=
0.5-0.5*TANH((Init Monthly Cost Used[Products,CustSegments]-(Cust
Acceptable Amt[CustSegments]+Cust Ideal Amt[CustSegments])/2)
*CalibConst[CustSegments])
~
Dmnl
Monthly Cost For Used Veh[Products,CustSegments]=
((1+Maintenance Penalty)*Residual Value[Products]-Scrap Residual
Value[Products,CustSegments])*
Discount Factor Used[Products,CustSegments]
~
Init
Dollars
CustValueForPrice [Products,CustSegments]=
0.5-0.5*TANH((Init Monthly Cost New[Products,CustSegments]-(Cust
Acceptable Amt[CustSegments]
+Cust Ideal Amt [CustSegments] ) /2) *CalibConst [CustSegments])
~
Dmnl
Monthly Rate Used[Products]=
(1+APR Rate Used[Products]
)A (1/12)-l
108
Discount Factor Used [Products, CustSegments]=
Monthly Rate Used[Products]*
(1+Monthly Rate Used[Products])^Normal
TradeInTimeUsed[Products,CustSegments]/
((1+Monthly Rate Used[Products])^ANormal
TradeInTimeUsed [Products, CustSegments] -1)
TradeInValue[Products,CustSegments]=
Eff of Used Inv on Res Value[Products]*
"Reference ResPercent Vs. Avg Age LOOKUP"[Products] (Normal
TradelnTimeNew[Products,CustSegments])*
MSRP[Products]
~
Dollars
Eff of Price on Used Prod Attractiveness [Products,CustSegments]=
CustValueEqForPrice Used[Products,CustSegments]
Dmnl
CustValueEqForPrice Used[Products,CustSegments]=
0.5-0.5*TANH((Monthly Cost For Used Veh[Products,CustSegments]-(Cust
Acceptable Amt[
CustSegments]+Cust Ideal Amt [CustSegments] ) /2) *CalibConst [CustSegments])
Dmnl
APR Rate Used[Products]=
0.07
-
Dmnl
Scrap Residual Value [Products,CustSegments]=
"Reference ResPercent Vs. Avg Age LOOKUP"[Products] (Avg Age of Used Veh
Inv [Products] +Normal TradeInTimeUsed [Products, CustSegments])
Init Eff of Price on NewProdAttr[Products,CustSegments]=
Init CustValueForPrice[Products,CustSegments]
~
Dmnl
Maintenance Penalty=
0.1
-
Dmnl
Init
Monthly Cost New[Products,CustSegments]=
(Init New Veh Price[Products]-Init
TradeInValue[Products,CustSegments])*
Init
Discount Factor [Products, CustSegments]
109
Init TradeInValue[Products,CustSegments]=
MSRP[Products]*
"Reference ResPercent Vs. Avg Age LOOKUP"[Products] (Normal
TradelnTimeNew[Products,CustSegments])
Effect of Price on Product Attractiveness[Products,CustSegments]=
CustValueEqForPrice New[Products,CustSegments]
Dmnl
Present Vlaue of Total Cost[Products,CustSegments]=
New Veh Price[Products]-TradeInValue[Products,CustSegments]
~
Dollars
Monthly Interest Rate New[Products]=
(1+APR rate New[Products])^(1/12)-l
~
Dmnl
CalibConst[CustSegments]=
2*0.5*LN((1+(2*IdealAmtValue[CustSegments]-1))/
(1-(2*IdealAmtValue[CustSegments]-1)))/
(Cust Acceptable Amt[CustSegments]-Cust Ideal Amt[CustSegments])
Cust Ideal Amt[CustSegments]=
252.2,247,227.5,256.75,252.2
This value corresponds to a 80% customer value for price.
Discount Factor New[Products,CustSegments]=
Monthly Interest Rate New[Products]*
(1+Monthly Interest Rate New[Products])^Normal
TradeInTimeNew[Products,CustSegments]
((1+Monthly Interest Rate New[Products])^Normal
TradeInTimeNew[Products,CustSegments]-1)
IdealAmtValue[CustSegments]=
0.65
~
Acceptable amount value is
(1-IdealAmtValue)
APR rate New[Products]:=
GET XLS DATA('Fl.XLS','APR New'
,
'A'
,
'B2'
110
Monthly Cost for New Purchase [Products, CustSegments] =
Present Vlaue of Total Cost[Products,CustSegments]*Discount Factor
New[Products,CustSegments
CustValueEqForPrice New[Products,CustSegments]=
0.5-0.5*TANH((Monthly Cost for New Purchase[Products,CustSegments](Cust Acceptable Amt
[CustSegments]+Cust Ideal Amt[CustSegments])/2)*CalibConst[CustSegments])
Cust Acceptable Amt[CustSegments]=
523.8,513,472.5,533.25,523.8
~
Dollars
This value corresponds to roughly a 20% customer value for price
Actual Brand Quality[FORD]=
SUM(New Product Quality[Fords!]*New Product Share of
Brand[Fords!]*NewPrdWght+
High Mileage Product Quality[Fords!]*Used Product Share of Brand[Fords!]*
(1-NewPrdWght)) ~~I
Actual Brand Quality[CHRYSLER]=
SUM(New Product Quality[Chryslers!]*New Product Share of
Brand[Chryslers!]*NewPrdWght
+High Mileage Product Quality[Chryslers!]* Used Product Share of
Brand[Chryslers!]*(l-NewPrdWght))
Actual Brand Quality[TOYOTA]=
~~|
SUM(New Product Quality[Toyotas!]*New Product Share of
Brand[Toyotas!]*NewPrdWght+
High Mileage Product Quality[Toyotas!]*Used Product Share of
Brand[Toyotas!]*(l-NewPrdWght))
TGW
NewPrdWght=
0.8
~
Dmnl
AORU[Products]=
SUM(Attrition OnRoadUsed[Products,CustSegments!])
Init
Actual New Product Quality[Products]=
Quality TGW Vs Time LOOKUP[Products] (Initial Time in Service)
~
TGW
Init Actual Used Product Quality[Products]=
Quality TGW Vs Time LOOKUP[Products] (High Mileage Time in Service)
111
New OnRoad Qlty Factor[Products]=
Avg ProdAttribute New OnRoad Veh[Products,Quality]/Init Avg
ProdAttribute of New OnRoad veh[Products,Quality]
Dmnl
Init UsedProdQlty[Products]=
INITIAL(
Actual Used OnRoad Prod Qlty[Products])
Month
Actual New OnRoad Prod Qlty[Products]=
Quality TGW Vs Time LOOKUP[Products] (Avg Age of New OnRoad
Veh [Products]) /New OnRoad Qlty Factor [Products]
TGW
Actual Used OnRoad Prod Qlty[Products]=
Quality TGW Vs Time LOOKUP[Products] (Avg Age of Used OnRoad
Veh [Products]) /Used OnRoad Qlty Factor [Products]
TGW
Actual2InitialQltyRatioNew[Products]=
SMOOTH3(Actual New OnRoad Prod Qlty[Products]/
Init NewProdQlty[Products],12)
~
Dmnl
Actual2InitialQltyRatioUsed[Products]=
SMOOTH3(Actual Used OnRoad Prod Qlty[Products]/
Init UsedProdQlty[Products],12)
~
Dmnl
Used OnRoad Qlty Factor[Products]=
Avg ProdAttribute of Used OnRoad Veh[Products,Quality]/
Init
Avg ProdAttribute of Used OnRoad Veh[Products,Quality]
Dmnl
Init NewProdQlty[Products]=
INITIAL(
Actual New OnRoad Prod Qlty[Products])
TGW
Initial Qlty Factor[Products]=
DELAY FIXED(NewProductAttributeFactors[Products,Quality],
Initial Time
in Service ,NewProductAttributeFactors [Products,Quality]
~
~
Dmnl
I
High Mileage Product Quality[Products]=
Quality TGW Vs Time LOOKUP[Products] (High Mileage Time in Service)/
112
High Mileage Qlty Factor[Products]
TGW
High Mileage Qlty Factor[Products]=
DELAY FIXED(NewProductAttributeFactors[Products,Quality], High Mileage
Time in Service, NewProductAttributeFactors[Products,Quality]
Dmnl
New Product Quality[Products]=
Quality TGW Vs Time LOOKUP [Products] (Initial
Initial Qlty Factor[Products]
TGW
~
This is an exogenous input
Time in Service)/
Initial Time in Service=
3
Month
High Mileage Time in Service=
60
-
Month
PBVI[Manufacturer]=
SUM(Perceived Brand Value
Index [Manufacturer, CustSegments!
)/ELMCOUNT (CustSegments)
BAI[Manufacturer]=
SUM(Brand Awareness
Index [Manufacturer, CustSegments!] )/ELMCOUNT (CustSegments)
Qlty Changes UsedVehInv[Products]=
Avg ProdAttribute of Used Veh Inv[Products,Quality]/Init Avg
ProdAttribute of Used Veh Inv[Products,Quality]
Dmnl
Used Veh Inv Quality[Products]=
Quality TGW Vs Time LOOKUP[Products] (Avg Age of Used Veh
Inv [Products]) /Qlty Changes UsedVehInv[Products]
~
TGW
Avg ProdAttribute New OnRoad Veh [Products,Attributes]= INTEG
Change in ProdAttribute of New OnRoad Veh[Products,Attributes],
Init Avg ProdAttribute of New OnRoad veh[Products,Attributes])
~
Index
113
Init
Avg ProdAttribute of New OnRoad veh[Products,Attributes]=
Init NewProduct Attributes[Products,Attributes]
BCI[Manufacturer]=
SUM(Brand Consideration Index[Manufacturer,CustSegments!])
Index
UVMS[Products]=
SUM(Used Veh Market Share By Prod[Products,CustSegments!])
~
Dmnl
PotBuyersFrac[CustSegments]=
Potential Buyers[CustSegments]/SUM(Potential Buyers[CustSegments!])
Dmnl
UPA[Products]=
SUM(Used Product
Attractiveness [Products, CustSegments!] *PotBuyersFrac [CustSegments!])
NPA[Products]=
SUM(PotBuyersFrac[CustSegments!]*New Product
Attractiveness[Products,CustSegments!])
NVMS[Products]=
SUM(New Veh Market Share By Prod[Products,CustSegments!])
~
Dmnl
RelNewProdAttrRunAvgRatio [Products, CustSegments]=
SMOOTH (RelNewProdAttrRatio [Products,CustSegments] ,0.25)/
SMOOTH (RelNewProdAttrRatio [Products, CustSegments] ,36)
~
Dmnl
SUM (TradeInNew [Products, CustSegments!
)
TiN P[Products]=
TiU P[Products]=
SUM (TradeInUsed [Products, CustSegments!])
UVS[Products]=
SUM(Used Veh Sales[Products,CustSegments!])
114
NVS[Products]=
SUM(New
Veh Sales [Products,CustSegments!])
CustSegment Percentages [Products, CustSegments]=
(Ini Pot Buyers[CustSegments]/SUM(Ini Pot Buyers[CustSegments!]))*
((Init NewProdAttr[Products,CustSegments]+
Init
UsedProdAttr[Products,CustSegments] )/
SUM(Init
NewProdAttr [Products!,CustSegments] +Init
UsedProdAttr[Products!,CustSegments]))
~
Dmnl
VehAvailVsInvRatio LOOKUP(
[(0,0)-
(2,1)], (0,0), (0.0795107,0.0921053),
(0.122324,0.197368),
(0.171254,0.315789),
(0.183486,0.442982), (0.201835,0.596491), (0.220183,0.723684), (0.250765,0.85526
3), (0.342508,0.951754), (0.507645,0.97807), (0.782875, 1), (1, 1) (2, 1))
~
Dmnl
Veh Availability[Products]=
ACTIVE INITIAL
VehAvailVsInvRatio LOOKUP(Inv Ratio[Products]),1)
~
Dmnl
ZeroVehRefLevel=
500
CustRelLevel[Products,CustSegments]=
Customers[Products,CustSegments]/ZeroVehRefLevel
PotBuyersRelLevel[CustSegments]=
Potential Buyers[CustSegments]/ZeroVehRefLevel
UVIRelLevel[Products]=
Used Veh Inv[Products]/ZeroVehRefLevel
ORVNewRelLevel[Products]=
OnRoad Veh[Products,New]/ZeroVehRefLevel
ORVUsedRelLevel[Products]=
OnRoad Veh[Products,Used]/ZeroVehRefLevel
115
Total New Veh Attractiveness [CustSegments]=
SUM(New Product Attractiveness[Products!,CustSegments])
NVIRelLevel[Products]=
New Veh Inv[Products]/ZeroVehRefLevel
Dmnl
Eff of Qlty on TiTUsed[Products,CustSegments]=
TradeInTimeUsedVsQuality LOOKUP(Actual2InitialQltyRatioUsed[Products])
~
Dmnl
I
~
RatioOfUsed2RefInv[Products]=
XIDZ(Used Veh Inv[Products],Reference Used Veh Inv[Products],100)
Dmnl
-
Init UsedVehInv[Products]=
(Init ProdCapacity Based on Sales Forecasts[Products]Init OnRoadVeh ByUsedProd[Products]*SUM(Init Veh Fraction By
Segment [Products,CustSegments!] / (Avg Veh Life [Products]
Normal TradeInTimeNew[Products,CustSegments!])))*
(Avg Veh Life[Products]-SUM(Veh Fraction By
Segment[Products,CustSegments!]*
Normal TradeInTimeNew [Products, CustSegments!]))
Vehicles
Eff of Qlty on TiTNew[Products,CustSegments]=
TradeInTimeNewVsQuality LOOKUP(Actual2InitialQltyRatioNew[Products])
~
Dmnl
Init OnRoadVeh ByNewProd[Products]=
Init ProdCapacity Based on Sales Forecasts[Products]/
SUM(Init Veh Fraction By Segment[Products,CustSegments!]/
Normal TradeInTimeNew [Products, CustSegments!])
Vehicles
Reference Used Veh Inv[Products]=
INITIAL(
Used Veh Inv[Products])
~
Vehicles
Normal TradeInTimeNew [Products, CustSegments]=
45.45,45.45,45.45,45.45,45.45;45.45,45.45,45.45,45.45,45.45;49.08,49.08
,49.08,49.08,
49.08;
~
Month
Init OnRoadVeh ByUsedProd[Products]=
116
+
SUM(Init Used Veh Sales[Products,CustSegments!])/
(SUM(Init Veh Fraction By Segment[Products,CustSegments!]/
Normal TradeInTimeUsed [Products, CustSegments!] )
SUM(Init Veh Fraction By Segment [Products,CustSegments!]/
(Avg Veh Life[Products]-Normal TradeInTimeNew[Products,CustSegments!])))
~
Vehicles
Normal TradeInTimeUsed [Products, CustSegments]=
59.1,59.1,59.1,59.1,59.1;59.1,59.1,59.1,59.1,59.1;65,65,65,65,65;
~
Month
~
55
Calibration for Eff of Price on UsedProdAttr[Products,CustSegments]= INITIAL(
Init UsedProdAttr[Products,CustSegments]/Init Used Prod
Attract[Products,CustSegments])
~
Dmnl
Init BrandValueIndex[FORD,CustSegments]=
SUM(Init ProductValueIndex[Fords!,CustSegments]*Init
OnRoadProdFractionPerBrand[Fords!]) -Init
BrandValueIndex[CHRYSLER, CustSegments]=
SUM(Init ProductValueIndex[Chryslers!,CustSegments]*
Init OnRoadProdFractionPerBrand[Chryslers!]) ~~Init BrandValueIndex[TOYOTA,CustSegments]=
SUM(Init ProductValueIndex[Toyotas!,CustSegments]*
Init OnRoadProdFractionPerBrand[Toyotas!])
~
Index
Indicated Brand Value Index[FORD,CustSegments]
SUM(New Product Value[Fords!,CustSegments]*New Product Share of
Brand[Fords!]+
Used Product Value[Fords!,CustSegments]*Used Product Share of
Brand[Fords!])/
Max Value
Indicated Brand Value Index [CHRYSLER, CustSegments]=
SUM(New Product Value[Chryslers!,CustSegments]*New Product Share of
Brand[Chryslers!]+
Used Product Value[Chryslers!,CustSegments]*Used Product Share of
Brand[Chryslers!])
Max Value
Indicated Brand Value Index [TOYOTA, CustSegments]=
SUM(New Product Value[Toyotas!,CustSegments]*New Product Share of
Brand[Toyotas!]+
Used Product Value[Toyotas!,CustSegments]*Used Product Share of
Brand[Toyotas!])/
Max Value
~
~
Index
Need to be aggregated from individual products with the indices
of \
Products,
CustSegments
117
Max Value=
10
-
Index
Init
/
ProductValueIndex[Products,CustSegments]=
(SUM(Init NewProduct Attributes[Products,Attributes!]*
AttributeCustSegWghts [Attributes!,CustSegments] )
SUM (AttributeCustSegWghts [Attributes!,CustSegments]))/
Max Value
~
Index
Init OnRoadProdFractionPerBrand[Fords]=
(Init OnRoadVeh ByNewProd[Fords]+Init OnRoadVeh
ByUsedProd [Fords]) /SUM (Init
OnRoadVeh ByNewProd
[Fords!]+Init OnRoadVeh ByUsedProd[Fords!]) -- I
Init
OnRoadProdFractionPerBrand [Chryslers] =
(Init OnRoadVeh ByNewProd[Chryslers]+Init OnRoadVeh
ByUsedProd [Chryslers] )/SUM (Init
OnRoadVeh ByNewProd
[Chryslers!]+Init OnRoadVeh ByUsedProd[Chryslers!]) ~
Init OnRoadProdFractionPerBrand[Toyotas]=
(Init OnRoadVeh ByNewProd[Toyotas]+Init OnRoadVeh
ByUsedProd [Toyotas] )/SUM (Init
OnRoadVeh ByNewProd
[Toyotas!]+Init OnRoadVeh ByUsedProd[Toyotas!])
Dmnl
/
New Product Value[Products,CustSegments]=
SUM(Perceived New ProductAttributes[Products,Attributes!]*
AttributeCustSegWghts [Attributes!, CustSegments] )
SUM (AttributeCustSegWghts [Attributes!, CustSegments])
Init
Avg ProdAttribute of Used Veh Inv [Products,Attributes]=
Init NewProduct Attributes[Products,Attributes]
~
Index
/
Used Product Value [Products,CustSegments]=
SUM(Avg ProdAttribute of Used Veh Inv[Products,Attributes!]*
AttributeCustSegWghts [Attributes!, CustSegments] )
SUM (AttributeCustSegWghts [Attributes!, CustSegments])
Init
Avg ProdAttribute of Used OnRoad Veh [Products,Attributes]=
Init NewProduct Attributes[Products,Attributes]
~
Index
Avg ProdAttribute
of Used Veh Inv
[Products,Attributes]=
INTEG(
Change in Avg ProdAttribute of Used Veh Inv[Products, Attributes],
Init
Avg ProdAttribute of Used Veh Inv[Products,Attributes])
~
Index
118
Avg ProdAttribute of Veh flowing in to Used Veh Inv [Products,Attributes]=
ZIDZ(
Avg ProdAttribute New OnRoad
Veh[Products,Attributes]*SUM(TradeInNew[Products,CustSegments
!])+Avg ProdAttribute of Used OnRoad
Veh[Products,Attributes]*SUM(TradeInUsed[ProductsCustSegments!]),
SUM(TradeInNew[Products,CustSegments!])+
SUM(TradeInUsed[Products,CustSegments!])
Index
Change in ProdAttribute of Used OnRoad Veh [Products,Attributes]=
(Avg ProdAttribute of Used Veh Inv[Products,Attributes]-Avg
ProdAttribute of Used OnRoad Veh[Products,Attributes])/
Dilution Time of Used OnRoad Veh[Products]
~
Index/Month
Change in Avg ProdAttribute of Used Veh Inv [Products,Attributes]=
(Avg ProdAttribute of Veh flowing in to Used Veh
Inv[Products,Attributes]-Avg ProdAttribute of Used Veh Inv
[Products,Attributes])/Dilution Time of Used Veh Inv[Products]
Index/Month
Change in ProdAttribute of New OnRoad Veh [Products,Attributes]=
(Perceived New Product Attributes[Products,Attributes]Avg ProdAttribute New OnRoad Veh[Products,Attributes])/
Dilution Time of New OnRoad Veh[Products]
~
Index/Month
Avg ProdAttribute of Used OnRoad Veh [Products,Attributes]= INTEG(
Change in ProdAttribute of Used OnRoad Veh[Products,Attributes],
Init
Avg ProdAttribute of Used OnRoad Veh[Products,Attributes])
Index
Change in Attribute Perception[Products,Attributes]=
(NewProductAttributes[Products,Attributes]-Perceived New Product
Attributes[Products,Attributes])/
Time to Change Attribute Perception[Attributes]
~
Index/Month
NewProductAttributes [Products,Attributes]
Init
NewProductAttributes[Products,Attributes]*
NewProductAttributeFactors [Products,Attributes]
~
Index
Perceived New Product Attributes [Products,Attributes]= INTEG
Change in Attribute Perception[Products,Attributes],
Init NewProduct Attributes[Products,Attributes])
~
Index
119
Change in TradeInTime Vs ValueRatio LOOKUP(
[(0,0)(3,1)], (0,1),
(1,1),
(1.59633,0.609649),
~
Dmnl
(1.13761,0.754386),
(2.30887,0.557018),
(1.26605,0.679825), (1.41284,0.635965),
(3,0.517544), (20,0. 5))
NewProAttrRunAvgRatio [Products, CustSegments]=
SMOOTH(New Product Attractiveness[Products,CustSegments],
SMOOTH(New Product Attractiveness[Products,CustSegments],
~
0.25 )/
36
Dmnl
Desired Production Capacity[Products]=
MAX
( 0,Expected New Veh Sales[Products]-(Inv Gap[Products]/
Decided Time to Correct Inv[Products]))
~
Vehicles/Month
Production Capacity [Products]= INTEG (+ProdCapacity Change Rate [Products],
Init ProdCapacity Based on Sales Forecasts[Products])
~
Vehicles/Month
Max Prodn Cap[Products]=
42000,32000,14000
-
Vehicles/Month
ProdCapacity Change Rate[Products]=
Production Change Decision[Products]*
(MIN(Max Prodn Cap[Products],Desired Production Capacity[Products])Production Capacity[Products])/Time to Change Prodn Capacity[Products]
Vehicles/Month
Time to Change Attribute Perception[Attributes]=
3
'A'
GET XLS DATA('F1.XLS', 'Quality'
,
'A'
,
NewProductAttributeFactors [Products, Safety]
=
GET XLS DATA('F1.XLS','Safety'
,
'A'
,
'B2'
)
~~I
'B2'
)
~~I
'B2'
'B2'
'B2'
NewProductAttributeFactors [Products, Comfort]
GET XLS DATA('F1.XLS', 'Comfort' , 'A'
NewProductAttributeFactors [Products, Styling]
GET XLS DATA('F1.XLS', 'Styling'
, 'A'
,
=
)
,
)
GET XLS DATA('Fl.XLS', 'PowerPerf'
NewProductAttributeFactors [Products, Quality]
)
NewProductAttributeFactors [Products, PowerPerf]:=
=
,
=
,
NewProductAttributeFactors [Products,Handling]:=
GET XLS DATA('F1.XLS','Handling'
~
Dmnl
,
'A'
,
'B2'
120
Init
NewProduct Attributes
[Products,Attributes]
=
GET XLS CONSTANTS('Fl.xls', 'InitAttributes'
Index
,
'B2'
,
'InitAttributes'
'B14'
'B15'
,
'B16'
)
=
,
,
'B17'
)
'InitAttributes'
,
'B18'
)
GET XLS CONSTANTS('Fl.xls',
)
GET XLS CONSTANTS('F1.xls',
AttributeCustSegWghts [Attributes, IndependentAdventurers]
)
AttributeCustSegWghts [Attributes, FunctionalTechnology]=
AttributeCustSegWghts[Attributes,Stylish]=
GET XLS CONSTANTS('Fl.xls',
'InitAttributes'
AttributeCustSegWghts [Attributes, FamilyEnabler]
GET XLS CONSTANTS('Fl.xls',
=
'InitAttributes'
AttributeCustSegWghts [Attributes, FashionStatement]
GET XLS CONSTANTS('Fl.xls',
'InitAttributes'
=
Avg Used Veh Age[Products]=
(OnRoad Veh[Products,Used]*Avg Age of Used OnRoad Veh[Products]+
Used Veh Inv[Products]*Avg Age of Used Veh Inv[Products])/
(OnRoad Veh[Products,Used]+Used Veh Inv[Products])
Avg Age of Used Veh Inv[Products]= INTEG
Change in Avg Age of Used Veh Inv[Products]+Used Veh Inv Aging,
Init Avg Age of Used Veh Inv[Products])
~
Month
Init Avg Age of Used OnRoad Veh[Products]=
123.79,123.81,136.47
~
Month
Veh Fraction By Segment [Products, CustSegments]=
(Customers[Products,CustSegments]*VehsPerCustomer[CustSegments]/SUM(Cus
tomers[Products,CustSegments!]*VehsPerCustomer[CustSegments!]))
Init Veh Fraction By Segment[Products,CustSegments]=
CustSegment
Percentages[Products,CustSegments]*VehsPerCustomer[CustSegments]/
SUM(CustSegment
Percentages[Products,CustSegments!]*VehsPerCustomer[CustSegments!])
Init Customers[Products,CustSegments]=
(Init OnRoadVeh ByNewProd[Products]/Init Percent of New OnRoad
Veh[Products])*
(CustSegment
Percentages [Products, CustSegments] /VehsPerCustomer [CustSegments])
121
customers
Attrition[Products]=
(Used Veh Inv[Products]/
(Avg Veh Life[Products]-SUM(Veh Fraction By
Segment [Products, CustSegments!] *TradeInTimeNew
[Products,CustSegments!])))*
NonZeroProtect LOOKUP (UVIRelLevel [Products])
~
Vehicles/Month
Attrition
OnRoadUsed [Products, CustSegments]=
OnRoad Veh[Products,Used]*
Veh Fraction By Segment[Products,CustSegments]
/(Avg Veh Life[Products]-TradeInTimeNew[Products,CustSegments])
Vehicles/Month
TradeInUsed[Products,CustSegments]=
(1/TradeInTimeUsed[Products,CustSegments])*OnRoad Veh[Products,Used]*
Veh Fraction By Segment[Products,CustSegments]*
NonZeroProtect LOOKUP (ORVUsedRelLevel [Products])*
NonZeroProtect LOOKUP (CustRelLevel [Products, CustSegments])
~
Vehicles/Month
Should be an array
TradeInNew[Products,CustSegments]=
(1/TradeInTimeNew[Products,CustSegments])*OnRoad Veh[Products,New]*
Veh Fraction By Segment[Products,CustSegments]*
NonZeroProtect LOOKUP (ORVNewRelLevel [Products])*
NonZeroProtect LOOKUP (CustRelLevel [Products, CustSegments])
~
Vehicles/Month
~
Should be an array
(
OnRoad Veh[Products,New]= INTEG
SUM (New Veh Sales [Products, CustSegments!]TradeInNew[Products,CustSegments!]),
Init OnRoadVeh ByNewProd[Products])
OnRoad Veh[Products,Used]= INTEG
SUM(Used Veh Sales [Products,CustSegments!]TradeInUsed [Products, CustSegments!]-Attrition OnRoadUsed
[Products,CustSegments!]),
Init OnRoadVeh ByUsedProd[Products])
~
Vehicles
TradeIns[Products,CustSegments]=
((TradeInNew[Products,CustSegments]+TradeInUsed[Products,CustSegments]+
Attrition
OnRoadUsed[Products,CustSegments] ) /VehsPerCustomer [CustSegments])
~
customers/Month
Indicated Used Prod Attractiveness[Fords,CustSegments]=
Eff of Price on Used Prod Attractiveness[Fords,CustSegments]*
122
Brand Consideration Index[FORD,CustSegments]*
Eff of Quality on Used Prod Attractiveness[Fords]*
Calibration for Eff of Price on UsedProdAttr[Fords,CustSegments] I
Indicated Used Prod Attractiveness[Chryslers,CustSegments]=
Eff of Price on Used Prod Attractiveness[Chryslers,CustSegments]*
Brand Consideration Index[CHRYSLER,CustSegments]*
Eff of Quality on Used Prod Attractiveness[Chryslers]*
Calibration for Eff of Price on UsedProdAttr[Chryslers,CustSegments]
Indicated Used Prod Attractiveness[Toyotas,CustSegments]=
Eff of Price on Used Prod Attractiveness[Toyotas,CustSegments]*
Brand Consideration Index[TOYOTA,CustSegments]*
Eff of Quality on Used Prod Attractiveness[Toyotas]*
Calibration for Eff of Price on UsedProdAttr[Toyotas,CustSegments]
Init Used Prod Attract[Fords,CustSegments]=
Init BCI[FORD,CustSegments]*Init Eff of Qlty on
UsedProdAttr[Fords]*Init Eff of Price on UsedProdAttr
[Fords,CustSegments] -~I
Init
Used Prod Attract [Chryslers,CustSegments]=
Init BCI[CHRYSLER,CustSegments]*Init Eff of Qlty on
UsedProdAttr[Chryslers]*Init Eff of Price on UsedProdAttr
[Chryslers,CustSegments] ~~I
Init Used Prod Attract[Toyotas,CustSegments]=
Init BCI[TOYOTA,CustSegments]*Init Eff of Qlty on
UsedProdAttr[Toyotas]*Init Eff of Price on UsedProdAttr
[Toyotas,CustSegments]
~
Index
Used Reduction Factor[Products,CustSegments]=
1
Dmnl
-
~
initial \
Assumed reduction in Used Veh Attractiveness wrt New for the
condition
Init BCI[Manufacturer,CustSegments]=
Init BrandValueIndex[Manufacturer,CustSegments]*
Init BrandAwarenessRating[Manufacturer,CustSegments]*
Init OverallCustSat[Manufacturer]
Index
Init New Veh Price[Products]=
(1+DealerMarginVsInvRatio LOOKUP(l))*Dealer Price[Products]
~
Dollars
Init NewProdAttr[Explorer,CustSegments]=
0.8,0.8,0.71,0.8,0.8
~~1
Init
NewProdAttr [GrandCherokee, CustSegments]=
0.67 ,0.41, 0.8 ,0.62 ,0.77 ~~1
Init NewProdAttr[FourRunner,CustSegments]=
123
0.13
,0.17
Index
10.73
,0.3 ,0.4
Calibration for Eff of Price on NewProdAttr[Fords,CustSegments]= INITIAL(
(Init NewProdAttr[Fords,CustSegments]/
((1-OnOff BCI)+OnOff BCI*Init BCI[FORD,CustSegments]))/
((1-OnOff EoP)+OnOff EoP*Init Eff of Price on
NewProdAttr[Fords,CustSegments]))
~~
Calibration for Eff of Price on NewProdAttr[Chryslers,CustSegments]=
(Init
NewProdAttr[Chryslers,CustSegments]/
((1-OnOff BCI)+OnOff BCI*Init BCI[CHRYSLER,CustSegments]))/
((1-OnOff EoP)+OnOff EoP*Init Eff of Price on
NewProdAttr[Chryslers,CustSegments]) ~~I
Calibration for Eff of Price on NewProdAttr[Toyotas,CustSegments]=
(Init NewProdAttr[Toyotas,CustSegments]/
((1-OnOff BCI)+OnOff BCI*Init BCI[TOYOTA,CustSegments]))/
((1-OnOff EoP)+OnOff EoP*Init Eff of Price on
NewProdAttr[Toyotas,CustSegments])
~
Dmnl
Init PercBrandQuality[FORD]=
SUM(Init Actual New Product Quality[Fords!]*Init New Product Share of
Brand[Fords!]*NewPrdWght+
Init Actual Used Product Quality[Fords!]*Init Used Product Share of
Brand[Fords!]*(l-NewPrdWght)) --I
Init PercBrandQuality[CHRYSLER]=
SUM(Init Actual New Product Quality[Chryslers!]*Init New Product Share
of Brand[Chryslers!]*NewPrdWght+
Init Actual Used Product Quality[Chryslers!]*Init Used Product Share of Brand
[Chryslers!]*(l-NewPrdWght)) ~~I
Init PercBrandQuality[TOYOTA]=
SUM(Init Actual New Product Quality[Toyotas!]*Init New Product Share of
Brand[Toyotas!]*NewPrdWght+Init Actual Used Product Quality[Toyotas!]*Init
Used Product Share of Brand [Toyotas!] * (1-NewPrdWght))
~
TGW
Init
Percent of New OnRoad Veh[Products]=
1/(1+(Init OnRoadVeh ByUsedProd[Products]/
Init OnRoadVeh ByNewProd[Products]))
~
Dmnl
Customers[Products,CustSegments]= INTEG
Buys[Products,CustSegments]-TradeIns[Products,CustSegments],
Init Customers[Products,CustSegments])
~
customers
Init Dealer CustSat[Manufacturer]=
INITIAL(Customer Satisfaction Dealer[Manufacturer])
~
Index
Init OverallCustSat[Manufacturer]=
124
*
PercQualityOnCustSat LOOKUP(Init
PercBrandQuality[Manufacturer]/VMIN(Init PercBrandQuality[Manufacturer!]))
Init Dealer CustSat[Manufacturer]
~
Index
Init Used Product Share of Brand[Fords]=
Init OnRoadVeh ByUsedProd[Fords]/
SUM(Init OnRoadVeh ByNewProd[Fords!]+Init OnRoadVeh ByUsedProd[Fords!])
Init
Used Product Share of Brand[Chryslers]=
Init OnRoadVeh ByUsedProd[Chryslers]/
SUM(Init OnRoadVeh ByNewProd[Chryslers!]+
Init OnRoadVeh ByUsedProd[Chryslers!]) ~~I
Init Used Product Share of Brand[Toyotas]=
Init OnRoadVeh ByUsedProd[Toyotas]/
SUM(Init OnRoadVeh ByNewProd[Toyotas!]+
Init OnRoadVeh ByUsedProd[Toyotas!])
Init New Product Share of Brand[Fords]=
Init OnRoadVeh ByNewProd[Fords]/SUM(Init OnRoadVeh
ByNewProd[Fords!]+Init OnRoadVeh ByUsedProd[Fords!])
Init New Product Share of Brand[Chryslers]=
Init OnRoadVeh ByNewProd[Chryslers]/
SUM(Init OnRoadVeh ByNewProd[Chryslers!]+
Init OnRoadVeh ByUsedProd[Chryslers!]) ~~I
Init New Product Share of Brand[Toyotas]=
Init OnRoadVeh ByNewProd[Toyotas]/SUM(Init OnRoadVeh
ByNewProd[Toyotas!]+Init OnRoadVeh ByUsedProd[Toyotas!])
~
Dmnl
Init BIC BrandQuality= INITIAL(
VMIN(Perceived Brand Quality[Manufacturer!]))
~
TGW
Init Eff of RelProdAttr on TradeInTime[Products,CustSegments]=
Change in TradeInTimeVsRelValRatio LOOKUP
(Init NewProdAttr[Products,CustSegments]/VMAX(Init
NewProdAttr[Products!,CustSegments]))
Init
TradeInTimeNew [Products, CustSegments]=
TradeInTimeNewVsQuality LOOKUP(Quality TGW Vs Time
LOOKUP[Products] (Init Avg Age of New OnRoad Veh[Products]))*
Init Eff of RelProdAttr on TradeInTime[Products,CustSegments]
Init
Used Prod Market Share [Products, CustSegments]=
Init UsedProdAttr[Products,CustSegments]/
SUM(Init NewProdAttr[Products!,CustSegments]+Init
UsedProdAttr[Products!,CustSegments])
125
Init
Used Veh Sales [Products,CustSegments]=
Init Used Prod Market Share[Products,CustSegments]*
Ini Pot Buyers [CustSegments]*VehsPerCustomer[CustSegments]
Init
TradeInTime Used[Products,CustSegments]=
TradeInTimeUsedVsQuality LOOKUP(Quality TGW Vs Time
LOOKUP[Products] (Init
Avg Age of Used OnRoad Veh[Products]))*
Init Eff of RelProdAttr on TradeInTime[Products,CustSegments]
Init ProdCapacity Based on Sales Forecasts[Products]=
SUM(
Init New Prod Market Share[Products,CustSegments!]*
Ini Pot Buyers[CustSegments!]*VehsPerCustomer[CustSegments!]
~
Vehicles/Month
Init
New Prod Market Share [Products,CustSegments]
Init
NewProdAttr[Products,CustSegments]/
SUM(Init NewProdAttr [Products!,CustSegments] +Init
UsedProdAttr[Products!,CustSegments])
Buys[Products,CustSegments]=
(New Veh Sales [Products,CustSegments] +Used Veh
Sales [Products, CustSegments]) / VehsPerCustomer[CustSegments]
~
customers/Month
*
Used Veh Sales[Products,CustSegments]=
Potential Buyers[CustSegments]*VehsPerCustomer[CustSegments]*
Used Veh Market Share By Prod[Products,CustSegments]*
NonZeroProtect LOOKUP (UVIRelLevel [Products] )
NonZeroProtect LOOKUP (PotBuyersRelLevel [CustSegments])
~
Vehicles/Month
Rate of Change in UsedProdAttr [Products, CustSegments]=
(Indicated Used Prod Attractiveness[Products,CustSegments]-Used Product
Attractiveness [Products,CustSegments] ) /Time to Change UsedProdAttr
~
Index/Month
Init
UsedProdAttr[Products,CustSegments]=
2*Init NewProdAttr[Products,CustSegments]
~
Index
Total Used Veh Attractiveness [CustSegments]=
126
SUM(Used Product Attractiveness[Products!,CustSegments])
Index
(
Residual Value[Products]= INTEG
Change in Residual Value[Products],
Init ResValue[Products])
~
Dollars
New Veh Market Share[CustSegments]=
Total New Veh Attractiveness[CustSegments]/
(Total New Veh Attractiveness[CustSegments]+Total Used Veh
Attractiveness[CustSegments])
~
Dmnl
Target Residual Value[Products]=
MIN(Nominal Residual Value[Products]*Eff of Used Inv on Res
Value[Products],0.85*New Veh Price[Products])
~
Dollars
I
~
Nominal Residual Value[Products]=
MSRP[Products]*"Reference ResPercent Vs.
Avg Age LOOKUP"[Products] (Avg
Age of Used Veh Inv[Products])
~
Init
Dollars
ResValue [Products]=
Nominal Residual Value[Products]*Eff of Used Inv on Res Value[Products]
Dollars
Time to Change UsedProdAttr=
1
-
Month
"Reference ResPercent Vs. Avg Age LOOKUP"[Explorer](
[(0,0)(120,1)],
(12,0.901), (24,0.726), (36,0.638), (48,0.556) , (60,0.523),
(0,1),
54), (84,0.395), (96,0.301), (108,0.263), (120,0.232))
~~I
(72,0.4
"Reference ResPercent Vs. Avg Age LOOKUP"[GrandCherokee](
[(0,0)(120,1)],
(12,0.928), (24,0.82),
(0,1),
(36,0.759), (48,0.588),
9), (84,0.4),
(96,0.344),
(108,0.3), (120,0.25))
~~1
(60,0.519), (72,0.44
"Reference ResPercent Vs. Avg Age LOOKUP"[FourRunner](
[(0,0)(120,1)],
(0,1),
(12,0.959), (24,0.853), (36,0.773), (48,0.679) ,(60,0.64), (72,0.55
5), (84,0.401),
(108,0.325), (120,0.291))
(96,0.35),
Dmnl
~
Each product can have a curve.
Quality Vs Used Prod Attr LOOKUP(
127
-
[(0,0)
(4,1)], (0,1), (0.40367,0.982456), (0.733945,0.934211), (1,0. 85)
(1.6,
45), (2.45872,0.399123), (2.91743,0.372807), (3.81651,0. 355263))
0. 55), (2, 0.
Eff of Used Inv on Res Value[Products]=
ResValueModFactor Vs Used2RefRatio LOOKUP(RatioOfUsed2RefInv[Products])
~~
Dmnl
Used Product Attractiveness [Products,CustSegments]= INTEG
+Rate of Change in UsedProdAttr[Products,CustSegments],
Init UsedProdAttr[Products,CustSegments])
Index
ResValueModFactor Vs Used2RefRatio LOOKUP(
[(0,0)(20,2)],
(0,1.34211), (1,1),
(1.65138, 0.780702), (2.38532, 0.54386), (3.42508,
737), (4.64832, 0.315789), (6.54434, 0.245614), (12 .3547, 0. 114035), (20, 0))
Dmnl
0.394
Used Veh Market Share By Prod[Products,CustSegments]=
(Used Product Attractiveness[Products,CustSegments]/Total Used Veh
Attractiveness [CustSegments] )*(1-New Veh Market Share [CustSegments])
~
Dmnl
Production Change Decision[Products]=
IF THEN ELSE(Inv Ratio[Products]>Inv Ratio Limit for
ProdnCuts[Products] :OR: Inv Ratio
[Products] < Inv Ratio Limit for ProdnIncrease
[Products],1,ContinuosOrDiscreteControl)
~
Dmn
~
This is to make sure that we try to correct excess inventory with
marketing incentives first before we reduce production capacity
CustShareVsBrandAwareness LOOKUP(
[(0,0)-(2,2)],
(0.00611621,0.894737),
(1,1), (1.99388, 1.49123))
ContinuosOrDiscreteControl=
1
~
0-Discrete;
1-Continous
----
This is for the production capacity
control
Test MarketShareByProduct[Products]=
SUM(New Veh Market Share By Prod[Products,CustSegments!])
128
INTEG
(
New Veh Inv[Products]=
+Production[Products]-SUM(New Veh Sales[Products,CustSegments!]),
Production[Products]*Desired Months of Inv[Products])
~
Vehicles
MSRP[Products]=
1.15*Dealer Price[Products]
~
Dollars
~
15% normal dealer margin is considered. If this is changed,
the
Dealer \
Margin Vs. Inv Ratio LOOKUP will also need to be changed/viceversa
Change in Residual Value[Products]=
(Target Residual Value[Products]-Residual Value[Products])/Time to
Change Residual Value
~
Dollars/Month
Time to Change Residual Value=
2
~
Month
TradeInTimeUsedVsQuality LOOKUP(
(1.5,2.5)],
[(0.5,0)(0.503058,2.17105),
~
(0.717125,1.35965),
(1,1),
(1.5,0.6))
Month
This could be fn of CustSeg\!\!\!
Change in TradeInTimeVsRelValRatio LOOKUP(
[(0,0)(5,2)], (0,0.447368), (0.183486,0.482456), (0.336391,0.561404), (0.504587,0.67543
9), (1,1), (1.42202,1.30702), (1.85015,1.54386), (2.4159,1.7193), (3.21101,1.77193
),(3.97554,1.7807),(4.96942,1.80702))
Dmnl
TradeInTimeNewVsQuality LOOKUP(
[(0.5,0)(1.5,2.5)],
(0.503058,2.17105) , (0.717125,1.35965),
Month
~
(1, 1), (1.5,0.6))
This could be fn of CustSeg\!\!\!
RelNewProdAttrRatio [Products, CustSegments]=
New Product Attractiveness[Products,CustSegments]/
VMAX(New Product Attractiveness[Products!,CustSegments])
~
Dmnl
OnOff BCI=
1
129
0-off 1-on
OnOff EoP=
1
~
0-off 1-on
Test AggregatedProdAttr[Products]=
SUM(New Product Attractiveness[Products,CustSegments!])
New OnRoad Veh Aging=
1
Months/Month
Avg Age of New OnRoad Veh[Products]=
INTEG
(
~
Change in Age of New OnRoad Veh[Products]+New OnRoad Veh Aging,
Init Avg Age of New OnRoad Veh[Products])
~
Month
Used OnRoad Veh Aging=
1
~
Months/Month
Avg Age of Used OnRoad Veh[Products]= INTEG
Change in Age of Used OnRoad Veh[Products]+Used OnRoad Veh Aging,
Init Avg Age of Used OnRoad Veh[Products])
~
Month
Used Veh Inv Aging=
1
~
Months/Month
Test EffofPricePerProduct[Products]=
SUM(Effect of Price on Product Attractiveness[Products,CustSegments!])
Test CustPerCustSegment[CustSegments]=
SUM(Customers[Products!,CustSegments])
Test CustPerProduct[Products]=
SUM(Customers[Products,CustSegments!])
130
Test BrandConsidIndexByManufact [Manufacturer]=
SUM(Brand Consideration
Index[Manufacturer,CustSegments!])/ELMCOUNT(CustSegments)
Decided Time to Correct Inv[Products]=
0.5
-
Month
Inv Gap[Products]=
New Veh Inv[Products]-Desired Inventory[Products]
Desired Inventory[Products]=
New Veh Inv[Products]/Inv Ratio[Products]
~
Vehicles
Time to Average Veh Sales=
4
~
Month
Expected New Veh Sales[Products]=
SMOOTH(SUM(New Veh Sales[Products,CustSegments!]),Time to Average Veh
Sales)
~
Vehicles/Month
Inv Ratio Limit for ProdnIncrease[Products]=
0.5
~
Month
Time to Change Prodn Capacity[Products]=
~
Month
~
This may need enhancements to have differentiation between
increasing and \
decreasing capacity (using an IF THEN ELSE based on the sign of
the diff \
in actual & desired months of Inv)
Inv Ratio Limit for ProdnCuts[Products]=
3
~
Dmnl
Inv Ratio[Products]=
Months of Inv Based on Sales[Products]/Desired Months of Inv[Products]
~
Dmnl
131
Rate of Change in NewProdAttr [Products, CustSegments]=
(Indicated New Product Attractiveness[Products,CustSegments]-New
Product Attractiveness [Products, CustSegments] )/Time to Change NewProdAttr
~
Index/Month
Dilution Time of New OnRoad Veh[Products]=
MAX(0.01,
MIN(1000,XIDZ (OnRoad Veh[Products,New] ,SUM(New
Veh
Sales[Products,CustSegments!]),1000))
~
Month
~
OnRoad Veh[Products,New]/SUM(New Veh
Sales[Products,CustSegments!])
New Product Attractiveness [Products,CustSegments]= INTEG
+Rate of Change in NewProdAttr[Products,CustSegments],
Init NewProdAttr[Products,CustSegments])
Index
~
Manufacturer
Sales
[FORD]=
SUM(New Veh Sales[Fords!,CustSegments!]+Used Veh
Sales[Fords!,CustSegments!]) --I
Manufacturer Sales [CHRYSLER]=
SUM(New Veh Sales [Chryslers!,CustSegments!] +Used Veh
Sales[Chryslers!,CustSegments!]) ~~
Manufacturer Sales[TOYOTA]=
SUM(New Veh Sales[Toyotas!,CustSegments!]+Used Veh
Sales[Toyotas!,CustSegments!])
~
Vehicles/Month
Months of Inv Based on Sales[Products]=
New Veh Inv[Products]/Expected New Veh Sales[Products]
~
Month
Time to Change NewProdAttr=
1
-
Month
New Veh Price[Products]=
IF THEN ELSE(Time=0
:OR:
Time>Rebate Expiry[Products],
(1+Dealer Margin[Products])*Dealer Price[Products],
(1+Dealer Margin[Products])*Dealer Price[Products]-Rebate
Amount[Products])
~
Dollars
Rebate Expiry[Products]=
IF THEN ELSE (:NOT:
Initiation
Rebate Initiation Time[Products],
0,Rebate
Time [Products] +Rebate Duration[Products])
132
Rebate Duration[Products]=
4
Month
Rebate
Initiation Time[Products]=SAMPLE IF TRUE(
IF THEN ELSE (Manufacturer Rebate Decision[Products], (Time) >(Rebate
Initiation
Time [Products] +Rebate Duration [Products]) ,0) ,Time , 0)
~
Month
Acceptable Dealer Margin[Products]=
0.034,0.034,0.023
Rebate Amount[Products]=
3000,3000,3000
~
Dollars
Manufacturer Rebate Decision[Products]=
IF THEN ELSE(Dealer Margin[Products]
Margin[Products], 1 , 0
~
< Acceptable Dealer
Dmnl
Dealer Margin[Products]=
DealerMarginVsInvRatio LOOKUP(Inv Ratio[Products])
~
Dmnl
DealerMarginVsInvRatio LOOKUP(
[(0,0)-
(4,0.2)], (0,0.15), (1,0.15), (1.99388,0.0763158), (3.00917,0.0140351),
Dmnl
(4,0.01))
Rel New Veh Price[Products]=
VMIN(New Veh Price[Products!])/New Veh Price[Products]
~
Dmnl
NonZeroProtect LOOKUP(
[(-2,0)-(100,1.5)],
(-
2,0), (0,0),
(0.960245,0.131579), (2.10398,0.546053),
),(9.54128,0.967105), (15,1), (5e+006,1))
(5.17431,0.868421
Dealer Price[Products]=
23697,24957,25705
~
Dollars
Brand Consideration Index[Manufacturer,CustSegments]=
133
Brand Awareness Index[Manufacturer,CustSegments]*
Brand Opinion Index[Manufacturer,CustSegments]
~
Index
Time to Change Brand Awareness=
3
~
Month
Rate of Change of PercBrandValue[Manufacturer,CustSegments]=
(Indicated Brand Value Index[Manufacturer,CustSegments]-Perceived Brand
Value Index[
Manufacturer, CustSegments] ) /Time to Change PercBrandValue
~
Index/Month
Brand Awareness Index [Manufacturer,CustSegments] = INTEG
+Rate of Change of Brand Awareness[Manufacturer,CustSegments],
Init BrandAwarenessRating[Manufacturer,CustSegments])
Index
Desired Months of Inv[Products]=
1.5
~
Month
45 days of inventory for all products
Perceived Brand Value Index[Manufacturer,CustSegments]= INTEG
Rate of Change of PercBrandValue[Manufacturer,CustSegments],
Init BrandValueIndex[Manufacturer,CustSegments])
~
Index
This is currently assumed to be 1. This is a 2-D array of
Manufacturer and \
Customer segments. Aggregation will done on Product Value of the
Products \
of a given manufacturer, but kept distinctly for different
customer \
segments
Time to Change PercBrandValue=
12
-
Month
Rate of Change of Brand Awareness[Manufacturer,CustSegments]=
(Indicated Brand Awareness Index[Manufacturer,CustSegments]Brand Awareness Index [Manufacturer,CustSegments])/
Time to Change Brand Awareness
~
Index/Month
Brand Opinion Index[Manufacturer,CustSegments]=
Perceived Brand Value Index[Manufacturer,CustSegments]*Brand Customer
Satisfaction[Manufacturer]
134
Index
Init BrandAwarenessRating[Manufacturer,CustSegments]=
GET XLS CONSTANTS('F1.xls',
~
Index
'InitAttributes'
,
'B8'
Init Avg Age of New OnRoad Veh[Products]=
45.45,45.45,49.08
~
Month
Dilution Time of Used OnRoad Veh[Products]=
MAX(0.01,
MIN(1000,
XIDZ(OnRoad Veh[Products,Used],SUM(Used Veh
Sales[Products,CustSegments!]),1000)))
Month
OnRoad Veh[Products,Used]/SUM(Used Veh
Sales[Products,CustSegments!])
Change in Age of New OnRoad Veh[Products]=
(0-Avg Age of New OnRoad Veh[Products])/Dilution Time of New OnRoad
Veh[Products]
Change in Age of Used OnRoad Veh[Products]=
(Avg Age of Used Veh Inv[Products]-Avg Age of Used OnRoad
Veh[Products])/Dilution Time of Used OnRoad Veh[Products]
~
Month/Month
Change in Avg Age of Used Veh Inv[Products]=
(Avg Age of Veh flowing in to Used Veh Inv[Products]-Avg Age of Used
Veh Inv[Products])/ Dilution Time of Used Veh Inv[Products]
Dilution Time of Used Veh Inv[Products]=
MAX(0.01,
MIN(1000,
XIDZ(Used Veh Inv[Products],
SUM(TradeInNew[Products,CustSegments!]+TradeInUsed
[Products,CustSegments!]),1000)))
~
Month
Avg Age of Veh flowing in to Used Veh Inv[Products]=
ZIDZ(
Avg Age of New OnRoad
Veh[Products]*SUM(TradeInNew[Products,CustSegments!])
+Avg Age of Used OnRoad Veh[Products]*
SUM(TradeInUsed[Products,CustSegments!]),SUM(TradeInNew[Products
,CustSegments!] ) +SUM(TradeInUsed[Products,CustSegments!]))
~
Month
135
Quality TGW Vs Time LOOKUP[Fords](
[(0,0)(200,10)], (0,0),
(3,2.309), (12,3.559), (36,5.011), (75,6.3),
(132,6.5))
Quality TGW Vs Time LOOKUP[Chryslers](
[(0,0)(200,10)],
(0,0),
(3,3.68421), (12,4.846), (36.0856,5.48246),
66667)) -Quality TGW Vs Time LOOKUP[Toyotas](
(200,10)],
[(0,0)(0,0),
~
TGW
(75,6.31579),
(132,6.
(3,1.835), (12,2.586), (36,4.237), (75,4.7807), (132,5.04386))
High Mileage Quality\!\!\!
Used Product
OnRoad
Used Product
OnRoad
Used Product
OnRoad
~
Share of Brand[Fords]=
Veh[Fords,Used]/SUM(OnRoad Veh[Fords!,NewOrUsed!]) ~~
Share of Brand[Chryslers]=
Veh[Chryslers,Used]/SUM(OnRoad Veh[Chryslers!,NewOrUsed!])
Share of Brand[Toyotas]=
Veh[Toyotas,Used]/SUM(OnRoad Veh[Toyotas!,NewOrUsed!])
~~I
Dmnl
New Product Share of Brand[Fords]=
OnRoad Veh[Fords,New]/SUM(OnRoad Veh[Fords!,NewOrUsed!])
New Product Share of Brand[Chryslers]=
OnRoad Veh[Chryslers,New]/SUM(OnRoad Veh[Chryslers!,NewOrUsed!])
New Product Share of Brand[Toyotas]=
OnRoad Veh[Toyotas,New]/SUM(OnRoad Veh[Toyotas!,NewOrUsed!])
~
~~I
Dmnl
Brand Customer Satisfaction[Manufacturer]= INTEG
CustSat Rate of Change[Manufacturer],Init OverallCustSat[Manufacturer])
Index
Perceived Brand Quality[Manufacturer]= INTEG
Rate of Change of Perceived Quality[Manufacturer],
Init PercBrandQuality[Manufacturer])
~
TGW
PercQualityOnCustSat LOOKUP(
[(0,0)(4,1)],
(0.40367,0.982456), (0.733945,0.934211), (1,0.85),
(0,1),
45) , (2.45872,0.399123), (2.91743,0.372807), (3.81651,0.355263))
~
Index
(1.6,0.55),
(2,0.
Time to Change CustSat=
3
~
Month
Indicated Brand Customer Satisfaction [Manufacturer]=
136
1*Customer Satisfaction Quality[Manufacturer]*Customer Satisfaction
Dealer[Manufacturer]
Index
CustSat Rate of Change[Manufacturer]=
(Indicated Brand Customer Satisfaction[Manufacturer]-Brand Customer
Satisfaction[Manufacturer])/ Time to Change CustSat
Index/Month
Customer Satisfaction Quality[Manufacturer]=
PercQualityOnCustSat LOOKUP(Rel Perceived Brand Quality[Manufacturer])
Index
Rate of Change of Perceived Quality[Manufacturer]=
(Actual Brand Quality[Manufacturer]-Perceived Brand
Quality[Manufacturer])/Time to Change PercQuality
TGW/Month
Rel Perceived Brand Quality[Manufacturer]=
Perceived Brand Quality[Manufacturer]/Init BIC BrandQuality
~
Dmnl
Time to Change PercQuality=
6
Month
Eff of RelServPrice On CustSat[Manufacturer]=
ServPriceOnCustSat LOOKUP(Dealer Service Price[Manufacturer]/Industry
Service Price Per Visit)
Dmnl
ServPriceOnCustSat LOOKUP(
[(0,0)(2,1)], (0.00611621,1), (0.987156,0.969298), (1.22324,0.912281), (1.41284,0.84210
5), (1.57798,0.754386), (1.73089,0.688596), (1.87156,0.640351), (1.99388,0.618421
~
Dmnl
Industry Avg Number of Dealers=
SUM(Number of Dealers[Manufacturer!])/ELMCOUNT(Manufacturer)
~
Dealers
Relative Number of Dealers[Manufacturer]=
Number of Dealers[Manufacturer]/Industry Avg Number of Dealers
~
Dmnl
137
Eff of VehAndService Availability on CustSat[Manufacturer]=
VehAndServAvailability RelNumOfDealers LOOKUP(Relative Number of
Dealers[Manufacturer])
~
Dmnl
Cost of Ownership[Products]=
Dealer Service Price[Manufacturer]
VehAndServAvailability RelNumOfDealers LOOKUP(
[(0.4,0)(2,2)], (0.5,0.75), (0.75,0.8), (1,0.85), (1.25,0.9), (1.5,0.95), (1.75,1))
Industry Avg Service Time=
SUM(Time to Get Service[Manufacturer!])/ELMCOUNT(Manufacturer)
~
Hours
Eff of RelDealerVol on Service Price [Manufacturer]=
RelDealerVolOnService Price LOOKUP(Relative DealerVolume[Manufacturer])
Dmnl
Eff of RelServTime On CustSat[Manufacturer]=
RelServTimeOnServCustSat LOOKUP(Relative Service Time[Manufacturer])
~
Dmnl
RelServTimeOnServCustSat LOOKUP(
[(0.2,0.2)(1.8,1)],(0.4,1),(0.611009,0.968421),(0.8263,0.915789),(l.00245,0.792982)
,(1.23731,0.652632),
(1.46728,0.561404), (1.71682,0.505263))
Dealer Service Price[Manufacturer]=
Eff of RelDealerVol on Service Price[Manufacturer]*Industry Service
Price Per Visit
Dollars
Time to Get Service[Manufacturer]=
(Dealer Volume[Manufacturer]/Dealer Service Capacity[Manufacturer])*120
~
Hours
Dealer Service Capacity[Manufacturer]=
18611,15149,6831
-
Vehicles/Month
~
I
Relative Service Time[Manufacturer]=
Time to Get Service[ManufacturerJ/Industry Avg Service Time
138
~~
Dmnl
Industry Service Price Per Visit=
200
Dollars
-
RelDealerVolOnService Price LOOKUP(
[(0,0.4)(1.8,2.2), (0.4,2), (0.6,1.8), (0.8,1.2), (1,1), (1.2,0.9), (1.4,0.8), (1.6,0.6)],
(0.4,2),
(0.655046,1.78947), (0.781651,1.37105), (1,1), (1.2,0.9),
(1.4,0.8),
(1.6,
0.6))
Dmnl
Relative Dealer Volume[Manufacturer]=
Dealer Volume[Manufacturer]/Industry Avg Dealer Volume
Dmnl
~
Industry Avg Dealer Volume=
SUM(Dealer Volume[Manufacturer!])/ELMCOUNT(Manufacturer)
~~
Vehicles/Month
Avgd Manufacturer Sales[Manufacturer]=
SMOOTH(Manufacturer Sales[Manufacturer], Averaging Time
~
Vehicles/Month
Sales Smoothed for 12 months
Averaging Time=
12
Manufacturer:
FORD,CHRYSLER,
~
TOYOTA
->
(Products:Fords,Chryslers,Toyotas)
manufacturer to products map
Dealer Volume[Manufacturer]=
4*Avgd Manufacturer Sales[Manufacturer]/Number of Dealers[Manufacturer]
Vehicles/Month
~
Volume
= 4*avg sales
Number of Dealers[Manufacturer]=
5000,2500,3000
-
Dealers
Used Veh Inv[Products]= INTEG
+SUM(TradeInNew[Products,CustSegments!]+TradeInUsed[Products,CustSegmen
ts!] )- Attrition[Products]-SUM(Used Veh Sales[Products,CustSegments!]),
139
~~
Init UsedVehInv[Products])
Vehicles
VehsPerCustomer[CustSegments]=
1
NewOrUsed:
New, Used
Potential Buyers[CustSegments]= INTEG
+New Entrants[CustSegments]Dropouts [CustSegments] +SUM (TradeIns [Products!,CustSegments]
Buys [Products!,CustSegments]) Ini Pot Buyers [CustSegments])
~
-
Production[Products]=
Production Capacity[Products]
~
Vehicles/Month
customers
Attributes:
PowerPerf ,
Quality, Safety,
Comfort, Styling, Handling
Products:
Explorer, GrandCherokee, FourRunner
Fords:
Explorer
Chryslers:
GrandCherokee
Toyotas:
FourRunner
Ini Pot Buyers[CustSegments]=
29500,18163,14628,19029,29384
customers
~
CustSegments:
140
FunctionalTechnology,IndependentAdventurers, Stylish, FamilyEnabler,
FashionStatement
~
Index
17 customer segments
Dropouts[CustSegments]=
5000
~
customers/Month
If "Potential Buyers" include the whole market, then this rate
will be \
governed only by the death rates. If "Potential Buyers" include
only one\
segment, then this rate will also include the "transfer into
other \
segments" rate.
.Control
Simulation Control Parameters
FINAL TIME
=
144
Month
The final time for the simulation.
~
INITIAL TIME
= 0
~
Month
The initial time for the simulation.
SAVEPER
~
=
1
Month
~
TIME STEP
~
~
The frequency with which output is stored.
=
0.125
Month
The time step for the simulation.
141