Application of System Dynamics to Corporate Strategy: An Evolution of Issues and
Frameworks
Henry Birdseye Weil
Senior Lecturer, MIT Sloan School of Management
© Henry Birdseye Weil 2007. All rights reserved.
Abstract
This paper discusses five landmark projects which highlighted issues and
produced frameworks that became important building blocks in the application of System
Dynamics to corporate strategy. The early models were primarily at the level of the firm.
The first model described in the paper captures the tension among conflicting
performance objectives and shows how the conflicts impact mid-term company
performance. The second model represents the behavior of an R&D organization as it
responds to changing pressures and direction from corporate management. Over time the
focus shifted to market behavior and competition. The third model explains powerful
long-term dynamics that lead to “commoditization" of products and services. Recent
work analyzed the social dynamics of markets, e.g., as they affect innovation and
technology substitution. The fourth model in the paper represents the fundamental
dynamics of innovative industries, building on an extensive body of research and
publications. The final model focuses on the market impacts of social factors, e.g., trust,
fashion, the characteristics of lead users, how trends are perceived and extrapolated, the
flow of information, bandwagon effects, and network effects. The progression started
with classical Industrial Dynamics, models of the firm which focused on orders,
production, and shipments. At each milestone more behavioral richness and complexity
were recognized and represented. What do we know now that we did not know when the
field was founded fifty years ago? The most important lessons are about the critical roles
of organizational, social, and psychological factors in important business decisions,
competitive behaviors, and the evolution of markets.
Introduction
This is a personal journey which began at MIT in the Fall Term of 1962 when, as
an undergraduate, I enrolled in course 15.581 Industrial Dynamics I. Jay Forrester was
the instructor, assisted by my future friend, colleague, and business partner Ed Roberts.
The following year I was in the first cadre of Jay’s innovative Undergraduate Systems
Program. This was a two-year, largely self-directed program of management education
based on Industrial Dynamics. Also in 1963 Ed Roberts and Jack Pugh founded Pugh-
Roberts Associates. | started working at Pugh-Roberts in 1966 while in graduate school.
During the forty years which followed I have specialized in applying System
Dynamics to corporate strategy. The early models were primarily at the level of the firm.
Over time my work shifted to market behavior and competition. Most recently I have
analyzed the social dynamics of markets, especially as they affect innovation and
technology substitution. Looking back I can see a series of landmarks, projects which
highlighted issues and produced frameworks that became important building blocks for
me, my colleagues, and my students. On the fiftieth anniversary of the founding of our
field I thought it would be useful to record them and discuss their significance.
Conflicting Objectives
One of my first consulting projects was with a large Canadian food manufacturer
and retailer. It focused on the dynamic interactions of multiple conflicting performance
objectives. “The opportunity for conflict among performance measures, and therefore the
lack of clarity as to how the measures mold behavior, is probably greatest in vertically
integrated companies. Not only do these firms offer the usual possibility of individual
measures conflicting to some extent (e.g., sales and profits), but also the opportunity
exists for interdivisional conflict as a result of the measurement process (e.g., the frequent
outcome of applying the profit center concept)” (Roberts 1978, p.427).
The model represented the manufacturing, distribution, and sale of bakery goods.
Most of the company’s manufacturing investment was in this area. The model centered
around two related flows: orders and goods, and cash. Orders to manufacturing were
based on historical sales data, but were modified by pressures from senior management.
Pressures to increase sales led to higher orders and pushing goods into the stores.
Pressures to improve gross margins produced the opposite behavior. Orders were scaled
back to minimize the sale at reduced prices of goods which had passed their “sell by”
date. Manufacturing had the option to modify the orders from retail. In an attempt to
increase its income contribution manufacturing frequently shipped 5-10% more than was
ordered. It also could offer lower transfer prices to stimulate more sales.
Management's efforts to boost sales conflicted with its efforts to increase gross
margin. To increase sales prices were cut and more goods pushed into the stores. To
improve margin management would do the opposite. Analyses with the model showed
that “...this conflict (and its resolution) is a critical determinant of how this part of the
company performs” (Roberts 1978, p.425). The dynamic behavior of the business results
from the interaction of four feedback loops shown in Figure 1.
Corrective actions regarding one performance objective, after a significant delay,
causes a variance in one or more of the other objectives. For example, if sales fell below
budget retail would reduce prices, spend more on advertising, and offer more specials
(balancing loop B2). However none of these actions had an immediate impact. Indeed
the variance easily could grow worse, stimulating even more aggressive corrective
actions. Finally sales were back on track. However lower prices and over-ordering
reduced gross margin(reinforcing loop R1 shown in dashed lines). Now a margin
variance grabbed management 's attention and the pressures and resulting behaviors
reversed (loops B3 and R1).
Budgeted Bakery
Income Income (ei) DELAY
Contribution Contribution
i Pressure Corrective
Variance ——> toCorect Action ~
Retail -oT TN
Budgeted .$ ,
7 * DELAY ~\
$ 7 Sales s~ (83) S
Sales 7 SMS
4 DELAY \
H Pressure Corrective \ \
Variance to Conect ation ;
| \
, @
DELAY
Retail ~§ ae —~ I |
Desired Gross I /
Gross 7 Margin (63) DELAY / /
Margin / \ 7 /
\ 7 .
! . Z ,
{ P SS é é a
i ——4 ressure. __ orrective “
Variance tp Comect “>” Action _
Figure 1: Conflicting C ontrol Loops
Analyses showed that continual oscillations among conflicting performance
objectives caused the company to fall short of all of its targets. The company wanted to
grow faster than the overall market, but this only could be accomplished through
aggressive pricing and advertising. That strategy would be possible if gross margin were
ignored, but a margin variance could not be ignored indefinitely. Moreover the company
could not sell more than it produced. Rapid growth required simultaneously stimulating
demand and increasing output while maintaining a satisfactory margin, e.g., through
economies of scale. It is a very difficult balance to achieve and sustain.
The need for alignment among strategic objectives and operating performance
measures is a recurring theme in the literature. Hamel and Prahalad emphasized the
critical role of a company’s strategic intent, i.e., its vision of market leadership.
“Strategic intent provides consistency to short-term action, while leaving room for
reinterpretation as new opportunities emerge” (Hamel and Prahalad 1989, p.65).
Other authors frame the issues in terms of bounded rationality (for example,
Simon 1957; Forrester 1961; Morecroft 1985; Sterman 2000; Sterman et al 2007). This
principle recognizes that decision makers rely on simple mental models which have
serious limitations. They become increasingly deficient as problems grow more complex,
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as the environment changes more rapidly, and as more people must participate in key
decisions “...agents make decisions using routines and heuristics because the complexity
of the environment exceeds their ability to optimize even with respect to the limited
information available to them” (Sterman et al 2007, p. 685).
This model was developed in 1967. It captured the dynamic tension among
conflicting performance objectives and showed how the conflicts impacted mid-term
company performance. These issues were central in subsequent projects. The resulting
model formulation became a key element of my solution repertoire and featured in many
later models, e.g., my work on commoditization (Weil 1996; Weil and Stoughton 1998).
Resource Allocation
Five year later a project with a major IT systems supplier became another
landmark. The project focused on R&D management at the strategic level. “Many
important R&D strategy issues pertain to the policies which govern the acquisition and
allocation of resources. The predominant resource in R&D organizations is people”
(Roberts 1978, p.326). Resource acquisition and allocation decisions are intimately
interwoven with aspects of technology strategy, e.g., the technical advancedness of the
company’s products, the mastery of the technologies used in products, the nature of new
technology programs, and the priorities for assigning resources to various activities.
The model represented the dynamic behavior of the R&D organization as it
responded to various pressures and direction from corporate management. It built on and
extended the pioneering work of Roberts (Roberts 1964; Roberts 1967). The model
consisted of five interrelated sectors: technology programs, new product exploration,
product development programs, performance measurement and control, and human
resources allocation. Innovations in technology enabled innovations in new product
exploration and then in major development programs. “Thus technological
advancements can have a cascading effect through the system, leading ultimately to the
introduction of more advanced products in the market” (Roberts 1978, p.329).
This structure is shown in Figure 2. The distinguishing feature of the model is its
focus on key characteristics of the company’s technology base, R&D activities, and
products in the market. Technology programs, new product exploration programs, and
product development programs were described in terms of their “advancedness,” i.e.,
how large a technological they represented relative to the previous generation. The
advancedness of these activities compared to the company’s level of technology
“mastery,” i.e., its depth of experience with the relevant technologies, determined the
productivity of technical staff. The series of linked stocks and flows in Figure 2
represented the sequential inter-relationship of these characteristics.
oO io)
en decay roe decay product decay
‘ADVANCEDNESS OF ‘ADVANCEDNESS OF ADVANCEDNESS OF
EXISTING PROVEN PRODUCT NEW PRODUCTS IN
TECHNOLOGY CONCEPTS THE MARKET
advancedness \ coil \ eae
of technology of new product of product
programs exploration programs
completion rate of
exploratory activities
resources assigned to
completion rate of
technology programs
product programs
productivity of
resources applied
to exploration productivity of
resources applied to
MASTERY OF Sceptre
EXISTING
TECHNOLOGY Ke
mastery decay
Figure 2: The Product Development Pipeline
The performance measurement and control sector dealt with the setting of
performance objectives and responses to performance problems. It captured the trade-off
between technical and schedule performance, the sequential interdependency of technical
activities, and the impacts of technical problems on staff productivity. The resource
allocation sector acquired and allocated two types of people, i.e., researchers and
engineers, to six activities: product performance problems, major product programs, new
product exploration, technology programs, technology idea nurturing, and product idea
nurturing. Skills constraints and management policies limited possible reallocations.
Analyses highlighted the causes and implications of “workflow bunching,” a
common pattern of oscillating and mal-distributed workload experienced by many R&D
organizations. The dynamics produce periodic bulges in workload at various stages of
the R&D pipeline, shortages of new product candidates, waves of new products entering
the market, insufficient mastery of new technologies, and unanticipated product
performance problems. “Looking at the output of the R&D process, the result of
workflow bunching appears as ‘bursts’ of product programs followed by ‘lulls.’ Within
the R&D organization, it looks like a series of waves, surging down the product
development pipeline” (Roberts 1978, p.337).
Workflow bunching is caused by the combination of dysfunctional resource
management policies, over-optimistic commitments, and changing corporate- level
pressures for diversification. Limiting growth in overall headcount and also allowing
rapid reallocation from one technical activity to another amplify the problem. Over-
optimistic commitments lead to initially unrecognized additional real workload. It is an
5
even more powerful driver of workflow bunching, since more reallocations of resources
are required. Changes in corporate priorities obsolete recent product exploration work
and require the R&D organization to develop a new generation of product candidates
under great schedule pressure.
These were fundamental concepts, not only in subsequent strategy applications of
System Dynamics, but also in the project management models (for example, Cooper
1980; Abdel-Hamid and Madnick 1991; Weil and Dalton 1992; Lyneis, Cooper, and Els
2001) which became the specialty of Pugh-Roberts. Applications to project management
illuminated the existence and critical impacts of “undiscovered rework,” i.e., work which
was considered to be complete and correct but actually was not. When this rework was
discovered later in the project, it represented unplanned workload which escalated costs,
delayed completion, and put dysfunctional pressures on management. Repenning (2001)
extended the concept to a multi-project development environment. He analyzed the
impacts of fire fighting. “In the product development context, fire fighting describes the
unplanned allocation of engineers and other resources to fix problems discovered late in a
product's development cycle” (Repenning 2001, p.5).
This model expressed in the System Dynamics paradigm concepts presented later
by Porter (1991) in his pursuit of a dynamic theory of strategy. Porter described how a
company’s competitive position is created over time. He called this dynamic process the
longitudinal problem and framed it in terms of the interactions between initial conditions
and managerial choices. “The earlier one pushed back in the chain of causality, the more
it seems that successive managerial choices and initial conditions external to the firm
govern outcomes” (Porter 1991, p.106). Porter's “successive managerial choices” is
captured in the model’s representation of a continuous flow of decisions regarding
staffing levels, resource allocation, and R&D objectives.
Commoditization
The experience of many technology-based markets suggested the existence of
powerful long-term dynamics that lead to "commoditization" of products and services.
This term is used to denote a competitive environment in which product differentiation is
difficult, customer loyalty and brand values are low, and sustainable advantage comes
primarily from cost (and often quality) leadership These industries exhibit recurring
cycles in investment, capacity utilization, prices, margins, and return on capital.
Examples range from lumber (West and Weil 1999) to IT (Carr 2003). Carr
argues that infrastructure technologies inevitably commoditize, citing railroads, electric
power, and communications. They become utilities that all businesses must use but not
longer are a source of competitive advantage. “Many of the major suppliers of corporate
IT... are battling to position themselves as dominant suppliers of web services - to tum
themselves, in effect, into utilities” (Carr 2003, p.45). The airlines have gone through
several boom and bust cycles since the US market was deregulated in the late 1970s.
My research illuminated the principal drivers of commoditization, key differences
among industries and markets, leverage points for influencing the dynamics, and
strategies for contending with commoditization. A generic market dynamics model was
developed. It has been used to analyze the behavior of a cross-section of markets at
different stages of maturity and liberalization, including airlines, telecommunications,
petroleum, iron and steel, ocean shipping, IT, media, and pharmaceuticals (the model is
described in Weil 1996; analysis results are presented in Weil and Stoughton 1998).
As depicted in Figure 3 this model represents the interactions among prices,
demand, capacity loading, and profitability. Unlike the two models discussed above the
unit of analysis is a market, not an individual company. Capacity planning, adoption of
new technology, pricing, service quality, the effects of new entrants, and intensity of
competition are modelled at the aggregate market level.
Profitability
Expenses.
S)
Capacity
Delivery Time
_ Planning
a Horizon ~
Revenues
‘> Projected__| _ | Desired_
Demand Capacity ~ _~
Sv
Demand Capacity
1a {a2) a Orders
/
Service Bl ea
d Quality Caneciy<
Prices NL
wa
Capacity
Figure 3: The Market Dynamics Model
The market dynamics model incorporates and builds upon the key features of
earlier landmark models. Tension between the conflicting objectives of target capacity
utilization and desired profitability strongly affect pricing and capacity decisions
(balancing loops B1, B2, and B3). Pipeline constraints lead to long delays between
capacity orders and when new capacity enters service, amplifying planning errors caused
by extrapolation of past demand trends and biases toward over-optimism (reinforcing
loop R2 shown in dashed lines).
This model differs in several important respects from a simple one-loop model of
industry development where entry occurs, capacity rises, and prices fall until profits are
driven out. First, increased capacity causes prices to decline and demand to grow,
leading to further capacity orders (loop R1 also in dashed lines). Commoditization often
creates a mass market which, for a period of time, grows much faster than would be
explained by exogenous drivers, e.g., GDP or population. As described above, the
second reinforcing loop (loop R2) raises capacity orders when the delivery time lengthens.
It accelerates the build-up of excess capacity, especially when there are significant
“barriers to exit.”
The oscillating emphasis between capacity utilization and profitability (shifting
dominance of loop R1 vs. loops R2 and R3) generate realistic dynamics observable in
many markets, e.g., periods of steeply falling prices separated periods of price stability.
The added complexity and richness of this model enable it to produce a wide range of
commoditization behaviors. They allowed me analyze why commoditization happens
very rapidly in some markets and more slowly in others.
Demand growth in commoditized markets tends to follow an irregular "stair step"
pattern, driven by the combination of recurring cycles of over capacity and price cutting
and macro-economic cycles. The distinctive pattern can be seen in Figure 4.
Demand
Capacity Order Rate
1985 1991 1997 2008 2009 2015,
Anes | $e Kiyrye)
Figure 4: Output from the Airline Model
Demand growth typically slows as an industry matures. This is both a cause and
result of commoditization. A point is reached where eroding margins produce pressures
which counter-balance the downward effects of poor capacity utilization on price.
Ambitious new entrants seeking to build share, established companies defending their
positions, and even governments backing national champions all have their limits. The
result is to moderate price cutting and thereby slow subsequent demand growth.
The effects of technology on the industry's cost structure also are significant. If,
as in the case of the airlines, the investment required per unit of capacity is rising the
industry becomes dominated by fixed costs. This makes margins extremely sensitive to
capacity utilization, and hence increasingly volatile. Conversely, as with telecoms during
most of the simulated period, if technology is driving fixed costs down steadily, margins
become less sensitive to utilization and in general more stable.
Capacity orders tend to become increasingly cyclical over time, with the down-
cycles becoming lower and more extended. This pattern, shown in Figure 4, results from
deteriorating industry profitability and increasingly strong profit and utilization effects on
order decisions. It has profound implications for both the industries in question and their
suppliers. Highly commoditized industries will have periodic opportunities to introduce
new technologies, but these will be quite limited both in duration and relative to the
installed base of capacity.
The combination of slowing demand growth, eroding profitability, and inherently
long asset lifetimes (generally 20-30 years in the industries studied) leads to stagnation of
the industry's portfolio of capacity. There are powerful incentives in a commoditized
industry to stretch asset lives and invest as little as possible. Significant "barriers to exit"
which make it more difficult and/or costly to eliminate capacity (e.g., governmental
support of national champions, protection by bankruptcy courts, or environmental
regulations which impose large clean-up obligations) exacerbate those dynamics.
“These results support the overarching conclusion that commoditization is driven
by excess capacity. And they show that complex interactions over time among industry
structure (e.g., the fragmentation and intemationalization of markets), management
policies (e.g., the response of pricing and investment decisions to capacity utilization and
profitability), and technology strategy (e.g., the impacts of technology on costs and
capabilities) underlie persistent excess capacity and, hence, commoditization” (Weil and
Stoughton 1998, p.40).
The model was used to test a range of scenarios for factors which influence the
industry-level dynamics, e.g., economic growth, technology trends, and regulation. The
formal process of scenario development and use is closely identified with the Shell group.
Wack (1985) and de Gues (1988) described why the process was initiated at Shell, how it
worked there, and the practical benefits that were achieved.
Porter (1985) defined a ten-step process for developing and using scenarios. It is
very significant that the first three steps involve a systematic micro-economic analysis of
the industry in question. What is its structure? How does it function? What are the
major uncertainties that might affect the industry? What are the sources of these
uncertainties? To address such questions, managers must rely on some type of model.
Another step requires managers to project the competitive situation under each scenario.
How are they to do this? Again, some type of model is required. And the final step -
monitoring key factors to anticipate changes in the industry - depends on an ability to
identify valid "leading indicators, " to interpret their movements correctly, and to know
when to act. Here, too, managers inevitably employ some type of model.
There can be a very powerful synergy between scenario development and System
Dynamics modelling. Scenarios consider multiple futures and force unconventional
thinking. They can cause a team of managers to be more creative about important aspects
of their business - operations, products, technology, markets, competition, government
regulations, economic conditions. And, in so doing, they can change the mentality of
senior managers. Scenario building requires managers to have a coherent view of their
business. System Dynamics models can help managers to acquire this view. They
provide a capability to identify which scenario variables are most important, to ensure the
consistency of scenario assumptions, and to monitor key environmental factors.
The market model and research into commoditization have stimulated many
Masters theses, five Doctoral dissertations (Stoughton 2000; Auh 2003; Ngai 2005;
Dattée 2006; and one which will be completed this year), and a series of publications (for
example, Weil 1996, Weil and Stoughton 1998, West and Weil 1999, Weil and Utterback
2005, and Dattée and Weil 2007). They have been enormously influential on my
subsequent applications of System Dynamics to corporate strategy issues.
Technology Substitution
Ongoing research, initiated two years ago, captures and analyzes the fundamental
dynamics of innovative industries with a System Dynamics model. By design the model
is simple and generic. It is intended to apply to a broad range of products and services:
assembled and process-based, complex and simple, physical and digital, business and
consumer, early stage and mature, 19" century and 21" century. That is what is meant by
the “fundamental dynamics” of innovative industries. In various combinations they can
explain the evolution of most markets.
This work builds on an extensive body of research and publications. Its roots lie
in the work of Abernathy and his collaboration with my colleague Jim Utterback. Over
the ensuing years a rich collection of empirical studies, conceptual frameworks, and
quantitative models of innovation were developed. (for example, Abernathy 1978;
Abernathy and Utterback 1978; Abernathy and Clark 1985; Tushman and Anderson
1986; Henderson and Clark 1990; Klepper and Graddy 1990; Utterback and Suarez 1993;
Utterback 1994; Milling 1996; Christensen 1997; Pistorius and Utterback 1997;
Christensen, Suarez, and Utterback 1998; Utterback and Afuah 1998; Christensen and
Overdorf 2000; Milling 2001).
The literature highlights dynamics which are fundamental to the sources of
innovations and their impacts on firms, markets, and industries. When disruptive
innovation enables a new generation of products or services the dominant companies in
the market often are complacent and slow to react (this is the behavior of incumbents
10
described in Sull 1999 and Christensen and Overdorf 2000). They seem unconcerned,
uninterested or even dismissive. The new technology may be considered “inferior” or “a
niche market.” It does not fit with the paradigm of the dominant companies and does not
appear to be a sufficiently large opportunity.
While the established companies may participate in the new technology they
usually focus most of their resources and attention on the older generation. Indeed, as
they feel more and more pressure from the new generation the incumbents often find
ways to substantially refresh the old technology, boosting its performance to much higher
level. But typically they struggle to be successful with the new technology. Innovation
obsoletes important aspects of the incumbents’ capabilities and knowledge, which tend to
become deeply embedded in their structure and processes (see Cooper and Schendel
1976; Utterback and Kim 1986; Henderson and Clark 1990; and Utterback and Suarez
1993). The most frequent outcome is a change in market leadership.
The basic structure of the model is presented in Figure 5. This model connects
the number of companies in the market, technology evolution, adoption of new
technology, and the profitability of the companies. The other fundamental dynamics of
innovation are represented, i.e., entry and exit of firms, improvements in cost and
performance, market growth, intensity of competition, and commoditization. The model
represents products based on two generations of technology. It first was applied to film
and digital cameras. The second case study is CD and MP3 music players.
R&D
Market Expenditure
Growth
Number of R&D
fo Semen P yee \
Profi -
\ of
\ inns of Me
Adoption of
Technology’ Competition
Product Cost
and Performance
Figure 5: Innovation and Technology Adoption
The entry of firms into a market and the subsequent exit of many or most
competitors are central to the dynamics of innovation (these dynamics are described in
Utterback 1994). The entry rate is determined by the expected growth and profitability of
the market and availability of finance. In the early fluid stage of a new generation of
technology the size of the prize is quite uncertain. Thus a “lemming effect” often occurs,
where the inflow of entrants reinforces the impression that this must be the “new big
thing,” attracts a large amount of investment, and thus encourages additional firms to
enter the market. In a relatively short time there can be a surprisingly large number of
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companies in the market. These self-reinforcing dynamics were conspicuous during the
dotcom boom.
The large number of firms generates a high rate of experimentation and
innovation. Continual innovation and the increasing number of users of a new
technology drive improvements in cost and performance. The adoption rate of products
or services based on a new technology depends on both the number of potential users and
their willingness to adopt. Customers’ willingness to adopt is affected by both objective
and emotional factors, i.e., price/performance, network effects, and perceived risks. “The
perceived risks of a new technology can be high in the early stage. It is unproven, and
potential users have reason to be skeptical and cautious. Things start to change as the
number of users increases. The quantity and quality of information about the new
technology improves, allowing more confident assessments and decisions” (Weil and
Utterback 2005, p.6).
As the market becomes more crowded and standards emerge, the intensity of
competition increases and the products or services commoditize. This has two
reinforcing effects on the number of firms. First, the entry rate slows because potential
entrants reassess the attractiveness of the opportunity. Second, a growing number of
firms exit the market. The rate of innovation slows and shifts from product to process.
Once again the model drew heavily on and added to the portfolio of building
blocks. Conflicting performance pressures, i.e., from market share and profit objectives,
influence the R&D expenditure of companies associated with both generations of
technology. There are significant delays in the product development pipeline before
R&D expenditures affect the level of technology in the companies and then the cost and
performance of products in the market. Commoditization drives the emergence of a mass
market for products based on the new technology but ultimately squeezes R&D, leading
to technological stagnation. Product cost-performance plateaus. Sales growth continues,
but with little or no profit.
Significant conclusions emerged from this work. The “lemming effect” has
enormous leverage. The inflow of companies stimulates technological progress, drives
improvements in cost and performance, increases willingness to switch, and accelerates
adoption of the new technology. The willingness to switch evolves in stages. First,
growth of the installed base overcomes initial skepticism and caution. Next,
improvements in cost and performance enhance the appeal of products based on the new
technology. Rising user requirements (influenced by increased marketing spend) add
momentum to the substitution dynamics.
Social Factors
Trust and brands are key elements in the dynamics of most markets. Powerful
brands make decisions easier when customers are faced with many possibilities. They
provide customers with a range of intangible benefits, e.g., peace of mind, recognition,
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individuality, status, a willingness to trust, and the confidence to empower. Brands
promote and reward loyalty, thus creating stickiness in relationships. Customer
information becomes the most valuable asset, especially in commoditized markets. Trust
is the essential prerequisite for the customer to reveal personal information, authorize use
of this information, and welcome the results. In the absence of sufficient trust likely
customer behaviors are deliberate deception, holding back, and fending off.
Customer information management drives a dynamic model of relationship value.
The model involves extremely powerful self-reinforcing mechanisms which can be either
virtuous or vicious. Growing satisfaction and trust leads the customer to be more open
regarding values and needs, and more willing to empower the service provider. As an
empowered agent it can search, evaluate, advise, and implement on behalf of the
customer. This “learn more, sever better’ model (Weil and Weil 2001) is shown in
Figure 6. Itis central to value creation in commoditized markets.
Learn More
Customer Provider
trust = greater personalization +
openness + push = greater
empowerment value added
of provider for customer
Provice’ Serve Better <
Figure 6: A Model of Relationship Value
“The easiest way to ruin a relationship is to frustrate, disappoint, irritate, or abuse
the customer, e.g., by over-stepping the bounds of what he or she at any point considers
‘acceptable behavior’” (Weil and Weil 2001, p.10) The provider needs to measure the
state of relationships, and not move too fast or too slowly. The imperative is demonstrate
commitment, respect, and trustworthiness.
My most recent work focuses on the market impacts of social factors, e.g., trust,
fashion, lead users, perception and extrapolation of trends, information flows, bandwagon
effects, and network effects. I am collaborating with two colleagues at Imperial College
London, Nelson Phillips and Brice Dattée (see Munir and Phillips 2002; Dattée 2006;
Dattée and Weil 2007). As described by Sterman (2000) bandwagon effects are driven
by media coverage and positive word of mouth which create the perception of a hot
product. Network effects involve a strong positive feedback loop. “As illustrated by the
VCR industry, the utility of a product often depends on how many others are also using
it” (Sterman 2000, p.370.). These factors are particularly significant in determining how
a market responds to innovative technologies, products, and business models.
My case study of the spectacularly successful iPod highlights the importance of
social factors in innovation and technology adoption. How did Apple de-commoditize
music? What exactly is innovative about the iPod? First, the timing was right.
Successful prosecution of Napster and individuals who flagrantly violated IP rights
publicized the criminal nature of unauthorized file sharing. The major music publishers
were experiencing declining sales of traditional media, belatedly recognized the potential
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of online distribution, and were ready to do a deal with a responsible distributor who
offered IP protection.
Equally important, A pple recognized that as a market matures and commoditizes
the sources of competitive advantage become increasingly intangible, e.g., brand,
customer insights, design, marketing, lead users, and the customer experience. Apple
used its counter-culture brand, elegant product design, and dramatic advertising to create
asocial phenomenon. The customer experience was great. The iPod made digital music
intuitive and easy (the interface, installing and using the software, downloading music).
Pine and Gilmore (1998) describe the progression of economic value from commodities,
to goods, services, and then experiences. “This transition from selling services to selling
experiences will be no easier for established companies than the last great economic shift,
from the industrial to the service economy” (Pine and Gilmore 1998, p.98).
Strong bandwagon and network effects reinforced the virtuous dynamics.
Network effects occur when the value of a product or service increases non-linearly with
the number of users, e.g., e-mail, text messaging, and social networks. Bandwagon
effects result from a fashion craze, something everyone must have. The combination
both effects is very powerful. The iPod became a fashion craze. Podcasting increased
the value of the iPod disproportional to the number of users. Two further lessons from
the iPod are the importance of complementary assets (iTunes and accessories) and of
continual incremental innovation to further reinforce the dynamics (new music players,
the photo iPod, and video iPod).
The commercial model of the iPod exploits these bandwagon, network,
complementary asset, and innovation effects. The iPod appliance is the principal revenue
and profit generator. The iTunes site offers copyrighted music tracks and video content
for fees and lists over 20,000 free podcasters. The role of iTunes is to make the iPod
more valuable to users. Apple has stimulated a rich ecology of independent accessory
suppliers, e.g., of cases, docks, speakers, and remote controls, and filled potentially
vulnerable gaps in its product range with low price basic models and a series of higher-
end models with increasingly sophisticated capabilities.
As noted above the model of innovation and technology adoption was applied to
CD and MP3 music players. A number of extensions to the original model were required
to capture the more complex dynamics of this market. They are shown in Figure 7.
Advances in product technology are strongly influenced by the cost and
performance of components, e.g., disk drives, flash memory, and USB interfaces for MP3
players. The key hardware innovations were at the component level, not in the
appliances. Increased ease of use of products based on the new technology accelerated
adoption. In the case of MP3 players the widespread adoption of broadband
communications dramatically improved the speed at which music and video files could
be downloaded. A strong positive feedback loop connected adoption with the availability
of complementary assets, in this case content and accessories. Finally another positive
feedback loop captured the bandwagon effect. As more people adopted products based
on the new technology they became fashionable, cool, and a must have.
14
Growth
“fashionable
*cool
Social or
Value Profitability
Adoption of
fe
R&D
Expenditure
shard disks
*fiash memory
*USB
Number of R&D
‘ompanies
i
Ease of Use
P ee
Ne Product
Intensity of. Technology
Product Cost
and Performance
Figure 7: The CD/MP3 Model
Number of Companies - MP3 Players
Component
Performance
and Cost
7
80
60
40
4 yis. after the peak 20
only 20% of the
companies survived nl
0 =
1990 1994 1998 2002 2006 2010 2014 2018
MP3 player sales exceed ume (ean) Ittakes 10 yrs. for MP3 to
CD in 8 yrs. (vs. 13 yrs. for Number of companies companies dominate the market (vs.
digital cameras) 20 yrs. for digital cameras)
Unit Sales Units in Use
60M 200M
45M oer i 150M
of -
30M zr 100M
:
:
15M NS 50M |
[oe wine el
0 Leslon™ if iam 0 Tod
1990 1994 1998 2002 2006 2010 2014 2018 1990 1994 1998 2002 2006 2010 2014 2018
Time (¥ ea) Time (¥ ea
CD players CD players units
MP3 players — — MP3 players - — — = units
Figure 8: Output from the CD/MP3 Model
Results from the CD/MP3 model are presented in Figure 8. Not surprisingly the
additional structure changed the dynamics. Driven by the positive feedback loops the
adoption of MP3 progressed rapidly. MP3 player sales exceed CD in 8 years vs. 13 years
for digital cameras. It takes 10 years for MP3 to dominate the installed base vs. 20 years
for digital cameras. The failure rate of market entrants is spectacular. In four years the
15
number of companies offering MP3 players declines from a peak of almost 80 to 15, i-e.,
only 20% have survived.
“A more complete model of technology diffusion that accounts for the dynamics
of social factors can replicate more accurately non-trivial substitution patterns. It also
indicates that more effective targeting of opinion leaders as early adopters could provide
effective leverage from communication because of their relevance and credibility as
reference users. Understanding, the dynamics induced by the structure of interpersonal
networks can... reduce the risk of misreading the market. Such risks include giving up
too soon, overconfidence, and the risk of a technological spark that fails to achieve
mainstream take-off” (Dattée and Weil 2006, p.1).
Epilogue
It has been a long and productive joumey. I started with classical Industrial
Dynamics, models of the firm which focused on orders, production, and shipments. At
each milestone I recognized and represented more behavioral richness and complexity.
Along the way the strategic issues on which I worked evolved from the internal business
dynamics of the firm to competition and the longer-term behavior of markets, particularly
markets where technology is an important factor.
Commoditization results from complex interactions among market structure,
competitive behavior, and new technologies. Aspects of organizational behavior are
central to these dynamics. New entrants usually put market share growth ahead of short-
term profitability for a period of time (often the first 2-3 years in “J-curve” business
plans). At some point the established incumbents feel compelled to sacrifice profitability
in order to defend their positions. Traditional barriers to entry and conservatism in
decision making are eroded by freely available financing. When an industry is “hot”
entrepreneurs have no difficulty raising the capital to launch new companies. As markets
grow more commoditized the sources of sustainable advantage become less tangible, e.g.,
IP, know-how, information, brand, reputation, relationships, trust, and the “customer
experience.” Competing on intangibles requires quite different capabilities from
competing on product or service price and performance.
My work on technology substitution took me deeper into the social and
psychological dynamics of markets. How potential adopters respond to something which
is new and unproven, but fresh and cool is quite complex. The fluidity and uncertainties
of the early stage poses significant risks for customers. Highly respected reference users
legitimize a new technology and make its selection much easier to defend. And products
or services based on the new technology can become a fashionable “must have.” This
happens in business markets as well as consumer markets, e.g., the rush by companies in
the late 1990s to get on-line. Then the risk is of not adopting, of being seen as “behind
the times” or “not getting it.” The “lemming effect,”. i.e., the wave of entrants into a
market during the early fluid stage of a new generation of technology, is a key driver of
experimentation, innovation, and technological progress.
16
A social perspective on innovation explains why it is necessary for most
companies to change the way they interact with customers. Successful innovations build
on customer needs and create positive behavioral change among large groups. Customers,
not suppliers, define “quality.” The innovations which create real value for companies,
e.g., the iPod, are not primarily about technology. The winners are companies who
manage the social side of technology: the complete customer experience.
What do we know now that we did not know forty years ago? The most
important lessons for me were about the critical roles of organizational, social, and
psychological factors in business decisions, competitive behaviors, and the evolution of
markets. I now think of business dynamics at three inter-related levels: the
organizational level, market level, and contextual level. My work highlights the critical
importance of human and intellectual capital, e.g., skilled scientists and engineers,
suppliers and other complementors, familiarity with technologies and markets, insights
regarding customer needs and values, and customers with an appetite for innovations.
To quote my favorite Chinese cookie fortune, “The road to success is always
under construction.”
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