Dynamics of Business Models
Long-ranging Impact Assessment of Business Models in the
Capital G oods Industry
Christian Lerch
Fraunhofer Institute Systems and Innovation Research
Competence Center Industrial and Service Innovations
Breslauer Str. 48
76139 Karlsruhe, Germany
Phone: +49/721 6809-320
Email: Christian.lerch@ isi.fraunhofer.de
Gregor Selinka
Fraunhofer Institute Systems and Innovation Research
Competence Center Industrial and Service Innovations
Breslauer Str. 48
76139 Karlsruhe, Germany
Phone: +49/721 6809-320
Email: Gregor.selinka@ isi.fraunhofer.de
In recent years, new business models are becoming increasingly more important for
manufacturers in the capital goods industry. However, manufacturers of plants still
hesitate to offer these customer-oriented solutions, due to existing uncertainties result-
ing from economic risks. The offer of innovative business models requires a stronger
integration of the supplier into the life cycle of a plant and hence into the production
phase of the customer, leading to the consequence that manufacturers have to restruc-
ture their previous activities extensively. Due to the financial risk connected herewith,
decision models are required, which identify and assess the impacts resulting from the
implementation of these innovative business models. Aspects like time delay, due to the
reorganization of the service department or the set up of adequate human resources
have to be considered. Therefore, the aim of this contribution is to develop a system
dynamics model for the analysis of long-ranging consequences due to the implementa-
tion of an exemplary business model.
Business Models, Capital Goods Industry, Availability Guarantee, System Dynamics.
1 Introduction
Business models become more and more important for enterprises of the capital goods
industry (Grénroos 2000; Baines et al. 2007). On the one hand, business models are able
to increase the competitiveness of industrial enterprises and on the other hand, they help
to open up new markets that are not accessible by means of traditional products
(Wise/Baumgariner 1999). Accordingly, the offer of these customer-oriented solutions
as a combination of product and services hold high potentials for manufacturers.
However, due to existing uncertainties manufacturers of plants still hesitate to offer
these innovative solutions to their clients. One reason for this situation might be intra-
company changes becoming necessary due to the implementation of business models
(Homburg et al. 2003). To offer these innovative solutions, suppliers of plants have to
restructure their previous activities extensively. The decisions connected herewith have
long-ranging impacts for the change from a manufacturer to a customer-oriented solu-
tion provider (Oliva/Kallenberg 2003; Gebauer 2004). Due to financial risks connected
herewith, decision models are required, which identify and assess the impacts resulting
from the implementation of business models.
Aspects like time delay due to the reorganization of the marketing department or the set
up of adequate human resources have to be considered, just as the transfer of market
risks from the client to the supplier (Baines et al. 2007). Furthermore, there are a lot of
different factors conceming industrial changes which often influence each other and are
characterized be feedback structures and delays (Forrester 1961, Jahangirian et al.
2010). Finally, enterprises changing from a manufacturer to a customer-oriented solu-
tion provider can hardly estimate the impacts of a decision during that process.
This paper tries to show the dynamics arising from implementing new business models
in the capital goods industry with the aim of supporting manufacturers during this stra-
tegic change. Hence, the procedure of this paper is as follows: First, the methodology of
business models will be discussed to work out the dynamics and its long-ranging conse-
quences resulting from the offer of business models. Afterwards, a system dynamics
model will be presented regarding an exemplary business model from the capital goods
industry. The work closes with a recapitulation of the results.
2 Dynamics of business models
2.1 Observed dynamics of business models
The chances and risks arising due to an implementation of innovative business models
can be regarded in various case studies (Lay et al. 2007). For example, some enterprises
noticed the lack of some competences needed to offer the new services only after their
market entry. Other enterprises calculated a lower effort as actually needed for offering
these services. Accordingly, these services involved a negative profit margin and the
new implemented business model failed after a short period of time (Lay et al. 2007).
Other case studies give positive examples. Numerous companies highlight the high cus-
tomer satisfaction leading to a long-ranging customer loyalty and hence to a higher
competitiveness. Others report from delayed cross-selling effects onto other product
groups due to innovative service offers (Lay et al. 2007). Finally it gets obvious, that
implementations of innovative business models have impacts onto a company that often
occur delayed and after a longer period of time. Moreover, business models have influ-
ences on other factors that affect the enterprise, too. Hence, the introduction of new
business models seems to bring up dynamic coherencies with a high degree of complex-
ity and a long time horizon.
These facts lead to the possibility of regarding the impacts of innovative service offers
by means of system dynamics with the aim of explaining the long-ranging interdepen-
dencies arising from an implementation of business models. For a better understanding,
the methodology of business models will be discussed as well as existing approaches
regarding their dynamic components.
2.2 Methodology of business models
In literature, there exist numerous classifications describing business models and their
subtypes (Mathieu 2001; Molinari-Tosatti et al. 2002; Davies et al. 2007; Lay et al.
2009). A widespread typology, basing on Tukker 2004, distinguishes into three different
kinds of business models (fig. 1).
sed new business model
Value
Service content mainly in
e ‘Result Pure
nted riented service
(intangible) service
Figure 1: Typology for business models (Tukker 2004).
content
= Product-oriented business models describe the core product, flanked with tradi-
tional service like maintenance and repair, installation or financing. Manufactur-
ers use these services to stimulate the sales of their core products, only.
= The second type are use-oriented services that aim to increase the utilization of
plants. Possible examples are the availability guarantee or the supply of tempo-
rary production capacities by means of mobile machines.
= Customers of result-oriented services pay the provider for a result and not for the
sheer product anymore. This kind of business model describes outsourcing con-
tracts or classic BOT-models.
There are a lot of different possibilities to configure these business models. The differ-
ent business models in the capital goods industry differ in shares of product and service
contents. The range of services offered includes product-oriented services, which are
necessary to use the product (e.g. maintenance), use-oriented services which contain the
possibility of a customer to use a specific product (e.g. product leasing or pooling), and
so-called result-oriented services, where the tangible product plays a minor role and the
costumer and manufacturer agree on a result and make no agreements on how this result
is reached. The share of the service component increases when product-service systems
contain product-, use- and result-oriented services (Tukker 2004).
The term business model has become common to describe a business concept or a busi-
ness strategy of a company. The economic sciences use a more specialized definition of
this term. Herein, a business model consists of three parts (Timmers 1998; Lehmann-
Ortega/Schoettl 2005):
= Value proposition: Defines the kind of utility for the customer or other players
resulting from the business activities.
= Revenue Model: Constitutes the type of payment from the customer to the pro-
viders.
= Value chain configuration: Describes the different players, their roles and their
contributions towards the value creation.
By means of these criteria it is possible to describe various types of business models.
The offer of a classic business model, classified as product-oriented service (fig. 1),
comprises the value proposition of a product with high quality and productivity. The
provider gets paid by the client in the form of the sales price. Moreover, the provider
builds the plant while the operation is done by the customer. In comparison to this, a
BOT-model includes a value proposition where the provider delivers serial parts to the
clients. The provider builds and operates the plant and gets paid for every delivered
serial part.
Finally, the question arises on what are the impacts of these different business model
criteria when it comes to dynamic coherencies existing between the implementation of a
business model and its success afterwards.
2.3 Basic dynamic structure of business models
It seems likely to build a dynamic structure of business models by means of the criteria
“value proposition”, “revenue model” and “value chain configuration”. Accordingly
these three components will be analyzed in terms of their interactions with the aim to
build a first generic structure for the dynamics of business models (fig. 2). The dynamic
consequences of the value proposition can be described by means of the price and the
service quality. The customer expects a special quality level defined by the value propo-
sition. Moreover, the client compares the sales price with the expected quality. Both
parameters configure the utility for the customer. Consequently, the higher the utility,
the higher is the number of sold business models. This factor has high impacts on the
component revenue model.
The revenue model holds three factors. The first factor describes the revenues, depend-
ing on a high degree on the sold number of business models. The second one is defined
by the costs arising from the offer of the business model and depending on its value
chain configuration. Moreover, the number of sold business models has impacts onto
the price level by means of learning effects. Finally, the revenue model has influences
on the value proposition and hence onto the satisfaction of the customers. The differ-
ence of costs and revenues leads to the benefit for the enterprise, influencing the last
component of business models, the type of value chain configuration.
The value chain configuration is described by one factor only. The development of in-
ternal resources and structures defines the activities necessary for a company to supply
such business models. The higher the benefit of a business model, the higher the inter-
ests of an enterprise to enhance the internal resources and structures. This component
seems to be the critical driver for successful changes of manufacturers to solution pro-
viders as described above. Consequently, the question arises which strategies lead to
successful changes and which strategies should be avoided. Moreover, this critical driv-
er cannot be described by means of one factor only. The reason for this situation is as
follows: Observing the generic dynamic structure of business models, the assumption
arises that the value proposition as well as the revenue model are independent from the
kind of business model. In comparison to this, the value chain configuration depends on
a high degree on the kind of business model. Accordingly, there exists no generic struc-
ture for this component. For further analysis, the individual dynamic structures have to
be developed for every single business model.
VALUE PROPOSITION |
model =m)
‘Customer
+ satisfaction
VALUE CHAIN Specialization of
CONFIGURATION intemal resourves ‘Average cous pat fs
business model
—~ Number of sold
business models
Overall costs for
business models
Benefit
‘Revenues from sold
+ business models
REVENUE MODEL.
Figure 2: Basic dynamic structure of business models.
For these reasons a system dynamics model is developed in order to analyze the differ-
ent dependencies and influence factors within the new business model describing main-
tenance contracts offering guaranteed availabilities. Hence, the focus of this system dy-
namics model lies on the value chain configuration and its influences on the other fac-
tors. The aim of such a model is to help support manufacturers in the capital goods in-
dustry when planning to introduce a new business model offering maintenance contracts
including guaranteed availability.
3 Exemplary business model “Availability guarantee”
3.1 Background and impacts
In reality it is nearly impossible to design a plant which runs without any failures over
its life cycle. This fact holds high potentials for designing innovative business models
with the aim to distinguish a company from its competitors. The classic buyer supplier
relationship implicates on the one side, that the customer buys a plant and runs it using
its own personnel. On the other side the provider builds the plant and delivers it to its
customer. One exemplary type of innovative business models is to supply a contract
which guarantees a specific availability of a plant. The aim of this contract is to reduce
downtimes and hence, to increase the productivity of a plant. In contrast to the classic
business model, the plant manufacturer arranges repairs and additional preventive main-
tenances. The provider and the client agree on a certain availability the plant has to de-
liver. The manufacturer then has to arrange all maintenance tasks necessary to reach the
agreed guaranteed availability. Furthermore, the provider and the client have to agree
about the payment. In general, two scenarios are possible: If the plant reaches the agreed
availability, the client has to pay the normal rate defined in the contract. If the plant
does not reach this availability, the provider has to bear the arising costs. (Lay et al.
2009).
Maintenance contracts offering guaranteed availability have far reaching consequences
on corporate and product design of the manufacturer. The producer needs its own main-
tenance staff to perform all the maintenance tasks or has to engage special maintenance
companies. The product design has to be adjusted to achieve the required availability,
too. As the manufacturer has to perform all needed maintenance tasks including preven-
tive and reactive maintenance tasks, he also must have the relevant information about
the condition of the plant. Furthermore, the producer must plan the different mainten-
ance tasks and their durations in order to plan the arising downtimes (Lay et al. 2009).
Regarding figure 2, it gets obvious that there exist interdependencies between obtained
availability and customer satisfaction and hence, between obtained availability and
long-reaching success of the business model. Therefore, a specific dynamic structure for
the business model “Availability guarantee” will be developed, basing on the conclu-
sions of figure 2.
3.2 Dynamic structure
As described above, the customer satisfaction depends on the compliance of the value
proposition. Furthermore, the value proposition gets influenced by the organization of
internal resources. For the business model “Availability guarantee” the internal resource
“staff” will be required. Accordingly, the question arises of what are the interdependen-
cies between staff and availability and how both factors are developing over time.
Moreover, it is of interest what strategies are possible for an enterprise to control the
availability and hence, the long-ranging success over time.
Due to the definition of the availability it seems likely to regard its individual factors
and to work out the dynamics in this way. The availability gets calculated using the
Mean Time Between Failures (MTBF), which describes the running time of the plant
between two breakdowns, and, the Mean Time To Repair (MTTR), which defines the
average time needed for one repair. Therefore, two loops will be regarded controlling
the availability. The MTBF-loop describes the influencing factors for the mean time
between failures whilst the MTTR-loop observes the factors influencing the mean time
to repair (fig. 3).
The availability of a plant has impacts onto the number of sold business models because
the higher the availability, the higher the customer satisfaction and hence, the higher the
sold business models (see fig. 2, too). Consequently, the stock of customers increases,
leading to higher numbers of repairs. This results in a higher workload for the responsi-
ble staff of maintenance and repairs. Like shown in figure 3, the staff workload has cen-
tral influences on both, the MTBF-loop and the MTTR-loop. On the one hand, a higher
staff workload leads to a lower number of preventive maintenance tasks. If the number
of preventive maintenance tasks decreases, the mean time between failure decreases
after some time, too. Finally, the lower the MTBF, the lower is the availability of a
plant. On the other hand, the staff workload has influences on the MTTR-loop, too. The
higher the staff workload, the greater is the MTTR and hence, the greater are the down-
times. Consequently, a higher downtime leads to a lower availability. An overview
about the complete dynamic structure gives figure 3.
Preventive
MTBF maintenance tasks
a
MTBF Loop
/ ‘ustomer stock
Availability
\wuall Staff workload
Sold business hee
models
Ga Loop
Downtime
a
Figure 3: Dynamic Structure of the business model “Availability guarantee”.
3.3 Modeling stocks and flows
The sector modeling MTBF, MTTR and availability form the central part of the model.
The connections within this sector are shown in figure 4. The stock “defective equip-
ment” describes the overall number of plants which are currently not useable because of
running repairs, running preventive maintenance tasks and waiting times. If a failure
occurs, the number of unusable equipment increases and then again decreases after the
respective MTTR. The downtime due to preventive maintenance tasks is modeled in the
same way. Planned maintenance tasks increase the number of unusable equipment for
the duration of the maintenance task. Depending on the monthly number of unusable
plants the average monthly downtime of a plant can be calculated. Together with the
operating time the resulting availability is evaluated.
The factors that influence the unusable equipment and hence the availability are the
number and duration of preventive and reactive maintenance tasks. A higher number of
preventive maintenance tasks results in a higher downtime. But a higher preventive
maintenance effort may increase the MTBF and reduce the number of occurring fail-
ures. Thus a higher preventive maintenance effort can reduce the downtime due to
needed repairs and optionally the overall downtime. However, a higher maintenance
effort also has negative effects. As the overall staff capacity is fixed, a higher number of
maintenance tasks reduce the available staff capacity for required repairs. A higher re-
maining staff capacity decreases the overall MTTR by reducing waiting times for avail-
able staff in case a failure emerges. This results in shorter downtimes and higher plant
availability.
The pictured feedback loop in figure 4 is closed by the connection between availability
and preventive maintenance effort. If the availability increases, the maintenance effort
will be reduced. The arrangement of this connection is a major strategic decision that
has to be made when planning the implementation of the new business model.
Preventive
— mintenance efor
/ Duration of
‘Mean time between
Overall staff
capacity
faire (MTBF) ‘Maintenance tasks maintenance
per month
Operating time
Defective equipment
Staff capacity for
~\ S| permonth ‘Maintenance tasks |
done per month
Repairs done per > avatiabilty
mosh, a
\ time to repair
(MTTR)
Figure 4: Stocks and flows for the maintenance and repair sector.
Besides the described sector, the model includes additional sections that describe the
market behavior and the revenue model. The market is described by using the diffusion
model of Bass, wherein the new customers are split up in so-called innovators and im-
itators. Imitators are customers characterized by the willingness to invest in new and
innovative products. Buying well-established and well-known products is typical for
imitators. Shortly after introducing a new business model the amount of innovators is
high compared to the number of imitators. But this relation changes over time, when
offering new business models (Bass 1963). A third group of customers are repurchasers
- customers that are satisfied with the performance of their plant will invest in a new one
after their maintenance contracts have expired.
In this model the success of the business model “Availability guarantee” is examined by
comparing the net present value which arises in different simulation runs. The costs of
the new business model, offering maintenance contracts with guaranteed availability,
consist of personnel costs, costs of material, manufacturing costs for the plants and pe-
nalties in case of non-achievement of the agreed availability. Furthermore, costs for the
implementation of the new business model are taken into consideration. Sales revenues
form the positive component of the net present value.
3.4 Simulation runs
Beyond the background described above, two major questions might be of interest for
enterprises planning to provide availability guarantees. Therefore, the following aspects
will be analyzed in this contribution:
= What are the impacts on the long-ranging success of the described business
model, depending on the level of availability?
= What are the influences of the staff capacity and the staff workload on the long-
ranging success of the business model?
The main focus of the model should lie within the strategy aspects concerning mainten-
ance staff and level of availability. First, the user of the model has to decide how many
workers to employ for all emerging preventive and reactive maintenance tasks. Second-
ly, the connection between the real availability and the offered availability must be spe-
cified. In order to describe this connection, the percentage of the actual availability that
will be subject-matter of the following contracts must be set.
Customer base development
(depending on theratio of the guaranteed availability to theactual availability)
— 3%
a: 96%
95%
Figure 6: Running contracts.
Figure 6 shows the number of running availability guarantees over time depending on
the percentage of the guaranteed availability at the actual availability. The contract pe-
tiod is fixed at 36 months in all simulation runs. A fter three years, the former customers
become potential customers and, depending on their satisfaction, have to decide whether
they want to invest in new plants and maintenance contracts. This causes the characte-
istic steps at month 36 and 72. The probability of becoming a repurchaser is greater the
higher the degree of fulfillment of the maintenance contract. Figure 7 shows the corres-
ponding net present value development.
If the manufacturer offers 97% of the actual availability as guaranteed availability he
will not be able to fulfill the contract as the staff capacity is fixed during the simulation
runs. Therefore, the customers become dissatisfied and do not invest in new plants after
their contracts have expired. This results in a decreasing number of customers (see fig-
ure 6). As the manufacturer has to pay high contractual penalties to its customers, the
net present value is decreasing, too (see figure 6).
The staff capacity is high enough to perform all needed maintenance tasks to achieve an
availability of 96% or lower. Offering a lower guaranteed availability the manufacturer
sells less maintenance contracts and therefore only generates lower revenues. However,
he is able to fulfill the contract conditions, the customers are satisfied and therefore in-
vest in new plants after the contract period. Over time the manufacturer can generate
higher revenues compared to the run in which a higher percentage of the actual availa-
bility is subject matter of the maintenance contract.
Net present value development
(depending on theratio of the guaranteed availability to theactual availability)
12,000.000,00
10,000.000,00
8,000.000,00
6.000.000,00
4.000.000,00
present value
2,000.000,00
0,00 eer
2.000,000,00
4.000.000,00
6.000.000,00
Months
Figure 6: Net present value development
4 Results and findings
For a better understanding of the dynamic complexity and the coherencies of the differ-
ent factors on the net present value, multiple runs were simulated. Therefore, various
levels of availability were regarded as well as different numbers of employees. The re-
sults of these simulation runs is visualized in figure 8. Results containing a negative
value are plotted grey, a net present value greater than zero is plotted black. The runs
shown in figure 6 and figure 7 are marked by a star.
The table shows the existing coherency between the number of employees and the of-
fered availability on the net present value. Moreover, the values show the trade-off be-
tween chances and risks resulting from an implementation of the regarded business
model. The lowest net present value reaches a level of -18.37 million euro whereas the
highest net present value achieves a level of 11.09 million euro.
Furthermore, the success of an implementation depends on both the availability and the
staff capacity. If the staff workload is too low, the costs for employees are higher than
the revenues of the new business model. On the other hand, a staff workload reaching
nearly 100 percent leads to a lower customer satisfaction and hence to a breakdown of
the system.
The observed influence factors and their complex connections (which often include
feedback structures and delays) have a great impact on the success of a new business
model. Regarding the two major questions discussed above, the following findings can
be made:
= Both, the offered level of availability as well as the staff capacity has high influ-
ences on the long-ranging success of the exemplary business model. Moreover,
there exists a coherency between these factors and the net present value, shown
in figure 8.
= Furthermore, enterprises offering availability guarantees should regard the staff
workload. Like described above, a corridor exists between the availability and the
staff capacity leading to a long-ranging success of the business model.
The focus of the model described in this article is placed on maintenance staff and its
workload. During the simulation it has been recognized that the planning of emerging
preventive and reactive maintenance tasks is a very important factor when looking at the
success of the business model “Availability guarantees”. The described system dynam-
ics model is a first approach to help manufacturers in the capital goods industry when
planning to introduce availability guarantees. As mentioned before there are a lot of
aspects that have not yet been considered or may be modeled in different ways.
Number of employees
Net present value
4 5 6 7
95% 8.46 Mio. 4.46 Mio.* 0.49 Mio. -3.54 Mio.
Ratio of the 96% -12.81 Mio. 9.41 Mio.* 5.41 Mio. 1.41 Mio.
Het and he Roi Mio. | -3.37Mio* | 11.09Mio. | 7.09 Mio.
offered
availability 98% Mio. -14.04 Mio. -9.79 Mio. 10.13 Mio.
99% -18.89 Mio. -18.37 Mio. -1.44 Mio. 10.98 Mio.
*) These runs are shown in figure 6 and figure 7
Figure 8: Overview about simulation runs.
Finally, it gets obvious that system dynamics is able to analyze and to describe long-
ranging consequences resulting from the implementation of new business models. The
exemplary business model “Availability guarantee” was able to show, the existence of
feedback loops and delays between the observed factors, leading to different net present
values in dependence to various strategies.
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