Interaction of Product and Service Innovations
An Analysis of the Dynamics in
Industrial Companies
Christian Lerch
Fraunhofer Institute for Systems and Innovation Research ISI
Competence Center Industrial and Service Innovations
Breslauer Str. 48
76139 Karlsruhe, Germany
Phone: +49/721 6809-320
Email: Christian.lerch@ isi.fraunhofer.de
Industrial companies increasingly rely on services to stand out from the crowd. Along-
side direct strategic and economic advantages, these industrial services can also pro-
vide impulses for the further development of the producer’s good. Likewise, modifica-
tions of the material product may lead to new or advanced service offers. Hence, prod-
uct modifications may lead to service adaptations, which again lead to product en-
hancements. Consequently, product and service innovations seem to interact, driving a
dynamic loop and accelerating the innovation activities of a company. This article ana-
lyzes the causes by means of a literature review. Afterwards a system dynamics model is
constructed to describe the assumed interaction system of product and service innova-
tions in industrial companies and show first consequences resulting from this dynamic
loop. Finally, the impacts of some drivers are tested, to give some insights into the be-
havior of this system.
System Dynamics, Manufacturing Industries, Innovation, Industrial Services
1 Introduction
In the past, industrial companies tended to focus strongly on material innovations.
However, it has since become widely accepted that a more comprehensive understand-
ing of innovation, one which takes innovations in services into account alongside ma-
terial product innovations, can have far-reaching consequences for boosting competi-
tiveness (see Dreher et al. 2005; Kirner et al. 2009). Services offered in addition to the
core product, such as maintenance, repairs, training or engineering services, for exam-
ple, aim to both bind customers and increase sales (see e.g. Boyt/Harvey 1997,
Wise/B aumgartner 1999).
Apart from the strategic and economic significance of services, additional effects can be
found in practice, such as, for example, additional information feedback from customer
contacts due to services (see Lay et al. 2007). Beside these practical experiences, this
phenomenon is also described in research (see e.g. Goffin/New 2001; Hobday et al.
2005). Such feedback can in tum provide incentives to improve or modify material
products. An information cycle can be created due to the contact between the compa-
ny’s employees and the product and/or customers which can deliver impulses for new
ideas for products of industrial companies.
Thus, the offer of industrial services can serve as a stimulus for new products, on the
one hand. On the other hand, other authors assume that the further development of
products is also accompanied by changes or modifications in the range of services of-
fered. The technological further development leads to additional or changed service
needs on the part of the customer or in the customer's production process (see Mey-
er/Bliimelhuber 1998; Busse 2005). In this context, the systemic character of products
and services in industrial firms is emphasized, which results from such an integrated
offer (see Duschek 2002).
Summing up, offering industrial services can function as an information channel for the
further development of products. Furthermore, the enhancement of the material product
leads to adaptations of the service offer. This leads to the speculation that there is a
process over time in which product and service innovations encourage each other. The
aim of this paper is to analyze these observed interdependencies and to investigate the
following questions:
(1) How can this interactive system between product and service innovations be de-
scribed and represented in a formal system dynamics model?
(2) How do various influential factors affect the system behavior and what possibili-
ties do enterprises have to trigger this interaction process?
In order to answer these key questions, first of all a literature analysis is carried out
which deals with the impacts both of products on service innovations as well as services
on product innovations. By combining these two approaches, a dynamic hypothesis is
derived and converted into a causal diagram. Based on the information from the litera-
ture, and with the help of the causal diagram, a formal system dynamic model is con-
structed which can illustrate the interaction of the product-service innovation system.
Thereafter, simulation nuns and tests will be conducted to analyze the impacts of indi-
vidual factors and to obtain first insights into systems behavior. The article ends with a
conclusion regarding the stimulus to the interaction process and an outlook to future
research needs.
2 Linking product and service innovations
In order to be able to classify the relevance of services as triggers of new products based
on the literature, a short review is made of existing work in this field. The literature
analysis focuses on those papers which deal with the information channel of services or
service contacts to customers and assesses its relevance as a source of inspiration for
innovations.
Several articles have been written over the past few years on the identification or evalu-
ation of industrial services as an information channel for triggering product innovations.
Hobday et al. (2005), for instance, assume that the expansion of services demanded by
customers automatically implies that closer contacts to customers will result. If these
services are provided, a broader information basis results for the manufacturer, which
involves accelerated learning processes due to feedback loops, and provides insights
into potentials for product development (see Hobday et al. 2005).
The authors of another paper also assume that industrial services can make a decisive
contribution to product development as a source of information (see Goffin/New 2001).
They conducted a literature review in combination with five case studies. However, they
state that industrial companies hardly use this channel as a source of inspiration for in-
novations. A formalized process supporting the flow of information between service
workers and product developers is actually very rare. In addition, the majority of indus-
trial companies organize the provision of services and the development of products sep-
arately, which halts the flow of information to a large extent.
Furthermore, several authors verified that the experiences garnered from using a product
or maintaining or repairing it can be an important source of information for developing
or improving products. And yet, in practice, this information was either not collected at
all, or not used to develop products (see Markeset/K umar 2003; Molenaar et al. 2002;
Petkova et al. 1999; Thompson 1999). Similarly, Bitrain and Pedrosa (1998) see cus-
tomer wishes and complaints as a useful source of information which ought to be used
as feedback.
Finally, it should be noted that, the various articles agree that services could be an im-
portant source of information for industrial companies in product development. Howev-
er, the information cycle resulting from this seems to be hardly used in practice or is not
being recognized as such.
On the other hand, several authors describe the delayed development of service innova-
tions after product modifications were carried out. Thus Meyer and Bltmelhuber
(1998), for instance, assume that changes or adaptations to the product trigger a demand
for services in the customer. However, the resulting need would have to be recognized
through a targeted monitoring of the production process. This could lead to the emer-
gence not only of service modifications, but also completely new industrial service of-
fers (see Meyer/Blimelhuber 1998).
This assumption is also discussed by Busse (2005). In this context, the author empha-
sizes the systemic character of innovations in the industrial goods sector. Due to the
numerous compatibilities and interfaces which services should have to material prod-
ucts, adaptations to the service offers would then follow product innovations. Because
of this systemic character, service innovations would thus depend largely on innova-
tions in the material sector (see Busse 2005).
Finally, we can determine that some articles proceed from a direct influence of product
innovations on service innovations. If this effect is coupled with the assumption of the
impacts of service offers on product innovations, a loop emerges which accelerates the
development of product and service innovations over time. The linking of these two
mentioned effects can be formulated as a dynamic hypothesis in a causal diagram. This
loop is depicted in Figure 2-1.
ie
Product Need of service
+ modification adaptation
+ +
Potential product BL Product
innovations innovation RL Service
innovation
+
+
Knowledge of
improvement opportunities Service contacts
Figure 2-1: Loop for the interaction of product and service innovations
Starting from service contacts to clients, knowledge about improvement opportunities
can be generated e. g. via maintenance and repairs to the material product. The em-
ployee gathers knowledge over time and therefore the accumulation of knowledge is to
be seen as stock of the knowledge flow. Increased knowledge about improvement po-
tentials can thus lead in the long term to an increase in the number of product innova-
tions (effect described e.g. in Hobday et al. 2005; Goffin/New 2001). As illustrated
above, innovations to the product increase the number of product modifications, which
again causes a rise in demand for services on the part of the client. Recognition of this
new and modified service demand, which also represents a learning process, again re-
sults in a higher number of service innovations (effect described e.g. in Duschek 2002;
Busse 2005). This reinforcing cycle (Loop R1) is slowed down by a balancing loop. The
technological potential of the product is exhausted by product innovations, making it
more difficult to implement innovations in the material product (see Dosi 1982; Loop
B1).
Thus the dynamic hypothesis shows that industrial firms with a range of services of-
fered would more frequently carry out product developments than industrial enterprises
without service offers. Equally, it can be proved that firms with a greater number of
service contacts to customers exhibit greater innovation activities, not only for products,
but also for services than companies with fewer service contacts. A first simulation
model was constructed to demonstrate these assumptions, whereby the above causal
diagram was converted into a stock and flow structure. The system structure is de-
scribed in the next section.
3 System structure and basic elements
The simulation model has a simple system structure, which consists of three sub-
systems. The sub-systems are described as the product development, the service devel-
opment and the client structure.
The product development was greatly simplified and depicted according to Stumpfe
(2003) (see Figure 3-1). Starting from a product potential, which describes the sum of
all product innovations, product inventions are initially generated via two different
channels. The product inventions thereby describe the technological status of the prod-
uct, respectively, the possibilities of the firms to improve the product (Stumpfe 2003).
In view of the problem, the R&D department as well as the service department were
considered as the sources of innovation. The opportunities for product improvement of
the R&D department depend on the present technological status of the product. The
more exhausted the potential, the more difficult it will be to generate product innova-
tions (see Stumpfe 2003, Dosi 1982).
If the dynamic hypothesis is followed, the company also has the possibility to generate
product innovations via information gathered from customer contacts. This information
channel therefore depends on the number of customer contacts, the number of custom-
ers, and the sum of the services offered. This channel is stimulated by the diffusion of
the product in the market. How successfully this information channel can be utilized,
however, depends to a great extent on the ability of the staff to recognize improvement
opportunities in the product ("Leaming Factor per Contact"). This constant can be
changed from 0 per cent (no knowledge generation via customer contacts) up to 100 per
cent (every single customer contact increases the knowledge stock). The product inven-
tions are then introduced to the market and implemented in product innovations. The
innovation rate describes the product innovations implemented per month.
The service development was also implemented in the model structure (see Figure 3-1).
As only gradual differences appear to exist between the innovation processes of prod-
ucts and services (see Kanerva et al. 2006), the development process for services was
aligned to that of products. There is a potential of service innovations which the service
department fully utilizes over time. It becomes increasingly difficult over time for the
firm to extend the knowledge stock about service potentials through the same structure.
This knowledge status is described by the stock of service inventions. The services in-
troduced to the market are represented as service innovations and the innovation rate
depicts the service innovations per month. If an industrial firm offers services, then
there is a possibility, because of the systemic nature (see Busse 2005, Duschek 2002), to
carry out improvements or modifications in the service range by means of product inno-
vations.
Maximum Contacts
for Leeming Leaming Factor per
\ Contact
Product invention
rate (services) v
Potential Product Product Product
Tmovation Inventions rovcton rate Innovations
BS point
Product invention
rate (R&D)
Gy
Adaptation Factor
per Product
Innovation
Adaptation rate
Potential Service Service Service
Innovation | Sonicetvention |_lventions [> 2s | Innovations
rate service
Figure 3-1: Stock and flow diagram for product and service development
By this means, the potential for service innovations can be raised, in contrast to the ma-
terial product. However, new service innovations are not necessarily generated through
the offer of new products. The constant “Adaptation Factor per Product Innovation”
describes the ability of companies to recognize the new demand of customers for ser-
vices, and thus to increase the knowledge stock about improvement possibilities for
services. This constant can be varied from 0 per cent (no ability to recognize the service
needs), up to 100 per cent (the ability to generate one service innovation exactly from
each product innovation).
The third sub-system refers to the diffusion of the product in the market (see Figure
3-2). For this purpose, Bass's diffusion model for demand development was modified
which takes repeat purchases into consideration (adopted from Sterman 2000; Bass
1969). A certain customer potential exists at product introduction, which is exhausted
over time by the demand. "Customers" describe the client stock and express the com-
pany's installed base of industrial products. "Obsolescence" describes the period in
which a product ages and must be re-purchased, when the client becomes a potential
customer again (see Sterman 2000; Stumpfe 2003). Alpha and beta are factors for de-
scribing the diffusion process (see Bass 1969) for initial purchases.
Time
Obsolescence
Potential i
Oustemes _ |, Customers [a
Obsolescence Customer Contacts
“ee 2 per Month
Service Contacts per
= = Service and Month
Demand
alpha eid
Figure 3-2: Demand development of the product with repeat purchases
From the number of customers we can calculate the number of customer contacts, which
results from the sum of services offered (stock of service innovations from Figure 3-1)
and the average service contacts per month and client. These service contacts are passed
into the information channel for new products (see Figure 3-1). The three sub-systems
are already sufficient for a simple model to depict the dynamic hypothesis, carry out
simulation runs, and gain first insights.
4 Simulation runs and tests
In order to test the dynamic hypothesis, the study focused on the constants "Learning
Factor per Contact" and "Adaptation Factor per product Innovation", as well as their
impacts on the innovation rates of products and services. An overview of these two con-
stants as well as the innovation potentials of products and services assumed for this
study can be seen in Table 1.
= The "Base" run describes an industrial company without any service offerings.
Companies belonging to this type enhance their material products only.
"The mun “Service Provider A” illustrates the same company, but generating five
service innovations over the life cycle of the product. Moreover, this company is
not able to adapt the service offer due to the implementation of product en-
hancements. Second, the employees conducting the services are not able to leam
anything from customer contacts resulting from the service offer.
= Companies belonging to the nun “Service provider B” generate 5 service innova-
tions also, but their employees generate knowledge from 20 per cent of all cus-
tomer contacts resulting from services. Moreover, they are able to adapt the ser-
vice offer for 20 per cent of all developed product innovations.
" The runs “Impact Leaming” and “Impact Adaptation” validate the simulation
model, on the one hand, and on the other hand, show what happens under ex-
treme conditions. The run “Impact Diffusion Progress” shows the difference in
innovation rates of services and products between various diffusion progresses.
Adaptation Factor
Potential Product Potential Service Learning Factor pra
Aun Innovation Innovation per Contact per Prod. beta
Innovation
(1) Base 25 0 0 0 01
(2) Service provider A 25 5 0 0 0.1
(3) Service provider B 25 5 20 20 O14
(4) Impact Learning 25 5 100 0 0.1
(5) Impact Adaptation 25 5 0 100 01
(6) Impact Diffusion Progress 25 5 20 20 0.15
Table 1: Overview of the assumed innovation potentials and constants for the simula-
tion runs
All other parameters were fixed for the runs. The graphs containing core information are
shown in the following. Other diagrams with additional information are listed in the
appendix.
41 Results of base runs
The product innovations and the innovation rate of products are illustrated in Table 1
for the base runs. The run “Base” and “Service Provider A” show the same curves for
both diagrams. Of course, an industrial company without any service offers is not able
to stimulate the circulation of the dynamic hypothesis (see “Base”). But when the inno-
vation rate of products is observed, the typical trend of innovation progress over time
can be realized, as described by Utterback/Abernathy (1975). At the beginning, there
are high innovation rates and the lower the residual potential becomes, the harder it is to
generate innovations for the company.
Almost the same result emerges for the second run. Indeed “Service Provider A” offers
services and is able to generate service innovations, but the enterprise is not able to
learn from service contacts or to improve the service offer as a result of implemented
product innovations. Actually, there is no dynamic interaction between product and ser-
vice innovations, but both services and products are developed independently of each
other. Finally, there are no advantages for this service provider against industrial com-
panies without service offers concerning the interactive innovation progress.
The run “Service Provider B” produces another result, because this enterprise is able to
adapt services due to product innovations and to generate knowledge about product im-
provements as a result of service contacts. This enterprise exceeds the number of 20
product innovations after 64 months, whereas the other runs do not reach this number
until 90 months. Actually, there is a time lag of 26 months and consequently a competi-
tive advantage in favour of “Service Provider B”. All in all, this advance of innovation
starts in month 40 and remains over the whole observation period of 120 months (see
Figure 4-1).
Product Innovations
40
§ 30
8
: 20
: 10
0 tL
0 12 2% 36 48 60 72 84 96 108 120
Time (Month)
Innovation rate product
04
¢
Z 03
: 0,2
4
: Ol
0
0 12 24 36 48 60 72 84 96 108 120
Time (Month)
Innovation rate product : Base 1 4 1 4 1
Innovation rate product : Service Provider A. ——2———_2—__2__2_
Innovation rate product : Service Provider B 3———3—3—_3-__3-
Figure 4-1: Base runs showing product innovations and the innovation rate of products.
In contrast to the other runs, the innovation rate of products does not decrease after 20
months. Due to the learning effect resulting from service contacts, this company is able
to keep the innovation rate at a high level and not decrease until 60 months. To sum up,
this company is able to tap the product potential much faster and earlier than companies
that are not able to learn from service contacts.
The results considering service innovations are illustrated in Figure 4-2. The lines for
the “Base” run remain at the zero level, because there are no service offers. But conse-
quently, there are large differences between “Service Provider A” and “Service Provider
B”. “Service Provider A” reaches the level of five service innovations only by 90
months. The innovation rate of services decreases already at about 15 months. A fter 60
months there are almost no kinds of progress concerning service innovations. In contrast
to this, enterprises that are able to improve services due to product innovations can de-
liver much more than only these five innovations. “Service Provider B” already exceeds
the number of five service innovations between 40 and 50 months. Moreover, this com-
pany generates almost twice the number of innovations than the companies which do
not improve their service offers. Regarding the innovation rate of services, the decrease
is clearly slower than in the run “Service Provider A”.
Service Innovations
10
g 75
Bo5
g
Bos
0
0 12 24 36 48 60 72 «684 «©6986 ~S 108120
Time (Month)
Innovation rate service
0,2
2
&
€
i
2
&
0 120 24 36 «48 60 72 84 96 108 120
Time (Month)
Innovation rate service : Base 1 1 1 1 1 1
Innovation rate service : Service ProviderA ——-2——2—_2____2_
Innovation rate service : Service Provider B. -3 3-333
Figure 4-2: Base runs showing service innovations and the innovation rate of services.
Taking a cross view between product and service innovations, it is obvious that there is
a peak after 60 months in the innovation rate of products and a lower decrease for the
10
service innovation rate. This peak is the combination of the maximum of the diffusion
progress (see demand in Figure 7-1), reaching first the product innovations (due to
learning) and afterwards directly service innovations (due to adapting).
4.2 Impacts of learning, adaptation and diffusion progress
The results shown above describe the impacts of innovation processes, differentiated
between companies using the interaction of product and service innovations and com-
panies that are not able to use this feedback loop. This section points out the impact of
each single factor regarded above.
The first graph shows the innovation rate of the product, including the first three runs
discussed above and nuns four to six (compare to Table 1). The first finding is that there
is no difference between the “Base” run, the run “Service Provider A” and the run “Im-
pact Adaptation”. The reason for this is that the capability to improve services due to
product innovations has absolutely no impact on product innovations. Furthermore,
there are great impacts, if the company is able to discover potentials to improve the
product due to service contacts. The result is a permanent increase of the innovation rate
in about the 46th month (compare to Figure 4.2-1).
Innovation rate product
06
\
Product Innovation/Month
0 —
o 6 2 i 2% 30 36 42 48 St 60 66 72 78 84 90 96 102 108 114 120
Time (Month)
4
Innovation rate product : Base 1 A
Innovation rate product: Service Provider A
Innovation rate product: Service Provider B
Innovation rate product : Impact Leaming
Innovation rate product : Impact Adaptation -- — — —
Innovation rate product : Impact Diffusion —6- be be be be
Figure 4.2-1: Innovation rate of products for all runs
The last run follows the impact of diffusion progress on the innovation rate of the prod-
uct. If there is faster progress (compare to Figure 7-1), there are more customer contacts
in early phases and the company is able to learn faster and hence to generate more prod-
uct innovations in a shorter time. Summing up, the role of diffusion progress is not a
subordinate one with regard to the innovation rate of the product.
11
The second diagram shows the innovation rate for industrial services (compare to Fig-
ure 4.2-2). The first three runs were already explained once above. The runs four to six
describe the impacts of the three various factors. Consequently, this time there are no
impacts on the innovation rate of services for the run “Impact Learning”. The ability to
discover potentials for improvements of the product does not affect the number of ser-
vice innovations. On the other side, there are great impacts due to the capability to adapt
services as a result of product innovations. With a percentage rate of 100 per cent, this
company would be able to generate exactly one service innovation for each imple-
mented product innovation. This is the reason for the high innovation rate over such a
long time for the run “Impact A daptation”.
Innovation rate service
04
¢
£
i
01 oh
SSS
a oe 1_.|
Peas |
oF 1 1 1 1 7 as
o 6 2 i 2% 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120
Time (Month)
Innovation rate service : Base 4 4 -- 4 4 ai 1
Innovation rate service : Service Provider A
Innovation rate service : Service Provider B
Innovation rate service : Impact Learning 4 4 4 4 4 4
Innovation rate service : Impact Adaptation 5 5. 5. 5 5 5
Innovation rate service : Impact Diffusion —6 6. 6. 6. 6 6 &
Figure 4.2-2: Innovation rate of services for all runs
With regard to diffusion progress, there is a little difference between the run “Service
Provider B” and the run “Impact Diffusion”. Finally, diffusion also has impacts on the
innovation rate of services.
5 Conclusions and Outlook
In summing up, it can be stated that a system dynamic model is able to map the dis-
cussed dynamic hypotheses derived from the literature. With the help of various simula-
tion runs, we could show that industrial firms that offer additional services related to the
core product are in a position to generate higher innovation rates than enterprises that do
not offer services. This enables an innovation lead which can lead to competitive advan-
tages. Similarly, product innovations can result in improvements to the services offered.
12
The interaction triggered off between product and service innovations can also acceler-
ate this cycle. In order, however, to stimulate this interactive innovation process in a
targeted manner, industrial firms must be in a position, on the one hand, to generate
knowledge through customer contacts via services, and on the other hand, to recognize
customer (service) needs resulting from product innovations, and to respond to them in
the form of new service offers. If only one of the two necessary processes can be real-
ized, the interactive innovation process comes to a standstill and the cycle stops
abruptly/ is broken.
However, this study is only a first approach, based on a simple system dynamic simula-
tion model. Resource limitations were not considered, nor were the strategic decisions
of companies taken into account. The model targets only the feasibility of the dynamic
hypothesis, with the aim of identifying initial influential factors and pointing out differ-
ences in the innovation processes of various firms in the interaction of product and ser-
vice innovations. This contribution should be regarded as a first approach to further re-
search.
13
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15
7 Appendix
Customers
600
6
450
i
300
150
hess 6
0 6 12 #18 #+%2 30 36 42 48 %S4 60 66 72 78 8 90 96 102 108 114 120
‘Time (Month)
Customers : Base. ————2 4. _____1_ Customers: Impact Leaming 44
Customers : Service Provider. 22-2. Customers: Impact Adaptation 55
Customers : Service Provider B. 33 Customers: Impact Difsion 66
Demand
20
0
0 6 12 18 24 30 36 42 48 54 60 66 2 78 84 90 96 102 108 114 120
‘Time (Month)
Demand : Base, —————}_____2.___1_ Demand : Impact Leaning 44 __4__
Demand : Service ProviderA. 22-2 Demand : Impact Adaptation —3——5—5—
Demand : Service Provider B. 33 Demand : Impact Diffusion ——6——6 6
Figure 7-1: Customer and demand behaviour depending on diffusion progress (all runs)
Adaptation rate
o4
03
f
3
g 02
8
01
vest Me as
——__3—__—>
!
OK 4 49 4 2 yp gg gg OB eg 12
o” 6 2 18 24 30 36 42 48 St 00 66 7 78 @4 90 96 102 108 il 120
Time (Month)
Adaptation rate :Base 2-1. ‘Adaptation rate : Impact Leaming 44
Adaptation rate : Service Provider A. ———2______2 Adaptation rate: Impact Adaptation. 5 5
Adaptation rate : Service Provider B. 3 ____— Adaptation rate: Impact Diffusion 6 6 —______6
Figure 7-2: Adaptation rate, ability to adapt services due to product innovations (all
runs)
Customer Contacts per Month
2,000
1 4 4
0 6 2 ig 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120
‘Time (Month)
‘Customer Contacts per Month : Base 1 1 1 1 1
‘Customer Contacts per Month : Service Provider A — —
‘Customer Contacts per Month : Service Provider B 3 3 3 3
Customer Contacts per Month : Impact Leaming
‘Customer Contacts per Month ; Impact Adaptation -
‘Customer Contacts per Month : Impact Diffusion 6. 6 6. & 6 6
Figure 7-3: Customer contacts due to service offers (all runs)
