A NEW METHOD FOR MONITORING INDUSTRIAL
PRODUCT-SERVICE SYSTEMS BASED ON BSC AND AHP
Michael Abramovici and Feng Jin
Ruhr-University Bochum, Universitätstr. 150, Bochum, Germany
Keywords: BSC, AHP, Monitoring, Industrial Product-Service System (IPS²), Performance Measuring.
Abstract: Driven by the increased competition pressure in the last few years, a number of manufactures are shifting
their focus from products towards Industrial Product-Service Systems (IPS²). However, the shift to IPS² is
also accompanied by risks. The monitoring of IPS² could support executives in identifying the IPS² risks in
time and could serve as the basis for optimizing future IPS². In this paper a new method for the hierarchical
monitoring of IPS² is developed based on Balanced Scorecard (BSC) and Analytic Hierarchical Process
(AHP). The performances and the imbalance degrees of IPS² on different levels are calculated to show IPS²
comprehensively. BSC is applied to define IPS²-specific perspectives and indicators. AHP is used to
construct a hierarchical monitoring structure and to generate weights for different IPS²-specific perspectives
and indicators. Finally a case study is introduced to validate this method.
1 INTRODUCTION
An Industrial Product-Service System (IPS²) is
defined as “an integrated offering of product and
service that delivers values in industrial application”
(Meier et al., 2010). It can also be considered as an
innovation that extends the traditional functionality
of a physical industrial product by incorporating
additional services (Baines et al., 2007). The shift to
IPS² can enhance competition and generate more
customer benefits, but complex combination among
different products and services in IPS² increases the
risks (Cook et al., 2006; Sundin et al., 2009). A
quick and precise monitoring of IPS² could support
executives in identifying the IPS² risks in time and
could serve as a basis for optimizing future IPS². At
present, however, executives of IPS² suppliers can
only gain IPS²-related information from reports
submitted by their employees. It largely impairs
executives’ work efficiency in monitoring IPS².
Hence, a new method for monitoring IPS² is
urgently needed.
This paper proposes a new hierarchical
monitoring method with three levels (i.e. the overall
IPS² level, the perspective level and the indicator
level) based on Balanced Scorecard (BSC) and
Analytic Hierarchical Process (AHP) for executives
to monitor IPS² quickly and precisely. BSC has been
applied to define IPS²-specific perspectives and
indicators. AHP has been used to construct the
hierarchical monitoring structure based on these
IPS²-specific perspectives and indicators and to
generate weights for them. In order to process the
indicators with different measurement units,
percentages are used to standardize the measurement
of different indicators. Furthermore, executives need
to know about the imbalances among the various
aspects, so that they can determine whether IPS² is
running in balance or not. Thus, the performances
and the imbalance degrees of IPS² on different levels
are both calculated to show the status of IPS²
comprehensively. To verify this new method, the
paper concludes with a case study about the
monitoring of micro-machining PSS.
2 RELATED WORKS
2.1 The Balanced Scorecard (BSC)
The Balanced Scorecard was introduced by Kaplan
and Norton (1992) as a management system to align
an organization’s performance measures with its
strategic plan and goals. As exclusive reliance on
financial measures in a management system is
insufficient, the BSC highlights a balance between
190
Abramovici M. and Jin F..
A NEW METHOD FOR MONITORING INDUSTRIAL PRODUCT-SERVICE SYSTEMS BASED ON BSC AND AHP.
DOI: 10.5220/0003483001900196
In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 190-196
ISBN: 978-989-8425-53-9
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
financial indicators and non-financial indicators
(Craig and Moores, 2010; Yang, 2009). The BSC
suggests that an organization should be evaluated
from four different perspectives: financial
perspective, customer perspective, perspective of
internal processes and perspective of learning and
growth. Each perspective considers several related
performance measuring indicators.
Though originally developed as a performance
measurement tool, the BSC has evolved into an
organizing framework, an operating system, and
even a strategic management system (Kaplan
and Norton, 1996). Of course, the choice and
definition of the perspectives and indicators depend
on the characteristics of the individual BSC
implementation. BSC can be adapted as a tool for
executives to monitor IPS², but the main weakness
that all indicators have the same weight hides the
different importance of the considered indicators.
2.2 The Analytic Hierarchical Process
(AHP)
The AHP method was developed by Saaty (1980) as
a tool for modeling the complex decision problems.
It allows both qualitative and quantitative
approaches to solve complex decision problems
(Wong and Li, 2008). In the qualitative aspect, the
problem is modeled to a hierarchy consisting of an
overall goal, a group of criteria and sub-criteria, and
a group of alternatives. In the quantitative aspect,
numerical weights for criteria are generated by
making pairwise comparisons among all criteria at
each level to distinguish in general the more
important criteria from the less important ones.
To improve validity recognizing that participants
may be uncertain or make poor judgments in some
of the comparisons, redundant comparisons are
involved in AHP. This redundancy can lead to
numerical inconsistencies. Saaty (1994) suggested
the error in these measurements is tolerable only
when it is of a lower order of magnitude (0.1) than
the actual measurement itself.
In order to distinguish the more important
indicators in the monitoring of IPS² from the less
important ones, the AHP method is mainly used to
generate weights for all indicators and perspectives
(Wang, 2009). The above-mentioned process of
weight generation and verification can ensure that all
weights are assigned meaningfully and objectively.
3 THE MONITORING METHOD
FOR IPS²
3.1 The IPS²-specific BSC
For the monitoring and measuring the performance
of IPS², executives require different information in
different perspectives. With reference to the
structure of BSC, as recommended by Kaplan and
Norton, four specific perspectives have been
considered for the IPS²-specific BSC: the customer
perspective, the perspective of IPS² lifecycle, the
perspective of IPS² resources and the financial
perspective (figure 1). The overall IPS² goal takes a
central position.
Figure 1: The IPS²-specific BSC perspectives.
In general, the fulfillment of customer needs and
customer satisfaction are the main goals of an IPS²
offering. The quality of the IPS² affects customer
satisfaction directly. The acquisition and integration
of different IPS² resources are prerequisites for each
successful IPS². They are also the foundation of the
innovation and creativity for IPS². Moreover, the
high efficiency of IPS² resources can reduce cost and
improve the financial status. Satisfied and loyal
customers can also lead to increased revenues, i.e.
improvement of the financial status. The balance of
these four perspectives can ensure a successful IPS².
In order to measure and monitor the IPS²
performance, several indicators have been defined
and assigned to considered perspective. In order to
explain the monitoring process clearly, only three to
four indicators have been shown in figure 2.
Generally, indicators can be divided into two
categories: quantitative indicators and qualitative
indicators (CIDA, 1996).
Quantitative indicators can be defined as
measure of quantity, such as revenue of IPS².
Qualitative indicators can be defined as
people’s judgments and perceptions about a
subject, such as customer satisfaction.
IPS² Goal
Financial
Do IPS² costs exceed
its budget?
Customer
how does the customer
see our IPS² and us?
IPS² Resources
Can we integrate IPS²
resources well?
IPS² Lifecycle
What is the current
status of IPS²?
A NEW METHOD FOR MONITORING INDUSTRIAL PRODUCT-SERVICE SYSTEMS BASED ON BSC AND AHP
191
Figure 2: The three-level structure of the monitoring method (adapted from Yuan and Chiu, 2009).
3.2 Three-level Structure of the
Monitoring Method
According to the structure of the IPS²-specific BSC,
a hierarchical structure incorporating three-levels
has been constructed for this monitoring method, as
shown in figure 2. The first level is the overall IPS²
goal. The second level shows four perspectives in
agreement with the IPS²-specific BSC. The third
level defines performance measuring indicators.
Usually the performance of an indicator is its
actual value. Since different indicators have different
measurement units, it is impossible to compare and
to integrate indicators with different units. Thus,
percentage is used to unify the measurement of all
indicators. In this method the performance of an
indicator can be calculated as follows:
If a>t is the most expected result
=(1−
−
) × 100%
(1)
If a<t is the most expected result
=(1+
−
) × 100%
(2)
Where : performance, : target, : actual value
As an example, the performance of “On-time
delivery of IPS²” indicator can be calculated using
(2) and based on the target finish time and the actual
finish time. All quantitative indicators can be
measured using similar calculation. However, for
qualitative indicators these two equations cannot be
used, but their principles must be kept. The
performance of qualitative indicators should be
manually measured according to their measurement
standards, and then converted to percentages.
The performance of indicators is the basis for the
further calculation. The performance of each
perspective is the weighted average of all indictors
under it. The overall performance is the weighted
average of its four perspectives.
Within the BSC method all indicators and
perspectives have the same weight. In fact different
indicators or perspectives have different weights.
The assignment of weights provides executives with
more precise information about the performance of
IPS². The AHP method is used to generate the
weights. Since indicators are organized by the
perspectives, the indicators in different perspectives
are not associative. The weights of perspectives and
the weights of indicators under each perspective
should be generated separately.
In order to show the imbalance among different
indicators or perspectives, the imbalance degree are
calculated using the method of standard variance
that is usually used as a measure of how far a set of
numbers are spread out from each other.
The hierarchical structure gives executives a top-
down view to monitor IPS². Based on the
performances of IPS² on these three levels,
executives can determine whether the IPS² has been
well implemented or not. At the same time the
imbalance degree show executives whether IPS² has
been implemented in a balanced way or not.
3.3 Methodology
By combining the BSC and the AHP method and
adapting them to IPS², a five-step calculation
method has been developed to generate the weights
of different indicators and perspectives, and to
calculate the performances and imbalance degrees of
IPS² on different levels.
The performances and imbalance degrees of the
overall IPS², the customer perspective, the IPS²
lifecycle perspective, the IPS² resources perspective
and the financial perspective must be calculated
IPS² Goal
Indicator
Level
Perspective
Level
IPS²
Level
Customer
Productivity improvement
of customer
Customer relationship
Customer satisfaction
…
IPS² Lifecycle
IPS² quality
On-time delivery of IPS²
Response to customer
needs
Cooperation with IPS²
component suppliers
…
IPS² Resources
Energy efficiency
Equipment efficiency
Efficiency of human
resources
Efficiency of external
resources
…
Financial
Purchase cost of IPS²
components
Cost of materials ratio
Revenue of IPS²
…
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
192
separately. Since their calculation process are the
same, the customer perspective serves as an example
to explain this calculation method.
Step 1: Construct the Comparison Matrix
The comparison matrix is constructed based on the
pairwise comparisons of each two indicators. It is
the prerequisite for the calculation of weights. In
order to determine the quantitative matrix, a
standardized comparison scale of nine levels is used
(table1).
If the number of customer indicators is , the
pairwise comparison matrix is an × matrix .
Where
represents relative importance between
indicator
and
. This matrix satisfies:
>0,
= 1/
,, = 1,2,· · · ,.
Table 1: The comparison scale for the comparison matrix
(Saaty, 1980).
Element Numerical scale Meaning
1
has equal importance as
3
has moderately more importance as
5
has strongly more importance as
7
has very strongly more importance as
9
has extremely more importance as
2, 4, 6 and 8
Intermediate values between two
successive qualitative judgments
Step 2: Calculate the Weight Vector
The comparison matrix of the customer indicators is
the matrix .
is the weight vector of customer
indicators. The weight of indicator
can be
calculated as (3):
=
∑
where
=
∑
and
∑
=1
(3)
Step 3: Examine the Consistency Ratio
The consistency property of the matrix is then
verified to ensure the consistency of judgments in
the pairwise comparison. The Consistency Ratio
() are defined as (4):
=
(4)
where =
(Consistency Index) is the average
consistency.
is the maximum eigen value of
the comparison matrix, and is the size of matrix.
is the average random index taken as in Table 2.
If < 0.1 , the comparison matrix is
considered to be consistent. In contrast, the matrix
results are inconsistent and it needs to be modified
for the further analysis.
Table 2: Random Index values for matrix (Saaty, 2008).
Size of matrix (n) 1 2 3 4 5
RI - - 0.58 0.9 1.12
Size of matrix (n) 6 7 8 9 10
RI 1.24 1.32 1.41 1.45 1.49
Step 4: Calculate the Performance
If the comparison matrix is consistent, it can be used
for the calculation of the performance of the
customer perspective. The performance of all
customer indicators should be calculated
beforehand. If
={
,
,…,
} is the
performance vector of customer indicators, the
performance of the customer perspective can be
calculated using the following equation (5):
=
(
)
=
⋅
⊺
(5)
Step 5: Calculate the Imbalance Degree
In essence, the imbalance degree of the customer
perspective is the standard variance of all customer
indicators. Their weighted variance is calculated
using (6). Subsequently their standard variance can
be calculated using (7):
=
∑
(
−(
))
∑
(6)
Where
∑
=1, then:
=
(
−
)
=
(
−
)
(7)
By using the same process, the performance of
the IPS² lifecycle perspective, the perspective of
IPS² resources and the financial perspective can be
calculated as
,
and
respectively. Their
imbalances can also be calculated as
,
and
.
The overall performance and imbalance degree
of IPS² can be calculated using equations (8) and
(9):
=
⋅
⊺
(8)
where
is the performance vector of four
A NEW METHOD FOR MONITORING INDUSTRIAL PRODUCT-SERVICE SYSTEMS BASED ON BSC AND AHP
193
perspectives:
=
{
,
,
,
}
={
,
,
,
}
is the weight vector of perspectives
=
(
−
)
(9)
4 APPLICATION OF THE
PROPOSED METHOD
In order to verify this method, it has been
prototypically applied to monitor the micro-
manufacturing PSS (Product-Service System) that is
provided by MicroMan solutions Co. This IPS²
offers customers an integrated solution including
micro-machining technology and related services,
such as condition monitoring, financing, process
optimization, maintenance, training, and so on.
This monitoring method has been applied based
on the structure shown in figure 2. Tables 3-7 show
the comparison matrixes of customer indicators,
IPS² lifecycle indicators, IPS² resource indicators,
financial indicators, and perspectives respectively,
which are created by several experts in the field of
IPS². Their weight vectors are calculated using
equation (3) and are listed as follows:
={0.64,0.12,0.24}
={0.43,0.16,0.33,0.09}
={0.26,0.16,0.49,0.08}
={0.55,0.23,0.22}
={0.36,0.12,0.21,0.31}
Table 3: The comparison matrix of customer indicators.
Customer I
1
I
2
I
3
I
1
1 8 3
I
2
1/8 1 1/5
I
3
1/3 5 1
I
1
: Productivity improvement of customer
I
2
: Customer relationship, I
3
: Customer satisfaction
Table 4: The comparison matrix of IPS² lifecycle
indicators.
IPS² Lifecycle I
1
I
2
I
3
I
4
I
1
1 3 1 5
I
2
1/3 1 1/3 2
I
3
1 3 1 3
I
4
1/5 1/2 1/3 1
I
1
: IPS² quality, I
2
: On-time delivery of IPS²
I
3
: Response to customer needs
I
4
: Cooperation with IPS² component suppliers
Table 5: The comparison matrix of IPS² resource
indicators.
IPS² Resource I
1
I
2
I
3
I
4
I
1
1 2 1/3 3
I
2
1/2 1 1/4 2
I
3
3 4 1 5
I
4
1/3 1/2 1/5 1
I
1
: Energy efficiency, I
2
: Equipment efficiency
I
3
: Efficiency of human resources, I
4
: Efficiency of external resources
Table 6: The comparison matrix of financial indicators.
Financial I
1
I
2
I
3
I
1
1 2 3
I
2
1/2 1 1
I
3
1/3 1 1
I
1
: Purchase cost of IPS² components
I
2
: Cost of materials ratio, I
3
: Revenue of IPS²
Table 7: The comparison matrix of perspectives.
Perspective P
1
P
2
P
3
P
4
P
1
1 2 3 1
P
2
1/2 1 1/2 1/2
P
3
1/3 2 1 1/2
P
4
1 2 2 1
P
1
: Customer, P
2
: IPS² lifecycle, P
3
: IPS² resources, P
4
: Financial
In order to examine the consistency ratio of their
comparison matrix, their CR values are calculated as
follows:
=0.034
=0.018
=0.019
=0.016
=0.044
None of the values exceed 0.1. Thus, these five
matrixes are considered consistent, and the five
calculated weight vectors can be used to calculate
the performance and imbalance degree of IPS² on
different levels.
For a micro-machining PSS, the performance of
its all indicators have been calculated and listed in
column 5 of table 8. Column 4 shows the weight of
each indicator. The performance and imbalance
degree of each perspective have been calculated
using equations (5) and (7), and are listed in column
2. The overall performance and imbalance degree
have been calculated using equations (8) and (9)
based on the results of column 2. They are shown in
column 1 of table 8.
In comparison to the above calculation, the
monitoring process is a top-down process. From
column 1, executives can derive the overall status of
IPS². Critical values can be taken to identify those
IPS² that have poor performance or are not in
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
194
Table 8: The calculation of the performances and imbalance degrees for a micro-machining PSS.
IPS² Perspective Indicator Weight Performance
=96.0%
=0.0879
Customer
(
=0.36)
=107.0%
=0.049
Productivity improvement of customer 0.64 110%
Customer relationship 0.12 95%
Customer satisfaction 0.24 105%
IPS² Lifecycle
(
=0.12)
=96.1%
=0.092
IPS² quality 0.43 100%
On-time delivery of IPS² 0.16 80%
Response to customer needs 0.33 100%
Cooperation with IPS² component
suppliers
0.09 85%
IPS² Resources
(
=0.21)
=85.4%
=0.042
Energy efficiency 0.26 84%
Equipment efficiency 0.16 95%
Efficiency of human resources 0.49 84%
Efficiency of external resources 0.08 90%
Financial
(
=0.31)
=90.3%
=0.013
Purchase cost of IPS² components 0.55 85%
Cost of materials ratio 0.23 113%
Revenue of IPS² 0.22 80%
balance. Usually
should not fall below 95%
and
should not exceed 0.1. Column 2 provides
executives with more detailed information of IPS²,
i.e. its four perspectives. Using critical values,
problematic perspectives can be found out easily.
The performance of each indicator in column 5
shows a concrete measurement of IPS². Executives
can find the concrete problem of a problematical
IPS² from the indicators whose performance is
insufficient. That way, executives can determine
problematic IPS² quickly and fix the problems
precisely by using the proposed hierarchical
monitoring method.
5 CONCLUSIONS
This paper has introduced a new method for the
hierarchical monitoring of IPS² based on BSC and
AHP to meet the IPS² monitoring requirements of
executives. The BSC method offers a framework to
comprehensively and precisely define IPS²-specific
perspectives and indicators. The assignment of
weights to different indicators and perspectives
gives executives an opportunity to monitor and to
measure the performance of IPS² by highlight in
indicators or perspectives with different weights, and
the AHP method ensures the generation of
meaningful and objective weights. Moreover, the
use of percentages as the unified measurement unit
eliminates the measurement differences among
different indicators and simplifies the expression of
IPS² performances. Next to performances, imbalance
degrees of IPS² have been calculated on different
levels to offer executives a fast view of whether IPS²
is running in balance or not.
The AHP employs a suitable method (i.e.
pairwise comparison matrix and consistency
examination) to ensure the generation of objective
weights. If, however, a comparison matrix is very
big, it is highly complex and a lot of time is needed
to adjust it to pass consistency examination. Thus,
an easier method for weight generation should be
added in future as an alternative to avoid having to
deal with too large comparison matrixes.
Since the main information, which is required by
executives in IPS² monitoring, originates from the
IPS² lifecycle management (LM) platform
(Abramovici et al., 2008; Abramovici et al., 2009), it
seems to be very efficient to integrate this
monitoring method in the IPS² LM platform. Thus,
in future, this monitoring method will be
programmed as a function module in the IPS² LM
platform to validate and to improve it in the actual
application by IPS² suppliers. As executives tend to
pay more attention to their products after the
economic crisis, the described monitoring method
can be adjusted and used further in other areas to
monitor different products or services.
A NEW METHOD FOR MONITORING INDUSTRIAL PRODUCT-SERVICE SYSTEMS BASED ON BSC AND AHP
195
ACKNOWLEDGEMENTS
We express our sincere thanks to the German
Research Foundation (DFG) for financing this
research within the Collaborative Research Project
SFB/TR29 on Industrial Product-Service Systems –
dynamic interdependency of products and services
in the production area.
REFERENCES
Abramovici, M., Michele, J., and Neubach, M., 2008.
Extension of the Product Lifecycle Management
Approach for PSS – Requirements and Challenges, In
Seminar Proceeding of Product Service Systems.
Abramovici, M., Neubach, M., Schulze, M., and Spura, C.,
2009. Metadata Reference Model for IPS² Lifecycle
Management, In Proceedings of the 1st CIRP IPS2
Conference.
Baines, T. S., Lightfoot, H. W., Evans, S., and Neely, A.,
2007. State-of-the-Art in Product-Service Systems,
Proc. IMechE Part B: Journal of Engineering
Manufacture, Vol. 221 No. 10, pp. 1543–1552.
CIDA, 1996. Guide to Gender-Sensitive Indicators, CIDA.
Quebec.
Cook, M., Bhamra, T., and Lemon, M., 2006. The
Transfer and Application of Product Service-systems:
from Academia to UK Manufacturing Firms, Journal
of Cleaner Production, Vol. 14 No. 17, pp. 1455–
1465.
Craig, J., and Moores, K., 2010. Strategically Aligning
Family and Business Systems Using the Balanced
Scorecard, Journal of Family Business Strategy, Vol.1
No. 2, pp. 78–87.
Kaplan, R. S., and Norton, D. P., 1996. Using the
Balanced Scorecard as a Strategic Management
System, Harvard Business Review, pp. 75–85.
Kaplan, R. S., and Norton, D. P., 1992. The Balanced
Scorecard Measures that Drive Performance, Harvard
Business Review, pp. 71–79.
Meier, H., Roy, R., and Seliger, G., 2010. Industrial
Product-Service System - IPS², CIRP Annals -
Manufacturing Technology, Vol.59, pp. 607–627.
Michalska, J., 2005. The Usage of the Balanced Scorecard
for the Estimation of the Enterprise's Effectiveness,
Journal of Materials Processing Technology, Vol.
162, pp. 751–758.
Saaty, T. L., 1980. The Analytic Hierarchy Process:
Planning Setting Priorities, Resource Allocation,
Mcgraw-Hil. New York.
Saaty, T. L., 1994. How to Make a Decision: The Analytic
Hierarchy Process, Interfaces, Vol. 24 No. 6, pp. 19–
43.
Saaty, T. L., 2008. Relative Measurement and Its
Generalization in Decision Making. Why Pairwise
Comparisons are Central in Mathematics for the
Measurement of Intangible Factors. The Analytic
Hierarchy/Network Process, Rev. R. Acad. Cien. Serie
A. Mat., Vol. 102 No. 2, pp. 251–318.
Sundin, E., Sandström, G. Ö., Lindahl, M., Sakao, T.,
Rönnbäck, A. Ö., and Larsson, T. C., 2009.
Challenges for Industrial Product/Service Systems:
Experience from a Learning Network of Large
Companies, In Proceedings of the 1st CIRP IPS2
Conference.
Wang, Y., and Xia, Q., 2009. A Fuzzy AHP and BSC
Approach for Evaluating Performance of a Software
Company Based on Knowledge Management, In
Proceedings of 1st International Conference on
Information Science and Engineering.
Wong, J., and Li, H., 2008. Application of the Analytic
Hierarchy Process (AHP) in Multi-Criteria Analysis of
the Selection of Intelligent Building Systems, Building
and Environment, Vol. 43 No. 1, pp. 108–125.
Yang, J. H., 2009. A Balanced Performance Measurement
Scorecard Approach for Product Service Systems, In
Proceedings of International Conference on Business
Intelligence and Financial Engineering.
Yuan, F., and Chiu, C., 2009. A hierarchical Design of
Case-Based Reasoning in the Balanced Scorecard
Application, Expert Systems with Applications, Vol.
36 No. 1, pp. 333–342.
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
196
