DATA MINING ON THE INSTALLED BASE INFORMATION
Possibilities and Implementations
Rashid Bakirov
Center for Sensor Systems (ZESS), University of Siegen, Siegen, Germany
Christian Stich
ABB Corporate Research Germany, Ladenburg, Germany
Keywords:
Data mining, Installed base, Proposals to customers, Failure prediction.
Abstract:
Managing the installed base at customer sites is a key for customer satisfaction. Hereby installed base com-
prises installed systems and products at customer sites which are currently being serviced by the producer
company. The purpose of the present study is developing use cases for data mining on the installed base infor-
mation of a large manufacturing company and specifically ABB, and constructing data mining models for their
implementation. The aim is to use the available information to enhance customer-tailored sales and proactive
service. This includes recommendations to customers and failure prediction. The developed models employ
association rules mining, classification and regression, realized with the help of data mining tools Oracle Data
Mining and Weka. Results have been evaluated using statistical means, as well as discussed with the experts
at the company. These results suggest that with the reasonable amount of data, installed base information is a
potential source for data mining models useful for business intelligence.
1 INTRODUCTION
At the present time, with the improvement in data
storage software and hardware systems, the amount
of available data about each individual or organization
is rapidly increasing. Today’s intense global competi-
tion, cost pressure, unstable markets and empowered
customers are the reasons why industrial enterprises
have to fully utilize this knowledge. An important as-
pect of knowledge discovery for customer-specific of-
fering and services is using historical data to find hid-
den relations using advanced data analysis techniques
known as “data mining”.
To enable customer tailored services, it is impor-
tant for companies to keep track of their installed
base, in other words, the products they produce and
need to maintain. An installed base system could be
used as a basis for data mining, identifying possible
relations between products, discovering patterns of
service jobs, predicting interest of customers to prod-
ucts and in many other various applications. Results
will lead to improved customer sensitive offerings and
service which would ultimately increase the sales of
products, lower costs for the customers and predict
service needs for the providing company.
In this work theoretical applications of data min-
ing on the installed base are analyzed, and implemen-
tations are shown. They are implemented on an inter-
nal data ware house of ABB, which is called ServIS,
the Service Information System. ABB is a multina-
tional corporation headquartered in Zurich, Switzer-
land. ABB’s core businesses are power and automa-
tion technologies and it holds market-leading posi-
tions in most key product areas. ABB employs more
than 116,000 people and operates in approximately
100 countries (ABB Group, 2010). ServIS is a com-
prehensive information system, that encompasses all
of the products and systems, customers’ services and
a range of additional data from all five divisions of
ABB. It was developed by ABB Corporate Research
Center Germany and provides a tool for outlining
and maintaining ABB’s installed base information.
Goal of ServIS is to operatively provide ABB’s field
service and technicians with information about cus-
tomers’ sites and equipment installed there.
The purpose of this paper is to present use cases
for data mining which target real business problems,
and show their realization using available technolo-
649
Bakirov R. and Stich C..
DATA MINING ON THE INSTALLED BASE INFORMATION - Possibilities and Implementations.
DOI: 10.5220/0003186506490654
In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART-2011), pages 649-654
ISBN: 978-989-8425-40-9
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
gies. The first part of the work gives information
about similar research and demonstrates possible us-
ages of data mining on a large production company’s
installed base data. The second part describes the ap-
plication the aforementioned use cases to the real data
warehouse of ABB, evaluates the results and identi-
fies possible shortcomings of these approaches. The
final part concludes the paper, giving a summary of
the conducted work and outlines perspectives for the
future works this area. Paper provides references for
used algorithms.
2 DATA MINING ON THE
INSTALLED BASE
INFORMATION
2.1 Review of Similar Works
Data mining on installed base presents many different
interesting opportunities for companies but not much
research work has been done in this field. Most of
related work deals only with subsets of installed base.
DaimlerChrysler, now Daimler, has successfully
employed data mining on their installed base. In the
paper “Forecasting the Fault Rate Behavior for Cars”
(Lindner and Studer, 1999), authors forecast the num-
ber of complaints for separate details of the cars that
will be produced in the future using neural networks
and decision trees. The implementation of the sys-
tem proposed by the authors was successfully tested
and has been transferred to the product controlling de-
partment.
In whitepaper “Data Mining in Equipment Main-
tenance and Repair: Augmenting PM with AM” (Ex-
clusive Ore Inc., 2003), the possibility of anticipatory
maintenance based on previous services data is dis-
cussed. A database of a large locomotive manufac-
turer was examined as a case study. Authors con-
cluded that, data mining of equipment maintenance
and repair data can help discover anticipatory main-
tenance (AM) procedures, improper repairs or other
maintenance, ways to improve repairs, undocumented
repair methods, as well as warn about likely failures
in advance. This all would result in less future fail-
ures, and lower downtime by failures.(Exclusive Ore
Inc., 2003).
Data mining was also used by Xerox to analyze
the service need of their installed base including ser-
vice of customer replaceable units (CRU). Aim of the
project was “measurable cost reduction in services de-
livery, including field service and consumable sup-
plies replenishment” (Minhas, 2003). Xerox used Or-
acle database for storing the information and Oracle
Data Mining (ODM) to for clustering of customer us-
age based on various attributes. (Minhas, 2003)
2.2 Use Cases of Data Mining in
Installed Base
The usage of data mining in the specific installed
base database will naturally depend on the data, data
model, structure and quality of data. This section de-
scribes its use cases. It is assumed that the database
has full information about products, customers and
services, as well as any necessary additional data.
Databases like that need to be constructed from vari-
ous datasets, tables and databases acquired from dif-
ferent divisions of company.
One use of data mining is cross-selling and mak-
ing recommendations to customers. Cross-selling in-
volves selling additional products to the same cus-
tomer, or bundling products in packets and selling
them together. Using data mining methods would
help to identify which products would interest spe-
cific customers. It is possible not only to create bun-
dles of products, but also to recommend interesting
products to the customers. Two approaches to make
a recommendation are possible. One is to show what
did other customers, who bought a particular product,
also purchase, as seen on Amazon Internet store (Lin-
den et al., 2003). This requires relatively few informa-
tion - only transaction ID, and ID’s of products which
appear on this transaction. Second approach is to rec-
ommend the products which are purchased by simi-
lar customers. The similarity criteria could be com-
bined from different inputs, for example company lo-
cation, size, industry type etc. These two described
approaches can be used together. They can be applied
to to identify suitable target audiences of marketing
campaigns, directed both to attract new customers and
make offers to the existing ones. Here, it is attempted
to discover customers, who will be interested in of-
fer, rather than offers, which might interest the cus-
tomer. The customers could then be assigned with
response scores for the particular campaign. An ex-
ample of this use case can be the following. Suppose
that two products, A and B are frequently bought to-
gether. Then, a company can offer a product B to the
customer that purchases A. In the second approach,
company can search for the customers, who have only
A, to use them as marketing targets for B.
A potential area of data mining’s application is
failure prediction. It should be possible to use the re-
pair history of the product, or of the other products
on the same site to make predictions about future fail-
ures. This method is less expensive than real time
ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence
650
condition monitoring using sensors and probes. Dis-
covery of common repair sequences would also assist
in improving the repair process itself. (Exclusive Ore
Inc., 2003). Suppose that product B regularly fails af-
ter failure of product A. Then the servicing company
can check the status of B each time after A fails. Also,
if it is concluded that the reason of B’s failure is the
failure of A, while repairing B, the parts of it which
has connection with A could have higher probability
of malfunction and can be checked first.
Another usage of data mining, is prediction of cus-
tomer leaving. There are two different models regard-
ing this topic. One is to predict if the customer will
leave in certain amount of time and another is to pre-
dict, for how long will customer stay with the com-
pany. Data mining can also be applied to estimate
customers’ prospective value, a revenue that customer
will bring during his remaining lifetime. This can
be used in identifying “good” and “bad” customers
(Berry and Linoff, 2004). Both models of attrition
prediction and other applications of data mining can
assist in corporate planning. Predicting amount of
failures for automobiles has successfully been applied
at DaimlerChrysler (see section 2.1). Same methods
could be applied on general installed base data, to pre-
dict failure rates of various products for financial, lo-
gistical and other planning.
Data quality control is another important task, par-
ticularly when managing a large installed base data
warehouse with many various information sources.
With the help of data mining, it is possible to improve
automation of this task. Two possible approaches in
this case are discovery of numerical outliers and iden-
tification of cases that do not fall into general patterns
identified by other data mining techniques. Identifi-
cation of cases that do not fall into general patterns
can also lead to discovery of holes in data. Discover-
ing and treating outliers is an important preprocessing
task before applying other data mining algorithms.
3 DATA MINING ON ABB’S
INSTALLED BASE
ABB’s ServIS is based on a data warehouse which
stores data in Oracle relational database. This
database consists of more than 200 tables with in-
stalled base and administrative data. Information on
the ABB products, materials, customers, services etc.
which is included in the system, can be used as a ba-
sis for data mining. Amount of available data was
quite large, encompassing about 30,000 products or
700,000 equipment units. Nevertheless, its quality
was not always on desired level for the data mining
purposes. These are missing data such as NULLs,
“other” or “unknown” entries, as well as scarcity of
historical data and lack of information that could be
useful for data mining purposes.
As the ServIS is based on Oracle 10g database,
the first choice of software for data mining was Ora-
cle Data Mining (ODM) (Oracle Corporation, 2010),
which is an built-in option of the database, with Ora-
cle Data Miner GUI. Additionally free Weka (Witten
and Frank, 1999) software was chosen as an alterna-
tive, to estimate results from ODM. Following sec-
tions present information on tested data mining mod-
els on ABB’s installed base information.
3.1 Association Rules on ABB Products
This model aims to discover ABB products that tend
to be on the same site - plant, factory, etc. An imple-
mentation of association rules discovery is done using
Apriori algorithm (Agrawal and Srikant, 1994). Simi-
lar method is also used by Amazon to find suggestions
for the customers (Linden et al., 2003) This algorithm
is available in most data mining packages including
ODM.
After constructing a data table, algorithm was ex-
ecuted with different generalization levels of prod-
ucts. To find optimal level, as suggested by Berry
and Linoff (Berry and Linoff, 2004), we used a dy-
namic approach, considering the item of the most spe-
cific generalization level and comparing amount of
item’s occurrences to heuristically identified thresh-
old value. If this amount is less than threshold, we
considered the next less specific generalization level,
continuing the process until the generalization level
which satisfies the threshold condition is found. Ex-
perimental threshold values of 50, 100, 200, 400 were
used, based on the number of occurrences in the mid-
dle generalization level, with the maximum of 506
and average of 23. 10% confidence and 1% support
thresholds were used for models.
We got at most 14 rules as a result of a single
model. This was dynamic generalization model with
200 occurrences threshold. This performance can be
explained by a specific nature of ABB. Even while
the number of products is quite large, 22,000 com-
pared with an average of 50,000 products in large su-
permarket (Nestle, 2002), ABB has lesser diversity of
products and fewer amount of customers when com-
pared to a supermarket. Customers of ABB that come
from the same industries tend to purchase similar mix
of products. Combining results from all models, we
get 9 rules with confidence levels higher than 75%
with various support levels ranging from 1% to 3%.
This method is easily reproducible and reusable.
DATA MINING ON THE INSTALLED BASE INFORMATION - Possibilities and Implementations
651
It can be used in combination with other data min-
ing or statistical methods, such as prediction of cus-
tomer interest described in the next section, for better
final outcome. Association rules on products can be
applied to provide aid in cross-selling and make rec-
ommendations to customers. It is possible to realize
automatic recommendation system on company’s In-
ternet portal, where customers reviewing a particular
product are notified of products, that were purchased
together with this one.
3.2 Prediction of Customer Interest
This model tries to determine customers with possible
interest in a given product. The idea is to use all the
data about customers that might be relevant in deci-
sion making and a binary attribute, showing whether
customer bought particular product. The approach is
first to build a classification model, predicting that bi-
nary variable on sample data, then apply the result
model to all available data and make predictions about
each single case. The used data included informa-
tion about technical site (location, industry, number of
products and materials on the site), information about
the company which owns the site (location, number of
sites the company owns, relation to ABB). Addition-
ally, binary variables that show whether the popular
products in the database exist on the given site were
included. This greatly improved the accuracy of mod-
els.
Models were built to predict the existence of some
of ABB’s more widespread products on the site. The
chosen decision trees classification technique pro-
duces a whitebox model - in other words, the pos-
sibility to see resulting rules and determine on which
attributes does the final result depend the most. ODM
implements a variation of CART decision tree algo-
rithm (Breiman et al., 1984). As there were not any
missing values in input table and decision tree is resis-
tant to outliers and does not require normalization, no
other pre-processing methods except from gathering
data from different tables were used. To build mod-
els, we used stratified sample, which keeps original
distribution of data, of around 10,000 cases for each
model. 60% of sample were used in tree building and
40% in its testing. An example of sample build data
for prediction for product A with distribution of class
(goal) attribute can be seen in figure 1.
Models were built and tested for several most fre-
quent products in ABB’s installed base to make state-
ment more significant. We will review results on an
example of As prediction. After the decision tree is
built, it is applied to test data. With the default proba-
bility threshold value of 0.5, model correctly predicts
Figure 1: Sample build data for product A prediction.
99.19% of negative and 36.73% of positive cases. In
some cases a user might want to bias the prediction in
the direction of making more correctly identified pos-
itive cases at the cost of less correctly identified neg-
atives, or in the reverse direction. This can be imple-
mented by altering the threshold value. Optimal value
for thresholds - the one which maximizes true pos-
itive rate and minimizes the false positive rate, may
be found using ODM ROC-chart (Receiver Operator
Characteristic) (figure 2). In our case it’s 0.11. If we
use this threshold, model correctly predicts 91.71% of
negative and 92.86% of positive cases.
Figure 2: Prediction ROC of A. Area under curve is 0,96.
When we test the model with all of the available
data, which also includes training data, with the same
threshold, we get similar results - 91.88% (35,703 of
38,859) true negatives and 90% (909 of 1009) true
positives. Ideally, training data has to be excluded
from test set. In our case, since training data is only
roughly 16% of all the available data and the results
are similar with results of the model bulidng test, this
ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence
652
slight error in evaluation is neglected.
Achieved results show that the models perform
well when predicting product type that appear fairly
often in installed base, as well as on those which ap-
pear less often. In our case, and the most of related
cases true positive predictions is more important than
true negatives. We achieve more true positives by al-
tering prediction threshold. Of course, more frequent
does product appear in dataset; more significant are
model’s results. With products that rarely appear, a
very heavy cost matrix biasing has to be used for a
model to make positive predictions. This greatly de-
creases true negatives accuracy rate. Industry branch
of the site was identified as the most significant at-
tribute, being the first splitting criterion in 3 out of
4 models for different products. Other significant at-
tributes were location of the site and amount of prod-
uct types on the site.
A drawback of this approach is that a separate
model has to be constructed for every product, on
which predictions are made. This approach can be
combined with the association rules mining to achieve
even more accurate predictions. Results of customer
interest prediction models can be used for recommen-
dations to customers for marketing purposes, as well
as data quality control.
3.3 Prediction of Future Repair Jobs
This model attempts to determine amount of repair
jobs for various materials in installed base. It also
aims to identify attributes that have the largest ef-
fect on amount of repairs. Different variations of
this model were tested; regression - predicting exact
amount of repair jobs and classification, splitting goal
value into none/low/high categories (ternary classifi-
cation) or making binary “yes”/“no” categories (bi-
nary classification) predictions. This approach is sim-
ilar to the method of Lindner and Studer (section 2.1).
Both approaches use amount of repairs that happened
before to forecast repairs that will happen in the fu-
ture. The difference is the lack of available historical
data for us to use. Instead, we use additional infor-
mation about site and material which can be found
in ServIS. General information about site of location
(see previous section) as well as information about
material (type, age) and repairs history from previous
years were used as predictors for the model. Because
of data quality problems, dataset used for models in
this section consisted of only 778 cases. This results
in lower significance of achieved results.
Considering that they are based on a very limited
dataset, models in this section have fairly high accu-
racy rates. For instance, ODM regression model that
Figure 3: Ternary classification results.
Figure 4: Binary classification results.
is based on Support Vector Machines (Vapnik, 1995)
has 27.48% predictive confidence. Predictive confi-
dence shows how much better given model is than a
“naive” model, a model that predicts average value
for the dataset. Predictive confidence is the percent-
age increase in accuracy of prediction gained by the
tested model over a naive model. Binary and ternary
classification results can be seen on the figures 3 and
4. We can see that in both cases, Weka J48 (imple-
mentation of C4.5 decision tree algorithm) provides
better results. The most important predictor for deci-
sion tree models was average of repair jobs on mate-
rial in previous years. Other significant attributes for
both classification algorithms were type of material,
amount of materials, industry branch as important.
Models described in this section can help in finan-
cial and logistical planning. For example, the budget
of company can be adjusted to include repair costs
and the spare parts could be prepared beforehand.
This knowledge can also be a factor for choosing prof-
itable service contract terms. Another usage of the
models is optimization of proactive service (section
DATA MINING ON THE INSTALLED BASE INFORMATION - Possibilities and Implementations
653
2.2). Choice of a particular model will depend on a
use case, for example when assessing the behavior of
a product, ternary classification could be enough, but
planning service budget or preparing spare parts for
product is better done with numerical values. Other
factors in choosing model are its accuracy and impor-
tance of obtaining rules, as for example ODM regres-
sion does not provide any. Another interesting data
mining approach to service jobs history is discovering
common sequences of failures on the site and using
these results to make prognoses about future repairs.
This was not implemented in our work due to scarce
data.
4 CONCLUSIONS
In this paper we have analyzed the possibilities of ap-
plication of data mining techniques on the installed
base in form of use cases. Data mining helps to dis-
cover relationships between products, customers, ser-
vice jobs etc. This information can be helpful in many
areas, among them cross-selling, marketing, proactive
service, contracts management, data quality control,
corporate planning. We implemented the use cases
using both mining tools. After tests and assessment
we have reached a conclusion that results of prelim-
inary tests were good and that it was worth continu-
ing research in this direction. Prediction of customer
interest in products have provided the best results.
These results have proven that data mining methods
could be successfully implemented on installed base
data.
With the increased amount of data and improve-
ment of its quality, more data mining models de-
scribed in section can be applied to ABB’s installed
base system. Then the obtained results are expected
to be more accurate and significant. Some refinement
and restructuring of data is necessary in order to ap-
ply certain models. After development and tests of
various models, the ones that provide useful results
for the outlined use cases can be implemented in real
business applications. Model training and applica-
tion processes can be extracted from ODM in form
of PL/SQL packages, that may be used in respective
software or web-interfaces to show data mining re-
sults. For instance, results of association rules mining
can be used to make automated suggestions to cus-
tomers in company’s web portal. PL/SQL packages
can be scheduled to run automatically to keep models
and results up to date with new data.
Successful business applications based on data
mining on the installed base information would cre-
ate a win-win situation both for a business and a
customer, offering appropriate products and systems,
identifying cross-selling opportunities and predicting
the maintenance needs. This would result in easier
product selection and decrease of downtime costs for
the customer and increased profits for the business.
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