Download Business Processes Reengineering Based on Data Mining

Business Processes Reengineering Based on Data Mining
DENG Zhonghua, SUN Limei
Center for Studies of Information Resources of Wuhan University, Wuhan, P. R. China
[email protected]
Abstract: As market competition intensifying, Business Process Reengineering (BPR) has become the
inevitable choice for enterprises to develop their competitive advantages. Effectively promoting the
implementation of BPR is an important way to scale the business for enterprises. As a new type of
data-processing tools in dealing with massive data, Data Mining technology has an important role in
transforming data into valuable information, supporting enterprises in business process reengineering,
and so on. This paper discusses the data mining technology applied to support BPR with the help of the
data analysis software -- SPSS. In this article presents the data mining technology in analyzing of
critical success factors, identifying core business processes, optimizing the flow of information and
feedback.
Keywords: Data Mining, Data Warehouse, BPR, Core Business Process
1 Introduction
Business Processes Reengineering was proposed by Dr. Michael Hammer in 1999. The core of BPR
is considered as that facing to the fierce market competition, enterprises should strengthen the process
control, keep re-think and reorganizing the existing business processes so as to improve the cost, quality,
service and speed that are the elements reflecting enterprises competitive abilities [1].
The rapid development of information technology makes the content and function of the
information systems have been considerable progress in breadth and depth. Information systems,
management models and management thinking are gradual integrated, such as the ERP (Enterprise
Resource Planning), CRM (Customer Relationship Management), and SCM (Supply Chain
Management) and so on. With the scale expanding, more and more companies become produce
diversified and scale, the present business process of the company can no meet the needs of market
competition, thus it is necessary for enterprises to implement BPR. However, in the current economic
times characteristic of the “customers”, “change” and “competition”, enterprises are facing a data
processing mixed and diversified. As a result, how to face the fierce and changing market competitive
environment, diverse and frequently changing business data, have become the bottleneck of the
constraints for enterprises in effective implement BPR[2]. Clearly, the traditional operation of the
database cannot meet the Business Process Re-engineering the information needs; we need to import
data mining. And how to extract the needed information from the large amounts of data and how to find
the potential link between information, to guide production, to forecast production targets and to provide
decision support for policy makers, all of these are the issue solved by data mining. Therefore, the
decision of the enterprise business process reengineering based on data mining must enhance the
competitiveness of enterprises.
2 BPR and its implementation
2.1 BPR and its characters
BPR is the conjoint result of inner and outer environmental interval affections, but its direct driving
force is for enterprises to meet customers’ changing needs much better. In today’s consumer-oriented
era, rapid response to changing market environment and effectively satisfying customers with products
and services are the fundamental pursuit of modern enterprises.
The characteristics of BPR are as following:
With the center of core business processes: During the process of BPR, we should be in the light of
the enterprise’s development agencies to carrying out strategic restructuring. The present management
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resources should be integrated to carding business processes and to establish core business processes
with the center of customers demands.
With the character of flat-organization[3]: BPR wants to achieve customer-oriented, flat level
organization so that to increase control scope, to reduce the middle management level, and then to
reflect the market more sensitive, to improve work efficiency and at the meantime, to reduce
management staff.
With the oriented of resources integration [4]: The competition of current era is not a single
department competition or a single internal enterprise competition, it is unitary competition among
corporations, between client-oriented and suppliers, which requires integrate resources appearing to be
scattered to enable enterprise not only remain flexible in services but also access scale economies.
With the target of customer satisfaction: Customers play a decisive role in enterprise’s
development. Process reengineering takes customer demand into consideration to determine the content
of business, in order to update business process thoroughly. Through the adjustment, information
feedback, full participation and so on to get durative improvement.
2.2 The implementation steps of BPR
There are 6 steps in implementation course of BPR, as follows [5-6]:
Stage 1: Planning and preparation. The implementation of BPR must be in the consideration of the
height of the strategy from enterprise executives: whether the present processes needed to be changed
fundamentally? Which process is the core business process of the current enterprise? Only on these
issues have a clear understanding, the follow-up process of reengineering can be conducted in an orderly
manner. What’s more, corporate executives must set a clear goal of the reorganization process, set up a
special BPR leading group to develop detailed project planning.
Stage 2: Process diagnosis. It is analyzing the existing processes and its sub-processes to built
model: analyzing the processes and finding the bottleneck of the process, discovering the existing
problems in the process, and then anchor the business process reengineering accurately, which make
preparations for the next stages.
Stage 3: Process design and optimization. Process design and optimization, which also can be
called the new process design, is on the basis of the analysis of the original process to built new
processes prototype and support its IT infrastructure.
Stage 4: Examining and approval. Examining and approval stage assess whether the re-designed
processes have achieved the executives’ strategic goals. If this approval is not passed, then the flow has
to go back to the second stage or the third stage on some conditions; but if this approval has adopted,
then go to the next stage.
Stage 5: Putting the new process into practice. During this stage, process reengineering is put into
practice, formally implementation.
Stage 6: Evaluation and feedback. After the formal stages, it is to assess the processes with the
goals setting at the beginning of the project to see whether this reengineering has optimized the structure,
whether this reengineering has improve the income, whether the new process has achieved the expected
results. Besides, it is necessary to put some experiences and results of this BPR implementation, such as
diagnosis, process design and optimization, and information feedback, into next BPR implementation
for next reengineering.
In addition, the Business Process Re-engineering is not an overnight project, one implementation
does not mean the BPR completed, otherwise, the completion of the entire project needs continuous
improvement, which means constant analysis and re-design.
3 The application of data mining
Data Mining (DM), known as data exploitation or data excavating, is the process that extract
connotative, useful information and knowledge which is unknown in advance from large incomplete,
noisy, fuzzy random data[7]. It is the product combined from database technology, artificial intelligence,
machine learning, statistical analysis, and fuzzy logic.
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As the continue expansion of enterprise business scale, the volume of data in the organization is
very huge but in contrast, the valuable information is really limited. And most of the existing databases
remain in the query, retrieval stage; abundant knowledge hidden in the database is far not fully explored
and used. Therefore it is stared in the face that getting decision-making information conductive to
business operation and competitiveness improvement from the databases by data mining to support the
business process reengineering.
In the shallow level, it makes use of the query, retrieval and reporting functions included in the
existing database management system, combined with multi-dimensional analysis and statistical
analysis to carry through on-line analytical processing (OLAP), from which statistical analysis of
information available for decision-making can be obtained; In the deeper layer, it finds the
unprecedented and implicit knowledge from the database.
Data mining technology has the following characteristics [8-10]:
(1)The scale of processed data can be very large, which can reach to TB, GB level. In addition, it
can make rapid and accurate response to the data that is dispersive sourced, large scaled, nonstandard
formatted and frequent updating to provide decision support, and this data can not be processed by the
traditional database management methods.
(2)It can study independently in accordance with the requirements of users, to complete imprecise
requirements for users.
(3)In data mining, the founding of rules is based on the statistics of the rules. Therefore, the
discovered rules need not to be applied to the whole data, and it can be seen as effective at the point of
reaching a certain threshold. That means a lot of rules can be found through the data mining technology.
Generally speaking, data mining consists of three main stages which are data preparation, mining
operation, and interpretation of the results [11]. In the stage of data preparation, the work needed to do is
to resolve the semantic ambiguity between phases, process the omission data, and clean dirty data.
Mining operations is the course that hypothesis producing, synthesis, verification and amendment
diffusing, aiming at building a model. Expression and interpretation of results stages are to express the
extracted useful information in accordance with the purpose of decision-making for the user. The data
mining process is not linear, but the repeated cross-cutting cycle.
The commonly used data mining technologies includes sequence analysis, classification analysis,
forecasting, clustering, association rules analysis, rough-and-theory methods. This paper used the cluster
analysis provided by SPSS mathematical statistics analysis software to identify the core business
processes of the enterprises to support the business process reengineering.
Basing on the principle of “Birds of a feather flock together”, Clustering refers to the process that
gather the samples which are not grouped and in a natural free state into different groups, and describe
each of those groups [12]. Its purpose is to make the samples similar to each other of the same group
and the samples belong to different groups not to similar as possible, that is, the smaller the similarity
between different groups and the greater the similarity in the same group. Different from analysis of
classification, in the process of clustering it isn’t known that how many and what groups the samples
will be divided into in advance, neither or the rules to define groups. Its purpose is to find the function
relationship between data attributes.
After bringing the data mining techniques into business process reengineering, in the planning and
preparation stage, decision-making information required by the high-level managers and related
indictors are delivered to the data mining group, and then the group diagnose the original business
processes and determine the enterprise’s core business processes basing on the analysis of key success
factors. In short, data mining technology runs through the entire business process reengineering, and
meanwhile the implementation of data mining can be uncovered by the feedback of effect of process
reengineering.
The steps of implementation can be described in the Figure 1.
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preparation
preparation
1. strategic decision
2. project beginning
key factors
3.process diagnosis
implemention
core business
process
Data
Mining
4. design and optimization
auditing
5.put into practice
Project over
6.system switching
data
warehouse
7.evaluation and feedback
Figure 1: The implementation steps of BPR, based on DM technology
4 Case study
4.1 Background of the case
A key high-tech enterprise mainly produces fine chemical materials. Through 12 years
development, it has possessed a quite big scale for sale and has become a leading enterprise with fairly
good profitability in industry [13]. With the scale expansion, the enterprise is now promoting two
strategic changes with full strength by effective managements, one change from a median-small
enterprise to a big one, and the other from a regional enterprise to a global one. However, the problems
come continuously, such as managements getting out of touch, failure on decision information’s
delivery on time, decrease in response to customer demands, etc. In other words, the former business
processes have become unable to meet the demands for the scale expansion of the enterprise itself, with
quantities of problems appearing in the business processes.
This is an enterprise that mainly produces fine chemical materials. The stock processes are like this:
Sale Department’s receiving the customer’s order—Quality Supervision Department’s providing the
sample—to send the sample to warehouse for material choice—to produce the goods in
workshops--Logistics company’s delivery and distribution. During the expansion, the enterprise often
receives the customers’ complaints that the delivery time is too long so that the company is unable to
achieve the promise of sending the goods in 2 days after the cash arrives.
4.2 Former solutions
To solve the problem, the broad of directors retained a professional consulting company to reform
the company’s management. Through overall inspection to the company for two months, the
programmer brings up that the original stock process should be rearranged to solve some key problems
such as the speed, the costs and the service. Especially the speed problem should be solved, only by this
could add to the customers’ satisfaction.
(1) Problems
Through the analysis of the existing business process model of the enterprise, problems are mainly
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concentrated in the following areas:
From a managerial point of view, it is lack of professional management persons, management
system is imperfect, top management has never seriously considered the optimization of business
processes or the integration of information resources. In accordance with their respective management
experiences and habits, various departments are lack of appropriate monitoring mechanisms, in which
information feedback seriously lagged behind and the phenomenon of inter-departmental shift
responsibility often occurred. In the current market competition, market changes more quickly, customer
needs more diversified, under the condition of the original inefficient management system, the too-long
reaction cycle undermined the competitiveness of enterprises.
From the perspective of business operations, sales and workshop processing sector are seriously out
of line, sales department lacks the capabilities of market research and forecast, which can not provide
the market demand forecasts, making workshop processing sectors not be able to adjust production and
often resulting in delays in delivery times. In addition, the workshop production sector and sales sector
lack of information communication, not sharing information and the information can not be conveyed as
soon as possible also lead to the delays in delivery times.
From the view of enterprise organizational structure, the current organizational structure is small,
while its senior management is large, and its reaction to market is insensitive, work efficiency is low. By
the way, there were also several problems, such as no customer demand-oriented, non-core business
processes and so on.
(2) Process diagnosis
After investigation and analysis of the factors of each department one by one, main factors
affecting the speed are concluded:
Sale Department: 1) speed of checking the orders; 2) speed of checking the cash; 3) speed of
sending the notice for stock to the Quality Supervision Department.
Quality Supervision Department: 1) getting samples to match the orders; 2) providing the
parameters of matching techniques; 3) sending the parameters to warehouse for records.
Warehouse management Department: 1) choosing the materials according to the technique
parameters; 2) preparing the materials according to the order number; 3) sending the materials to
workshops for records.
Workshop: 1) speed of stopping producing semi-finished goods to spare the machines; 2) speed of
the processing machines; 3) speed of packing the finished products; 4) speed of informing the Quality
Supervision Department for scientific examinations.
Connecting with the Logistics company: 1) Quality Supervision Department’s informing the Sale
Department to arrange goods delivery; 2) Sale Department’s informing the Logistics company to load
goods; 3) Logistics company’s delivery and distribution.
4.3 Identify core business processes using data mining
(1) Principal component analysis
Critical success factors are the key management points, which ensure the organization of the
implementation of development strategies and achieve its objectives. According to the analysis of the
enterprise through the critical success factors, we can understand where the enterprise's key business
process is [14].
Business process reengineering aims to faster and better satisfy customer’s needs. Therefore, when
analyzing the critical success factors, we would convert customer satisfaction to response time, and set
the various departments, the people number of departments, work content, the transmission time of
information between departments, and departments’ preparing production time as ordered factors.
According to the key success factors analysis, the people number of departments and departments within
the normal preparing production time can be attributed to the department of internal factors.
And then the SPSS11.5, statistical analysis software of data mining, is used to do an analysis of the
main factors. The main factors affecting response time are concluded, as following:
Sales department: the speed of checking orders and ordered a notice served on the Quality and
Technical Supervision Bureau.
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Store management department: the speed of preparation of materials and the time of sending the
preparing materials to workshop.
Workshop: stop the production of semi-finished, the speed of releasing the plant; the speed of
finishind packing and notify quality scientific examination.
(2) Identification of the core business processes
After the analysis, we can find that critical success factors can be affected by many factors.
However, by comparing these factors, we can find out where the enterprise’s larger issues are, whether
it became bottleneck of seriously impacting the development of the enterprise’s scale economies, which
leads to determine the business processes in the need to restructure the core business processes. Here,
we are doing the cluster analysis from the efficiency, effectiveness, efficiency, encoding the critical
factors from the previous analysis of workers in several departments and preparing time respectively
into 1, 2 in SPSS; encoding sales, quality inspection branch, store management branch, the workshop,
the TAC before the convergence of logistics companies into another variable y of 1, 2, 3, 4, 5. In the
analysis of core business processes, it turns out to be the workshop and warehouse.
5 Summaries
By means of data mining technology, we can quickly identify the critical success factors in the
many impact factors and then re-establish its core business processes. On the one hand, two months of
workload of professional consulting firm can be completed efficiently within two weeks, and this two
weeks mainly for information-gathering personnel to collect the factors that may affect the enterprise;
On the other hand, it is higher accuracy that using data mining technology to identify key factors and
core business processes. We can learn from the case above, five main factors were obtained by using the
old method, but only three by data mining analysis. It can greatly reduce the workload and more
efficiently in identifying the core business processes latter.
It is a try to using data mining in BPR, using cluster analysis in determining the core business
processes. Since the case in this paper is belong to small and medium enterprise, the complexity and the
diversity of its data is limited, its integrity of function is not embodied. But the advantages in its
business processes reengineering reflected well: data mining technology was used to determine the
critical success factors and core business processes, which are two important contents in BPR.
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