BUSINESS PROCESS PERFORMANCE MANAGEMENT USING BAYESIAN BELIEF NETWORK
Yeong-bin Min1, Yongwoo Shin2, Kim Jeehong1, Dongsoo Kim3, Suk-ho Kang1 1
Department of Industrial Engineering, Seoul, National University, +82-2-880-7360, [email protected]; [email protected]
2
Entrue Consulting BU, LG CNS, +82-2-880-7360, [email protected] 3
Department of Industrial and Information Systems Engineering, Soongsil University, +82-2-820-0688, [email protected]
Abstract
As business environment changes dynamically, efficient management strategy and risk management are required for a company to survive. To cope with such requirements, BPMS (business process management system) has been developed. BPMS enables companies to manage and improve their processes continuously. Most of earlier studies on business process management have been focused upon process modeling, execution, and monitoring. Therefore, there are a few researches that investigate how to improve business processes. This paper proposes a method for business process performance management that ranges from business activity monitoring to escalation. To consider dependencies between tasks, Bayesian belief network is employed and mathematical model is designed to determine the tasks to be escalated.
Keywords: business process management system (BPMS), key performance indicator (KPI), Bayesian belief network (BBN), escalation
1. Introduction
As business environment changes dynamically and competition becomes fierce, it is important for enterprises to handle risks and to build efficient management strategies. Under these changes in business environment, enterprises need to define their own critical success factors and key performance indicators (KPIs) to evaluate the present state of operations, and then they try to find the method for improving performance. Several performance measurement systems are in use today, and each has its own group of supporters. One of the most influential approaches that have been implemented in many companies is performance measurement which is commonly based on the Balanced Scorecard (BSC) [7].
However, the performance measurement systems based on the BSC usually take a vertical view. This means that the structure of the BSC often mirrors the organization charts of enterprise to be measured. The inclusion of the organizational units is important, but it is insufficient. Küng et al [9] proposed a performance measurement system considering business processes to take a horizontal flow. One of the most widely used approaches considering the horizontal flow is business process management system (BPMS). BPMS extends the functionality of workflow management systems (WfMS) beyond automation into areas such as analysis, monitoring and cross-organizational interactions [15]. The BPMS enables all stakeholders to have an understanding of an organization and its performance, and to facilitate process improvement.
The research issues of the BPMS are categorized in the perspective of the process life cycle into four groups: modeling, execution, monitoring, and improvement. Most of earlier researches on the BPMS have been focused upon process modeling, execution, monitoring. Therefore, there are a few researches that investigate how to improve business processes using monitored process data. And the researches related to process improvement suggest general guidelines through establishment of a framework rather than specific escalation methods. Also, most of them assume that tasks or components of a process are mutually independent.
This paper proposes a method for business process performance management that ranges from business activity monitoring to improvement. To consider dependencies between tasks, Bayesian
evaluate performance of a task.
The rest of the paper is organized as follows. In section 2, we reviewed related work. Section 3, present the business process escalation framework, and section 4 describes the process escalation strategies with experimental results. Finally, conclusions and future work are summarized in section 5.
2. Related work
2.1. Process monitoring
Monitoring encompasses the tracking of individual process instances, so that information on their state can be visualized, and statistics on the performance of processes can be provided. This information can be used to work with every participant in processes, so that problems in operations can be identified and corrected. The degree of monitoring depends on what information the business wants to evaluate and analyze and how business wants it to be monitored. Business activity monitoring (BAM) coined by Gartner group is solution for monitoring of business activities. BAM extending monitoring tools generally provided by BPMS refers to the aggregation, analysis, and presentation of real-time information about activities inside organizations.
Most general approach for performance measurement using operational data is to compare 'as-is' state and 'to-be' state. Based on the comparison of these two states, Rozinat et al [14] suggested the method of workflow simulation for operational decision support. Also, decision support method was proposed to build rule-based event processing which is considering the dependencies of state information [8]. The existing rule-based approaches define the rules as a form of ‘If condition Then action’. The rules are usually extracted from historical log data or designed by domain experts manually. To extract the meaningful correlations, other methods which are applying decision tree [4], and genetic algorithm [1] were developed for monitoring of the process instances.
However, these rule-based approaches have a limit to evaluate the performance reactively, rather than proactively [6]. Rules are usually extracted from the attributes of completed instances from historical log data. However, an ongoing instance has only partial information, composed of collected attributes of events until observation period. This causes that a rule-based monitoring system waits until all the conditions of predefined rules are observed in ongoing processes.
2.2. Process escalation
According to the definition of the WfMC process escalation is a procedure executed when predefined constraints or conditions are not fulfilled [16]. This means that the escalation is an additional activity to prevent monitored process instances from causing uncontrollable states of a process.
As the abnormal states and their escalation methods can be differently defined according to the contexts of business processes and environments, it is difficult to provide general solution for the process escalation. General approach to abnormal state analysis and escalation method decision is simulation-based. Simulation is repeatedly performed to confirm whether a process instance can be terminated successfully in varying the value of the performance indicators under the current conditions [11, 14]. After this investigation of the process instance through simulation, the target of escalation is identified. And then, appropriate actions will be served to the instance according to the extent of abnormality on the performance indicators. For example, intensity of the escalation can be controlled by the extent of overtime [2, 5, 12]. To determine the extent of the abnormality, Grigori et al [3] set up the rule using classification techniques like decision tree and showed the escalation taken by re-arrangement of the task priorities.
In this paper, we propose a method for business process performance management that ranges from business activity monitoring to improvement. To evaluate the performance and abnormality of tasks, cost and time indicators are considered. And escalation is conducted in a way that additional resources (e.g. cost and time) are supplied to tasks.
3. Business Process Escalation Framework
Figure 1 illustrates the escalation framework in BPMS. The abnormal state analysis and the escalation engine are covered area in this paper.
Figure 1. Escalation framework in BPMS
3.1. Abnormal state analysis
KPIs seen as a means of quantifying the efficiency and effectiveness of tasks can be used to the current state of a process. As a process is a collection of related, structured tasks that produce specific actions, the evaluation of a process can be done by aggregation of task-level KPIs. However, it is unsuitable to use task-level KPIs directly to evaluate the performance of a process [3]. Because each task has different KPIs and it is difficult to identify relationships between the values of task-level KPIs in each task. Hence, each task is evaluated as a status corresponding to the combination of its KPI values. In table 1, we can summarize the status of a task using the following states.
Table 1. Status of a task
state description
Best All the KPI values in a task obtain good results
Good The KPI values in a task lead positive effects to following tasks Normal General results when a task is ended
Bad Not serious, but the task concluded contains possible risks affecting to following tasks Worst Monitored results in a task leading negative effects to following tasks
Based on the historical data of process execution, the status of a present task is determined. To formulate the status of a task, ordinary least squares linear multiple regression is used. Table 2 describes the regression model used in this paper to determine the status of a task. At the end of every single task, the status of a task is evaluated assigning KPI values to corresponding variables in the model.
Table 2. Regression model
0 2 1 2 1
...
i i i i i i in in i
Y
x
x
x
i
Y state value of ith task
ij
x j
th
KPI’s normalized value of ith task
ij
coefficient
State 0~a a~b b~c c~d d~1 n number of KPIs in i
th task
State prediction module which to target tasks remained or undone is operated every time the status of a task is determined. It is assumed that a currently finished task or the determined status of a task can have influence on other unfinished tasks. For example, if the status of a currently finished task is evaluated as ‘worst’, following tasks are affected by the negative status of the previous task, so that it is likely that these unfinished tasks will have lower performances. To consider these dependencies between tasks, a Bayesian network is employed. The reason why we use a Bayesian network is as follows.
As a Bayesian network can be represented as a directed acyclic graph, it is easy to model a process which consists of a sequence of connected tasks.
A Bayesian network provides a clear semantic interpretation of the model parameters. Unlike neural network models, which usually appear to the user as a black box, all the parameters in a Bayesian network has an understandable semantic interpretation.
It is easy to handle domain expert knowledge. In Bayesian modeling, domain knowledge can be coded as prior distributions, prior meaning that probability distributions are defined before and independently of processing any possible sample data. This allows for combining expert knowledge with statistical data in a very practical way.
From the domain knowledge and the historical data, parameters needed to build a Bayesian network are estimated. Using this network, the expected status of unfinished tasks can be calculated probabilistically and we can inference last task in a given process whether ends up successfully or not. 3.2. Escalation
To execute a task, tangible or intangible resources related to business process are required. The kinds of such resources are cost, time, human resources, materials, etc. As available resources have a strong influence on the performance of a task, we assume that the more resources are assigned to a task, the better performance can be achieved. In this paper, we only consider cost and time. Because not only earlier work done by [2, 10, 13] considered cost and time, but also these two resources are related to the performance indicators directly and indirectly.
Escalation is a procedure which is invoked if last task in a process have a high probability that will be a negative state. Escalation is done by comparison between the worst state probability of last task in a process and allowable limit. In other words, based on the finished task, the worst state probability of last task is calculated and then, escalation is aimed to lower this probability than maximum limit of the worst state probability. Determination of tasks to be escalated is performed by following model in table 3. Using the model described above, tasks to be escalated and necessary escalation cost are determined.
4. Experiments
Experiments are done to apply and evaluate the proposed escalation method to the process composed of sequentially connected fifteen tasks. To show the efficiency of the proposed escalation method, escalation policies are designed as below.
Rule-based escalation
Rule-based escalation represented as a form of ‘If condition Then action’ is a most widely used approach in BPMS. We considered following two rules.
(1) If the previous task is evaluated as bad or worst, escalation will be done to the following task by raising the normal state.
(2) If the previous two tasks are evaluated as (worst, worst) or (bad, worst), escalation will be done to the following task by raising the good state.
Table 3. Escalation model
N
a set of unfinished tasksi
s state of task i
i
x
decision variable which indicates whether unfinished task i will beescalated or not ijk
c ,tijk
additional cost and time related to improve the state of task i from state j to k. The indices j, k are on a scale of 1 to 5. The indices are larger, the better performance can be achieved.
c
w ,
w
t weights corresponding to escalation cost and time, wherew
c
w
t
1
1 2 1
( | , ,..., )
worst n n
P s s s s
the worst state probability of task n, derived from the Bayesian network
P maximum limit of the worst state probability
B, T available budget and time for escalation
15 15
min
c ijk i t ijk ii N j j k i i N j j k i
c
t
w
x
w
x
c
t
(1) (1) Objective function is formulated asa weighted sum of normalized escalation cost and time. And it will minimize the effort for the escalation. (2) the worst state probability of last task in a process is less than allowable limit (3) budget constraint (4) time constraint 1 2 1 ( | , ,..., ) worst n n P s s s s P (2) ijk i i N j j k c x B
(3) ijk i i N j j k t x T
(4) {0,1} ix ,si{worst bad normal good best, , , , }
No escalation
There are no escalation actions to tasks in a process. Cost used in this case is zero.
Varying the maximum limit of the worst state probability, escalation cost and the ratio of instances not finished in worst state are compared. Each escalation policy is executed 500 times. Results are shown at figure 2.
Figure 2. Comparison experiments
As shown in figure 2, the proposed escalation method use less cost compared to other policies, because it has little chance of escalation to tasks which have low dependencies with others. Also, the proposed escalation method shows high performance in the case of rigorous limit.
5. Conclusions
In this paper, we propose a method for business process performance management that ranges from business activity monitoring to escalation. To consider dependencies between tasks, Bayesian belief network is employed and mathematical model is designed to determine the tasks to be escalated. And we show that the proposed escalation method is useful in the case of rigorous limit and has a good performance compared with the rule-based escalation.
There still remain several further research issues to be dealt with. The model can represent various structural features of process. In the model introduced here, tasks are connected sequentially. Processes in real world, however, can have other structures such as parallel, condition, and iteration. Also, techniques to represent non-linear relationships between performance indicators can be considered.
Acknowledgement
This work is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 20110016160).
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