Business Process Management Journal
Business process modelling, simulat ion and reengineering: call cent res Razvi Doomun, Nevin Vunka Jungum,
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Business process modelling,
simulation and reengineering:
call centres
Razvi Doomun and Nevin Vunka Jungum
Department of Computer Science and Engineering,
University of Mauritius, Reduit, Mauritius
Abstract
Purpose– The purpose of this paper is to develop a flexible framework through which business processes can be modelled, simulated and reengineered in a cost-effective way.
Design/methodology/approach– This paper is mainly based on a review of the literature and the methodology is discussed in the context of a typical call centre business.
Findings– Reengineering business processes involve changes in people, processes and technology over time. In this paper, a flexible business process modelling, simulation and reengineering (BPMSR) approach is presented. Modelling starts with precisely defining model objectives and boundaries, and carrying extensive data analysis. Simulation modelling allows testing and analysis of different scenarios to understand their impact on a broader “system” and evaluate feedback before moving forward with reengineering implementation plans. The need for a flexible and adaptive methodology is stressed to augment efficiency and effectiveness of reengineering cycle.
Originality/value– Flexibility and adaptability in the reengineering cycle are effective to identify early modelling incompatibility and simulation defects. It adds intelligence to BPMSR and accommodates for any technical or process changes that may subsequently arise. This approach is reliable for future process improvement or reengineering endeavours due to its flexible configuration, which can be adapted to both radical or incremental change.
KeywordsBusiness process, Reengineering, Modelling, Simulation, Call centres Paper typeLiterature review
Introduction
Information and communication technology (ICT) sector companies nowadays are under increasing pressure to adapt their business processes to persistent technological, organisational, political and other changes (Davenport and Perez-Guardado, 1999). A group of process innovation techniques known collectively as business process reengineering (BPR) has emerged to address this challenge (Colin and Rowland, 1996; Davenport and Short, 1990; Grover and Kettinger, 1995; Hammer, 1993; Kubeck, 1995; Kettingeret al., 1997a,b). Reengineering is not about fine-tuning or marginal changes, rather it is for ambitious companies willing to make substantial changes to achieve major performance improvements. BPR is an organisational initiative to fundamentally re-examine and redesign business processes with the objectives of achieving competitive breakthrough in quality, responsiveness, cost, satisfaction and other critical process performance measures. Despite, the widespread use of BPR tools and methodologies, however, significant process innovation initiatives fall short of delivering the
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expected results. While they typically aim for dramatic or radical change, they often result in only incremental improvements (Jaarvenpaa and Stoddard, 1998). Wright and Yu (1998) defined the factors to be considered before actual BPR starts and developed a model for identifying the tools for BPR. To support the good conduct of a reengineering project, various modelling and simulation tools are designed to be used. Kallio et al. (1999) analysed 32 BPR projects and concluded that most projects were focused on streamlining current business processes, while only in a few cases were business processes radically redesigned. Based on the results, they developed a framework to help managers choose the most appropriate BPR strategies. Some researchers argue that one of the major problems that contribute to the “failure” of BPR projects is the lack of tools for evaluating the effects of designed solutions before implementation (Paolucciet al., 1997). Errors brought about by process reengineering can only be realised once the redesigned processes are implemented, when it is too late, costly and difficult to correct wrong decisions (Tumay, 1995). How can we ensure that the radical redesign will in fact lead to achieve dramatic improvements instead of organisational anarchy? This paper is structured as follows: we first give an overview of methodologies for reengineering. We then describe process modelling and simulation, and present a flexible reengineering lifecycle. Then, we discuss how a call centre process can be redesigned by systematically performing modelling, simulation and reengineering; and conclude.
Methodologies for business process modelling, simulation and reengineering (BPMSR)
A comprehensive survey (Kettingeret al., 1997a,b) of current BPR techniques identified the following categories of tools relevant to the redesign stage of process innovation: integration definition (IDEF) modelling, data modelling, including data flow diagramming, flow charting, case-based information, engineering tools, process simulation, creativity techniques, including brainstorming, out-of-the-box thinking, nominal group and visioning.
Most BPR methodologies share common phases and features, but they also differ in the way they approach reengineering. Their main differences are, whether or not they recommend detail modelling and analysis of current situation; whether they support incremental or radical changes to business processes and; whether they suggest the study of successful organisations before embarking a BPR project. Consolidated methodology for reengineering provide a structured approach and to facilitate understanding. This model was developed from five previously proposed methodologies (Muthuet al., 1999).
Modelling is an essential step in studying current and proposed structure business processes from a systems perspective. The classification of literature on modelling and analysis of BPR is based on six major tool/techniques used that include (Gunasakaran and Kobu, 2002): conceptual models, simulation models, object-oriented models, IDEF models, network models and knowledge-based models. However, there are contradictory aspects about modelling and analysing current processes. In favour of it are researchers who believe that understanding and analysing current business processes is fundamental for a successful BPR effort. Against it are researchers who stress that as-is modelling is a time-consuming step, which prevent creative thinking and going beyond traditional ways of doing business. If continuous improvement is the case then detail as-is modelling can help in identifying problems, bottlenecks and opportunities of small changes that will improve performance. During a BPR effort though, as-is modelling should not be detailed.
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It should rather help a BPR team to understand current process and not analysing it. Many business-modelling methods do not lead to a precise enough model of the underlying business knowledge. Hence, a model should be comprehensive enough to allow for a systematic study and precise formulation of the BPR.
In order to describe and communicate the future state of a process efficiently, the “to-be” process is visualised. In most cases there are more than one redesign options. These options are evaluated against expected benefits and the strategic objectives of the organisation. The best of them is selected and is further analysed to identify neglected problems. Simulation analysis can be very beneficial in this stage, because it provides a way to simulate the operation of the future process and identify its strength and potential problems. A BPR methodology that concludes to a continuous improvement model is very strong, because it is positioned within a process management system that enables the investigation, monitoring and refinement of organisation processes. If this is the case then process improvement becomes an every day task and both radical redesign and continuous process improvement becomes part of processes’ lifecycle.
Process modelling and simulation
Business processes consist of a series of logically connected entities that use organisation’s resources. Davenport and Short (1990) define a process as “a structured, measured set of activities designed to produce a specified output for a particular customer or market”. In a majority of definitions, the common elements relate to the process itself (usually described as transformation of input, work flow or a set of activities), process input and process output, usually related to creating value for a customer, or achieving a specific goal (Paul et al., 1998). In order to reengineer a business process, both internal and external process capabilities need to be integrated. Flexible simulation has an important role in modelling and analysing the activities in introducing BPR since it enables quantitative estimations on influence of the redesigned process on system performances (Bhaskaret al., 1994). The simulation of business processes represents one of the most widely used applications of operational research. It allows understanding the fundamentals of business systems, identifying opportunities for change, and evaluating the impact of proposed changes on key performance indicators. The design of business simulation models is proposed as a suitable tool for BPR projects (Swami, 1995). Dynamic process models afford the analysis of alternative process scenarios through simulation, that provides a structured environment in which one can understand, analyse and improve business processes. The reasons for the introduction of simulation modelling into process modelling can be summarised as follows:
. Simulation enables modelling of process dynamics.
. Influence of random variables on process development can be investigated. . Anticipation of reengineering effects can be specified in a quantitative way. . Process visualisation and animation are possible.
. Communication between clients and an analyst is facilitated by simulation
models.
Modern simulation software tools are able to model dynamics of the processes and show it visually, which then can enhance generating the creative ideas on how to
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redesign the existing business processes. Depending on the business scenario, one or a combination of several methods for process modelling can be adopted, including data and functional modelling, information modelling, activity modelling, activity based coasting, simulation and CASE or functional economic analysis.
Reengineering lifecycle
Process reengineering projects can be decomposed into a number of important sub-phases, as shown in Figure 1, and in order as follows:
(1) Changes needed. It begins with identifying external factors, criticising existing process, comparing best practices elsewhere to determine priority of changes needed.
(2) Defining modelling objectives. Specific business objectives such as cost reduction, time reduction and output quality improvement are defined. The limitations and scope of changes are also established in order to conduct simulations later with a reasonable model.
(3) Defining modelling boundaries. It involves awareness of information technology capabilities and its influences on process design. Thus, resource implications are considered and scope of model is detailed.
(4) Data collection and analysis. This phase is concerned with the collection of relevant actual business data and their analysis. Collected data give a quantitative criterion to assess the advantages and disadvantages of changes to the process. At this point, if there are anomalies, then there is loopback to clarify modelling objectives.
(5) Business process model development. Process models are used to facilitate understanding of “how” a process currently operates and “what” it actually does. Then, actions are taken to improve process. The model building phase is where simulation modelling expertise and creativity comes into picture. However, the construction of process models is a resource-intensive activity. The purpose is to understand the problems and to recognise the constraints with the information flows and seek optimal solutions for improving the overall performance of the system. Common process modelling techniques and their associated tools are: flow chart technique, data flow diagrams, role-activity diagram and role interaction diagrams for more detailed descriptions of a process, Gantt chart, IDEF for high level process modelling and UML. The model is built using modular technique, so that variables can be very easily modified to perform “what if” analysis. (6) Business process simulation. Modelling is followed by analytical and simulation
steps. With a simulation tool, we can take a dynamic picture of models. Some simulation tools available are Metis, Arena and SimProcess. A number of researchers postulate that simulation is the only suitable technique for BPR; business processes are too complex and confusing (Fathee, 1998) because:
. Many business processes are undetermined and include random variables. . Activities and resources that are main business process elements have
interactions.
. Business processes of organisations affect each other and are changed by
agents outside the organisation.
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Figure 1. A flexible BPMSR lifecycle
Business changes needed?
Defining modelling objectives
Deciding on model boundaries
Data collection & Analysis
Business Process Modelling
Business Process Simulation
Model Testing
Model Experimentation
Output Analysis
Business Process Change Recommendations
Major or minor changes?
Improving existing process
New Process Performance Analysis
Goals satisfied?
Reengineering & Implementing new process Minor
No
No
Yes Yes
Major
Defects & Remodel
Clarif
ication & Gaps
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(7) Model testing. Model testing provides the ability to analyse business processes in the following areas:
. Determining bottlenecks and wastage.
. Planned reviewing of processes for improving the performance. . Choosing better-designed processes to get better-results. . Cost evaluation.
. Measuring the performance of new processes.
(8) Model experimentation. Experimentation gives correct and reliable estimate of results. This step helps analysis in the following areas:
. Time variable properties of many processes.
. Time-based processes (changing the state of system by time). . Nonlinear relations between elements of process.
. Randomness property of real processes.
. Unwanted events and occurrence in business environment.
During the experimentation phase, the model is run multiple times and some critical statistical tests are conducted to identify the steady state and run length of the model:
(9) Output analysis. The output from the model experimentation phase is analysed in order to check whether the result obtained has met the expected output. Identifying and measuring performance indicators are a key issue to process evaluation. If performance indicators are unsatisfactory to proceed with recommendation phase, this points out that previous stages (experimentation, testing or data collection and analysis) must be reconsidered. Hence, early defects are remedied by this adaptive mechanism.
(10) Business process change recommendation. Based on successful output analysis, recommendation of process reengineering or improvement is made and the implementation plan is finalised.
(11) Reengineering and improvement. When enough confidence with the model has been achieved and taking into consideration the recommendation, the organisation is ready for reengineering or improvement phase. The goals that made visible the need for a reengineering or improvement are implemented. This phase involves the design of the new process. Depending on particular circumstances either an approach of incremental improvement or an approach of radical change is adopted. Existing processes need to run in parallel until the complete installation of new ones without disturbing the environment in which they both operate.
(12) New process performance analysis. The new implementation is finally examined when fully deployed and operational to evaluate its real performance.
Flexibility and adaptability in the reengineering cycle are effective to identify early modelling incompatibility and simulation defects. It adds intelligence to BPMSR and accommodates for any technical or process changes that may subsequently arise. This approach is reliable for future process improvement or reengineering endeavours owing to its flexible configuration
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which can be adapted to both radical or incremental change. The main advantage is that the probability of finishing the project is much higher, in much less time: the invested time is spent much better, the average return on invested time is higher. An additional advantage is that the proposed solution is focused on the issues where most can be gained.
Call centre example
In this section, we outline the application of BPMSR for call centre processes using the stages presented in previous section. A typical call centre, as shown in Figure 2, consists of the following main components: an automatic call distributor (ACD), an interactive voice response (IVR) and agent work stations. PABX’s supports IVR and ACD functionality. To every PABX a number of extensions are connected; the ACD switch is able to select an extension with a free agent for a call coming in over a certain line. Call centre is assumed, but not restricted, as queueing models (Gans et al., 2003). An incoming call is connected, when one or more trunk lines are free. First, the call is routed to an IVR, which provides standard messages and guides the caller through a menu to select the requested service. Before speaking to an agent, the call is handled by the ACD, which based of several criteria, finally routes the call to free agent.
Figure 2. Schematic model of a call centre
Customers
PABX
IVR
ACD Scheduler
End of service Agents Agents
Multiple queues
Abandon
Customer database
server End of service
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In a large, best-practice call centre, many hundreds of agents can cater to many thousands of phone callers per hour; agent utilisation levels can average between 90 per cent and 95 per cent; no customer encounters a busy signal and, in fact, about half of the customers are answered immediately; the waiting time of those delayed is measured in seconds, and the fraction that abandon while waiting varies from the negligible to a mere 1-2 per cent. However, most call centres – even well-run ones – do not consistently achieve such simultaneously high levels of service quality and efficiency. The potential reasons for changes required for call centre reengineering initiatives are:
. Better queueing performance models and control models required for
multiple-server systems.
. Human resources problems associated with personnel scheduling, hiring and
training.
. Higher service quality, and complex customer and agent behaviour. . Statistical analysis of call centre data for higher overall performance.
Modelling objectives of complex processes, such as queues, abandonment’s, retrials, multi-skill, routing policies, general service time distributions have to be first clearly defined (Mehrotra and Fama, 2003). Modelling is always a compromise between the scope of the model and the complexity. If the call centre process model is too complex to solve satisfactorily, then decreasing the model scope is an option. This has the risk that the influence on system parts that are not modelled is ignored. This influence can be important enough to change the proposed solution to the problem.
A large call centre generates many gigabytes of call-by-call data each month. Its IVR(s) and ACD are special purpose computers that use data to arbitrate the flow of calls. It records call’s identification number, action taken, time elapsed since the previous action, numbers of arrivals and abandonment, average service times, agent utilisation, and the distribution of delay in queue. Quantitative analysis of these data is used both to measure actual system performance and for call centre process redesign. It results in building a mathematical model of the call centre, estimating all relevant parameters, and drawing conclusion from a thorough analysis of the model data. One common difficulty that arises is the lack of relationship between call-by-call data stored at the IVR level and downstream, aggregate data, tracked by workforce planning systems. Collection of high-quality data and subsequent in-depth statistical analysis is an important pre-requisite for better understanding of call centres, which in turn is a pre-requisite for advanced simulation modelling. Data mining techniques (Paprzycki
et al., 2004) (such as linear neural networks, multi-layered perceptions and neural network approach) can be applied to the problem of predicting the quality of service in call centres. The data analysis phase with feedback arrangement allows designers to identify problems early, such as missing call centre process parameters, inconsistencies in objectives and boundaries. Hence, there is a chance to correct any gaps and refining the model objectives and boundaries.
Static design problem determines staffing levels according to which agents are assigned to work schedules. Dynamic control problem resolves the real-time assignment of incoming calls to agents. These two goals are clearly inter-related and are modelled simultaneously.
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When a model is run, calls arrive, go to queues, transfer to available agents or an IVR, and are completed. The model can keep track of what agents are available, following rules, skills restrictions, transfers in and out of IVR, and virtually any complexities that are to be studied. Consequently, this enables call flow analysis and bottlenecks or inefficiencies identification. Once, the “basic mode” model is working, it is possible to create “traps” within the logic to capture specific information to assist in analysing the model. Example is the number of calls handled partially by an IVR and then transferred to an agent.
All simulation conditions that can influence performance require testing, analysing simulation data and evaluating results accurately. Any simulation defects are tackled by either checking the experimentation, testing and simulation structure again or reinitiating the model design. The testing and experimentation phase collect and report on call centre model metrics – service levels, agent utilisation, telephone trunk line utilisation and longest wait for a caller. The baseline results are compared to the scenario(s) under evaluation, and the differences are analysed. These outcomes provide quantitative information to use in the business case for the changes recommended. Inefficiencies in the call centre process model are checked before providing recommendations to optimise the process. Finally, reengineering of a new call centre process or improving existing process can be undertaken.
Conclusion
In this paper, we have presented an effective and flexible methodology to support modelling, simulation and reengineering business process. The importance of modelling and analysis of BPR was discussed as a core part of reengineering. A flexible and iterative BPMSR methodology is recommended on the basis that feedback control and stepwise performance analysis minimise reengineering failure risks by resolving early modelling and simulation problems. The initial costs, in terms of time, human and financial resources to apply the proposed BPMSR approach to any business process are recovered in efficiency improvements or cost avoidance of technology or process change errors. The call centre example has been considered to show the different aspects of the proposed reengineering lifecycle approach. A future work will be to test the proposed flexible reengineering model in other real-life business environments. Areas of research being pursued by the authors are knowledge-based models including artificial intelligence (AI) that can be used in the future to minimise the complexity of modelling and analysis of BPR. Call centre models and their associated tools incorporate a number of AI techniques for agent modelling and actions. Finally, Modelling tools should be flexible so that process benchmarking can be carried out in a more effective manner for successful reengineering of business processes. Moreover, BPMSR is an “endless” process. A reengineered process may reach a satisfactory state of efficiency but the organisation as a whole will never stop competing in a continuously changing environment, which leads to the transformation of business strategy and vision, and as a result to the continuous need for improved or innovative processes and functions.
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Corresponding author
Razvi Doomun can be contated at: r. doomun@uom.ac.mu
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