Business process modelling, simulation and reengineering: call centres Razvi Doomun, Nevin Vunka Jungum,
Business Process Management Journal
Business process modelling, simulat ion and reengineering: call cent res Razvi Doomun, Nevin Vunka Jungum,
Article information:
To cite this document: Razvi Doomun, Nevin Vunka Jungum, (2008) "Business process modelling, simulation and
reengineering: call centres", Business Process Management Journal, Vol. 14 Issue: 6,pp. 838-848, doi:
10.1108/14637150810916017 Permanent link t o t his document : Downloaded on: 30 March 2017, At : 21: 06 (PT) Ref erences: t his document cont ains ref erences t o 21 ot her document s.To copy t his document : permissions@emeraldinsight . com The f ullt ext of t his document has been downloaded 5303 t imes since 2008*
Users who downloaded this article also downloaded:
(2008),"Assessing readiness for business process reengineering", Business Process Management Journal, Vol. 14 Iss 4 pp. 497-511 http://dx.doi.org/10.1108/14637150810888046
(2007),"Business process reengineering: critical success factors in higher education", Business Process
Management Journal, Vol. 13 Iss 3 pp. 451-469 http://dx.doi.org/10.1108/14637150710752344 Access t o t his document was grant ed t hrough an Emerald subscript ion provided by emerald-srm: 602779 [ ]For Authors
If you would like t o writ e f or t his, or any ot her Emerald publicat ion, t hen please use our Emerald f or
Aut hors service inf ormat ion about how t o choose which publicat ion t o writ e f or and submission guidelines are available f or all. Please visit www. emeraldinsight . com/ aut hors f or more inf ormat ion.About Emerald www.emeraldinsight.com
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) Emerald is a global publisher linking research and pract ice t o t he benef it of societ y. The company
manages a port f olio of more t han 290 j ournals and over 2, 350 books and book series volumes, as well as
providing an ext ensive range of online product s and addit ional cust omer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.- Relat ed cont ent and download inf ormat ion correct at t ime of download.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-7154.htm
BPMJ Business process modelling,
14,6 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. Keywords Business process, Reengineering, Modelling, Simulation, Call centres Paper type Literature 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).
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT)
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; Kettinger et 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,
Business Process Management Journal
however, significant process innovation initiatives fall short of delivering the
Vol. 14 No. 6, 2008 q pp. 838-848 Emerald Group Publishing Limited The authors are grateful to the anonymous referees and the editor for their constructive comments
1463-7154 on the earlier version of the manuscript, which helped to improve the content of the paper. expected results. While they typically aim for dramatic or radical change, they often result
Business process
in only incremental improvements (Jaarvenpaa and Stoddard, 1998). Wright and Yu (1998)
modelling
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 (Paolucci et 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 (Kettinger et 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
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) model was developed from five previously proposed methodologies (Muthu et 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
It should rather help a BPR team to understand current process and not analysing it. Many
BPMJ
business-modelling methods do not lead to a precise enough model of the underlying
14,6
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 (Bhaskar et 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
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) 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 redesign the existing business processes. Depending on the business scenario, one or a
Business process
combination of several methods for process modelling can be adopted, including data
modelling
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
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) 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 BPMJ 14,6 No Business changes needed?
Yes Defining modelling objectives Deciding on model boundaries ication & Gaps
Data collection & Analysis Clarif
Business Process Modelling Business Process Simulation Model Testing
Defects & Remodel Model Experimentation Output Analysis
Business Process Change Recommendations Major or minor changes? Major
Minor Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT)
Reengineering &
Improving existing process
Implementing new process New Process Performance AnalysisNo Yes Figure 1.
Goals satisfied? A flexible BPMSR lifecycle
(7) Model testing. Model testing provides the ability to analyse business processes
Business process
in the following areas: .
modelling .
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
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT)
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 which can be adapted to both radical or incremental change. The main
BPMJ
advantage is that the probability of finishing the project is much higher, in
14,6
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.
Customers PABX End of service
IVR Multiple queues
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) Abandon ACD
Scheduler Customer database server
Figure 2.
Agents Schematic model of a call centre
Agents End of service In a large, best-practice call centre, many hundreds of agents can cater to many
Business process
thousands of phone callers per hour; agent utilisation levels can average between
modelling
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
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) 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.
When a model is run, calls arrive, go to queues, transfer to available agents or an IVR,
BPMJ
and are completed. The model can keep track of what agents are available, following rules,
14,6
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
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) 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.
References Bhaskar, R., Lee, H.S., Levas, A., Petrakian, R., Tsai, F. and Tulskie, B. (1994), “Analysing and reengineering business processes using simulation”, in Tew, J.D., Manivannan, S., Sadowski, D.A. and Seila, A.F. (Eds), Proceedings of the 1994 Winter Simulation Conference (Lake Buena Vista, Florida, USA), Vol. 1994, pp. 1206-13. Colin, A. and Rowland, P. (1996), Managing Business Processes: BPR and Beyond, John Wiley & Business process Sons, New York, NY. modelling
Davenport, T.H. and Perez-Guardado, M.A. (1999), “Process ecology: a new metaphor for reengineering-oriented change”, , Kluwer Academic Publishers, Norwell, MA, pp. 25-44.
Davenport, T.H. and Short, J.E. (1990), “The new industrial engineering: information technology and business process redesign”, Sloan Management Review, pp. 11-27. Fathee, M. (1998), “The effects of complexity on business processes reengineering: values and limitation of modelling and simulation technologies”, Proceeding of 1998 Winter
Simulation Conference, p. 1339.
Gans, N., Koole, G. and Mandelbaum, A. (2003), “Telephone call centres: tutorial, review, and research prospects, invited review paper”, , Vol. 5 No. 2, pp. 79-141.
Grover, V. and Kettinger, W.J. (Eds) (1995), Business Process Change: Concepts, Methodologies and Technologies, Idea Group, Harrisburg, PA. Gunasekaran, A. and Kobu, B. (2002), “Modelling and analysis of business process reengineering”, , Vol. 20 No. 11, pp. 2521-46. Hammer, C.M.J. (1993), Reengineering the Corporation: A Manifesto for Business Revolution, Harper Business, New York, NY. Jaarvenpaa, S. and Stoddard, D.B. (1998), “Business process redesign: radical and evolutionary change”, , Vol. 41 No. 1, pp. 15-27. Kettinger, W.J., Teng, J.T.C. and Guha, S. (1997a), “The process reengineering life cycle methodology: a case study”, in Grover, V. and Kettinger, W.J. (Eds), Business Process
Change: Concepts, Methodologies and Technologies, pp. 211-44.
Kettinger, W.J., Teng, J.T.C. and Guha, S. (1997b), “Business process change: a study of methodologies, techniques, and tools”, , Vol. 21 No. 1, pp. 55-80. Kubeck, L.C. (1995), Techniques for Business Process Redesign, John Wiley & Sons, New York, NY. Mehrotra, V. and Fama, J. (2003), “Call center simulation modelling: methods, challenges, and opportunities”, pp. 135-43. Muthu, S., Whitman, L. and Cheraghi, S.H. (1999), “Business process reengineering:
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) a consolidated methodology”, Proceedings of the 4th Annual International Conference on Industrial Engineering Theory, Applications and Practice, San Antonio, Texas, USA.
Paolucci, E., Bonci, F. and Russi, V. (1997), “Redesigning organisations through business process re-engineering and object-orientation”, Proceedings of the European Conference on Information Systems, Cork, Ireland, pp. 587-601.
Paprzycki, M., Abraham, A. and Guo, R. (2004), “Data mining approach for analyzing call center performance”, , Ottawa, Canada, pp. 1092-101.
Paul, R.J., Hlupic, V. and Giaglis, G. (1998), “Simulation modelling of business processes”, in Avison, D. and Edgar-Neville, D. (Eds), Proceedings of the 3rd UK Academy of Information Systems Conference, McGraw-Hill, Lincoln, UK, June, pp. 311-20.
Swami, A. (1995), “Building the business using process simulation”, Proceedings of the 1995 BPMJ Winter Simulation Conference, SCS, pp. 1081-6.
14,6 Tumay, K. (1995), “Business process simulation”, in Alexopoulos, A., Kang, K., Lilegdon, W.R. and Goldsman, D. (Eds), Proceedings of the WSC’95 – Winter Simulation Conference, Washington, DC, pp. 55-60.
Wright, D.T. and Yu, B. (1998), “Strategic approaches to engineering design process modelling”, , Vol. 4 No. 1, pp. 56-71. Corresponding author Razvi Doomun can be contated at: r. [email protected]
Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) To purchase reprints of this article please e-mail: [email protected] Or visit our web site for further details: www.emeraldinsight.com/reprints
This article has been cited by:
1. Andrew Greasley Aston University Birmingham United Kingdom of Great Britain and Northern Ireland Chris M Smith University of Manchester Manchester United Kingdom of Great Britain and Northern Ireland . 2017. Using activity based costing and simulation to reduce cost at a Police communications centre. Policing: An International Journal of Police Strategies & Management 40:2. . [
2. LeyerMichael Michael Leyer [email protected] MoormannJürgen Jürgen Moormann [email protected] University of Rostock, Rostock, Germany Frankfurt School of Finance & Management, Frankfurt a.M., Germany . 2017. Action learning approach to teaching business engineering methodology. Business Process Management Journal 23:1, 130-154.
3. Patrick Afflerbach, Martin Hohendorf, Jonas Manderscheid. 2016. Design it like Darwin - A value-based application of evolutionary algorithms for proper and unambiguous business process redesign. Information Systems Frontiers . ] 4. Bransford Brandon, Tor Guimaraes. 2016. Increasing Bank BPR Benefits by Managing Project Phases.
Knowledge and Process Management 23:2, 136-146. [
5. Philipp Bergener European Research Center for Information Systems, University of Münster, Münster, Germany. Patrick Delfmann European Research Center for Information Systems, University of Münster, Münster, Germany. Burkhard Weiss European Research Center for Information Systems, University of Münster, Münster, Germany Axel Winkelmann Department of Business Administration, University of Würzburg, Würzburg, Germany. . 2015. Detecting potential weaknesses in business processes. Business
Process Management Journal 21:1, 25-54. [ Abstract ] [ Full Text ] [ PDF ]
6. Youseef Alotaibi Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia and Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia Fei Liu Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia . 2014. An empirical study of a novel managing customer power model and business performance in the mobile service industry. Business Process Management Journal 20:6, 816-843. [
7. Michael Leyer ProcessLab, Frankfurt School of Finance & Management, Frankfurt, Germany Maximilian Hollmann ProcessLab, Frankfurt School of Finance & Management, Frankfurt, Germany . 2014.
Introduction of electronic documents: how business process simulation can help. Business Process Management Journal 20:6, 950-970. [
8. Kristian Rotaru, Leonid Churilov, Andrew FlitmanPhilosophical positioning of discrete-event simulation Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT) and system dynamics as management science tools for process systems: A critical realist perspective 76-104.
]
9. Maria Christofi Information School, University of Sheffield, Sheffield, UK Miguel Nunes Information School, University of Sheffield, Sheffield, UK Guo Chao Peng Information School, University of Sheffield, Sheffield, UK Angela Lin Information School, University of Sheffield, Sheffield, UK . 2013. Towards ERP success in SMEs through business process review prior to implementation. Journal of Systems and Information Technology 15:4, 304-323. 10. Tor Guimaraes, Ketan Paranjape. 2013. Testing success factors for manufacturing BPR project phases.
The International Journal of Advanced Manufacturing Technology 68:9-12, 1937-1947. [
University, Cookeville, Tennessee, USA . 2013. Important factors for success in hospital BPR project phases. International Journal of Health Care Quality Assurance 26:8, 729-745. [
12. Gregor ZellnerDepartment of Management Information Systems, University of Regensburg, Regensburg, Germany. 2013. Towards a framework for identifying business process redesign patterns. Business Process Management Journal 19:4, 600-623. [
13. Professor T.C. Melewar and Dr Sharifah AlwiS.J. GoraneDepartment of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, India Ravi KantDepartment of Mechanical SCM enablers: an integrated ISM‐fuzzy MICMAC approach. Asia Pacific Journal of Marketing and Logistics 25:2, 263-286. [ ]
14. Youseef AlotaibiDepartment of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia Fei LiuDepartment of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia. 2013. Average waiting time of customers in a new queue system with different classes. Business Process Management Journal 19:1, 146-168. [ ]
15. Nazanin EftekhariMads Clausen Institute, University of Southern Denmark, Sønderborg, Denmark Peyman AkhavanDepartment of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran. 2013. Developing a comprehensive methodology for BPR projects by employing IT tools.
Business Process Management Journal 19:1, 4-29. [ Abstract ] [ Full Text ] [ PDF ]
16. Daisy Mathur JainNational Law University, Jodhpur, India and Institute of Management Technology, Ghaziabad, India Reema KhuranaInstitute of Management Technology, Ghaziabad, India. 2013. Need for sustainable global business model in software outsourcing. Business Process Management Journal 19:1, 54-69.
17. Jonna JärveläinenInformation Systems Science, Turku School of Economics, University of Turku, Turku, Finland. 2012. Information security and business continuity management in interorganizational IT relationships. Information Management & Computer Security 20:5, 332-349.
18. D. Dobrilovic, V. Jevtic, I. Beker, Z. StojanovShortest-path based model for warehouse inner transportation optimization 63-68. [
19. Guido NassimbeniUniversity of Udine, Udine, Italy Marco SartorUniversity of Udine, Udine, Italy Daiana DusCASSCC, University of Torino, Torino, Italy. 2012. Security risks in service offshoring and outsourcing. Industrial Management & Data Systems 112:3, 405-440.
20. Gregor ZellnerDepartment of Management Information Systems, University of Regensburg, Regensburg, Germany. 2011. A structured evaluation of business process improvement approaches. Business Process Downloaded by UNIVERSITY OF INDONESIA At 21:06 30 March 2017 (PT)
Management Journal 17:2, 203-237. [ Abstract ] [ Full Text ] [ PDF ] 21. Kijpokin KasemsapThe Role of Business Process Reengineering in the Modern Business World 87-114
]
22. Kijpokin KasemsapThe Role of Business Process Reengineering in the Modern Business World 1802-1829. [