White Paper Business Process Modeling and Simulation WP0146 May 2014 Bhakti Stephan Onggo Bhakti Stephan Onggo is a lecturer at the Department of Management Science at the Lancaster University Management School, United Kingdom. His research interests are in the areas of simulation methodology (modeling paradigms and conceptual modeling), simulation technology (parallel and distributed simulation, cloud-based simulation) and business process modeling and simulation applications. He has done a number of consultation projects in healthcare, manufacturing and public sector services including the European Commission and a UK police department. A simulation model of a real system is a simplified version of the real system implemented in a computerized form so that it can be run on a computer. This allows decision makers to carry out experiments using the simulation model on a computer (in-silico experiments) instead of carrying out the experiments in the real system. To take the analogy from the aviation, carrying out the simulation experiment is similar to playing with a management flight simulator. Typical objectives of using a simulation model for experimentation include increasing understanding of the real system, finding out the main issues with the current system (such as bottleneck analysis), estimating how the real system is likely to perform under various scenarios or estimating the optimum setting for the real system. In the context of business, the real system is often the business process. Based on the above description of the simulation, we can appreciate that a simulation model can be a natural next step after we map a business process. While a process map provides a snapshot of the business process, the simulation of the process map allows us to show the dynamic behavior of the business process over time. In the context of business process reengineering, this functionality provides a useful analysis before any changes to the business process is implemented in the field. In the context of business process analysis, the functionality provides a tool for us to analyze the performance of an existing business process. More importantly, once we have mapped a business process, we have actually carried out the first few steps of the simulation modeling as I will explain later on in white paper. Access our free, extensive library at www.orbussoftware.com/community
Advantages Before I explain the steps for carrying out a simulation modeling process, I will list a number of advantages that can be obtained from carrying out experiments using a simulation model on a computer instead of experimenting with the real system. In my opinion, the key advantages are as follows: We do not disrupt the continuous operations of the real system. This will minimize any risk that can be attributed to the unforeseeable effects of conducting experiments in the real system. We do not have to buy or hire extra resources that would otherwise be needed if experimenting with the real system. If an experiment in the real system takes a long time to complete, computer simulation allows us to compress the time. An experiment that would take one year if done in the real system might only take one minute on a computer. We can conduct an experiment even if the real system does not exist (for example when we are planning for a new system). In this case, experimenting with the real system is not possible. We can produce an exact replica of what has happened during a computer experiment. This is difficult to achieve in a real system and in most cases it is impossible (for example we cannot control the real system in such a way that every customer who arrived on Monday will arrive at same arrival time for the same transaction on Tuesday). It is sometimes illegal and dangerous to conduct an experiment in the real system, especially for systems that may cause irrecoverable damages or human fatalities. When the use of simulation is not appropriate Despite its advantages, there are a number of situations when computer simulation is not appropriate. These situations include: when a simple calculation can solve the problem when a mathematical formula exists to represent the problem (for example in simple queuing systems) when carrying out experiments in the real system is feasible and cheaper when the data needed to solve the problem using the simulation model is not adequate when the total cost for carrying out a complete simulation modeling project cannot be justified (for example against the expected benefits) 2
Steps in Simulation Modeling Modern simulation software packages have made simulation modeling easier. However, someone who is good at using a simulation software package does not always mean that the person is also good at simulation modeling. In fact, without adequate training, people can easily misinterpret the simulation results. Hence, it is important that we follow the correct simulation modeling steps. In this section, I will summarize the steps in simulation modeling. Problem structuring Before investing our resources to simulation modeling work we need to make sure that we are attempting to solve the correct problem (instead of fixing a symptom or, worse, a completely different problem). The main objective of this step is to identify the root cause of the problem at hand. This step requires close contact with the domain expert (someone who knows the system well) and the problem owner. We can also build our understanding about the context of the modeling work within the broader objectives of the problem owner. Conceptual Modeling A conceptual model represents our mental model of the system being studied. This includes our understanding of the objectives of the model, the inputs to the model, the outputs of the model, the boundary of the model (such as a decision on whether we should include three departments or four departments in the model), the level of detail (such as decision on whether we should model the system at an individual level, work unit level or department level) and the structure of the model (how the components in the model are linked). Most simulation projects are carried out in a team. Hence, it has become increasingly important that we make the conceptual model tangible, for example, by representing it using an appropriate diagram. A tangible conceptual model allows us to discuss and evaluate the conceptual model with the other team members. Appropriate diagrams that can be used to represent a conceptual model are discussed in Onggo (2010) which includes Objective Diagram to represent the objectives of the model, Influence Diagram to represent the model input and outputs, and BPMN to represent the boundary, structure and level of detail of a conceptual model. Figure 1 shows an example of a conceptual model describing a process carried out by a clerk at a health clinic using BPMN. The process is triggered when a patient has finished the consultation session with a doctor. The clerk will then notify the patient that payment will be taken. When taking the payment, the clerk will check whether the patient is covered by an insurance policy and take payment accordingly. The patient will be notified when the payment has been taken. Figure 1 is a 3
part of the example used in an earlier white paper (Onggo 2013). Hence, the earlier process when the clerk checked for another payment method in case the insurance would not cover the treatment is not shown. Taking payment yes Check insurance detail Valid? no Finish Start taking consultation payment Have insurance? no Self payment Finish taking payment Figure 1: An example of a conceptual model using BPMN People who are familiar with business process modeling may notice that Figure 1 is not different from a process map. This reinforces my earlier point that when we have mapped a business process, we have completed the first few steps in simulation modeling. Computer Implementation To enable the simulation of a business process model (or a conceptual model), we need to add additional information to the diagram. The main parameters needed to enable the simulation of a process map are time (i.e. the duration of a task), control (i.e. to specify how the sequence flows are controlled) and resources (i.e. to specify the resources needed by a BPMN element). In more complex cases (for example, tasks competing for the same resources), we will need information to specify the tasks priority and information to allow flexible logical rules. Figure 2 shows a few examples of extra information needed to simulate the business process shown in Figure 1. I have shown the information in an XML format to emphasize that the format is machine readable (and we can also read the content). Vendors use various formats to store the extra information but whatever format they use, it should provide similar key information. Most commercial software packages such as Process Simulator (http://www.orbussoftware.com/products/iserver/ integration-modules/process-simulator/) provide a friendly user interface (such as the use of forms). Hence, users do not need to know about how to write the detailed information in a machine readable format. 4
Figure 2: Examples of additional information needed to simulate the conceptual model Due to space limitation, I do not show all parameters in Figure 2. However, the following examples should give a clear idea on what kind of parameters needed for key BPMN elements used in the model. The first example is shown at the top left XML. This XML contains information about the probability that a patient says that s/he is covered by insurance. In this example, the probability is 60%. The second example (bottom left XML) specifies that 40% of patients say that they are not covered by any insurance. The third example (top right XML) specifies that the time for the clerk to check for the insurance detail varies from 5 minutes to 10 minutes. Likewise, the last example (bottom right corner) specifies that the time for the clerk to process the self-payment option varies between 1 minute and 3 minutes. Model Validation Simulation modeling involves running an experiment using a simulation model instead of experimenting with the real system. Hence, it is important that the model represents the real system correctly. A model validation process is needed to ensure that a model can sufficiently represent the real system to achieve the objectives of the simulation modeling project. There are techniques that can be applied for model validation such as white box and black box methods (Pidd, 2004). It is also important to involve the problem owner and domain experts in the validation process to improve the credibility of the model. It should be noted that a simulation model is a simplified version of the real system so all models are inherently incorrect (they are different from the real system). Even though a model is simpler than the real system, if it is built with sufficient level of detail and focusing on the important parts of the system, the model can provide useful insights. It is because of its simplicity that we can understand the behavior shown by the model better. The insights gained from the model will complement our understanding of the real system and provide us with a better judgment on what could work in the real system. 5
Experimentation Knowledge Acquisition Once we have built a sufficient level of confidence in the model, we can carry out experiments using the model. A well designed experiment should increase our knowledge about the real system through the model. If the objective of the modeling is to optimize the real system, many simulation software packages can set the design of the experiments automatically based on well-known algorithms. It should be noted that although I explain the simulation modeling steps sequentially, in practice, the steps are done iteratively. For example, if during the validation process we identify a mistake, we may need to revisit either the computer implementation step or the conceptual modeling step. Conclusion In this white paper, I have explained that simulation modeling is a natural next step after we have completed a business process modeling or a business process reengineering project. I have listed the key benefits of computer simulation and at the same time I have also listed the situations when computer simulation may not be suitable. One of the main reasons why computer simulation is beneficial to business process modeling is that it provides the ability to observe the expected performance of a business process (existing or new) over time. This is difficult, if not impossible, to obtain simply by analyzing the business process map alone. It is not feasible to explain about computer simulation in detail in this white paper but I have provided a summary of the steps in simulation modeling. Readers who are interested to know more about computer simulation may consult any of the popular textbooks such as Pidd (2004), Banks et al. (2005), Robinson (2004) and Law (2007) 6
References Banks J, Carson II J S, Nelson B L and Nicol D M. (2005) Discrete-event system simulation. 4th edition. Pearson Education: Upper Saddle River, NJ. Law A.M. (2007) Simulation Modeling and Analysis, 4th Edition. McGraw-Hill Onggo, B.S.S. (2010) Methods for Conceptual Model Representation, in Robinson S., Brooks R., Kotiadis K. and van der Zee, D-J. (Eds) Conceptual Modelling for Discrete-Event Simulation. Boca Raton, FL:Taylor and Francis, pp. 337-354. Onggo, B.S.S. (2013) Agent-Oriented BPMN. Orbus Software white paper series. London, UK: Orbus Software. Available from http://www.orbussoftware.com/downloads/white-papers/ agent-oriented-bpmn/ Pidd M. (2004) Computer simulation in management science. 5th edition. John Wiley & Sons: Chichester, England. Robinson S. (2004) Simulation: The Practice of Model Developmentand Use. John Wiley & Sons: Chichester, England. Copyright 2014 Orbus Software. All rights reserved. No part of this publication may be reproduced, resold, stored in a retrieval system, or distributed in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the copyright owner. Such requests for permission or any other comments relating to the material contained in this document may be submitted to: marketing@orbussoftware.com Orbus Software 3rd Floor 111 Buckingham Palace Road London SW1W 0SR United Kingdom +44 (0) 870 991 1851 enquiries@orbussoftware.com www.orbussoftware.com