PMSD: Data-Driven Simulation Using System Dynamics and Process Mining ?

Abstract

Process mining extends far beyond process discovery and conformance checking, and also provides techniques for bottleneck analysis and organizational mining. However, these techniques are mostly backward-looking. PMSD is a web application tool that supports forwardlooking simulation techniques. It transforms the event data and process mining results into a simulation model which can be executed and validated. PMSD includes log transformation, time window selection, relation detection, interactive model generation, simulating and validating the models in the form of system dynamics, i.e., a technique for an aggregated simulation. The results of the modules are visualized in the tool for a better interpretation.

Keywords

• Process mining
• Simulation
• System Dynamics analysis

Introduction

Process mining uses stored event data of organizations, i.e., event logs, to provide actionable insights for organizations [ 1 ]. Di erent tools address process discovery, performance analysis, bottleneck analysis, and deviation detection. Yet, the gap between the backward-looking and the forward-looking process mining techniques remains. Traditional forward-looking techniques as mentioned in [ 2 ], use events in the process as a basis of simulation. They aimed to mimic the process at the level of detail and simulate it. In more recent simulation tool such as [ 3 ], di erent level of detail for simulation is acquired, e,g., duration of activities and the ow of activities are used. Moreover, the Monte Carlo technique is used in the pm4py tool1 for simulating discovered Petri nets.

In PMSD, we use the idea that a simulation model can be learned from the event data at an aggregated level. The traditional connections between process mining and simulation mainly use a descriptive model discovered in the discovery step to enrich the simulation models at the level of the process instances, e.g., Discrete Event Simulation (DES). The presented tool is the result of our

Event Log

Tool Scope PreEvpernotcLeosgsing SD-Log ModeRleGlaetinoenrDaettieoNcntoion Preparation Generation SDLog CLDModelGeneration TimSeeleWctiniodnow ASnDa-lLyosgis SFDModelGeneration

Simulation

Model Validation Yes Refinement

Further Prediction approach in generating simulation results for business processes at an aggregated level providing the option to add external factors into the simulation [ 4 ]. Figure 1 shows the overview of the approach starting from an event log and ending with a scenario-based simulation model. The steps indicated in the highlighted parts are supported by the tool. We extract possible variables from the process in different steps of time instead of taking the events into account for the simulation as shown in Fig. 2.

The Model generation module is introduced in [ 6 ] and the preprocessing step is presented in [ 5 ]. The event log is transformed into a set of variables over time and the values of these variables form the System Dynamics logs (SDLogs). To generate more stable SD-Logs, we use time series analysis over the values. The relations between variables over time in the SDLog are used for creating the system dynamics models. We support both causal loop diagrams vvFasi.rgi.aP2bMl.eSsDT(mr.a)WdiotevioeernxattilrmaceStismtpeuoplssast(iibkol)ne. (dCynLaDm)iacsndmsotodcekls- tohwe dsiyasgtreammss a(nSdFDt)h.eSirysrteelamtions with the environment [ 8 ]. CLDs represent these conceptual relationships and SFDs model the underlying equations using stock, ow and variable notations. Flows add/remove to/from the values of stocks, also, variables a ect/get a ected by the ows, other variables. PMSD provides insights through the processes over time which can be hidden from the user, e.g., a nonlinear relation between the workload of resources and the speed of performing tasks. 2

Description of Functionalities

In our approach, the possible process variables are extracted over time, e.g., arrival rate per day and average service time per day. The newly generated log (SD-Log) is the cornerstone of the simulation. The preprocessing step and extracting the best parameters in the framework by means of time series analysis

Visualized Validation Results User proposed in [ 5 ]. To form a valid system dynamics model, we have to discover all the relations, i.e., linear and nonlinear correlations, between the generated process variables over time as introduced in [ 6 ]. Analyzing a process and creating aggregated features of the process over time (process variables) for further analyses is the main focus of the tool.

PMSD is being designed in such a way that in all the steps, the outputs are accessible for users. Figure 3 depicts the data ow diagram of the application. The inputs and generated outputs in each module and the interactions with the user are shown. The generated SD-Logs including active steps in the processes as well as all the steps for the di erent selected time windows in the form of .csv are captured. Also, all the designed CLDs and SFDs in the .mdl format are stored locally for the user. To run the tool locally, the home page can be accessed via any browser using the http://127.0.0.1:5000 URL. All the modules are designed as di erent tabs and are visually accessible. PMSD is a fully interactive tool with a user interface based on Python and Flask technology. The results of the steps are shown graphically to provide an easier interpretation possible. It contains 8 tabs and each tab can be run separately with di erent inputs/output of the other modules/tabs. Currently, the following components are available: { Event log transformation indicates the main attributes of the event log, discovers the directly follows graph, and presents the event log's information. { Time window selection assesses the quality of the user's preference for selecting a time window for generating simulation data. { Simulation log generator uses the transformed event log and the selected time window to generate simulation data (SD-Log). It generates an SD-Log for di erent aspects and levels, i.e., general process, organizational, and activity aspects. For instance, an SD-Log of the general aspect of a process includes the arrival rate of the process, and average service time in the process and other possible measurable variables per day. { Relation detection investigates whether there is any strong relationship between the variables in the extracted SD-Log. Furthermore, the user can look for the relations between variables in di erent steps of time. { Detailed relations, presents the existing relations between every two variables in the SD-log for further investigation on the types of relations. { Interactive conceptual model generation provides the option for the user to choose between all the strong relations discovered in the relation detection module and creates CLD, i.e., e ects and relations between process variables. It generates both the graphical model in the tool and the .mdl (text format) le to be used in most of the system dynamics tools, e.g., Vensim2. { Interactive stock- ow diagram generates SFDs graphically in PMSD and the (.mdl) le. The relations are directly transformed from the CLD (previous step) and the user can map the process variables to the SFD elements. { Simulation and validation simulates the SFD model using the values in the SD-Log and validates the results using the pair-wise comparison of the SDLog and simulation results values and their distributions. 3

Maturity of the Tool

The evaluation results of our proposed forward-looking approaches in process mining are represented using di erent modules of the tool. PMSD along with a tutorial and a screen-cast is available on GitHub.3 It has also been used in some industrial projects, e.g., in the project of Internet of Production in the context of Industry 4.0. In [ 7 ], part of the results of using PMSD for the production line is presented. By an example, i.e., an event log of a call center designed by the CPN tool, we show some similar results.

We use di erent suggested time windows to extract values over time for the possible process variables using the time window test. The result in Fig. 4 shows the selected time windows by the user and the errors of trained models for each time window.

Figure 5 represents the user interface for selecting the strong detected relations between the variables. Finally, by uploading the generated SFD and SD-log (both are automatically genFig. 4. Stability test showing the error of erated), the automatic simulation is training models for the time windows. performed and the validation results are shown in validation module. The results include a comparison between the real values and the simulated ones and their distributions for the selected variables. 4

Conclusion

In this paper, we introduced PMSD to support designing system dynamics models for simulation in the context of business processes. Using PMSD, we look into 2 www.vensim.com 3 https://github.com/mbafrani/PMSD PMSD the processes at di erent aggregation levels, e.g., hourly or daily, as well as different aspects, e.g., overall process or organizational aspects. The provided user interface and the graphical outputs make the interpretation of the results easy. Applying PMSD, the underlying e ects and relations at the instance level can be detected and modeled in an aggregated manner. Besides the option to simulate and validate the models directly in the tool, the models can be simulated or re ned by adding external variables using simulation software like Vensim.

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