Identifikasi Bottleneck Pada Hasil Ekstraksi Proses Bisnis ERP Dengan Membandingkan Algoritma Alpha++ Dan Heuristics Miner

Saat ini banyak Perusahaan menggunakan sistem informasi untuk menunjang proses bisnis. Namun Kenyataannya hanya beberapa Perusahaan yang melakukan evaluasi untuk proses bisnis pada sistem informasi tersebut. Evaluasi ini didapat dari data Event log yang merupakan hasil proses ekstrasi ERP. Cara melakukan evaluasi yaitu dengan process mining. Process mining berfungsi untuk menggali proses transaksi sehingga terbentuk suatu workflow proses bisnis yang actual. Workflow proses bisnis ini akan digambarkan dalam bentuk Petri Net, selanjutnya dari Petri Net inilah akan dilakukan analisis untuk mengidentifikasi adanya Bottleneck. Bottleneck merupakan peristiwa pada suatu transaksi yang memiliki waktu tunggu yang lebih lama dibandingkan transaksi lainnya dalam suatu proses bisnis. Dengan adanya PROM Tools maka penggambaran mengenai proses bisnis ERP dapat terbentuk. Penggambaran model tersebut menggunakan dua algoritma, yaitu Algoritma Alpha ++ dan Heuristics Miner. Kedua algoritma ini digunakan untuk mencari perbedaan bottleneck yang terjadi. Dari penelitian didapatkan hasil bahwa algoritma sangat mempengaruhi letak bottleneck. Letak tersebut didasarkan pada perhitungan waktu token yang ada pada place (tempat antara dua transisi atau dua transaksi) saat terbentuk model

Mining Product Data Models: A Case Study

This paper presents two case studies used to prove the validity of some data-flow mining algorithms. We proposed the data-flow mining algorithms because most part of mining algorithms focuses on the control-flow perspective. First case study uses event logs generated by an ERP system (Navision) after we set several trackers on the data elements needed in the process analyzed; while the second case study uses the event logs generated by YAWL system. We offered a general solution of data-flow model extraction from different data sources. In order to apply the data-flow mining algorithms the event logs must comply a certain format (using InputOutput extension). But to respect this format, a set of conversion tools is needed. We depicted the conversion tools used and how we got the data-flow models. Moreover, the data-flow model is compared to the control-flow model

Business Process Improvement of Production Systems Using Coloured Petri Nets

The quality of information systems affects the company's business performance. Therefore, it is necessary to analyze business processes to determine any discrepancies between the planned business processes and the actual ones. Based on the results of this analysis, the business process can be improved. The fundamental factor of manufacturing companies is production process. In reality, there are many discrepancies between the actual business processes with the pre-planned, so that there should be analyzed. The analysis can be performed by modeling the business process using Coloured Petri Nets (CPN). In this study, the objectives are to determine the level of conformance checking of business processes, reachability graph and the bottleneck analysis. The results of the analysis are used to construct a recommended model. Based on the analysis of the case study, e.g. a steel industry in Indonesia, the recommended model has a better value than initial model

Enhancing Decision Patterns Dicovered By Process Mining With Semantic Related Data

Business processes can be automatic, semiautomatic or manual processes. Semi-automatic and manual processes are involved in some parts by people. Understanding how people work or make judgments in processes can help management to evaluate their performance and suggest essential information to enhance their decision making. This paper describes a case study of using process mining to discover decision patterns of a worker in a semi-automatic business process. It was found that the discovered rules could be improved by enhancing the business execution log file with semantic related data. The experimental results before and after improvements were compared

Semantic concepts in product usage monitoring and analysis

Nowadays, complex electronic products, such as DVD players or mobile phones, offer a huge number of functions. As a consequence of the complexity of the devices, customers often have problems to use such products effectively. For example, it has been observed that an increasing number of technically sound products is returned due to, e.g., interaction problems. One possible root cause of this problem is that most product development processes are still too technologydriven, i.e., potential users are brought into contact with the product only at a very late stage. If early consumer tests are carried out, then these typically aim at abstract market evaluations rather than formulating concrete requirements towards the functionality of the product. As a result, products often have little meaning or relevance to the customers. Therefore, we need better ways to involve users in the development of such products. This can be achieved by observing product usage in the field and incorporating the gained knowledge in the product creation process. This paper proposes an approach to build automatic observation modules into products, collect usage data, and analyze these data by means of process mining techniques exploiting a novel semantic link between observation and analysis. This link yields two main benefits: (i) it adds focus to the potential mass of captured data items; and (ii) it reduces the need for extensive post-processing of the collected data. Together, these two benefits speed up the information feedback cycle towards development

Process oriented data virtualization design model for business processes evaluation (PODVDM) research in progress

During process enactment in the business process management (BPM) lifecycle, information collected on execution plans are stored in the form of log files and database tables by using information systems (IS).In the past decade, a new approach based on the applications of Business Intelligence (BI) in business process management has emerged. The approach implements process-oriented data warehouse and mining techniques.However, the main issue is providing the right information at the right time to facilitate process evaluation that can be used for performance analysis and improve business process.Existing techniques have limitations, including huge data in database log files, performance of Process Warehouse (PW), which is highly dependent on specific design), complexity of PW design, lack of convergence between business processes and PW specifications, and the need for real data during process evaluation stage.Objects such as processes, storage, and data repositories can be virtualized to address these limitations.The main aim of this study is to propose a process-oriented data virtualization design model for process evaluation in BPM.The model will be validated through expert reviews and prototype development as well as through a case study.In this paper, we describe the research motivation, questions, approach, and methodology related to addressing the described limitations by designing a model for evaluation in business processes using the Data Virtualization technique

Similarity Determination in Activity Sequences – A Supportive Framework

An increasing number of information systems support their users by helping them in reusing existing knowledge and experience. Often this is done by retrieving similar instances like similar documents, similar process executions or similar persons. While the recommendations use similarity as central concept, the selection of a suitable measure is often done by intuition. This paper introduces a framework that supports the application engineer in selecting and configuring a suitable similarity measure. The requirements of the intended framework are gathered before the architectural implications are detailed. The resulting framework is applied in a case study in which project performance prediction is to be supported by the similarity of the projects’ activity sequences. The results show the framework’s utility by allowing a comparably simple configuration to yield a considerable support in selecting and configuring a suitable similarity measure

57POR. Monitorando processos com uma arquitetura orientada a serviços: Uma aplicação no processo de gestão da produção

Muitas empresas têm implantado sistemas de informação a fim de apoiar e suportar seus processos de negócio. Em grande parte destes sistemas é comum o registro de eventos associados a execução real do processo. Por meio destes eventos, também chamados de logs de eventos, é possível a identificação de inconsistências e posterior diagnóstico para a melhoria do processo. No entanto, grande parte do registro de eventos realizado pelos sistemas de informação, possui uma estrutura específica, cada qual com suas peculiaridades. Esta diversidade na estrutura dificulta e/ou dependendo da situação, inviabiliza um diagnóstico mais aprofundado. Nesse contexto, a utilização de uma arquitetura orientada a serviços para o monitoramento da execução dos processos, possibilita a padronização dos logs de eventos para sua utilização em análises e diagnósticos de melhorias. O artigo propõe um modelo de arquitetura orientada a serviços capaz de monitorar um processo da gestão da produção. Para testar o modelo proposto, foi utilizada uma ferramenta que permite o desenho e a simulação de processos. Os resultados obtidos por meio da simulação são discutidos e indicam a aplicabilidade deste modelo. A principal contribuição deste trabalho está fundamentada nas áreas de extração de informação de processos (mineração de processos) e arquitetura orientada a serviços e como estas áreas, quando integradas, podem auxiliar na melhoria dos processos de negócio

PROCESS MODELS DISCOVERY AND TRACES CLASSIFICATION: A FUZZY-BPMN MINING APPROACH.

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain process, and describes causal dependencies between the various activities as performed within the process execution environment. In principle, one can use process discovery to obtain process models that describes reality. To this end, the work in this artcle presents a Fuzzy-BPMN mining approach that uses training events log representing 10 different real-time business process executions to provide a method for discovery of useful process models, and then cross-validating the derived models with a set of test event logs in order to measure the accuracy and performance of the employed approach. The method focuses on carrying out a classification task to determine the traces, i.e. individual cases that makes up the test event logs in order to determine which traces that can be replayed by the original model. Thus, the paper aim is to provide a technique for process models discovery which is as good in balancing between “overfitting” and “underfitting” as it is able to correctly classify the traces that can be replayed (i.e allowed) or non-replayable (disallowed) by the model. In other words, the study shows through the Fuzzy-BPMN replaying notation and the series of validation experiments - how given any classified trace (for the test events log) and discovered process model (the training log) it can be unambiguously determined whether or not the traces found can be replayed on the discovered model