Dissertations / Theses on the topic 'Business process discovery'

Business process analysis and integration are key endeavours for today's enterprises. Recently, Web services have been widely adopted for the implementation and integration of business processes within and across enterprises. In this dissertation, we investigate the problem of enabling the analysis of service interactions, in today's enterprises, in the context of business process executions, and that of service integration. Our study shows that only fraction of interactions in the enterprise are supported by process-aware systems. However, enabling above-mentioned analyses requires: (i) a model of the underlying business process to be used as a reference for the analysis, and (ii) the ability to correlate events generated during service interactions into process instances. We refer to a process model and the corresponding process instances as a "process view". We propose the concept of process space to refer to all process related information sources in the enterprise, over which various process views are defined. We propose the design and development of a system called "process space discovery system" (PSDS) for discovering process views in a process space. We introduce novel approaches for the correlation of events into process instances, focusing on the public processes of Web services (business protocols), and also for the discovery of the business protocol models from the process instances of a process view. Analysis of service integration approaches shows that while standardisation in Web services simplifies the integration in the communication level, at the higher levels of abstractions (e.g., services interfaces and protocol models) services are still open to heterogeneities. We characterise the mismatches between service interfaces and protocol specifications and introduce "mismatch patterns" to represent them. A mismatch pattern also includes an adapter template that aims at the resolution of the captured mismatch. We also propose semi-automated approaches for identifying the mismatches between interface and protocol specifications of two services. The proposed approaches have been implemented in prototype tools, and experimentally validated on synthetic and real-world datasets. The discovered process views, using PSDS, can be used to perform various analyses in an enterprise, and the proposed adaptation approach facilitates the adoption of Web services in business process integration.

In modern organisations business process modelling has become fundamental due to the increasing rate of organisational change. As a consequence, an organisation needs to continuously redesign its business processes on a regular basis. One major problem associated with the way business process modelling (BPM) is carried out today is the lack of explicit and systematic reuse of previously developed models. Enabling the reuse of previously modelled behaviour can have a beneficial impact on the quality and efficiency of the overall information systems development process and also improve the effectiveness of an organisation’s business processes. In related disciplines, like software engineering, patterns have emerged as a widely accepted architectural mechanism for reusing solutions. In business process modelling the use of patterns is quite limited apart from few sporadic attempts proposed by the literature. Thus, pattern-based BPM is not commonplace. Business process patterns should ideally be discovered from the empirical analysis of organisational processes. Empiricism is currently not the basis for the discovery of patterns for business process modelling and no systematic methodology for collecting and analysing process models of business organisations currently exists. The purpose of the presented research project is to develop a methodological framework for achieving reuse in BPM via the discovery and adoption of patterns. The framework is called Semantic Discovery and Reuse of Business Process Patterns (SDR). SDR provides a systematic method for identifying patterns among organisational data assets representing business behaviour. The framework adopts ontologies (i.e., formalised conceptual models of real-world domains) in order to facilitate such discovery. The research has also produced an ontology of business processes that provides the underlying semantic definitions of processes and their constituent parts. The use of ontologies to model business processes represents a novel approach and combines advances achieved by the Semantic Web and BPM communities. The methodological framework also relates to a new line of research in BPM on declarative business processes in which the models specify what should be done rather than how to ‘prescriptively’ do it. The research follows a design science method for designing and evaluating SDR. Evaluation is carried out using real world sources and reuse scenarios taken from both the financial and educational domains.

Les informations échangées dans les textes des courriels sont généralement concernées par des événements complexes ou des processus métier dans lesquels les entités qui échangent des courriels collaborent pour atteindre les objectifs finaux des processus. Ainsi, le flux d’informations dans les courriels envoyés et reçus constitue une partie essentielle, les activités métier de l’entreprise. L’extraction d’informations sur les processus métier à partir des courriels peut aider à améliorer la gestion des courriels pour les utilisateurs. Il peut également être utilisé pour trouver des réponses riches à plusieurs questions analytiques sur les employés et les organisations. Aucun des travaux précédents n’a résolu le problème de la transformation automatique des journaux de courriels en journaux d’événements pour éventuellement en déduire les processus métier non documentés. Dans ce but, nous travaillons dans cette thèse sur un framework qui induit des informations de processus métier à partir d’emails. Nous introduisons des approches qui contribuent à ce qui suit : (1) découvrir pour chaque courriel le sujet de processus qui le concerne, (2) découvrir l’instance de processus métier à laquelle appartient chaque courriel, (3) extraire les activités de processus métier des courriels et associer ces activités aux métadonnées qui les décrivent, (4) améliorer la performance de la découverte des instances de processus métier et des activités métier en utilisant la relation entre ces deux problèmes, et enfin (5) estimer au préalable la date/heure réelle d’un activité métier. En utilisant les résultats des approches mentionnées, un journal d’événements est généré qui peut être utilisé pour déduire les modèles de processus métier d’un journal de courriels. L’efficacité de toutes les approches ci-dessus est prouvée par l’application de plusieurs expériences sur l’ensemble de données de courriel ouvert d’Enron
Exchanged information in emails’ texts is usually concerned by complex events or business processes in which the entities exchanging emails are collaborating to achieve the processes’ final goals. Thus, the flow of information in the sent and received emails constitutes an essential part of such processes i.e. the tasks or the business activities. Extracting information about business processes from emails can help in enhancing the email management for users. It can be also used in finding rich answers for several analytical queries about the employees and the organizations enacting these business processes. None of the previous works have fully dealt with the problem of automatically transforming email logs into event logs to eventually deduce the undocumented business processes. Towards this aim, we work in this thesis on a framework that induces business process information from emails. We introduce approaches that contribute in the following: (1) discovering for each email the process topic it is concerned by, (2) finding out the business process instance that each email belongs to, (3) extracting business process activities from emails and associating these activities with metadata describing them, (4) improving the performance of business process instances discovery and business activities discovery from emails by making use of the relation between these two problems, and finally (5) preliminary estimating the real timestamp of a business process activity instead of using the email timestamp. Using the results of the mentioned approaches, an event log is generated which can be used for deducing the business process models of an email log. The efficiency of all of the above approaches is proven by applying several experiments on the open Enron email dataset

The big pharma drug discovery process is currently hampered by long lead times, high costs, and fre-quent failures. On the other hand the general view is that many small pharmaceutical and biotech com-panies are more successful in terms of preclinical drug project transitions, and many small companies and/or their projects are bought by big pharma. In this thesis we investigated if the Business Process Reengineering (BPR) and the Process and Enterprise Maturity Model (PEMM) approaches by Hammer are suitable for the big pharma R&D process and if these approaches could shed any light on where potential improvements in the process can be made. A number of people with work experience from both big pharma and biotech preclinical R&D were also interviewed regarding the BPR and PEMM approaches and also regarding general organizational and pro-cess differences between biotech and big pharma. Our findings suggests that where suitable, most of the BPR principles have already been implemented in big pharma R&D, and that the PEMM approach at large, is suitable for the industry. Further, the inter-views revealed that there is a stronger focus on the core science and project work, and much less focus on processes, in biotech compared to big pharma. Despite a higher focus on control in terms of pro-cesses and metrics, the big pharma R&D process suffers, indicating a need to strengthen the process cul-ture if these processes should remain and add value to the organization.

Business process discovery is one of the most fundamental steps of business process management (BPM) lifecycles. Incorrect, misleading or biased results of this stage can cause the whole BPM project to fail or the information systems that are created based on them to have great alignment problems with the reality of the organisation and how people carry out their work. The main problems of the business process discovery phase stem from two main sources. Firstly, the wrong attachment of BPM definitions and business process discovery techniques to the functionalist social paradigm whose only objective is the survival of the organisation through ensuring its efficiency and adaptability like a machine. This attachment to the functionalist paradigm has made BPM definitions to assume that organisations as social systems are in a unitary problem context, which means its constituents have similar beliefs and interests, they share common goals and objectives and they have all been involved in the decision-making. These assumptions are obviously far from the reality of today’s organisations which are normally either in pluralist or coercive problem contexts. The second source of problems in the business process discovery phase are BPM’s definitions and techniques over-reliance on human memory and cognition that has made them suffer, like any other knowledge acquisition technique, from human memory and cognition limitations. Using Design Science Research methodology, this research develops a conceptual framework in which new definitions for business task, business process and business process model in pluralist and coercive problem contexts will be presented. It will also be shown that conversation logs are a good source of information for business process discovery based on the new definitions and that using conversation logs can reduce the limitations caused by human memory and cognition. To develop the new conceptual framework, organisations as social systems have been analysed using the creative holism systems approach, and sound theories such as viable system model (VSM), i* framework, speech act theory, conversation for action diagrams and episodic memory have been leveraged. Based on the conceptual framework that consumes email messages as the conversation log and as its source of information, a method for business process discovery has been developed. Using two case studies it has been demonstrated that the proposed definitions and the developed methods are applicable in unitary, pluralist and coercive problem contexts; and taking advantage of the conversation logs as their information source, they suffer to a lesser extent from human memory and cognition limitations. As a consequence, the resulting business process models created from applying the proposed definitions and methods are closer to the realities of the organisations and can increase the success rate of the business process management projects and reduce the information system’s alignment problems.

Enterprise decision making is continuously transforming in the wake of ever increasing amounts of data. Organizations are collecting massive amounts of data in their quest for knowledge nuggets in form of novel, interesting, understandable patterns that underlie these data. The search for knowledge is a multi-step process comprising of various phases including development of domain (business) understanding, data understanding, data preparation, modeling, evaluation and ultimately, the deployment of the discovered knowledge. These phases are represented in form of Knowledge Discovery and Data Mining (KDDM) Process Models that are meant to provide explicit support towards execution of the complex and iterative knowledge discovery process. Review of existing KDDM process models reveals that they have certain limitations (fragmented design, only a checklist-type description of tasks, lack of support towards execution of tasks, especially those of the business understanding phase etc) which are likely to affect the efficiency and effectiveness with which KDDM projects are currently carried out. This dissertation addresses the various identified limitations of existing KDDM process models through an improved model (named the Integrated Knowledge Discovery and Data Mining Process Model) which presents an integrated view of the KDDM process and provides explicit support towards execution of each one of the tasks outlined in the model. We also evaluate the effectiveness and efficiency offered by the IKDDM model against CRISP-DM, a leading KDDM process model, in aiding data mining users to execute various tasks of the KDDM process. Results of statistical tests indicate that the IKDDM model outperforms the CRISP model in terms of efficiency and effectiveness; the IKDDM model also outperforms CRISP in terms of quality of the process model itself.

The volume of data related to business process execution is increasing significantly in the enterprise. Many of data sources include events related to the execution of the same processes in various systems or applications. Event correlation is the task of analyzing a repository of event logs in order to find out the set of events that belong to the same business process execution instance. This is a key step in the discovery of business processes from event execution logs. Event correlation is a computationally-intensive task in the sense that it requires a deep analysis of very large and growing repositories of event logs, and exploration of various possible relationships among the events. In this dissertation, we present a scalable data analysis technique to support efficient event correlation for mining business processes. We propose a two-stages approach to compute correlation conditions and their entailed process instances from event logs using MapReduce framework. The experimental results show that the algorithm scales well to large datasets.

Il presente elaborato esplora l’attitudine delle organizzazioni nei confronti dei processi di business che le sostengono: dalla semi-assenza di struttura, all’organizzazione funzionale, fino all’avvento del Business Process Reengineering e del Business Process Management, nato come superamento dei limiti e delle problematiche del modello precedente. All’interno del ciclo di vita del BPM, trova spazio la metodologia del process mining, che permette un livello di analisi dei processi a partire dagli event data log, ossia dai dati di registrazione degli eventi, che fanno riferimento a tutte quelle attività supportate da un sistema informativo aziendale. Il process mining può essere visto come naturale ponte che collega le discipline del management basate sui processi (ma non data-driven) e i nuovi sviluppi della business intelligence, capaci di gestire e manipolare l’enorme mole di dati a disposizione delle aziende (ma che non sono process-driven). Nella tesi, i requisiti e le tecnologie che abilitano l’utilizzo della disciplina sono descritti, cosi come le tre tecniche che questa abilita: process discovery, conformance checking e process enhancement. Il process mining è stato utilizzato come strumento principale in un progetto di consulenza da HSPI S.p.A. per conto di un importante cliente italiano, fornitore di piattaforme e di soluzioni IT. Il progetto a cui ho preso parte, descritto all’interno dell’elaborato, ha come scopo quello di sostenere l’organizzazione nel suo piano di improvement delle prestazioni interne e ha permesso di verificare l’applicabilità e i limiti delle tecniche di process mining. Infine, nell’appendice finale, è presente un paper da me realizzato, che raccoglie tutte le applicazioni della disciplina in un contesto di business reale, traendo dati e informazioni da working papers, casi aziendali e da canali diretti. Per la sua validità e completezza, questo documento è stata pubblicato nel sito dell'IEEE Task Force on Process Mining.

Dans chaque organisation, les processus métier sont aujourd’hui incontournables. Cette thèse vise à développer une méthode pour les améliorer. Dans le domaine de la santé, les organisations hospitalières déploient beaucoup d’efforts pour mettre leurs processus sous contrôle, notamment à cause de la très faible marge d’erreur admise. Les parcours des patients au sein des structures de santé constituent l’application qui a été choisie pour démontrer les apports de cette méthode. Elle a pour originalité d’exploiter les données de géolocalisation des patients à l’intérieur de ces structures. Baptisée DIAG, elle améliore les parcours de soins grâce à plusieurs sous-fonctions : (i) interpréter les données de géolocalisation pour la modélisation de processus, (ii) découvrir automatiquement les processus métier, (iii) évaluer la qualité et la performance des parcours et (iv) diagnostiquer automatiquement les problèmes de performance des processus. Cette thèse propose donc les contributions suivantes : la méthode DIAG elle-même qui, grâce à quatre différents états, extrait les informations des données de géolocalisation ; le méta-modèle DIAG qui a deux utilités : d’une part, interpréter les données de géolocalisation et donc passer des données brutes aux informations utilisables, et, d’autre part contribuer à vérifier l’alignement des données avec le domaine grâce à deux méthodes de diagnostic décrites plus bas ; deux algorithmes de découverte de processus qui utilisent la stabilité statistique des logs d’évènements ; une nouvelle approche de process mining utilisant SPC (Statistical Process Control) pour l’amélioration ; l’algorithme proDIST qui mesure les distances entre les modèles de processus ; deux méthodes de diagnostic automatique de processus pour détecter les causes des déviations structurelles dans des cas individuels et pour des processus communs. Le contexte de cette thèse confirme la nécessité de proposer de telles solutions. Une étude de cas dans le cadre de ce travail de recherche illustre l’applicabilité de la méthodologie DIAG et des fonctions et méthodes mentionnées
Business processes are everywhere and, as such, we must acknowledge them. Among all of them, hospital processes are of vital importance. Healthcare organizations invest huge amount of efforts into keeping these processes under control, as the allowed margin of error is so slight. This research work seeks to develop a methodology to endorse improvement of patient pathways inside healthcare organizations. It does so by using the indoor location data of patients. This methodology is called DIAG (Data state, Information state, Awareness, Governance). It is constructed of several different functions. The most important ones are as follows: (i) location data interpreting, (ii) automatic discovery of business process models, (iii) business process analyzing for evaluating the performance and quality of processes, and finally, (iv) automatic diagnosing of business processes. Along the former functions, the contribution of this thesis are: The DIAG methodology which, through four different states, extracts knowledge from location data; the DIAG meta-model which supports both the interpretation of location data (from raw data to usable information) and the alignment of the domain knowledge (which are used for the diagnosing methods); two process discovery algorithms which explore statistical stability in event logs, application of Statistical Process Control (SPC) for the “enhancement notation” of Process Mining; the ProDIST algorithm for measuring the distance between process models; two automatic process diagnosing methods to detect causes of structural deviations in individual cases and common processes. The state of the art in this dissertation endorses the necessity for proposing such solutions. A case study within this research work illustrates the applicability of the DIAG methodology and its mentioned functions and methods

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Process Mining is a recent area of research and is composed of techniques that allow the analysis and extraction of knowledge from the logs of the business processes obtained from Management Information Systems (MIS). The analyzes can be classified into three types: Process Discovery, Conformance Check and Process Improvement. With the current growth not only of quantity, but also of the types of algorithms that seek to fulfill the objectives of Process Mining, a classification that takes into account the performance of the algorithm in the various real situations of its application becomes important. The Evaluation and Comparison of the algorithms from the repository data could be done through the application of Quality Metrics or Machine Learning Techniques. This work presents a proposal of a set of Quality Metrics to allow the classification, evaluation and comparison of Process Discovery algorithms. The proposal is based on the review of algorithms and their families; the possible performance characteristics, that can be applied to any type of algorithm being tested; and in simulations of business process patterns. The results obtained by the work are promising in the sense of creating the conceptual basis and a methodology for future research to allow the construction of a framework for Evaluation and Comparison of new algorithms.
A Mineração de Processos (Process Mining) é uma área de pesquisa recente e é composta por técnicas que permitem a análise e a extração de conhecimento a partir dos registros de eventos (logs) dos processos de negócios obtidos de Sistemas de Informação Gerenciais (SIG). As análises podem ser classificadas em três tipos: Descoberta de Processos, Checagem da Conformidade e Melhoria de Processos. Com o atual crescimento não apenas da quantidade, mas também dos tipos de algoritmos que procuram cumprir os objetivos da Mineração de Processos, uma classificação que leve em consideração a performance do algoritmo nas diversas situações reais de sua aplicação se torna importante. A Avaliação e a Comparação dos algoritmos a partir dos dados do repositório poderiam ser feitas através da aplicação de Métricas de Qualidade ou Técnicas de Aprendizado de Máquina. Este trabalho apresenta uma proposta de um conjunto de Métricas de Qualidade que tem como objetivo permitir a classificação, avaliação e comparação de algoritmos de Descoberta de Processos. A proposta foi construída com base na revisão dos algoritmos e suas famílias; no levantamento das possíveis características de performance, que podem ser aplicadas a qualquer tipo de algoritmo sendo testado; e em simulações de registros de eventos de padrões de processos de negócio. Os resultados obtidos pelo trabalho são promissores no sentido de criar a base conceitual e uma metodologia para que futuras pesquisas permitam a construção de um arcabouço (framework) de Avaliação e Comparação de novos algoritmos.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
This search has as goal to validate the hypothesis of the applicability of data mining techniques in Bidding and Contracts database managed by the Account Court of Paraiba State, enabling the generation of rules and discovery of hidden knowledge or implicit, contributing to the process of decision making, supervision and celerity in this Court of Auditors. To the best comprehension of this work, It was made a literature revision bringing at first place a historic vision about the decision process, as well as this theme evolution studies and the relation between the tender processes sent to Account Court of Paraiba State and the fraud indication discovery process and irregularities through the data mining process using. We will bring to light the concept of Business Intelligence (BI) and for it`s main components, as well as the concepts of knowledge discovery in database, and a comparing between the using of the instruments of data mining. We expect from this implant of the data mining an increase in the productivity and also an increase in speed of lawsuit process from the public accounts analysis and public money fiscal control.
Esta pesquisa tem como objetivo validar a hipótese da aplicabilidade das técnicas de mineração de dados na base de dados de Licitação e Contratos gerenciada pelo Tribunal de Contas do Estado da Paraíba (TCE-PB), possibilitando a geração de regras e descoberta de conhecimento oculto ou implícito, contribuindo desta forma com o processo de tomada de decisão, fiscalização e celeridade processual no âmbito desta Corte de Contas. Para melhor compreensão desse trabalho foi realizada uma revisão de literatura abordando primeiramente um histórico sobre o processo de decisão, bem como a evolução dos estudos deste tema e da relação entre os processos licitatórios enviados ao TCE-PB e o processo de descoberta de indícios de fraudes e irregularidades através do uso de mineração de dados. São abordados os conceitos sobre a tecnologia de Business Intelligence (BI) e dos seus principais componentes, bem como os conceitos de Descoberta de Conhecimentos em Bases de Dados (Knowledge Discorevy in Databases), e uma comparação das funcionalidades presentes nas ferramentas de mineração de dados. Espera-se com a implantação desta ferramenta de mineração de dados, um ganho de produtividade e um aumento na celeridade do tramite processual decorrentes da análise das contas públicas e na fiscalização do erário.

碩士
大同大學
資訊工程研究所
89
We employ intelligent agent technology to develop application of Business-to-Business (B2B) Electronic Commerce (EC). The application based on the Collaboration Protocol Agreement (CPA) that is a business contract of the trading partners in the B2B EC. When entering the Internet transactions system, intended party can create agents on their behalf to matchmaking trading partner, negotiating the CPA, and concluding a transaction. The Internet Transactions system mainly consists of supplier agents, demander agents and facilitator agents, each kind of agents has their own functional requirements to execute tasks of business process in the transaction server. From the multi-agent aspect, this system provides a framework for interaction, negotiation, and collaboration each other agents in the business deal. In order to support the persistence for the system, we act up to the B2B EC standard — “ebXML”. In addition, intended companies can registry their fundamental information, advertise their goods, and query the information of the trading partner in the Registry and Repository of system. We have built a prototype, where intended user can create trading agents and customize trading strategies to participate business processes.

The aim of this thesis is to evaluate the existing sales process of the financial technology start up ABC to develop suggestions for process improvements that are intended to lead to a reduction in overall through put time. For the evaluation, this thesis uses state-of-the-art process mining techniques, analyzes the common discovery algorithms, and determines the most suitable algorithm for ABC. The discovered process model is extended by performance data to identify bottlenecks and develop suggestions for their elimination. This thesis concludes that chosen process mining techniques are well suited to evaluate business processes of ABC and develop proposals for process redesign.

abstract: 中医药是中华文明的瑰宝，中药材是中医药文化和产业的核心。随着近年来国家相关政策出台，中药材产业的发展备受瞩目。由于中药材产业链条长，层级多，各层级间信息不对称，因而中药材市场普遍具有“假”、“乱”、“杂”的问题。 A公司的中药材全产业链服务商模式，通过对上游各主要专营商的整合，形成一定的平台综合集采能力，并开始得到下游医药厂家、药店认可，在市场逐步形成品牌号召力。本文实证研究A公司商业模式的转型对中药材市场价格的影响，进而分析中药材全产业链服务商模式在中药材行业健康发展中所发挥的积极作用。研究结果表明，上下游产销结合的中药材全产业链服务商模式，只有在形成一定收购规模，对市场价格产生一定影响的时候，才能充分释放药材质量的信号，润滑药材交易市场，提高收购价格，增加市场波动率，发挥价格发现作用。由于中药材市场的信息不对称程度较高，如果产销结合模式仍处于初级开创阶段，产销结合模式释放的药材质量信号则不足以全面改善信息不对称的状况。
Dissertation/Thesis
Doctoral Dissertation Business Administration 2019

碩士
國立中央大學
工業管理研究所
101
In recent years, many companies use the process mining techniques in the management. The companies will use the process mining techniques to derive the business model. When the Business model and the real process do not match, it means that there may be a risk inside the companies and it also shows the lack of internal controls that may exist. In the past research, conformance checking focused on the difference between event logs and business model. But in this research, when checking the process in the event logs, there are different grains of process in the same event logs. It can represent the process from grain 1 to grain n according to the event logs, and the lower of grains means the finer of process. When using the process in event logs to mine the business model, it shows that using different grains of process from same event logs will mine out different business model. The purpose in this research is solving the problem that different grains of process from the same event logs will mine the different business model by using the dependency threshold of Heuristic Miner Algorithm. This research use the event logs from a stone processing industry for case verification. By setting two different grains of process, the business model of fine and coarse to compare the consistency. If giving the dependency threshold and consistent ratio from one of business model, the system will automatically give another grain of the business model dependency threshold.

博士
國立中央大學
企業管理學系
103
Effective time management is one of the most crucial characteristics of a successful business. For most businesses, time management is an area that can always be improved. Irregularities in execution duration of business processes impede corporate agility and can incur severe consequences, such as project failure and financial loss. Efficient managers must constantly identify potential irregularities in process durations to foresee and avoid process glitches. This paper proposes a k-nearest neighbor method for systematically detecting irregular process instances in a business by using a comprehensive set of activity-level durations, namely execution, transmission, queue, and procrastination durations. Moreover, because agents, customers, and other variables influence the progress of processes, contextual information is presented using fuzzy values. The values and corresponding membership functions are used to adjust the duration of each activity. This proposed method was applied to the system logs of a medium-sized logistics company to identify irregularities. Experts confirmed that 81% of the instances identified as irregular were abnormal.

碩士
國立臺灣科技大學
資訊管理系
97
For growth and business continuity, the enterprise looks for various ways to improve its survival condition and competition advantage. Since Howard Smith and Peter Fingar published “Business Process Management: The Third Wave ”on 2003 and raised a fever of Business Process Management(BPM), because of IT vendor’s promotion, lots of companies implement diverse Business Process Management System and this implementation suddenly becomes the synonym of BPM activity. But, this thinking neglects that the essence of BPM is actually an organization change, in order to make BPM project successful, it is necessary to control the factors and resistances that will affect BPM project’s success. So this research adopts a viewpoint form organization change and makes the conclusion through the unstructured interview with selected cases to discover the resistances and success factors during each steps of BPM project. The derivative model of this research which combines organization change theory with BPM methodology can be adopted as a methodology reference for the enterprise which wants to start a BPM project, and the derivative conclusion from this research that includes: the leader’s will, the support from high level executive, the leader’s authority, the participation of key departments and IT department, the strategy of starting from obvious problem, effective communication, developing the solution with its evaluation method, adopting prototyping methodology and concrete action provides the suggestions for the enterprise to control the success factors of a BPM project.