Применение технологии Process Mining в управлении цепями поставок
Currently businesses are constantly adapting their business processes to the ever-changing market conditions. This involves a continuous monitoring and improvement of business processes. Process Mining is a useful approach to automated reconstruction of business process models from event logs collected from company’s information systems, as well as to detecting deviations from the assumed process model and to monitoring of process’ KPIs. There is a growing trend towards implementing Process Mining tools for logistics and supply chain management. This paper presents the key concepts of Process Mining. The methods are illustrated with a practical example of a logistical process analysis. We consider three main types of Process Mining. Finally, an overview of research and business cases for Process Mining in logistics and supply chain management is provided.
Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a process model based on an event log. Process mining is not limited to process discovery and also includes conformance checking and model enhancement. Conformance checking techniques are used to diagnose the deviations of the observed behavior as recorded in the event log from some process model. Model enhancement allows to extend process models using additional perspectives, conformance and performance information. In recent years, BPMN (Business Process Model and Notation) 2.0 has become a de facto standard for modeling business processes in industry. This paper presents the BPMN support current in ProM. ProM is the most known and used open-source process mining framework. ProM’s functionalities of discovering, analyzing and enhancing BPMN models are discussed. Support of the BPMN 2.0 standard will help ProM users to bridge the gap between formal models (such as Petri nets, causal nets and others) and process models used by practitioners.
The issue contains the papers presented at the 7th Spring/Summer Young Researchers' Соllоquium оn Software Engineering (SYRCoSE 2013) held in Kazan, Russia on 30th and З1st оf Мay, 2013. Paper selection was based on a competitive peer review process being done by the program committee. Both regular and reseаrсh-in-рrogrеss papers were соnsidered ассeрtable for the colloquium.
The topics of the colloquium include modeling of computer systems, software testing and verification, parallel and distributed systems, information search and data mining, image and speech processing and others.
Process mining is a new direction in the field of modeling and analysis of processes, where using information from event logs, describing the history of the system behavior, plays an important role. Methods and approaches used in the process mining are often based on various heuristics, and experiments with large event logs are crucial for the study and comparison of the developed methods and algorithms. Such experiments are very time consuming, so automation of experiments is an important task in the field of process mining. This paper presents the language DPMine developed specifically to describe and carry out experiments on the discovery and analysis of process models. The basic concepts of the DPMine language, as well as principles and mechanisms of its extension are described. Ways of integration of the DPMine language as dynamically loaded components into the VTMine modeling tool are considered. An illustrating example of an experiment to build a fuzzy model of the process discovered from the log data stored in a normalized database is given.
Process mining is a research area dealing with, inter alia, the construction of models of various types from event logs. Fuzzy maps are an example of such models produced by different process mining tools, such as ProM and Disco. We proposed a new approach to mining fuzzy models which is based on logs representation in the form of relation databases. Fast and effective SQL queries to such logs are made as a part of a DPMine workflow model. Resulting datasets are processed and visualized by a special DPMine component working tightly integrated with VTMine modeling framework. The paper discusses the suggested approach in the context of customization aspects of VTMine framework with an embedded DPM engine.
Supply Chain Management in modern conditions requires close integration of business processes of transport companies and information technology. We know that today there are a large number of applications and information systems for the automation of logistics activities. Currently there is no complete and consistent classification of software products of the Transportation Management System (TMS). Their diversity is relevant in the context of the fourth industrial revolution ("Industry 4.0"). It's difficult to navigate existing and emerging information systems and choose the most appropriate. The most important class TMS products are designed to plan, organize, and account for the operation of the vehicle fleet. However, their practical use is often ineffective for several reasons. One of the common problems in the implementation of the information system is the lack of or inadequate investigation of all operating activities of the enterprise and its strategic position in the market, the analysis of information flows, evaluation of employees of business roles, mechanism of decision-making. The reason for this is the lack of logistics management competencies in the field of information technology, and on the other hand, often poor understanding of IT-managers of the transport processes. Therefore, a practical approach synchronization strategic goals, objectives, business processes, supply chain management with business logic implemented information system. The paper discusses the use of proper Zachman enterprise architecture framework as this approach. This proper framework is simple enough to understand, and is known for a long time in the IT industry. Therefore, its use in the development of the information supply chain management system in practice, it seems appropriate for small and medium-sized freight enterprises. It is known that the business processes of all transport companies in general are often very similar. However, in practice often requires a flexible adaptation of the information system for each of them.
To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the “Big Data” phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data. The lectures held at the Third European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., pattern and process mining, business semantics, Linked Open Data, and large-scale data management and analysis. Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.
It gives us great pleasure to welcome you on the 17th edition of the International Conference on Harbor, Maritime & Multimodal Logistics Modeling and Simulation (HMS 2015) part of the 12th International Multidisciplinary Modeling and Simulation Multiconference (I3M). HMS 2015 upholds a long tradition started in 1999 in the field of simulation and computer technologies applied to logistics, supply chain management, multimodal transportation, maritime environment and industrial logistics. As time goes by, HMS looks at the future of science and practice seeking to capture new and emerging development trends but not only. As challenges are put forward by the fast changing social, technical and economic situation, a great effort has been done to set up an advanced scientific program with lots of talks, seminars, research presentations and discussions. Valuable research experiences need to be shared for developing new knowledge and generating new groundbreaking ideas. This is, in essence, the inner meaning that HMS nurtures. Therefore HMS gives a not‐to‐be‐missed chance of networking among colleagues to set up new relations and strengthening long‐established ties on joint research interests. It’s our firm determination that HMS 2015, as indeed past and future editions, could end with some strong take‐home messages rewarding merits and scientific excellence. To this end, HMS provides the Authors of the best papers with the opportunity to extend their works for publication in International Journals Special Issues.
Process mining is a relatively new field of computer science which deals with process discovery and analysis based on event logs. In this work we consider the problem of discovering workflow nets with cancellation regions from event logs. Cancellations occur in the majority of real-life event logs. In spite of huge amount of process mining techniques little has been done on cancellation regions discovery. We show that the state-based region algorithm gives labeled Petri nets with overcomplicated control flow structure for logs with cancellations. We propose a novel method to discover cancellation regions from the transition systems built on event logs and show the way to construct equivalent workflow net with reset arcs to simplify the control flow structure.
Process-aware information systems (PAIS) enable developing models for interaction of processes, monitoring accuracy of their execution and checking if they interact with each other properly. PAIS can generate large data logs that contain the information about the interaction of processes in time. Studying PAIS logs with the purpose of data mining and modeling lies within the scope of Process Mining. There is a number of tools developed for Process Mining, including the most ubiquitous ProM, whose functionality is extended by plugins. To perform an object-aware experiment one has to sequentially run multiple plugins. This process becomes extremely time-consuming in the case of large-scale experiments involving a large number of plugins. The paper proposes a concept of DPMine/P language of process modeling and analysis to be implemented in ProM. The language under development aims at joining separate stages of the experiment into a single sequence, that is an experiment model. The implementation of the basic semantics of the language is done through the concept of blocks, ports, connectors and schemes. These items are discussed in detail in the paper, and examples of their use for specific tasks are presented ibid.
In this paper we consider choice problems under the assumption that the preferences of the decision maker are expressed in the form of a parametric partial weak order without assuming the existence of any value function. We investigate both the sensitivity (stability) of each non-dominated solution with respect to the changes of parameters of this order, and the sensitivity of the set of non-dominated solutions as a whole to similar changes. We show that this type of sensitivity analysis can be performed by employing techniques of linear programming.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The Working Paper examines the peculiarities of the Russian model of corporate governance and control in the banking sector. The study relies upon theoretical as well as applied research of corporate governance in Russian commercial banks featuring different forms of ownership. We focus on real interests of all stakeholders, namely bank and stock market regulators, bank owners, investors, top managers and other insiders. The Anglo-American concept of corporate governance, based on agency theory and implying outside investors’ control over banks through stock market, is found to bear limited relevance. We suggest some ways of overcoming the gap between formal institutions of governance and the real life.
At present many industries reveal tendency for setting up of vertically integrated companies (VIC) the structure of which unites all technological processes. This tendency proved its efficiency in oil industry where coordination of all successive stages of technological process, namely, oil prospecting and production -oil transportation - oil processing - oil chemistry - oil products and oil chemicals marketing, is necessary. The article considers specific features of introduction of "personnel management" module at enterprises of oil and gas industry.
vertically integrated companies; personnel management