Технология интеллектуального анализа бизнес-процессов для повышения конкурентоспособности предприятий

Process mining technology selection with spherical fuzzy AHP and sensitivity analysis

Modern realities dictate the requirements for effective optimization of production. Modernization of business, economics, production, healthcare and medicine in general is an urgent task of our time. One of the technologies for effective process analysis is process mining. Process mining technology allows you to extract knowledge from an event log, forming a process model based on it. Such tools make it possible to identify ineffective management structures and business processes, malfunctions in software, and incompetence of employees, solving many problems of organizing effective management in various industries. Today there are many software tools that implement process analysis methods: Disco, Apromore, ProM. Celonis Process Mining, UiPath Process Mining, etc. Selecting software for certain tasks is a complex process, which subsequently affects the implementation of the project as a whole. Therefore, it is extremely important to comprehensively analyze all the capabilities and limitations of existing process mining tools. The following criteria were selected: cost of the framework; availability of source code; programming language; the ability to build a decision tree based on the process; technical support; technical documentation; developed community of developers; the ability to create and add plugins; experience with the framework. Healthcare was chosen as the target application area. According to the results of the analysis, it was concluded that the use of the ProM framework is effective for solving problems in the field of healthcare.

Performance ImprovementVolume 40, Issue 7 p. 28-33 Article Award-winning performance & consulting an interview with Burt Huber† Gary Dickelman, Gary Dickelman Gary J. Dickelman is the Chief Knowledge Officer for Christensen/Roberts Solutions of Woodbridge, Connecticut, and continues to develop and promote unique performance support tools with Epiance (formerly Guru, Inc.). He specializes in the application of KM, human factors engineering, learning technologies, information technology, and business process engineering to the creation of systems that human beings can actually use. Dickelman's work includes electronic performance support systems for human resources management, customer service, financial services, health care, and business process re-engineering. He is a member of the International Society for Performance Improvement, the American Society for Training and Development, and the Association for Computing Machinery. He is a contributing author to Using Computers in Human Resources (Jossey-Bass, 1992) and The Instructional Technology Handbook (McGraw-Hill, 1993) and is the author of numerous articles for industry periodicals. He is webmaster for Innovations in Performance Centered Design (http://www.pcd-innovations.com), editor for epss.com! (http://www.epss.com), and guest editor for ISPI's Performance Improvement. Gary may be reached at gdickel-man@pcd-innovations.com.Search for more papers by this author Gary Dickelman, Gary Dickelman Gary J. Dickelman is the Chief Knowledge Officer for Christensen/Roberts Solutions of Woodbridge, Connecticut, and continues to develop and promote unique performance support tools with Epiance (formerly Guru, Inc.). He specializes in the application of KM, human factors engineering, learning technologies, information technology, and business process engineering to the creation of systems that human beings can actually use. Dickelman's work includes electronic performance support systems for human resources management, customer service, financial services, health care, and business process re-engineering. He is a member of the International Society for Performance Improvement, the American Society for Training and Development, and the Association for Computing Machinery. He is a contributing author to Using Computers in Human Resources (Jossey-Bass, 1992) and The Instructional Technology Handbook (McGraw-Hill, 1993) and is the author of numerous articles for industry periodicals. He is webmaster for Innovations in Performance Centered Design (http://www.pcd-innovations.com), editor for epss.com! (http://www.epss.com), and guest editor for ISPI's Performance Improvement. Gary may be reached at gdickel-man@pcd-innovations.com.Search for more papers by this author First published: 17 January 2008 https://doi.org/10.1002/pfi.4140400707 † Burton A. Huber is President and CEO of Ariel Performance Centered Systems, Inc. He recently shared his perspectives on KM and the solutions Ariel designs “with humans in mind” on Northwest Airlines' Business and Technology Report. In addressing the critical elements of performance support and KM solutions, Burt identifies integration as an essential part of such systems. Huber specializes in designing systems that interface well with the people who use them and ensures that a company's expertise, best practices, and knowledge—their key differentiators—are embedded in the designs. Burt focuses on creating new thinking in software design. Creating designs with humans in mind, Burt and Ariel have created value for dozens of clients including many Fortune 100 companies. Ariel specializes in designing business applications with integrated performance support and KM. Burt may be reached at bhuber@arielpcs.com AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume40, Issue7August 2001Pages 28-33 RelatedInformation

The article discusses the methodological basis and practice of using Process mining technology to improve the efficiency of business processes of Russian companies. The challenges of the digital economy and prerequisites for the development of Process mining methods are identified. The directions of transformation of approaches to process management in the conditions of application of Process mining methods are established. The main synergy effects of internal audit and Process mining are identified. The stages of implementation of the Process mining project and quality criteria for corporate data logs are considered. An algorithm of actions is proposed to prepare for the introduction of Process mining technology in Russian companies; to obtain the identified synergy effects by internal audit.There is no conflict of interests.

Process mining technology aims to automatically generate process models by analyzing events, thereby assisting the design and redesign of process models. Although many process mining methods have appeared, they all have deficiencies. These methods focus on mining from the behavioral aspects described by the log, while ignoring the structural nature of the process model itself. The complexity of the process describes the simplicity and ease of understanding of the process. Higher process complexity affects the readability of the process. Genetic programming has strong robustness. Its individual representation based on tree structure can describe the special structure of the process. The introduction of process complexity fitness enables it to consider the complexity of the process model itself while mining log behavior, so as to achieve nonlinear mining of complex processes. This paper analyzes the process mining based on genetic programming and proposes process individuals based on tree structure. By realizing the combination of process complexity measurement and process mining technology, noncomplex process mining can be realized. In this paper, the process complexity is combined with the process mining algorithm based on genetic programming, and a measure of process structural complexity is proposed, which is converted into complexity fitness and introduced into the fitness function of genetic programming, to realize the use of genetic programming. The research results show that the improved new adaptive genetic programming robust scheduling algorithm provides a new idea and method for the evaluation of college sports under different sports conditions. This makes the college sports evaluation management system more intelligent and improves the rational allocation of physical education teaching.

Work-related disorders account for a significant part of total healthcare expenditure. Traditionally muscle-skeletal disorders were predominant as source of work absenteeism but in the last years work activity-related disorders have increased remarkably. Too little activity at work, sedentarism, or too much work activity leads to stress. The individualized behavioural analysis of employees can support ergonomy experts in optimizing work environments. However, in order to understand which aspects of the working environment need to be improved a clear understanding on the behaviours and working conditions of employees is needed. This requires analyzing and summarizing the multi-dimensional set of variables that describes a work environment. Process Mining Technologies can offer a human understandable view of what is actually occurring in workplaces in an individualized way. In this paper, we present a proof of concept of how Process Mining technologies can be used for discovering employees work flows in order to support the ergonomy experts in the selection of more accurate interventions for improving occupational health.

The aim of the article is to analyze the impact of information technology on the efficiency of business processes in modern marketing. A business process optimization helps an enterprise increase its competitiveness by reducing the time from product development to market launch and reducing production and promotion costs. This helps to enhance the efficiency of marketing strategies and increase the profitability of the enterprise. Optimization of business processes in marketing, in particular through the use of process management, is an important element of the company’s strategy for achieving success and ensuring competitiveness in the market. Information technology plays a key role in today’s progress. Under modern business conditions, information technology becomes a powerful tool for increasing the competitiveness of an enterprise, becomes a necessary component of its management system, an important element of its international activity. The rapid progress of information technologies requires increased attention to the processes of their implementation and use. Ignoring current digital trends can lead to business degradation. By analyzing different types of information technology, such as customer relationship management systems, marketing process automation, analytics systems, internet marketing and mobile technologies, social media instruments, data analytics and machine learning platforms, the article explores how these tools contribute to improving productivity and achieving the strategic goals of enterprise marketing departments. The given examples of specific programs and platforms, their advantages and disadvantages, plus a detailed analysis illustrate the importance of using information technology for the successful implementation of marketing strategies and ensuring the competitiveness of enterprises in the modern business environment. Information technology plays a crucial role in the progress of modern marketing, assisting businesses in improving the efficiency of their business processes and ensuring a high level of competitiveness.

With the broad use of business process management technology, there are more and more business process models. Since the ability of different modelers is different, the quality of these models varies. A question arises here is that, can we refactor these models to improve the quality as practised in software engineering? Business process modeling can be regarded as declarative programming, and business process models can be used to drive the process aware information systems, which are generally developed with model driven architecture, so business process models are crucial for the efficiency of process aware information systems. In this paper, we propose a novel approach on how to systematically refactor business process models with parallel structures for sequence structures for the first time. More specifically, we analyze the real causal relations between business tasks based on data operation dependency analysis, and refactor business process models with process mining technology. After comprehensive model refactoring, parallel execution of business tasks can be maximized, so the efficiency of business processing can be improved, that is, the quality of business process models can be improved. Analysis and experiments show that our approach is effective and efficient.

Business process mining is a new method that amalgamates business process modelling and analysis with data mining and machine learning techniques, whereby knowledge from event logs stored in today's information system is extracted to automatically construct business process models, identify bottlenecks, and improve the business processes. Since process mining has recently received attention among the research community, there are many challenges. The most important issues have been defined in the process mining manifesto. However, none of the published works investigated, or the defined challenges, have been solved and are still being encountered. Therefore, this paper provides a comprehensive and critical review of the literature in the context of process mining challenges. The contribution of this paper is to increase the maturity of the field of process mining by providing researchers with the state-of-the-art of the most important challenges of process mining.

The main priority of this article is to develop a tool for numerical forecasting of the life cycle of information resources based on actual life cycle data in the form of event logs of the resource history. To solve this problem, we used the GERT network device. To build a GERT network describes the life cycle model of an information resource, we were used methods of intellectual analysis of processes (Process Mining) implemented with the ProM Framework. In the course of the work, an analysis of the life cycle of information resources of the Scientific electronic online library was carried out. The work considers the process of extracting data used for modeling the GERT network, as well as the process of identifying the law of distribution of the probability density of the duration of the life cycle stages. The model of the GERT network is constructed and the law of distribution of the probability density of the duration of the life cycle of an information resource was described. The obtained result confirms the applicability of Process Mining technology to probabilistic analysis and forecasting of the life cycle of information resources.

Process mining analyzes business processes using event logs. Existing tools generate models to facilitate this task and improve the original business process, but the results are often unsatisfactory due to the complexity of the obtained models. Among the challenges faced in this context, we identify the misalignment with specific business requirements, preventing managers from accessing key data and making effective decisions. In this paper, we propose a requirement-driven approach centered on meta-modeling, which can help the development of process mining tools specially tailored to organizational needs. Thus, we introduce a requirement-driven method to address the critical challenge of model misalignment with required information. The method employs Model-Driven Engineering to simplify how process mining results are formulated, analyzed, and interpreted. The proposed method is iterative and involves several steps. First, a service manager defines a specific business question. Second, service managers and developers collaboratively establish a meta-model representing the target data. Third, developers extract relevant data using appropriate analysis techniques and visualize it. Finally, service managers and developers jointly interpret these visualizations to inform strategic decisions. This requirement-driven methodology empowers developers to concentrate on relevant information. Unlike general-purpose frameworks (e.g., ProM, Disco), this method emphasizes specificity, iterative refinement, and close stakeholder collaboration. By reducing cognitive overload through focused modeling and filtering of irrelevant data, organizations adopting this approach can achieve faster response times to business questions and develop specialized in-house analytical tools. This requirement-driven methodology, therefore, improves decision-making capabilities within process mining and across related analytical domains. We illustrate our methodology through a real business process taken from the literature owned by the VOLVO group. We use several examples of process mining to illustrate the benefits of the proposed methodology compared to existing tools which are unable to provide the required information. • A requirement-driven meta-model method for standardized process-mining assessment. • An iterative approach for low-cost development of tailored in-house tools. • Improves decisions through focused data extraction and need-aligned analysis.

Business process management (BPM) is a well-established discipline comprising a set of principles, methods, techniques, and tools to continuously improve the performance of business processes. Traditionally, most BPM decisions and activities are undertaken by business stakeholders based on manual data collection and analysis techniques. This is time-consuming and potentially leads to suboptimal decisions, as only a restricted subset of data and options are considered. Over the past decades, a rich set of data-driven techniques has emerged to support and automate various activities and decisions across the BPM lifecycle, particularly within the process mining field. More recently, the uptake of artificial intelligence (AI) methods for BPM has led to a range of approaches for proactive business process monitoring. Given their common data requirements and overlapping goals, process mining and AI-driven approaches to business process optimization are converging. This convergence is leading to a promising emerging concept, which we call (AI-)augmented process execution: a collection of data analytics and artificial intelligence methods for continuous and automated improvement and adaptation of business processes. This article gives an outline of research at the intersection between process mining and AI-driven process optimization, classifies the researched techniques based on their scope and objectives, and positions augmented process execution as an additional layer on top of this stack.

Process Mining (PM) is a research area that focuses on helping organizations optimize their business processes. PM provides process analysis tools and techniques with a wide range of applications described in the literature. Additionally, PM can be associated with Root Cause Analysis (RCA), a problem-solving technique based on the assumption that a problem can only be solved by addressing its underlying cause. In this study, we performed a case on a real-world Brazilian telecommunication company that provides telephony, television, and broadband Internet subscription services. We applied PM and RCA to identify the possible causes for short and long-duration services, high rework rates, and activity repetition. We split the dataset into three aspects: Support, Cancellation, and Sales related services and analyzed them separately. For RCA, we applied both decision tree classification and rule mining. We identified 53 rules over 9 decision trees that were validated by process specialists from the tech company.

Artificial Intelligence (AI) has rapidly become a critical technology for businesses seeking to improve efficiency and profitability. One area where AI is proving particularly impactful is in service operations management, where it is used to create AI-powered service operations (AIServiceOps) that deliver highvalue services to customers. AIServiceOps involve the use of AI to automate and optimize various business processes, such as customer service, sales, marketing, and supply chain management. The rapid development of Artificial Intelligence has prompted many changes in the field of Information Technology (IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on the Business Value Impact of Artificial intelligence (AI) Powered IT Service Operations. It can help IT build optimized business resilience by creating value in complex and ever-changing environments as product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps creates business value and sustainability, basically how AIServiceOps makes the IT staff liberation from a low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation tool. The paper presents a case study of a company that implemented AI-powered service operations (AIServiceOps) and analyzes the resulting business outcomes. The study shows that AIServiceOps can significantly improve service delivery, reduce response times, and increase customer satisfaction. Furthermore, it demonstrates how AIServiceOps can deliver substantial cost savings, such as reducing labor costs and minimizing downtime.

In the food delivery sector, the usage of artificial intelligence (AI) technologies has grown significantly. An important player in the Indian market for online food ordering and delivery, Swiggy, has been looking into how AI could improve its business processes and customer support. This topic intends to analyze the research on artificial intelligence (AI) utilisation in the food delivery sector and investigate its possible uses for Swiggy. The paper covers several AI applications and their effects on many business elements, such as logistics optimization algorithms, NLP-based chat bots, and picture recognition-based recommendation systems. According to the study, Swiggy could boost customer service, increase delivery efficiency, and give users tailored recommendations if it used AI. However, it is essential to assure the moral use of AI and address any potential privacy and prejudice issues. The study comes to the conclusion that AI offers a lot of potential for enhancing Swiggy's business processes and customer service, which would eventually improve customer satisfaction and retention. To do the findings of this topic the sample size of 400 restaurants were taken into account to know the customer preference and the service provided by the restauramt partners in the Swiggy platform and to rectify there problems and issues faced by them in accepting the orders and their online presence in the Swiggy app so that there customer ratings and service can be increased.