Business Process Monitoring Research Articles

Improving the Development Technology of an Oil and Gas Company Using the Minimax Optimality Criterion

The article deals with the problem of adaptation of the Russian oil and gas company (Novatek, Russia) to the rapidly changing external environment, the avalanche of data from competitors, and the need to filter important information for business development and the prosperity of the industry as a whole. The approach is based on the system of integrated software monitoring of key business processes at the enterprise developed by the authors—from the formation of the idea of a new product to its implementation to paying customers. The scientific novelty lies in the use of an optimization model that allows for minimizing the maximum losses of the investor at all levels of decision-making, from the distribution of capital between companies, to the optimization of internal reserves to increase the competitiveness of the company. The toolkit is a minimax model that allows you to redistribute the shares of investor influence at the portfolio level, and then within the business processes of each company selected by investors, in order to achieve the optimal solution in accordance with the selected estimated indicators. Application of the well-known portfolio investment models of Markowitz, Tobin, Sharp, etc. is not possible due to the lack of necessary data on the basis of which the probabilistic parameters involved in the model are estimated. Even if we get them, it is necessary to take into account the level of correlation influence of the technological process in the composition of each subsystem, which is unacceptable for the data used, as it leads to a strong increase in errors. Using minimax and a systematic approach allows you to minimize such errors by choosing a balanced concentration of distributed assets for both the investor and the buyer. To this end, a three-way analysis of the company’s development was carried out and a technology for comprehensive improvement of the company’s activities was developed in the following areas: the company’s rating in the industry, financial condition, and interaction with counterparties using merchandising technologies. Tools for optimal image zoning at the Novatek site using the minimax approximation criterion have been developed. The technology provides a procedure for creating a comfortable mode of image perception based on high-tech visualization of merchandising, zoning of the screen area, and a mathematical approach that allows you to develop a calculation algorithm.

A Multi-View Deep Learning Approach for Predictive Business Process Monitoring

The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces based on the knowledge learned from historical event logs. In this article, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety of information possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods.

An Empirical Investigation of the Relationship Between Business Process Transparency and Business Process Attack

Business Process Management (BPM) is a management approach to discover, analyze, redesign, execute and monitor business processes. Implementing BPM concepts help and benefit organizations by increasing their productivity, achieving their strategies and operational excellence, and saving costs. Rosemann et al. identify business process transparency as one of the key values of BPM, and essential to achieving other BPM benefits. Business process transparency provides visibility about how operations/activities are conducted in a detailed way, sometimes with technical details, within an organization; which facilitates the identification of structural issues of the process model. A conducted content analysis of the literature shows that fraudsters have leveraged structural issues of the business process model to commit fraud. Such fraud can be labeled as a Business Process Attack (BPA). In analogy to information system security attack, BPA can be defined as the exploitation of a vulnerability in a business process model to commit fraudulent activities that influence the business negatively such as achieving invalid or unwanted results. This research aims to investigate the relationship between the degree of business process transparency and exposure to BPA. If the relationship is positive, appropriate security controls shall be implemented on the business process transparency to avoid BPA. The main research question is: What is the relationship between an organization's degree of business process transparency and exposure to BPA. A quantitative research method is employed to measure and understand the impact of business process transparency on BPA. An experiment is designed and conducted to assess the awareness of the existence of vulnerabilities in various process models and how to exploit them to commit BPA. Results show that there is a positive significant relationship between increased business process transparency and exposure to BPA. This research contributes towards understanding and highlights the negative impact of business process transparency, which motivates researchers to investigate this phenomenon and find appropriate solutions.

Upravljanje kvalitetom u industrijskim preduzećima - empirijska studija sistema upravljanja kvalitetom u Češkoj

Since monitoring, managing, sustaining and improving the quality are is so important to the competitiveness of the company, it is advisable to use a variety of Quality Management tools (QMTs) and techniques, in addition to comprehensive Quality Management Systems (QMSs). The main aim of this research study is to explore the connection between Quality Management System (QMS) and selected Quality Management tools (QMTs) in industrial companies in the Czech Republic. This study summarizes the results of the online questionnaire survey between April 2017 and July 2017. The final sample consisted of answers from 200 companies. It has been found that 46% of surveyed companies monitor and evaluate quality of their business processes. Furthermore, it has been found that 59% of surveyed companies are ISO 9001 certified. Larger companies tend to monitor and evaluate quality of their business processes and to have ISO 9001 certification. The relationships between the use of Quality Management tools (QMTs), monitoring and quality assessment of business processes (MQABP) and ISO 9001 certification have been found based on the Pearson's Chi-square Test of Independence, the Fisher's Exact Test of Independence and the Column Proportions Z-Test.

Improvement of bank strategic management based on econometric modeling of its activity efficiency

Some aspects of bank strategic management are defined in the article. Efficiency econometric modelling of one of the leading banks operating at Ukrainian financial market was performed using Statistica and Excel PCs. To establish appropriate correlations, the three inputs that were most affected during the coronavirus pandemic were identified: net interest margin, financial leverage ratio, and profitability of personnel costs. There are selected two parameters as one of the main indicators of banking institution efficiency: return on assets and return on equity. The dependences response surfaces of return on assets and return on equity based on the following combinations are constructed: net interest margin and financial leverage ratio; net interest margin and profitability of personnel costs; the financial leverage ratio and profitability of personnel costs. Correlations between different combinations of input variables and their effect on outputs have been established. The limits within which each input parameter should be in order to cause the best results of output parameters have been determined. In order to define those bank's activity areas that require the most attention, models of time dependences have been built for: net interest margin; financial leverage ratio; profitability of personnel costs; return on assets and return on equity. The milestones of the researched bank have been identified. A number of proposals have been developed to improve the strategic management of the banking institution based on the conducted analysis. It is defined that the effectiveness of banking institutions strategic management depends on the timeliness and objectivity of changes forecast, the reliability of information flows and the quality of monitoring and control of various business processes for customers, shareholders and society as a whole.

DEVELOPMENT OF A BUSINESS PROCESS MODEL FOR AUTOMATED MONITORING OF FINANCIAL OPERATIONS BY THE BANK TO COUNTER LEGALIZATION OF CRIMINAL INCOME

The general architecture of the automated information system of financial monitoring consisting of 4 levels is developed in the article: internal financial monitoring of economic agents (level 1), bank financial monitoring (Client-bank - level 2), state financial monitoring ( level 3), law enforcement and intelligence organs (level 4). The model was developed using the Bizagi Studio software product and modern BPMN 2.0 notation for automated monitoring of the business process of financial transactions through the "Client-Bank" system, which reveals the purpose and subject of the study. Relevant criteria for verifying the content of financial transactions are 10 factors unified for different economic agents and 13 verification criteria implemented directly at the second level of verification in the system "Client-Bank." The paper confirms that to implement an effective and efficient system of response to any alarming or suspicious actions related to financial transactions, it is necessary to use automated financial monitoring systems, conducting internal audits of economic agents' financial transactions to achieve quality monitoring in the "Client-Bank" system. This allows making the identification of potential threats with a high degree of probability. If there is a noncompliance with any of the verification criteria, the automated system of verification of financial transactions generates messages to the services of state financial monitoring, law enforcement, and intelligence agencies. The results of the developed business process models of the automatic system for internal monitoring of economic agents and the automatic business process monitoring of financial transactions by the bank can be implemented in the work of primary financial monitoring entities, directly, into the banks, credit unions, pawnshops, insurance companies, other financial institutions, and payment institutions and organizations.

Инновационный инструмент контроля бизнес-процессов диагностических работ на объектах ПАО “Газпром”

Инновационный инструмент контроля бизнес-процессов диагностических работ на объектах ПАО “Газпром”

Application of the modern information technologies for design and monitoring of business processes of transport and logistics system

The principles and functional diagram of the business processes of the transport and logistics system are presented in the article. Based on these, the use of modern information technologies for the design of business processes and their continuous monitoring is justified. The possibilities and areas of application of the computer assisted platform “AnyLogic” are illustrated. An example of designing of a distribution business process for transport using the cloud-based information system “AnyLogic” is shown.

An Empirical Investigation of Different Classifiers, Encoding, and Ensemble Schemes for Next Event Prediction Using Business Process Event Logs

There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process predictive monitoring, and we extend our previously published benchmark by studying the impact on the performance of different encoding windows and of using ensemble schemes. The choice of whether to use ensembles and which scheme to use often depends on the type of data and classification task. While there is a general understanding that ensembles perform well in predictive monitoring of business processes, next event prediction is a task for which no other benchmarks involving ensembles are available. The proposed benchmark helps researchers to select a high-performing individual classifier or ensemble scheme given the variability at the case level of the event log under consideration. Experimental results show that choosing an optimal number of events for feature encoding is challenging, resulting in the need to consider each event log individually when selecting an optimal value. Ensemble schemes improve the performance of low-performing classifiers in this task, such as SVM, whereas high-performing classifiers, such as tree-based classifiers, are not better off when ensemble schemes are considered.

ЖИТТЄСТІЙКІСТЬ ПІДПРИЄМСТВА ЧЕРЕЗ ПРИЗМУ ФІНАНСОВОГО УПРАВЛІННЯ БІЗНЕС-ПРОЦЕСАМИ

У сучасній методиці управління бізнес-процесами особливої актуальності набуває проблема підбору показників фінансового та антикризового управління, яка забезпечить підприємству здійснення поточної діяльності у запланованих межах, дасть змогу швидко адаптуватися до змін в умовах ризику та кризових явищ, сформує бачення перспективи розвитку підприємства. Фінансове та антикризове управління є основою для забезпечення життєстійкості підприємства в умовах глобальної економічної кризи. Життєстійкість передбачає здатність підприємства протистояти загрозам і швидко відновлюватися у разі якщо ці загрози стали реальністю і завдали відчутної шкоди. Здійснюючи ефективне управління бізнес-процесами, підприємство здатне ставити адекватні цілі та їх реалізувати, своєчасно забезпечувати свою діяльність зовнішніми та внутрішніми ресурсами, ефективно управляти ризиками.

CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

Explainable predictive business process monitoring using gated graph neural networks

ABSTRACT Predictive business process monitoring (PBPM) is a class of techniques designed to forecast future behaviour of a running process instance or the value of process-related metrics like times and frequencies. PBPM systems support process workers and process managers in making operational decisions. State-of-the-art PBPM systems apply deep-learning techniques with multiple hidden layers to infer from data which makes it difficult for system users to understand why a prediction was made. However, the user needs to see deeper causes to identify intervention mechanisms that secure process performance. The main contribution of this paper is a technique that makes a prediction more explainable by visualising how much the different activities included in a process impacted the prediction. This work is the first to use gated graph neural networks (GGNNs) to make decisions more explainable, and it’s also GGNN’s first application to PBPM. We use a process event data set to demonstrate our approach.

Development Of Agent Platform Architecture For Intelligent Analysis Of Business Processes

The success of a company in the market depends on many factors: the range of services offered, market saturation,marketing policy. An intelligent analysis of processes is to identify, analyze the efficiency, and optimize the businessprocesses of the enterprise. In the field of information technology, and in particular, in various areas of artificial intelligence, the use of multi-agent systems is of particular interest. In the context of the use of multi-agent systems for thedesign of intelligent systems for various purposes, the development and study of a model and software implementationof a prototype of an agent platform are relevant. The purpose of this work is to develop and research an agent platformthat can be implemented in the work of a company interested in modeling and monitoring business processes in realtime. The work is devoted to the analysis of existing approaches to the design of multi-agent software systems and thedevelopment of software components of the agent platform using the Kotlin programming language. A prototype ofan agent-based platform has been developed based on the use of formal methods for designing multithreaded systems.According to the test results, the developed prototype has advantages over the existing solutions in terms of processorload and memory usage. It is concluded that the number of agents is the main parameter affecting the reliability andefficiency of the developed agent platform

Seven Paradoxes of Business Process Management in a Hyper-Connected World

Business Process Management is a boundary-spanning discipline that aligns operational capabilities and technology to design and manage business processes. The Digital Transformation has enabled human actors, information systems, and smart products to interact with each other via multiple digital channels. The emergence of this hyper-connected world greatly leverages the prospects of business processes – but also boosts their complexity to a new level. We need to discuss how the BPM discipline can find new ways for identifying, analyzing, designing, implementing, executing, and monitoring business processes. In this research note, selected transformative trends are explored and their impact on current theories and IT artifacts in the BPM discipline is discussed to stimulate transformative thinking and prospective research in this field.

Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction

Predictive process monitoring aims at forecasting the behavior, performance, and outcomes of business processes at runtime. It helps identify problems before they occur and re-allocate resources before they are wasted. Although deep learning (DL) has yielded breakthroughs, most existing approaches build on classical machine learning (ML) techniques, particularly when it comes to outcome-oriented predictive process monitoring. This circumstance reflects a lack of understanding about which event log properties facilitate the use of DL techniques. To address this gap, the authors compared the performance of DL (i.e., simple feedforward deep neural networks and long short term memory networks) and ML techniques (i.e., random forests and support vector machines) based on five publicly available event logs. It could be observed that DL generally outperforms classical ML techniques. Moreover, three specific propositions could be inferred from further observations: First, the outperformance of DL techniques is particularly strong for logs with a high variant-to-instance ratio (i.e., many non-standard cases). Second, DL techniques perform more stably in case of imbalanced target variables, especially for logs with a high event-to-activity ratio (i.e., many loops in the control flow). Third, logs with a high activity-to-instance payload ratio (i.e., input data is predominantly generated at runtime) call for the application of long short term memory networks. Due to the purposive sampling of event logs and techniques, these findings also hold for logs outside this study.

Temporal Logics Over Finite Traces with Uncertainty

Temporal logics over finite traces have recently seen wide application in a number of areas, from business process modelling, monitoring, and mining to planning and decision making. However, real-life dynamic systems contain a degree of uncertainty which cannot be handled with classical logics. We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. We study the properties of the logic and provide automata-based mechanisms for deriving probabilistic inferences from its formulas. We then study a fragment of the logic with better computational properties. Notably, formulas in this fragment can be discovered from event log data using off-the-shelf existing declarative process discovery techniques.

Accounting agricultural business from scratch: management accounting, decision making, analysis and monitoring of business processes

The article reveals the key points of accounting and management actions when creating an agricultural business from scratch for growing seedlings of vegetables in a greenhouse. The main economic instruments for creating an agricultural business for growing vegetable seedlings in a greenhouse in Russia are: planning, management, accounting, organization, analysis and monitoring of business processes. Today, the state is increasingly focusing on the development of the industry, helping beginners and existing farmers, allocating land at preferential rates, subsidizing interest rates on loans and providing grants for the development of greenhouses. However, the main problem holding back the development of the industry remains high utility tariffs. One of the main conditions for obtaining reliable management information on business value is a clear definition of the costs incurred to create a business from scratch and its further sustainable development. The aim of the study is to develop management accounting criteria for organizing a business from scratch. The subject of the study is management accounting as an integrated mechanism in terms of creating and developing an agricultural business from scratch. In accordance with this goal, the main task was determined: to develop managerial decisions to create an agricultural business (greenhouse) for growing seedlings of vegetables for its implementation in the Udmurt Republic. It is concluded that achieving high profits is possible with a competent managerial approach, taking into account the further expansion and cultivation of other seedling crops, despite the complexity and specificity of this type of activity.

Assessment of Internal Control Systems of Technical Universities in Ghana

The relevance of internal control systems in organizations have been duly recognized as a process for assuring the achievement of set objectives in operational efficiency, reliable financial reporting and compliance with regulatory and international standards. For Technical Universities (formerly, the Polytechnics), as public institutions with the attendant problem of limited resources, it is imperative to implement effective internal controls for the judicious utilization, monitoring and management of business processes towards success and sustainability. The study, sets out to assess internal control systems at Accra Technical University, investigating the existence and implementation effectiveness and related challenges of such internal controls. The specific objectives were to find out the internal control systems at Accra Technical University, the extent of employee awareness of the control systems, consistency of the control systems with national and international standards, and the effective implementation of the control systems. The study used both primary and secondary data sources. The primary data was collected with a questionnaire from respondents comprising mainly staff from the Finance, Audit, Administration and Academic departments while secondary data was gathered from review of documentation at the Finance Office. The study reveals that Accra Technical University has instituted policies and procedures to enhance the implementation of internal controls, however, there is low staff awareness regarding such policies and violation penalties. This, it is noted, stems from Management’s inability to communicate information regarding internal control policies to staff promptly and effectively. Further, Accra Technical University exhibits high conformity to international standards and the present national regulatory regime in the preparation of its financial reports and fiscal management. Overall, the implementation of internal control systems at Accra Technical University is rated as “quite effective” and directed mainly towards conducting business in an efficient manner and ensuring financial information are accurate and complete. It is recommended, therefore, that Management improves staff awareness of policies regarding internal controls, and undertake continuous monitoring, identification and assessment of the internal control systems established and implemented to ensure they are functioning properly and meeting expectation.

Context-Aware Process Performance Indicator Prediction

It is well-known that context impacts running instances of a process. Thus, defining and using contextual information may help to improve the predictive monitoring of business processes, which is one of the main challenges in process mining. However, identifying this contextual information is not an easy task because it might change depending on the target of the prediction. In this paper, we propose a novel methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two phases. The first phase guides process analysts on identifying the context for the predictive monitoring of process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive monitoring technique that incorporates the relevant context information as input for the prediction. Our methodology leverages context-oriented domain knowledge and experts’ feedback to discover the contextual information useful to improve the quality of PPI prediction with a decrease of error rates in most cases, by adding this information as features to the datasets used as input of the predictive monitoring process. We experimentally evaluated our approach using two-real-life organizations. Process experts from both organizations applied CAP3 methodology and identified the contextual information to be used for prediction. The model learned using this information achieved lower error rates in most cases than the model learned without contextual information confirming the benefits of CAP3.

Prediction of Incompliance With Business Goals With Business-Related Data and Context Data

Proactive adaptation of business processes can prevent and mitigate future problems during the execution of business processes. For the proactive adaptation of business processes, adaptation decisions should be made based on whether a business process instance is heading to a business goal. Recently, much work struggles to improve predictive business process monitoring efficiency, i.e., accuracy and prediction earliness. As such, process designers can find more necessary adaptations and have more remaining time for the adaptations. Almost all of these previous efforts concentrate on business-related data generated by business processes. However, besides business-related data, context data has a huge impact on how a business process instance is unfolding to its completion. Context data should be noticed in the area of predictive business process monitoring. To remedy this shortage, in this paper, we propose a predictive business process monitoring framework based on a joint of business-related data and context data to predict incompliance with business goals. We evaluated the framework concerning the measures of accuracy, prediction earliness, and cost time based on a real-life online banana purchase process.