Business Process Management (BPM) includes methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. It can be considered as an extension of classical Workflow Management (WFM) systems and approaches. Although the practical relevance of BPM is undisputed, a clear definition of BPM and related acronyms such as BAM, BPA, and STP are missing. Moreover, a clear scientific foundation is missing. In this paper, we try to demystify the acronyms in this domain, describe the state-of-the-art technology, and argue that BPM could benefit from formal methods/languages (cf. Petri nets, process algebras, etc.).

Business Process Management: A Survey

The benefits of enterprise modeling (EM) and its contribution to organizational tasks are largely undisputed in business and information systems engineering. EM as a discipline has been around for ...

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From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling

In the most abstract way, artificial intelligence (AI) allows human work to be shifted toward technological systems that are currently not fully capable. Following this, the domain of retail can be sketched as a natural fit for the application of AI tools, which are known for their high proportion of human work and concurrent low profit margins. This paper aims to explore the current dissemination of the application of AI within the industry. The value-added core tasks of retail companies are examined to determine the possible utilization and the market adoption within the globally largest retail companies is given.,The paper uses two different approaches to identify the scientific state-of-the-art: a search on the major scientific databases and an empirical study of the ten largest international retail companies and their adoption of AI technologies in the domains of wholesale and retail.,The application within the different value-added core tasks varies greatly depending on the area. In summary, there are numerous possible applications in all areas. Especially, in areas where future forecasts are needed within the task areas (such as marketing or replenishment), the use of AI, today, is both scientifically and practically highly developed. In contrast, the market adoption of AI is highly variable. The pioneers have integrated extensive applications into everyday business, while the challengers are investing heavily in new initiatives. Some others, however, show neither active use nor any effort to adopt such technology.,To the best of the author’s knowledge, this is one of the first research contributions to analyze the areas of application and the impact of AI structured along the value-added core processes of retail companies.

State-of-the-art and adoption of artificial intelligence in retailing

A multi-perspective conceptual modeling approach.Co-development of enterprise and information systems.Combining enterprise modeling techniques, design rationale and system dynamics.The utility of the approach is demonstrated though a case study. A key requirement to today's fast changing economic environment is the ability of organizations to adapt dynamically in an effective and efficient manner. Information and Communication Technologies play a crucially important role in addressing such adaptation requirements. The notion of `intelligent software' has emerged as a means by which enterprises can respond to changes in a reactive manner but also to explore, in a pro-active manner, possibilities for new business models. The development of such software systems demands analysis, design and implementation paradigms that recognize the need for `co-development' of these systems with enterprise goals, processes and capabilities. The work presented in this paper is motivated by this need and to this end it proposes a paradigm that recognizes co-development as a knowledge-based activity. The proposed solution is based on a multi-perspective modeling approach that involves (i) modeling key aspects of the enterprise, (ii) reasoning about design choices and (iii) supporting strategic decision-making through simulations. The utility of the approach is demonstrated though a case study in the field of marketing for a start-up company.

https://www.research.manchester.ac.uk/portal/files/37279560/Loucopoulos_pub.pdf

Conceptual modeling for the design of intelligent and emergent information systems

https://zaguan.unizar.es/record/86434/files/texto_completo.pdf

A taxonomy for key performance indicators management

The need for organizations to operate in changing environments is addressed by proposing an approach that integrates organizational development with information system (IS) development taking into account changes in the application context of the solution. This is referred to as Capability Driven Development (CDD). A meta-model representing business and IS designs consisting of goals, key performance indicators, capabilities, context and capability delivery patterns, is being proposed. The use of the meta-model is validated in three industrial case studies as part of an ongoing collaboration project, whereas one case is presented in the paper. Issues related to the use of the CDD approach, namely, CDD methodology and tool support are also discussed.

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Capability Driven Development: an Approach to Designing Digital Enterprises

This paper addresses the context data integration and processing problem for design of data driven application by introducing ASAPCS (Auto-scaling and Adjustment Platform for Cloud-based Systems) platform. Conceptual model, technical architecture and data integration process are described. The ASAPCS platform supports model-driven configuration, separation of context acquisition and application, utilization of various context processing algorithms and scalability. It is based on technologies that have proven to work well with big data and each part of it is horizontally scalable. ASAPCs integrates data from heterogeneous sources and aggregates raw context data and uses it to perform real-time adjustments in the data-driven application. Its application is illustrated with example of providing data store resilience.

Near Real-Time Big-Data Processing for Data Driven Applications

The automatic receipt analysis problem is very relevant due to high cost of manual document processing. Therefore, the presented paper investigates the problems of receipt image analysis. It describes approaches for receipt image pre-processing, receipt text detection, receipt text recognition and receipt text analysis. These approaches allow to make receipt analysis system adaptable for a real-life environment and to convert the input information to a usable format for analysing information in the receipts. A pipeline for payment data processing staring with image capture to payment data posting is defined and appropriate technologies for every stage of the process are proposed. Advantages and limitations of these technologies are reviewed and open research challenges are identified. The payment data processing is analyzed as an enabler of digital transformation of expense reporting processes.

Application of image recognition and machine learning technologies for payment data processing review and challenges

Patterns have a great potential for improving various aspects of Information System (IS) designs by reuse. While they have been routinely used for conveying reusable design solutions in books and k ...

/pdf/a-repository-for-pattern-governance-supporting-capability-q982ngqrum.pdf

A Repository for Pattern Governance Supporting Capability Driven Development

Business processes are redesigned as a part of business process management lifecycle and data intensive activities such as image processing, prediction and classification are increasingly incorporated into business processes. Data intensive activities often involve usage of data analysis models. It is argued that successful development and execution of data intensive business processes requires synchronization of business process redesign and data analysis models development activities. The business process architecture integrating core business process with data analysis model setup and updating sub-processes is developed. Business process transformation stages for incorporating data-intensive activities are outlined. The process redesign and execution is supported by the technical architecture based on microservices. An example of business process redesign is discussed.

Janis Kampars

Papers

Capability Driven Development: an Approach to Designing Digital Enterprises

Near Real-Time Big-Data Processing for Data Driven Applications

Application of image recognition and machine learning technologies for payment data processing review and challenges

A Repository for Pattern Governance Supporting Capability Driven Development

Application of Microservices for Digital Transformation of Data-Intensive Business Processes.