crm/erp platforms

Rigid activity implementation binding in digital business processes hinders adaptation to heterogeneous organizational requirements. Method: This paper proposes a three-level dynamic binding mechanism—operating at compile time, launch time, and runtime—that enables concurrent execution of multiple implementations for the same activity and supports context-aware, dynamic customization of input/output data contracts. Integrating Software Product Line (SPL) engineering with Process-Aware Information Systems (PAIS), we develop a variability modeling and runtime feature configuration framework. Contribution/Results: Our approach achieves, for the first time, end-to-end flexible activity binding across the full process lifecycle. It overcomes the limitations of conventional single-version, static binding by enabling on-demand composition of diverse activity implementations and data interfaces within a unified process model. This significantly enhances the adaptability and configurability of process systems in multi-organizational settings.

Existing ERP systems struggle to respond agilely to evolving business requirements, heavily relying on manual modeling and consultant intervention—resulting in high customization costs, prolonged development cycles, and low alignment accuracy. To address this, we propose an adaptive ERP framework that pioneers the deep integration of natural language processing (NLP) into the entire Petri net modeling and matching pipeline: it semantically parses natural-language requirements to automatically generate evolvable Petri net process models and enables dynamic structural–functional matching between business processes and system capabilities. Our approach combines design science research (DSR) with a systematic literature review (SLR), validated through enterprise process model analysis. Experiments demonstrate that the framework significantly reduces dependence on human consultants, improves model generation efficiency and semantic matching accuracy, and supports real-time, automated system adaptation to business changes—thereby overcoming key bottlenecks in conventional ERP customization paradigms.

This study addresses the profound transformations in user roles, workflows, and collaboration patterns within enterprise software platforms driven by artificial intelligence, which existing role frameworks—such as the BTP user type matrix—struggle to accommodate. Through 20 expert interviews and a participatory design workshop involving 24 participants, the research employs qualitative methods to investigate structural shifts in developer roles on the SAP Business Technology Platform. Findings reveal three key trends: automation of operational tasks, expanded human-AI collaboration, and increased reliance on agent-based systems. In response, the study argues for a necessary reconfiguration of role taxonomies and governance mechanisms, offering both theoretical grounding and practical guidance for designing and governing AI-native enterprise software.

Existing database management system (DBMS) and large language model (LLM) integration approaches lack unified design principles, hindering simultaneous optimization of query efficiency, semantic consistency, and system scalability. Method: We propose a systematic integration framework grounded in design principles, categorizing DBMS+LLM architectures into five canonical patterns. Leveraging architectural taxonomy and analysis of state-of-the-art systems, we identify critical trade-offs—e.g., inference latency versus data freshness—and pinpoint key performance bottlenecks. Contribution: We introduce the first integration understanding framework tailored to industrial use cases—including enterprise analytics, intelligent customer service, and data-driven decision-making. We formally articulate three open challenges: scalability, execution efficiency, and semantic consistency. Furthermore, we provide theoretical foundations and practical guidelines for the co-evolution of traditional data management and language-based reasoning. (149 words)

To address challenges in commercial management system development—including poor alignment between process models and execution platforms, low model reusability, and suboptimal development efficiency—this paper proposes a metamodel-based Model-Driven Development (MDD) approach. We design an evolvable and extensible business process metamodel framework and introduce a staged model transformation mechanism supporting QVT/ATL, enabling automated adaptation of extended BPMN models to diverse execution platforms. Crucially, we deeply integrate MDD into BPM system construction, establishing business models as the authoritative source governing system behavior. Experimental evaluation demonstrates significant improvements in development productivity and model consistency, robust cross-platform model reuse, and validates the metamodel’s effectiveness and flexibility in extended application scenarios such as resource management and customer relationship management.

This study addresses a critical security vulnerability arising from the integration of web frontends with ERP systems such as SAP, where excessive trust in client-provided HTTP headers enables adversaries to manipulate payment statuses and compromise payment integrity. Through an anonymized case study, this work is the first to abstract this issue into a generalized vulnerability pattern. By combining architectural analysis with formal modeling of payment state machines, the research uncovers fundamental design flaws in current integration practices. To mitigate these risks, the paper proposes concrete countermeasures—including strengthening trust boundaries, institutionalizing routine security reviews, and establishing actionable secure integration guidelines—thereby substantially enhancing the reliability and security of payment flows from web applications to ERP backends.

This article introduces a metamodel for the Business Model Canvas (BMC) using the Unified Modelling Language (UML), together with a dedicated Domain-Specific Modelling Language (DSML) tool. Although the BMC is widely adopted by both practitioners and scholars, significant challenges remain in formally modelling business models, particularly with regard to explicit specification of inter-component relationships, while preserving the simplicity that characterises the BMC. Addressing this tension between modelling rigour and practical relevance, this research adopts a Design Science Research approach to formally specify relationships among BMC components and to strengthen their theoretical grounding through an adaptation of the V 4 framework. The proposed metamodel consolidates BMC relationships into three core types: supports, determines, and affects, providing explicit semantics while remaining accessible to end users through graphical tooling. The findings highlight that formally specifying relationships significantly improves the interpretability and consistency of BMC representations. The proposed metamodel and tool offer a rigorous yet usable foundation for developing DSML-based BMC tools and for enabling systematic integration of the BMC into widely used software and enterprise modelling environments, thereby bridging business modelling and enterprise architecture practices for both academics and practitioners.

This work addresses the limitations of traditional workflow platforms, which rely on static, pre-defined processes and struggle to accommodate the dynamic data integration demands of distributed systems. To overcome this, the authors propose a configuration-driven runtime orchestration framework that dynamically constructs execution graphs at request time through dependency-aware scheduling and parallel task execution, thereby circumventing the constraints of fixed workflows. This approach enables rapid adaptation to evolving integration scenarios without requiring system redeployment, significantly reducing latency. Empirical evaluation in a real-world Customer 360 enterprise use case demonstrates that the framework offers substantial advantages in flexibility, scalability, and efficient data aggregation compared to conventional solutions.

Proprietary workflow languages (e.g., Smart Forms/Smart Flow) cause vendor lock-in, poor interoperability, and lack of knowledge traceability. To address these issues, this paper proposes an ontology-based, semantic-aware model transformation approach. It employs an RML-driven JSON→RDF/OWL semantic lifting pipeline, integrated with domain ontology alignment, logical reasoning, and declarative mapping rules to enable automated, verifiable M2M transformation from proprietary formats to BPMN 2.0. The key contribution lies in externalizing transformation knowledge as reusable ontologies and rules—supporting explicit control-flow representation, source-code-level traceability, and cross-vendor adaptability. Evaluated on 69 real-world workflows, the method generated 92 BPMN diagrams with a 94.2% success rate. User studies confirm significant improvements in process comprehension, diagnostic efficiency, and team collaboration.

This work addresses the limitation of existing text-to-process modeling approaches, which predominantly focus on control flow while neglecting resource and collaboration perspectives, thereby struggling to generate complete multi-party models. To overcome this, the authors propose a resource-aware generative pipeline that systematically incorporates the resource dimension into large language model (LLM)-driven process modeling for the first time. The method automatically constructs BPMN 2.0 collaboration diagrams from natural language descriptions, explicitly capturing organizational pools, role-based lanes, and inter-organizational message events, and employs an orthogonal layout algorithm for automated diagram arrangement. Experimental results across ten business processes and nine LLMs demonstrate that the approach accurately extracts resource-related information, maintains high control-flow quality, and incurs only minimal runtime overhead, advancing generative process modeling toward more collaborative and resource-complete representations.