workday

To address the insufficient security and scalability of engineering workflow automation and cross-organizational collaboration in Industry 4.0, this paper proposes an engineering workflow management approach integrating Asset Administration Shells (AAS) with BPMN. We innovatively design a distributed, write-on-copy AAS infrastructure to ensure data consistency and access security, and develop a lightweight workflow engine prototype supporting native AAS operations, enabling automatic mapping and execution of BPMN processes onto AAS interactions. This method unifies digital twin representation, asset modeling, and business process logic, thereby significantly enhancing standardization of engineering data exchange, end-to-end process traceability, and multi-stakeholder collaboration efficiency. Experimental evaluation demonstrates the system’s feasibility for secure inter-organizational coordination and its horizontal scalability across heterogeneous industrial environments.

This study investigates core challenges confronting corporate financial management under remote and hybrid work models—specifically, weakened budgetary control, diminished financial transparency, and inefficient cross-departmental collaboration. Employing a mixed-methods approach, it integrates quantitative surveys of managers, HR professionals, and finance staff with ERP system log analysis, digital workflow assessment, and organizational practice framework modeling. It delivers the first empirical evidence on financial process performance in flexible work environments. Results indicate that ERP integration and digitized workflows significantly enhance budget execution controllability and procedural transparency; however, reduced demand forecasting accuracy and suboptimal interdepartmental communication persist as critical bottlenecks. Notably, improved employee stress mitigation and work–life balance yield positive spillover effects on financial operations. The study advances both theoretical understanding and practical guidance for reconfiguring finance functions to align with emerging work paradigms.

This study addresses data security challenges confronting cloud-based ERP systems during enterprise digital transformation. We propose a five-dimensional security assessment framework—integrating the CIA triad (Confidentiality, Integrity, Availability) with Zero Trust principles—encompassing Confidentiality, Integrity, Availability, Authentication, and Compliance. Our methodology combines weighted sub-indicators with qualitative security reviews. Innovatively, we embed fine-grained access control, end-to-end encryption, continuous identity verification, and business-process-driven security group mechanisms into a layered governance model. Evaluation of Workday—a leading cloud ERP—yields an overall security score of 0.86, demonstrating full compliance with GDPR, HIPAA, and SOC 2 requirements. The system’s adaptive defense capabilities and strict implementation of least-privilege access establish a novel, reusable benchmark for cloud ERP security assurance.

Small and medium-sized enterprises (SMEs) and freelancers face significant challenges in cash flow management due to data scarcity and limited computational resources, rendering mainstream enterprise-grade financial tools impractical. To address this, we propose a lightweight, two-module forecasting architecture tailored for few-shot learning scenarios: a front-end machine learning model performs binary classification of accounts receivable delay risk, while a back-end modular time-series model—designed to handle incomplete historical records—enables fine-grained cash flow forecasting. The system is deployed as a web application with deep integration into the Cluee financial platform. Evaluated in real-world operational settings, the prototype demonstrates substantial improvements in prediction accuracy and decision responsiveness under sparse-data conditions. This work bridges a critical gap in intelligent financial control for micro- and small-scale entities, both technically and practically.

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 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.

This study addresses the lack of systematic architectural analysis in current software-intensive Asset Administration Shells (AAS), which hinders their ability to meet the pressing demands of software modeling in digital manufacturing and AI-driven environments. To bridge this gap, the work proposes the first software integration taxonomy framework specifically tailored for AAS, integrating software quality attributes with representative manufacturing use cases. By employing architectural analysis, quality attribute evaluation, and scenario mapping, the framework provides systematic guidance on how software services should be integrated within AAS. This contribution fills a critical void between academic research and industrial practice, offering actionable architectural choices and interpretive guidelines for the standardized integration of software services in digital twins.

This study addresses a critical gap in existing research, which predominantly examines flexible work through productivity and organizational lenses while overlooking its entanglement with personal and caregiving responsibilities. Drawing on an asset-oriented qualitative approach, the authors conducted in-depth interviews with 20 flexible workers in Singapore to reconceptualize the blurred boundary between work and care as a set of negotiable resources. They propose “rhythm crafting” as a core practice of flexible work, revealing how individuals sustain daily rhythms through the synergistic orchestration of temporal, spatial, relational, and internal resources—specifically via temporal-spatial arrangements, relational negotiation, and self-care. This reframing offers novel theoretical insights and practical directions for human-computer interaction design aimed at supporting the integration of caregiving and work.

This study addresses the mismatch between temporary inter-organizational talent supply and demand, along with associated legal, ethical, and data governance risks, by proposing a privacy-preserving cross-enterprise workforce leasing framework (BWLS). Grounded in governance-by-design principles, the framework employs locally deployed connectors and a lightweight central coordination layer to enable enterprises to anonymously publish and discover skills, negotiate tasks, and record agreements. It innovatively reconceptualizes the platform as a governance-oriented digital infrastructure that integrates consent mechanisms, auditability, role-based access control, data minimization, and institutional accountability. Drawing on socio-technical governance, enterprise architecture, and privacy-by-design principles, the work yields a structured governance framework and a set of design artifacts suitable for expert review, stakeholder deliberation, prototyping, and compliance analysis, though it has not yet been empirically deployed.

This study addresses the inadequate awareness of privacy protection principles—such as Privacy by Design and data minimization—among ERP system developers and consultants, which hinders compliance with regulations like the GDPR. For the first time, the Fogg Behavior Model (FBM) is introduced into the ERP domain, integrated with qualitative thematic analysis to systematically construct privacy-related behavior models for these two key stakeholder groups. The research uncovers the motivational drivers and implementation barriers underlying their privacy practices. By extending the applicability of FBM to enterprise software privacy governance, this work not only advances theoretical understanding but also offers actionable insights for designing targeted interventions to enhance privacy compliance in ERP ecosystems.