business operations

OSCM faces challenges of theoretical fragmentation and operational complexity. This study develops the first encyclopedic, nonlinear knowledge organization framework for OSCM, integrating systematic literature review, multi-case comparative analysis, conceptual framework modeling, and practice mapping. It synthesizes dominant paradigms—including digital supply chains, lean management, and resilience-by-design—into a standardized, end-to-end value-chain knowledge system. Its contributions are threefold: (1) a unified, role- and context-agnostic OSCM knowledge graph; (2) structured coupling of theoretical rigor with industrial practice; and (3) support for nonlinear, scenario-driven knowledge retrieval. The framework has been adopted as core pedagogical material by multiple universities and enabled supply chain governance upgrades in three manufacturing and retail enterprises.

Business process optimization remains challenging due to fragmented methodologies across process mining, predictive process monitoring, and process-aware recommendation—each operating in isolation without a unified theoretical foundation or integration framework. Method: This paper proposes a closed-loop optimization framework that systematically integrates Alpha algorithm/Inductive Miner for process discovery, LSTM/Transformer for runtime prediction, collaborative filtering/graph neural networks for action recommendation, and explainable AI (XAI) for interpretability—enabling automated bottleneck identification, anomaly forecasting, and prescriptive optimization from event logs. Contribution/Results: We establish the first unified conceptual boundary, evolutionary taxonomy, and synergy paradigm across the three domains; construct a comprehensive classification schema covering 120+ studies; clarify application scopes and standardized evaluation benchmarks; and deliver an industrially actionable methodology selection guide with validated deployment pathways.

Existing enterprise process management approaches lack systematic modeling and quantitative optimization capabilities for sustainability. This paper proposes SOPA, a novel framework that— for the first time—deeply integrates environmental impact metrics (e.g., carbon footprint, resource consumption) across the entire BPM lifecycle. SOPA synergistically combines process mining, life cycle assessment (LCA), constraint solving, and graph neural networks to enable sustainability assessment, multi-objective co-optimization (efficiency, cost, environmental impact), and interpretable green process redesign. Its core contribution lies in establishing an environment-aware paradigm for process analysis and reengineering. Evaluated on manufacturing and logistics case studies, SOPA achieves an average 23.7% reduction in carbon emissions, a 41% improvement in process circularity, and maintains a 98.2% service timeliness compliance rate.

This study addresses task sequencing, resource allocation, and multi-constraint optimization during project planning, aiming to jointly minimize makespan and cost. We propose a sequential concession-based iterative task hierarchical decomposition method incorporating an “ideal point” concept to enable decision-makers to dynamically trade off time–cost preferences. We introduce the first modeling framework for synchronous task execution, integrated with Boolean programming to compute minimum-cost feasible schedules. Furthermore, we develop a hybrid qualitative–quantitative multi-objective decision support model that rigorously derives computable lower bounds for both makespan and cost, and generates tunable Pareto-optimal management plans. The model has been validated across educational, research, and industrial production scenarios, demonstrating significant improvements in plan robustness and execution efficiency.

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 challenge of automating workflows in complex industries—such as logistics, healthcare, and construction—where processes are fragmented across heterogeneous tools and involve multi-party collaboration. The work proposes orchestration as a core abstraction to enable effective automation by dynamically coordinating multi-step tasks, enforcing domain-specific constraints, managing human approvals, and integrating legacy systems. It introduces the novel concept of “orchestration bottlenecks” and develops a theoretical framework that unifies multi-agent systems, workflow modeling, constraint reasoning, and human–AI collaboration, while exposing critical gaps in current multi-agent approaches at the orchestration level. Based on distinct sources of operational friction across domains, the paper advocates for targeted architectural safeguards—such as constraint enforcement or explainability—and phased implementation strategies to provide actionable pathways for automation in complex operational environments.

This study addresses a critical gap in geriatric care research, which has predominantly emphasized operational efficiency while lacking robust connections to clinical health outcomes. Through a systematic review of 30 interdisciplinary studies at the intersection of industrial engineering and operations research applied to elderly care, the work categorizes existing literature into three thematic domains: home-based medical care, polypharmacy management, and chronotherapeutic clinical scheduling. It proposes a novel conceptual framework that explicitly links operational optimization with clinical outcomes, advocating a paradigm shift from isolated task-level improvements toward integrated, multi-layer decision-making. By incorporating human-centered design, systems analysis, and emerging technologies such as digital twins and large language models, the study highlights the field’s current overemphasis on workforce scheduling at the expense of health impact, thereby laying a theoretical and technical foundation for intelligent care decision systems spanning hospital and community settings.

This study investigates how task-queue ordering rules, worker autonomy, and queue visibility influence performance and quality in service operations. Through a preregistered online experiment integrating dynamically arriving real-world order-picking tasks, behavioral measures, and personality assessments—while controlling for task complexity and arrival dynamics—the research demonstrates that mandating an “easiest-first” sequencing rule significantly enhances accuracy by mitigating workers’ self-selection bias. Conversely, granting workers autonomy over task sequencing reduces overall performance. Although task-arrival notifications trigger short-term efficiency spikes, long-term removal of queue information does not impair sustained performance. These findings underscore the critical role of queue design in human–algorithm collaboration and identify specific personality traits predictive of error propensity.

Scheduling activities in business processes can improve efficiency (e.g., reduce makespan), but is challenging because the exact sequence of activities required to complete a case is often uncertain due to decisions based on data that emerges during execution. Nevertheless, probabilistic information regarding such decisions can often be estimated or derived from historical execution logs, and can help anticipate which execution paths are likely to lead to successful completion. Planning with particular execution paths affects feasibility, i.e., the probability of successful completion, and the expected number of superfluous activities that are planned but never executed. We frame the problem as a chance-constrained optimization problem and present two formulations: A decomposed approach with two stages, a planning stage that minimizes the expected number of superfluous activities subject to a feasibility constraint, and a scheduling stage that minimizes the makespan over the planned activities; and an integrated approach that combines planning and scheduling into a single formulation. Evaluation on two real-world and one synthetic dataset shows that the integrated approach yields superior makespans but is intractable at scale, while the decomposed approach scales to large settings.

This study addresses occupational burnout among Security Operations Center (SOC) practitioners, often stemming from misalignment between job demands and individual capabilities. Drawing on flow theory, the authors conduct an inductive content analysis of 106 global SOC job postings to systematically map the prevalence of certifications (e.g., CISSP), technical skills (e.g., Python, Splunk), and soft skills—particularly communication skills, mentioned in 50.9% of listings. The research reveals, for the first time, a structured pattern in the skill and certification requirements of SOC roles. These findings provide empirical grounding for achieving challenge–skill balance, refining recruitment practices, and guiding professional development. Furthermore, the study advances the discourse on flow-aligned person–job fit and sets the stage for future investigations into the impact of artificial intelligence on SOC workforce dynamics.