management consulting

Existing business process simulation predominantly relies on long-term, cold-start simulations, which are ill-suited for short-term performance prediction and operational decision-making under current runtime conditions or sudden disruptions (e.g., demand surges, resource shortages). To address this, we propose a short-term simulation method initialized from the real-time system state. Our approach uniquely integrates event-log-driven state reconstruction with process models to build an executable discrete-event simulation engine, enabling precise initialization of case progress and resource allocation. This eliminates the state mismatch inherent in conventional warm-up-phase simulations and significantly improves prediction accuracy under concept drift and abrupt behavioral shifts. Experimental results demonstrate that our method reduces prediction error for short-term KPIs—including response time and backlog volume—by 23%–41% compared to traditional long-term simulation, particularly excelling in dynamic operational environments.

Process mining often yields an overwhelming number of candidate process models, creating decision paralysis for managers seeking actionable insights. Method: This paper proposes a multi-criteria decision-making (MCDM) evaluation framework that jointly incorporates quantitative metrics (e.g., fitness, precision) and qualitative factors (e.g., organizational culture alignment). It systematically integrates MCDM techniques—including the Analytic Hierarchy Process (AHP)—into process model prioritization for the first time, moving beyond purely technical, performance-driven selection criteria. Contribution/Results: The framework enables structured, interpretable trade-offs between operational performance and strategic objectives. Evaluated in a logistics case study, it significantly improves contextual sensitivity and managerial alignment in model selection, facilitating robust, transparent decision-making under competing goals.

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.

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.

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 the persistent challenges faced by User Experience Research (UXR) teams—namely, stakeholder bias, reactive engagement, and fragmented insights—that hinder their ability to exert strategic influence. To overcome these limitations, the authors innovatively integrate structured strategic thinking into UXR function development, proposing an organizational maturity model grounded in a UXR Point-of-View (POV) framework. Complementing this model is a practical playbook that combines “offensive” and “defensive” strategies to guide implementation. This integrated approach systematically enables UXR teams to transition from tactical execution to strategic impact, significantly enhancing their capacity to forge strategic partnerships, generate actionable insights, and contribute meaningfully to long-term corporate strategy formulation.

Current what-if analysis lacks a unified conceptual framework, leading to terminological inconsistency across domains, structural ambiguity, and divergent interpretations. To address this, we conduct a systematic review of 141 papers in visual analytics and human-computer interaction, proposing Praxa—the first integrative framework that unifies scenario modeling, sensitivity analysis, and counterfactual analysis under a coherent paradigm. Praxa formally defines the underlying motivations, core components (hypothesis generation, intervention modeling, outcome evaluation), and a taxonomy of analytical types. It establishes a standardized terminology and structured model, exposing critical challenges including interpretability, causal modeling fidelity, and alignment with user intent. By clarifying conceptual boundaries and operational relationships among methods, Praxa significantly enhances cross-domain conceptual consistency and application clarity. The framework provides a rigorous foundation for theoretical advancement and the design of next-generation interactive analytical tools.

The rapid adoption of AI in software engineering introduces significant human factors challenges, yet existing research remains predominantly technology-centric, lacking systematic investigation into team adaptability and trust mechanisms. Method: Integrating organizational change theory and behavioral software engineering, we propose the first human-centered, nine-dimensional AI transformation framework—covering strategic design, collaboration, governance, and other critical dimensions—and derive corresponding design principles. Using a mixed-methods approach, we developed the initial framework via literature review, refined it through thematic analysis of 24 practitioner interviews, and validated and optimized it via a survey (N=105) and expert workshops (N=4). Contribution/Results: Findings indicate that skill development and AI strategic design are most prioritized (each consuming >15% of allocated resources), whereas socio-technical safeguards remain consistently under-resourced. This framework bridges a critical gap in human factors integration within technology-dominated AI adoption paradigms and provides an actionable, socio-technical pathway for responsible AI implementation in software engineering.

Contemporary BI dashboards lack a structured, iterative optimization framework, hindering their evolution from exploratory tools to robust decision-support systems. Method: This study proposes a feedback-driven, gap-analysis–informed four-stage iterative methodology, integrating a six-element data narrative framework—encompassing goals, context, insights, evidence, actions, and impact—and implements it in Power BI via DAX metric optimization and collaborative peer review. Contribution/Results: The framework demonstrably enhances narrative coherence and explanatory power. Empirical application uncovered critical issues: significantly lower gross margin for furniture (6.94% vs. 13.99% for technology), profitability erosion beyond a 20% discount threshold, and $1.35M in unrecovered freight costs—substantially improving decision accuracy. This work makes the first contribution of embedding structured narrative design directly into the BI dashboard iteration lifecycle, yielding a reusable, methodologically grounded framework.

This paper addresses the incompleteness and insufficient semantic coverage in existing translations from BPMN 2.0 to the AI planning language PDDL. We propose the first semantics-complete mapping method supporting all core BPMN elements—tasks, events, sequence flows, and parallel/inclusive gateways. Our approach establishes an end-to-end pipeline comprising BPMN parsing, semantics-preserving transformation, and non-deterministic PDDL model generation, enabling fully automated derivation of executable PDDL models from BPMN process diagrams. We further integrate a non-deterministic planner to perform execution-path reasoning and generate valid behavioral trajectories. Experimental evaluation confirms that all generated trajectories strictly adhere to BPMN semantics. The method significantly broadens the applicability of AI planning to business process automation, analysis, and optimization. By providing a scalable, formal foundation, it advances process intelligence for modeling, verification, and decision support in enterprise workflows.