total cost analysis

This study addresses the challenge of quantifying the complexity and cost induced by external requirement changes when detailed knowledge of a system’s internal logic is unavailable. To this end, the authors propose a black-box assessment method based on a directed graph of component coupling. By analyzing component interfaces and integrating multi-view modeling—graphical, algebraic, and tabular—the approach uniquely links interface characteristics to cost factors, enabling computable bounded estimates of change-induced complexity and associated costs. The method was validated through a large-scale integration case in a retail banking platform, demonstrating its effectiveness and providing architects and operations teams with actionable, quantitative insights for system design and maintenance.

This study addresses a critical gap in current AI efficiency evaluations, which typically focus only on isolated training or inference phases and fail to capture the full lifecycle resource consumption and environmental impact of AI systems. To overcome this limitation, the work introduces, for the first time, a comprehensive Life Cycle Assessment (LCA) framework tailored to machine learning. This approach systematically integrates energy use and embedded environmental costs across all stages—including hardware manufacturing, model training, and deployment—thereby transcending the narrow scope of conventional assessments. By providing a holistic and accurate methodology for evaluating sustainability, the proposed framework offers researchers, developers, and policymakers a robust tool to guide more environmentally responsible design, deployment, and regulation of AI technologies.

Existing LCA/LCC tools lack capabilities for parametric modeling, temporal dynamic analysis, uncertainty quantification, and integration with optimization algorithms, limiting the depth and rigor of integrated environmental-economic assessments. To address this, we develop *lcpy*, an open-source Python toolkit implementing a unified, modular LCA/LCC modeling framework that supports parametric specification, dynamic coupling, and uncertainty propagation. The framework adopts dictionary- and list-driven design principles, ensuring compatibility with forward-looking LCA ecosystem tools and optimization libraries (e.g., SciPy, Optuna). Its key innovation is the first implementation of explicit temporal dimension modeling in LCA/LCC, enabling seamless integration of static and dynamic analyses, alongside built-in Monte Carlo-based uncertainty propagation. *lcpy* supports JSON/YAML input formats, result visualization, and lightweight integration, and is publicly available on GitHub—significantly enhancing flexibility, scalability, and accessibility of environmental-economic evaluation.

Qualitative lean analysis of business process value addition is highly manual, time-consuming, and subjective. Method: This paper introduces large language models (LLMs) to qualitative lean analysis for the first time, proposing a two-stage structured framework: (1) automatic decomposition of high-level business activities into fine-grained operational steps; and (2) systematic classification of each step’s value-adding nature according to the lean value taxonomy. The method integrates zero-shot reasoning with interpretable, structured prompting to balance deep semantic understanding and transparent decision logic. Contribution/Results: Evaluated on 50 real-world business process models, the framework significantly outperforms zero-shot baselines in accurately identifying non-value-adding steps and systematically pinpointing waste sources. It extends the applicability of LLMs in process management and establishes a novel, low-human-effort, highly interpretable AI-assisted paradigm for process optimization.

Business professionals—non-technical domain experts—lack appropriate tools and methodologies for effective what-if analysis (WIA), hindering data-informed decision-making. Method: We conducted a two-phase mixed-methods user study—comprising contextual interviews and in-situ task-based evaluations—to systematically characterize their analytical behaviors for the first time. Contribution/Results: Based on empirical findings, we propose three domain-grounded design principles: business-contextual data preparation, risk-aware assessment, and domain-knowledge integration. We implemented and validated these principles in an interactive visual analytics prototype. The study identifies three critical support gaps, empirically confirms that six classes of what-if techniques significantly improve decision efficiency and confidence, and yields eight actionable design guidelines for commercial business intelligence systems. This work bridges a key theoretical and practical gap in WIA research concerning non-technical users.

This work addresses the challenges of low quality and poor transparency in build-or-buy decisions within enterprise software development, which often stem from reliance on unstructured experiential knowledge. To overcome these limitations—particularly in cold-start scenarios lacking historical data—the authors propose a structured approach that integrates a decision-factor ontology, rule-based reasoning, and reference-class matching. This method enables transparent, auditable evaluation of alternatives and represents the first application of combined ontology modeling and rule reasoning to build-or-buy decision-making. By revealing critical decision thresholds and supporting traceability, the approach enhances the rationality, transparency, and auditability of choices. Its practical efficacy is demonstrated through a lightweight tool validated in a financial industry case study, showing significant improvements in decision quality.

This study addresses the lack of a systematic framework for identifying critical input variables and conducting sensitivity analysis under uncertainty in complex simulations, particularly in military decision-making contexts. The authors propose a unified sensitivity analysis framework that integrates local and global methods—including variance-based, derivative-based, screening, and uncertainty quantification techniques—and strategically maps these approaches to specific decision objectives such as factor prioritization, fixing, variance reduction, and mapping. Innovatively, the framework introduces a “sensitivity audit” mechanism to enhance traceability of model assumptions and promote responsible model usage. By providing a structured guide for high-dimensional, complex simulation systems, this work significantly improves model interpretability, transparency, and the credibility of decisions derived from such models.

本文提出一种混合代理AI框架，通过协调代理解析用户意图并分配任务给专门代理，解决供应链分析中的决策难题，提高效率和成本效益。

This study addresses the challenges of control design in complex industrial processes characterized by multivariable coupled dynamics by proposing an automated control strategy generation framework that integrates large language models (LLMs) with Bayesian optimization. The approach decomposes control design into structured code generation steps, ensuring physical consistency through execution-based validation and feedback-driven repair. It pioneers the automatic synthesis of decentralized PI controller architectures and their tuning environments directly from dynamic process models. Evaluated on a nonlinear gas preheater benchmark, the generated control schemes—subsequently refined via Bayesian optimization—achieve a 26.5% improvement in closed-loop performance and significantly enhance the transient response of pressure loops, thereby demonstrating the method’s effectiveness and novelty.

This work addresses the limitations of existing CI/CD workflow analyses, which often focus narrowly on stage identification and struggle to assess reliability, maintainability, and optimization priorities. To overcome this, we propose a large language model–based CI/CD analysis pipeline that integrates repository context enhancement, anti-pattern detection, stage mining, and actionable recommendation generation. Our approach uniquely combines diagnostic reasoning, context awareness, and human-in-the-loop review to deliver observability tailored to cybersecurity engineering. Leveraging few-shot prompting, YAML parsing, and statistical tests (chi-square and Cramér’s V), the method identifies 434,769 anti-patterns across 75,201 workflows and generates an average of 8.25 syntactically valid optimization suggestions per repository, achieving a 96.1% compliance rate with YAML syntax standards.