论文标题
Sens：集成供应链模拟和分析的语义合成基准测试模型
SENS: Semantic Synthetic Benchmarking Model for integrated supply chain simulation and analysis
论文作者
论文摘要
供应链（SC）建模对于理解和影响SC行为至关重要，尤其是对于日益全球化和复杂的SC。现有模型以孤立的方式涉及各种SC概念，例如流程，层和生产，限制了由集成信息系统授予的丰富分析。此外，现实世界中数据的稀缺性阻止了在不同情况下，尤其是WRT的总体SC性能的基准测试。中断期间的弹性。我们提出Sens，这是一项基于本体的知识 - 格拉普（KG）（KG），配备了KPI的SPARQL实现，以纳入SC的端到端透视图，包括标准化的SCOR过程和指标。此外，我们提出了一种高度可配置的数据生成器Sens-Gen，它利用SENS在多个方案配置下创建合成语义SC数据，以进行全面的分析和基准测试应用程序。评估表明，通过SENS实现的显着改善的模拟和分析功能，促进了抓地力，控制并最终增强SC行为，并在破坏性方案中提高了弹性。
Supply Chain (SC) modeling is essential to understand and influence SC behavior, especially for increasingly globalized and complex SCs. Existing models address various SC notions, e.g., processes, tiers and production, in an isolated manner limiting enriched analysis granted by integrated information systems. Moreover, the scarcity of real-world data prevents the benchmarking of the overall SC performance in different circumstances, especially wrt. resilience during disruption. We present SENS, an ontology-based Knowlegde-Graph (KG) equipped with SPARQL implementations of KPIs to incorporate an end-to-end perspective of the SC including standardized SCOR processes and metrics. Further, we propose SENS-GEN, a highly configurable data generator that leverages SENS to create synthetic semantic SC data under multiple scenario configurations for comprehensive analysis and benchmarking applications. The evaluation shows that the significantly improved simulation and analysis capabilities, enabled by SENS, facilitate grasping, controlling and ultimately enhancing SC behavior and increasing resilience in disruptive scenarios.
