[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2413":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"view_count":15,"doi":25,"paper":26,"created_at":40},2413,"Predictive and Conceptual Models for KPI Dashboard Integration and Multi-Vendor Risk Monitoring in Government Systems","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijcsmt.vol.12.no3.2026.pg263.294","Government institutions increasingly rely on complex digital ecosystems involving multiple technology vendors, outsourced service providers, and distributed information systems. While these environments enable scalability and operational efficiency, they also introduce significant risks related to performance monitoring, accountability, and vendor coordination. Key Performance Indicator (KPI) dashboards have emerged as essential tools for real-time monitoring and decision support; however, many government implementations remain fragmented, lacking integrated predictive capabilities and standardized risk monitoring frameworks across multiple vendors. This review paper examines existing predictive and conceptual models designed to support KPI dashboard integration and multi-vendor risk monitoring in government information systems. The study synthesizes literature on performance analytics, risk management architectures, business intelligence dashboards, and predictive modeling techniques applied to public-sector digital governance. Particular attention is given to machine learning–driven forecasting models, data integration frameworks, and conceptual governance architectures that support continuous monitoring of vendor performance, service-level compliance, and operational risks. The review further explores how integrated dashboards can consolidate heterogeneous data streams from procurement systems, contract management platforms, cybersecurity monitoring tools, and operational service metrics. By analyzing existing approaches, the paper proposes a structured conceptual model that links predictive analytics with dashboard visualization layers to enhance transparency, accountability, and proactive risk mitigation in government technology ecosystems. The findings highlight the importance of interoperable data architectures, standardized KPI frameworks, and predictive risk scoring mechanisms for improving oversight of multi-vendor environments. Ultimately, this review contributes to the development of more resilient, data-driven governance models capable of supporting complex government digital infrastructures.","政府机构日益依赖由多个技术供应商、外包服务提供商和分布式信息系统构成的复杂数字生态系统。尽管这些环境能够实现可扩展性和运营效率，但也带来了与绩效监测、问责制和供应商协调相关的重大风险。关键绩效指标（KPI）仪表盘已成为实时监测和决策支持的重要工具；然而，许多政府实施仍然较为分散，缺乏跨多个供应商的集成预测能力和标准化风险监测框架。本文综述了旨在支持政府信息系统中KPI仪表盘集成和多供应商风险监测的现有预测模型和概念模型。本研究综合了应用于公共部门数字治理的绩效分析、风险管理架构、商业智能仪表盘和预测建模技术方面的文献。研究特别关注机器学习驱动的预测模型、数据集成框架以及支持持续监测供应商绩效、服务水平合规性和运营风险的概念治理架构。本文进一步探讨了集成仪表盘如何整合来自采购系统、合同管理平台、网络安全监测工具和运营服务指标的异构数据流。通过分析现有方法，本文提出了一个结构化概念模型，将预测分析与仪表盘可视化层相连接，以增强政府技术生态系统中的透明度、问责制和主动风险缓解能力。研究结果强调了可互操作的数据架构、标准化KPI框架和预测性风险评分机制对于改善多供应商环境监督的重要性。最终，本综述有助于开发更具韧性、数据驱动的治理模型，以支持复杂的政府数字基础设施。",null,"INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","2026-09-11T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为政府信息系统KPI仪表盘与多供应商风险监控，与三农、农业信息化、智慧农业、数字乡村无直接关联，相关性门槛未通过。",[19],{"name":10,"url":6},[21,22,23,24],"政府信息化","KPI仪表盘","多供应商风险","预测分析","10.56201\u002Fijcsmt.vol.12.no3.2026.pg263.294",{"doi":25,"openalex_id":27,"authors":28,"venue":10,"cited_by_count":15,"oa_url":31,"card":32,"direction":38,"ingested_from":39},"W7212218080",[29],{"name":30,"orcid":9},"Peter Chibwaye Irene","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 12 NO. 3 2026\u002FPredictive and Conceptual Models for KPI 263-294.pdf",{"tldr":33,"method":34,"finding":35,"direction":36,"opportunity":37},"综述政府KPI仪表盘集成与多供应商风险监控的预测与概念模型。","文献综述，综合绩效分析、风险管理架构、商业智能与机器学习预测模型。","提出连接预测分析与仪表盘可视化的概念模型，强调互操作数据架构与标准化KPI。","数字乡村与农业信息化","可将政府多供应商风险监控框架迁移至农业信息化项目，研究涉农多主体数据集成与KPI预警。","智慧农业 \u002F 农业物联网","openalex","2026-09-14T23:30:09.787050Z"]