On the background of integrated ERP development, activity-value-flexibility management (AVFM) is defined. By using economic-value-added (EVA) and corporate value creation as the objective of AVFM, custom value deviati...On the background of integrated ERP development, activity-value-flexibility management (AVFM) is defined. By using economic-value-added (EVA) and corporate value creation as the objective of AVFM, custom value deviating rate, capital cost deviating rate, cash-flow-out per purchase deviating rate and cash-flow-in per sell deviating rate are developed to be the key responding variates for AVFM, and they also decide the rational quantity range for AVFM tactics. Method for rational AVFM tactics solution could be got by means of redesigning activity information process on integrated ERP.展开更多
We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algori...We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.展开更多
目的探索在带量采购背景下院内骨科关节类高值耗材管理模式升级方案,控制原有管理风险,并为未来承接诊断相关组-预付费制度(Diagnosis Related Groups-Prospective Payment System,DRGs-PPS)落地,响应医疗体系改革做准备。方法深入分析...目的探索在带量采购背景下院内骨科关节类高值耗材管理模式升级方案,控制原有管理风险,并为未来承接诊断相关组-预付费制度(Diagnosis Related Groups-Prospective Payment System,DRGs-PPS)落地,响应医疗体系改革做准备。方法深入分析我院原有骨关节管理模式存在的局限点,充分结合骨关节管理创新理念、供应链协同优化理念,在信息化技术手段的支持下,构建我院基于实体智慧仓的数字化智慧化管理方案。结果基于骨科高值智慧仓管理模式,单件耗材入院清点核验时长从(1.53±0.07)min缩短至(0.62±0.05)min,单件耗材术后计费时长从(22.2±4.21)s缩短至(9.26±0.83)s,单台手术耗材全流程追溯比例从12.58%±3.71%提升至100.00%,且差异均具有统计学意义(P<0.05)。建设骨科高值耗材仓后管理效果明显优于模式建设前,控制了原模式下术前入院质量、术后计费、运转全流程追溯等环节的管理风险,同时提升数据管理精细化程度,实现了数字化管理。结论带量采购背景下的管理方案升级可有效管控原有风险,同时提升院内管理精细化程度,为承接DRGs-PPS落地奠定一定基础。展开更多
基金Supported by the National Natural Science Foundation of China (No. 70031020)
文摘On the background of integrated ERP development, activity-value-flexibility management (AVFM) is defined. By using economic-value-added (EVA) and corporate value creation as the objective of AVFM, custom value deviating rate, capital cost deviating rate, cash-flow-out per purchase deviating rate and cash-flow-in per sell deviating rate are developed to be the key responding variates for AVFM, and they also decide the rational quantity range for AVFM tactics. Method for rational AVFM tactics solution could be got by means of redesigning activity information process on integrated ERP.
文摘We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.
文摘目的探索在带量采购背景下院内骨科关节类高值耗材管理模式升级方案,控制原有管理风险,并为未来承接诊断相关组-预付费制度(Diagnosis Related Groups-Prospective Payment System,DRGs-PPS)落地,响应医疗体系改革做准备。方法深入分析我院原有骨关节管理模式存在的局限点,充分结合骨关节管理创新理念、供应链协同优化理念,在信息化技术手段的支持下,构建我院基于实体智慧仓的数字化智慧化管理方案。结果基于骨科高值智慧仓管理模式,单件耗材入院清点核验时长从(1.53±0.07)min缩短至(0.62±0.05)min,单件耗材术后计费时长从(22.2±4.21)s缩短至(9.26±0.83)s,单台手术耗材全流程追溯比例从12.58%±3.71%提升至100.00%,且差异均具有统计学意义(P<0.05)。建设骨科高值耗材仓后管理效果明显优于模式建设前,控制了原模式下术前入院质量、术后计费、运转全流程追溯等环节的管理风险,同时提升数据管理精细化程度,实现了数字化管理。结论带量采购背景下的管理方案升级可有效管控原有风险,同时提升院内管理精细化程度,为承接DRGs-PPS落地奠定一定基础。
