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A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement
International Journal of Machine Learning and Cybernetics ( IF 5.6 ) Pub Date : 2021-05-12 , DOI: 10.1007/s13042-021-01331-7
Ming-Fu Hsu , Sin-Jin Lin

This research introduces a fusion architecture that integrates balanced scorecards (BSCs) and network data envelopment analysis (NDEA) to conduct a performance evaluation task from multiple perspectives. The architecture is able to capture the dynamics of production processes and sub-processes, uncover some of the components behind successful business practices, and shed light on needed actions for decision makers. Furthermore, the architecture not only can support decision makers to plan for improvement, but also equip them with forecasting ability. To enhance its forecasting quality, this study goes beyond quantitative ratios and extends them to qualitative ratios (i.e., readability: the complexities of disclosure) borrowed from computational linguistics. The results indicate that a poor readability score is highly associated with bad operations. Finally, to enlarge the mechanism’s applicable fields, the study executes the genetic algorithm (GA) to extract the inherent decision logics and represents them in a human-readable manner. The mechanism, examined by real cases, is a promising alternative for performance evaluation and forecasting.



中文翻译:

基于BSC的网络DEA模型,该模型配备了用于评估和改进性能的计算语言学

这项研究引入了一种融合架构,该架构融合了平衡计分卡(BSC)和网络数据包络分析(NDEA),可以从多个角度执行性能评估任务。该体系结构能够捕获生产过程和子过程的动态,揭示成功的商业实践背后的某些组件,并阐明决策者需要采取的行动。此外,该体系结构不仅可以支持决策者制定改进计划,还可以使他们具备预测能力。为了提高其预测质量,本研究超出了定量比率,并将其扩展到了从计算语言学借来的定性比率(即可读性:披露的复杂性)。结果表明,较差的可读性评分与不良操作高度相关。最后,为扩大该机制的适用领域,该研究执行了遗传算法(GA)来提取内在的决策逻辑,并以人类可读的方式表示它们。经实际案例检验的机制是性能评估和预测的有前途的替代方法。

更新日期:2021-05-12
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