发表机构
Universitat de Girona; Woxsen University（赫罗纳大学; 沃克森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出补偿能力评估（CCA）方法，基于应付账款网络先验估计供应商选择的减免金额与时机，实验显示其与未来减免高度相关，能有效排序补偿机会。
AI 中文摘要
供应商选择不仅影响运营绩效，还影响新债务所进入的应付账款网络。本文开发了补偿能力评估（CCA），这是一种先验的买方-供应商度量，用于估计预期总应付账款减免及其可能的时间安排。CPM提供了有界结构核；凹形CCA变体增加了双边发票容量。该度量在2012年至2023年间发布的749,952张分析性发票上进行了测试，使用了冻结的九个月历史数据和未来的周、月、季度、半年度和年度窗口。周期受限和路径启用的清算作为独立的结果生成环境用于验证该度量，而非本研究比较的技术。在2022-2023年的七个完全观测季度中，log-CCA与未来综合减免的中位Spearman相关系数为0.638；持续关系承载了93.5%的减免，得分最高的关系捕获了买方特定最佳减免的92.5%。可预测性从周到年保持正向，季度重新校准在信号、覆盖范围和及时性之间提供了最佳操作平衡。时间分析显示，在两个验证环境中，89-91%的归属减免发生在发票开具后七天内，94-96%发生在三十天内，平均比合同到期日提前约十六天。Log-CCA与三十天减免的相关系数为0.564，与减免天数的相关系数为0.566。其最高四分位数的三十天补偿中位概率为96.5%，而最低四分位数仅为57.6%。条件等待时间预测较弱，因此CCA应排序及时补偿机会，而非预测确切付款日期。研究结果将供应商定位、财务循环性和营运资本敞口联系起来，同时推动部署、因果测试和季度漂移监控。
英文摘要
Supplier selection affects not only operating performance but also the payable network entered by a new obligation. This paper develops the Compensability Capacity Assessment (CCA), an ex ante buyer-supplier measure of expected gross payable relief and its likely timing. CPM provides the bounded structural kernel; concave CCA variants add bilateral invoice capacity. The measure is tested on 749,952 analytical invoices issued during 2012-2023, using frozen nine-month histories and future weekly, monthly, quarterly, semester, and annual windows. Cycle-restricted and path-enabled clearing are independent outcome-generating environments used to validate the measure, not technologies compared by this study. Across seven fully observed quarters in 2022-2023, log-CCA has a median Spearman correlation of 0.638 with future integrated relief; persistent relations carry 93.5% of relief, and the highest-scoring relation captures 92.5% of buyer-specific best relief. Predictability remains positive from week to year, with quarterly recalibration providing the best operating balance between signal, coverage, and timeliness. A timing analysis shows that, across the two validation environments, 89-91% of attributed relief occurs within seven days of invoice issue and 94-96% within thirty days, on average about sixteen days before contractual maturity. Log-CCA correlates 0.564 with thirty-day relief and 0.566 with relief-days. Its highest quartile has a 96.5% median probability of thirty-day compensation, compared with 57.6% in the lowest quartile. Conditional waiting-time prediction is weaker, so CCA should rank timely compensation opportunity rather than forecast an exact payment date. The findings link supplier placement, financial circularity, and working-capital exposure while motivating deployment, causal testing, and quarterly drift monitoring.
