Peter Sherman
2018年5月15日星期二
与Muthuraman Annamalai合著
Peter Sherman
Tuesday May 15, 2018
Co-authored with Muthuraman Annamalai
关键绩效指标(kpis)帮助组织根据目标度量进展。APICS字典将KPI定义为“与特定战略目标和目标相关联的财务或非财务度量，无论是战术上的还是战略上的”。常见的KPI包括销售、利润率、收益率、准时发货、未完成销售天数、客户满意度和事故率。但这些一定是管理企业的最佳标准吗?它们是领先指标还是落后指标?
Key performance indicators (KPIs) help organizations measure progress against a goal. The APICS Dictionary defines a KPI as “a financial or nonfinancial measure, either tactical or strategic, that is linked to specific strategic goals and objectives.” Common KPIs include sales, profit margins, yield, on-time shipment, days sales outstanding, customer satisfaction and accident rates. But are these necessarily the best gauges to manage a business? Are they leading or lagging indicators?
滞后KPIs是那些不能直接执行的KPI。它们缺乏必要的数据来揭示客户的感受或内部运营状况。当这些KPIs显示出负面趋势时，问题已经存在很长时间了。
Lagging KPIs are those that cannot be directly acted on. They lack the necessary data to reveal how customers feel or what internal operations are experiencing. By the time these KPIs show a negative trend, something has been wrong for a long time.
然而，领先的KPIs在本质上具有预测性。根据APICS字典，一个领先的指标是“一个特定的商业活动指数，表明未来的趋势。”“这对任何试图改善日常运营或实现战略目标的组织都具有特殊意义。领先指标可以作为强大的杠杆，在周期的早些时候实时修正航向。
Leading KPIs, however, are predictive in nature. According to the APICS Dictionary, a leading indicator is “a specific business activity index that indicates future trends.” This has particular relevance to any organization trying to improve day-to-day operations or achieve strategic goals. Leading indicators can be used as powerful levers to make real-time course corrections earlier in the cycle.
以下是识别领先指标的三种技术。
Following are three techniques for identifying leading indicators.
方法1:统计。预测统计将输入与输出或因果联系起来。这种关系的强度可以用相关系数来确定——换句话说，在一对分布中变量之间线性关系的程度。相关系数r从1.00到-1.00。如果有一个强关系，r将接近1或-1。应该使用散点图来显示这种关系。在散点图中，如果回归曲线是陡峭的，并且数据点紧密地聚集在直线周围，则存在很强的相关性。较弱的关系表现为平稳的回归和分散的数据点。值得注意的是，相关性并不意味着因果关系。
Approach 1: statistical. Predictive statistics link inputs with outputs, or causes and effects. The strength of that relationship can be determined using the coefficient of correlation — in other words, the degree of the linear relationship between variables in a pair of distributions. The coefficient of correlation, r, ranges from 1.00 to -1.00. If there is a strong relationship, r will be close to 1 or -1. A scatter plot should be used to show such relationships. Strong correlation exists if the regression line in the scatter plot is steep, with the data points tightly clustered around the line. Weak relationships are evidenced by a flat regression and scattered data points. It is important to note that correlation does not imply causation.
