Co-authored with Muthuraman Annamalai
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?
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.
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.
Following are three techniques for identifying leading indicators.
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.
As an example, consider a company with lead time — a lagging indicator — that averaged 5.5 days at its distribution center. Management identified several variables that affected lead time, including distance traveled, cargo weight and order volume. They collected 90 days of historical shipping data and quantified the predictive value of the three input variables by examining the correlation of each with the lead time. Based on the analysis, it was clear that order volume was the most predictive variable influencing lead time. Armed with this information, this business was able to better focus efforts on streamlining the fulfillment process.
Approach 2: value stream. This method is composed of three steps — identifying KPIs at the strategic level, such as on-time shipments, damaged or lost products, sales and profit margins; next, drawing the value stream; and finally, matching each KPI with the corresponding section in the value stream. Segments of the value stream that should be considered include product development, sales, procurement, operations, information technology and recruitment. With all the key stakeholders representing the value stream, ask: Why is the KPI not being met? and What influences or drives this KPI? Start at the end of the value stream, and continue posing these questions through to the beginning. The goal is to arrive at the leading or predictor indicators that largely determine the downstream or lagging KPIs.
A Fortune 500 manufacturing company used this method when it was struggling to meet a delivery reliability KPI. In 2008, delivery reliability was 40 percent. The management team evaluated if the right things were being measured and if delivery reliability was a leading or lagging indicator. Then, they laid out the value stream from order to delivery. They asked why service was so poor and what was driving delivery reliability. Turns out, the production team couldn’t keep up with the production schedule because it was constantly changing. The materials management team explained that this was happening because they were constantly running out of materials. The engineers then said that they constantly had to go back and forth with customers to clarify specifications. A sampling of customer orders confirmed that they were vague and often had missing information. In the end, what began as a shipping problem was revealed to be an order issue. The clean order rate emerged as a reliable pre-indicator of delivery reliability, and today, that KPI exceeds 99 percent.
Approach 3: role playing. Role playing with key stakeholders, as both business owner and customers, is an effective way to determine which KPIs are leading and lagging indicators. To do this, have everyone first pretend they own the business and write down on a sticky note one piece of information or metric they would need to properly manage it. Place the sticky notes on a flip chart next to the value stream. Then, have everyone act ask the customers, and tell them their company’s service or product is available from other businesses and is similarly priced. Ask them to write down on another sticky note how they would decide which company to choose. Include specific reasons. Again, place the sticky notes on a flip chart next to the value stream. More often than not, the lists will be starkly dissimilar. The final step to this approach, therefore, is to determine how to unite them so both stakeholders are satisfied.
Once these three techniques are complete, it’s time to ask some more questions: Are the indicators measurable? Does management understand how to use and interpret them? Are they willing to incorporate them on a regular basis to manage the business?
Finally, note that the objective is not to manage solely with leading indicators and neglect the lagging ones. Every organization needs both. Leading indicators provide earlier signals to better manage the business; lagging indicators demonstrate if progress is being made on the broader outcomes.
Peter J. Sherman, CSCP, is managing partner at Riverwood Associates, a process improvement training and consulting firm based in Atlanta, and the author of APICS magazine’s “Operational Excellence” department. Sherman is a Certified Lean Six Sigma Master Black Belt and served as lead instructor of Emory University's Six Sigma Certificate program.
Muthuraman Annamalai is a quality assurance engineer for the City of Charlotte, North Carolina. He is a Certified Six Sigma Black Belt, Certified Quality Auditor and Certified Quality Engineer. Annamalai also serves as the Section Chair for the ASQ Charlotte Section.