Choosing the Right KPIs: Leading vs Lagging Metrics for Manufacturing Excellence

Key performance indicators (KPIs) play a key role in manufacturing success. Measuring, collecting and using KPI data can increase uptime, enhance reliability, improve cost control, drive revenue growth and boost overall equipment effectiveness (OEE). 

The challenge? Choosing the right KPIs for the job. 

This starts with a decision: leading or lagging? Leading KPIs leverage data to predict future outcomes, while lagging KPIs measure outcomes after events occur. While both have value, over-reliance on lagging KPIs can leave manufacturers struggling to keep pace with the competition. 

Consider research firm Gartner, which found that 49% of manufacturers lack confidence in their manufacturing strategy to deliver on business outcomes over the next three years. While KPIs alone don’t determine strategy success, forward-looking performance indicators play an important role in improving visibility, alignment and execution. 

In this piece, we’ll explore the basics of KPIs, break down the difference between leading and lagging indicators and offer advice to help manufacturers build a balanced KPI framework.

What are KPIs in maintenance and manufacturing?

KPIs are actionable metrics.  While KPIs are calculated using data from sources such as CMMS platforms, sensors and user reports, they go beyond observation to suggest action.  

This is because KPIs link observations with business outcomes and provide a pathway for improvement. Consider a company looking to reduce the amount of scrap generated during component assembly processes. The organization sets a target goal, say 5%, and then uses this formula to calculate the scrap rate KPI: 

Scrap rate = (Total units scrapped / Total units produced) x 100 

If a production run of 1,000 results in 70 items being scrapped, the KPI is calculated as follows: 

Scrap rate = (70 / 1000) x 100 = 7% 

Equipped with this data, teams can take action to identify the source of scrap issues and bring the KPI in line with expectations. 

Other common manufacturing KPIs include OEE, cycle times, mean time between failure (MTBF) and on-time delivery (OTD). Companies can also build their own KPIs depending on business objectives and visibility goals. 

KPIs share four characteristics: 

• Relevant 

• Timely 

• Controllable 

• Aligned to business goals 

KPIs help drive decision-making at multiple organizational levels. For example, maintenance technicians may use KPIs to help identify root causes, while shop floor managers may leverage KPIs to streamline operational performance. C-suite executives, meanwhile, often connect KPIs to long-term business goals and strategies. 

What are lagging KPIs?

Lagging KPIs are metrics that measure outcomes after events occur. Historically, these KPIs have dominated manufacturing because they do not require pre-event analysis. Instead, all relevant data is provided after the fact. 

Some examples of lagging KPIs include: 

• Downtime 

• Mean time to repair (MTTR) 

• OEE 

• Failure rate 

• Maintenance cost per unit 

• Safety incidents 

Lagging performance indicators come with several benefits. They are objective, easy to measure and support clear reporting and benchmarking.  

These indicators also come with drawbacks. Because they are reactive by nature, they can’t prevent failures, only report on them. As a result, lagging KPIs often reveal problems after the damage is done. 

Consider machine downtime. Measurement of this KPI starts when a critical asset fails and ends when systems are back up and running. Tracking downtime incidents over weeks, months and years helps identify failure patterns and underpin maintenance efforts but comes with risk: Each time equipment or systems fail, businesses lose time and money. If root causes aren’t identified, failures will continue to occur, often without warning. 

What are leading KPIs?

Leading KPIs signal future outcomes before failures occur. They do so by collecting current and past performance metrics and then analyzing this data to determine both the probability of asset failure and its likely cause. 

Leading KPIs include: 

• PM compliance rate 

• Condition monitoring alerts 

• Percent of assets monitored by sensors 

• Work order backlog health 

• Inspection completion rate 

• Mean time between anomalies 

Collecting and applying these KPIs requires a combination of always-on equipment predictive maintenance sensors, connected IIoT assets and in-depth analytics. This is the primary challenge with leading indicators: They aren’t automatically available. Instead, companies must build transparent workflows that enable real-time data management. 

The biggest benefit of leading KPIs, meanwhile, is enabling the shift from reactive to proactive maintenance. Rather than waiting for machines to break before taking action, teams can use these KPIs to identify likely failure points and act to remove the risks.

Leading vs. lagging KPIs: Key differences

Lagging KPIs are historical and reactive. As a result, they’re often used for quarterly or monthly performance reviews to help manufacturers understand what happened and why. 

Leading KPIs are forward-looking and proactive. They may be used for daily or weekly operational control by providing insight about what could happen, what’s likely to happen and when. 

Ideally, manufacturers should use a combination of both leading and lagging KPIs to create an end-to-end view of operations.

Dimension

Leading indicators

Lagging indicators

Primary purpose

Predict and prevent future performance issues 

Measure results after events occur 

Timing

Forward-looking (before failures or losses) 

Backward-looking (after outcomes happen) 

Type of insight

Proactive and predictive 

Reactive and historical 

Control

High—teams can influence outcomes in advance 

Low—reflects events that already happened 

Typical use

Daily and weekly operational control 

Monthly and quarterly performance reporting 

Maintenance examples

PM compliance rate, condition monitoring alerts, inspection findings and addressing maintenance backlogs 

Downtime, MTTR, failure rate and calculating maintenance costs 

Manufacturing examples

Process deviation trends, SPC signals and equipment health indicators 

Scrap rate, yield loss, missed deliveries 

Reliability value

Enables failure prevention and predictive maintenance 

Confirms reliability performance after the fact 

Ideal use cases

Improving future performance and preventing losses 

Reporting, benchmarking and accountability 

KPI maturity model: From reactive to prescriptive maintenance

Many manufacturers progress through a four-stage KPI maturity model as they evolve from reactive to prescriptive maintenance practices. 

• Stage 1: Reactive: This stage is defined by lagging KPIs. Companies use lagging metrics to track patterns and reduce risk. 

• Stage 2: Preventive: Stage 2 introduces basic leading industrial maintenance KPIs, such as condition reports and common failure causes to create preventive maintenance programs. 

• Stage 3: Predictive: In stage 3, manufacturers leverage sensor-driven leading metrics to drive in-depth analytics that anticipate failures and plan maintenance accordingly.  

• Stage 4: Prescriptive: Finally, businesses deploy AI-driven solutions to combine leading and lagging indicators and provide targeted recommendations for maintenance. 

Why leading KPIs are critical for predictive maintenance

Leading indicators enable predictive maintenance by combining condition monitoring techniques and connected sensor data to create a complete picture of asset health. Predictive processes typically start with thresholds—circumstances that demand response immediately to limit the risk of failure. Next are trends. By combining KPI data with analytics, manufacturers can uncover patterns in asset behavior that may lead to unplanned downtime. 

Finally, leading KPIs set the stage for predictive insights that connect the dots across operations, maintenance and management. 

Leading signals can help reduce: 

• Unplanned downtime 

• Emergency repairs 

• Spare parts chaos 

• Industrial maintenance safety risks 

For example, if analysis of leading KPIs reveals a connection between recent worker injuries and a particular piece of equipment, teams can perform root cause failure analysis (RCFA) to identify the underlying cause.

How to build a balanced KPI framework

Businesses are best-served by a balanced KPI framework. Lagging indicators alone leave manufacturers in the dark about what comes next, while leading indicators in isolation can see teams miss critical trends. 

To find your best-fit framework, ask three questions: 

1. How many KPIs do we need? Both too few and too many KPIs can undermine maintenance and management efforts. Determine your ideal number by tying KPIs to clear business outcomes. Any lagging or lead indicators that don’t drive action or provide insight aren’t needed.  

2. How should leading and lagging KPIs be balanced? Balance is often defined by asset criticality. For example, lagging indicators may be sufficient for less-critical assets in cases where post-failure remediation causes minimal impacts. Leading indicators, meanwhile, are a must-have for assets that could trigger large-scale unplanned downtime upon failure. 

3. How do KPIs differ by role? Finally, consider the impact of KPIs by role. For example, technicians need asset-specific KPIs that track immediate issues, while executives and managers may benefit from higher-level KPIs that collectively define larger patterns. 

Enabling KPI measurement with technology

Effective KPI measurement and management depend on technology. The volume and complexity of production line data make it impractical for organizations to collect, analyze and apply KPIs manually. Leading KPIs are especially hard to measure without technology, since accurate data depends on proactive analysis of asset repair history, current behavior and future failure probability. 

Technologies that enable KPI measurement include: 

• Computerized maintenance management systems (CMMS) 

• Condition monitoring sensors 

• Reliability analytics 

• AI and machine learning tools 

In combination, these tools enable automated KPI generation, removing the need for manual data entry and reporting. When paired with accessible dashboards, these KPIs provide real-time visibility into trends and patterns that can affect cycle times, failure rates, OTD and overall product quality. 

Moving from reporting to predicting

Lagging KPIs track what happened, while leading KPIs help predict what comes next. 

Both are critical for companies to navigate the evolving landscape of Manufacturing 4.0, which is driven by always-connected, always-on and autonomous systems. While measuring post-event impacts is essential for ongoing improvements, a data-driven predictive maintenance culture offers the competitive edge manufacturers need.  

While it’s possible to handle the shift from reporting to predicting entirely in-house, businesses are often better served by partnering with experienced service and system providers. Key indicators of a potential partner include knowledge of predictive maintenance best practices, expertise with sensor installation and analytics reporting, and in-depth reliability expertise. 

Bottom line? The future of maintenance is predictive and KPI-driven. Build a balance of lagging and leading indicators to get the best of both worlds. 

Take the lead on maintenance and manufacturing KPIs with ATS. Let’s talk.  

References

Gartner, Inc. (2025, October 28). Gartner survey shows 49% of organizations lack confidence in future manufacturing strategy. https://www.gartner.com/en/newsroom/press-releases/2025-10-28-gartner-survey-shows-49-percent-of-organizations-lack-confidence-in-future-manufacturing-strategy