What is operational performance in manufacturing? Operational performance measures how effectively manufacturers can deliver outcomes. Better performance reduces wasted time and materials while improving customer satisfaction.
Poor performance, meanwhile, can lead to both immediate and long-term impacts. Consider a manufacturer that experiences unexpected equipment failure and misses their delivery targets. This not only creates the immediate impact of unplanned downtime, but also creates a bottleneck when machines are back up and running as teams try to make up for lost time. Customers, meanwhile, may not be willing to give the company another chance; according to research from Accenture, 87% of people surveyed say they’re likely to avoid a company after just one bad experience.
The result? Operational performance bridges the gap between strategy and execution, enabling both market competitiveness and production line resilience. Here, we’ll explore the key components, dimensions and metrics of operational performance, and offer practical advice to monitor and manage performance at scale.
What defines operational performance?
Operational performance is defined as the ability to run processes efficiently, reliably and safely. It covers machine uptime and availability, maintenance, supply chain management, quality control and workforce execution with a focus on improving consistency, predictability and visibility.
The broad nature of operational performance means that it is not a set-and-forget measurement. Instead, it requires continual observation and evaluation to ensure performance targets meet both day-to-day operational performance objectives and long-term strategic goals.
Key dimensions of operational performance
Operational performance is comprised of six dimensions. These dimensions are interdependent—changes in one dimension may positively (or negatively) impact multiple others.
- Efficiency: Operational efficiency is the ability to do more with less. For example, process automation can help reduce the number of manual operations required to produce components.
- Reliability: Reliability refers to consistent asset and process performance. Variability in these operations can lead to quality issues, duplicated work and unplanned downtime.
- Quality: Quality includes meeting product specifications and customer expectations. More identified defects mean more reworks and may prompt the need for failure analysis. Missed defects lead to end-user frustration, which may prompt customer attrition.
- Safety: Operational performance depends on safety. If staff are injured or operations fail because equipment is damaged or hasn’t been properly serviced, companies lose both time and money. Failure to address these issues, meanwhile, can lead to employee attrition and regulatory non-compliance.
- Delivery: Meeting schedules and commitments are a critical component of operational performance. For example, goods stuck in loading docks or returned due to logistics errors can lead to production bottlenecks.
- Cost control: Asset operation and lifecycle costs also impact performance. Higher average costs mean lower revenue, while unexpected costs create production process challenges.
Common metrics used to measure operational performance
Dimensions provide the framework; metrics provide the data. Consider a company that has identified issues with quality control across critical product lines. Recognizing this shortfall indicates the need for action, while metrics such as scrap, rework and cost-per-unit help quantify what needs to change.
Common key performance indicators used as operational performance measures include:
- Overall equipment effectiveness (OEE): OEE combines availability, performance and quality to provide a snapshot of overall effectiveness. The OEE formula is as follows, with all values represented as a percentage.
OEE = Availability x Performance x Quality
If availability is 95%, performance is 90% and quality is 85%, the result is as follows:
OEE = 0.95 x 0.90 x 0.85 = 0.72 or 72%
- Downtime and availability: Downtime is a measure of how often and for how long assets are not operational. Lower downtime means improved operational performance.
Availability includes unplanned downtime along with scheduled maintenance, cleaning, safety checks and other processes that render assets unusable.
- On-time delivery (OTD): On-time delivery is a measure of how many products are completed and delivered on-time to customers.
- Scrap and rework rates: Scrap and rework rates are manufacturing KPIs that track the total number of “good” outputs produced. Scrap rates refer to the number of products that fail quality control and cannot be salvaged. Rework rates are a measure of how many products must be remanufactured to meet specifications.
- Industrial Maintenance KPIs (MTBF, MTTR): Industrial maintenance KPIs such as MTBF and MTTR offer insight into operational processes. MTBF is the mean time between failures and is a measure of the average length of operation before a machine fails. Higher MTBF values mean longer stretches of uninterrupted operations. MTTR is the mean time to repair, that is, how long, on average, it takes to repair equipment after it fails. Lower MTTR means more efficient maintenance processes and higher operational performance.
- Cost per unit and operating cost trends: With a host of manufacturing operation metrics available, it’s important to select measurements that support performance management and production efficiency goals. For example, an organization facing sudden spikes and scrap and rework rates might prioritize metrics such as downtime, MTBF and MTTR. Other measurements, such as on-time delivery, meanwhile, are less relevant because they are a natural outcome of higher scrap and rework volumes.
The role of equipment reliability in operational performance
Equipment reliability can make or break operational performance.
This is because unplanned downtime disrupts the entire manufacturing process, from materials input and processing to quality control, shipping and delivery. More unplanned downtime means more time, money and effort spent trying to get operations back on track, leaving little room for process optimization.
Solid maintenance strategy drives improved equipment reliability; companies with mature maintenance strategies are less likely to experience downtime and are better prepared to resolve issues ASAP when they occur.
The primary driver of poor reliability? Reactive maintenance. This approach sees companies waiting until problems occur, then deploying maintenance teams in response. While this may work for simple failures with limited scope, reactive frameworks quickly become problematic when dealing with systemic issues or those that don’t have an obvious root cause.
How maintenance strategy impacts operational performance
Maintenance strategies impact both day-to-day operations and long-term operational performance management. Reactive strategies rely on reported failures to activate maintenance teams and trigger root cause failure analysis. This means manufacturers start with no data. Instead, they must collect and analyze this data even as unplanned downtime continues.
Proactive strategies take a different approach. They prioritize the use of data from sensors, staff and technologies such as computerized maintenance management systems (CMMS). This data allows teams to address potential problems before they occur.
To improve operational performance, many manufacturers adopt a multi-strategy framework that includes:
- Preventive maintenance: Preventive maintenance programs identify common issues and create schedules to address these issues before they lead to reduced performance or downtime. For example, production line machines that rely on high heat or pressure may benefit from monthly maintenance to check seals, confirm calibration and (if necessary) replace parts.
- Predictive maintenance: Predictive maintenance solutions use data analysis to identify failure trends and operational patterns. This allows maintenance teams to anticipate potential problems and take action to eliminate risk.
- Standardized maintenance workflows: Standardized maintenance workflows remove variability, which in turn improves reliability. Standardization includes maintenance scheduling, replacement parts procurement and regular training to ensure technicians are equipped to address emerging issues.
Equipped with knowledge about likely failure causes, symptoms and outcomes, manufacturers can improve maintenance planning and scheduling to minimize the risk, duration and impact of unplanned downtime.
In addition, a multi-maintenance strategy helps align maintenance best practices with production priorities to help meet cycle time and batch targets.
Operational performance and data visibility
Improving operational performance isn’t possible without data visibility. If data is fragmented or delayed, optimizing processes to improve performance becomes difficult, if not impossible.
Consider a manufacturer maintaining a production line machine with steadily increasing MTTR. Available data suggests that limited access to specialized parts is slowing maintenance efforts, prompting the company to invest in new supplier relationships that bolster existing inventory.
While the organization also commissioned a root cause failure analysis (RFCA), issues with data visibility meant delayed access to results. When RFCA results finally arrive, the company discovers that failing parts were a symptom of larger problems tied to disconnects between PLCs and equipment functions. The issue is resolved, but data delays led to extra time and money spent solving the wrong problem.
In practice, accurate and available data offers multiple benefits:
- Faster decision-making
- Enhance performance tracking
- Better root cause analysis and continuous improvement
- Improved alignment with Manufacturing 4.0 initiatives
- Support for lean manufacturing initiatives
Barriers to improving operational performance
Data is essential, but it is only one piece of the operational performance puzzle. Other common barriers include:
- Siloed teams and disconnected systems
- Inconsistent processes and metrics
- Labor shortages and skills gaps
- Legacy equipment and technology constraints
- Lack of clear ownership and accountability
Operational performance as a continuous improvement discipline
Operational performance is a moving target. Changes to workflows, production targets, environmental conditions and staff availability all impact performance values. As a result, manufacturers can’t treat operational performance as a one-time effort. Instead, they need to develop a continuous process improvement discipline that evolves alongside manufacturing conditions.
Four best practices can help enhance operational performance efforts:
1. Build for strategy, not speed: Improved operational performance is built over months or years, not days or weeks. Creating long-term strategies helps address current issues and lay the groundwork for long-term goals.
2. Identify clear baselines and benchmarks: To get where you’re going, you need to know where you are. Better performance starts with identifying operational baselines and comparing them to expected and industry-best benchmarks.
3. Create continuous feedback loops: Performance is a cyclical process. Baselines and benchmarks provide the impetus for change, and metrics help measure the impact. This drives process optimization, which creates new baselines and operational goals.
4. Align people, processes and technologies: Operational performance depends on people such as operators, technicians and maintenance leaders leveraging standardized processes to measure efficiency and identify opportunities for improvement.
Technologies such as computerized maintenance management systems (CMMS), enterprise asset management (EAM) software and enterprise resource planning (ERP) tools ensure that people have the data they need to implement, measure and monitor these processes.
Operational performance is the foundation of manufacturing excellence
Operational performance provides an overall measure of manufacturing health. In the short term, measuring performance helps teams identify immediate issues and prioritize quick wins. Over the long term, improving operational performance is a competitive advantage that improves reliability, reduces costs and ensures on-time delivery.
Enabling operational excellence, however, requires a disciplined approach. By prioritizing data visibility, addressing inconstant processes and building a long-term manufacturing operations management strategy, enterprises are better prepared to optimize efficiency and execution.
Improve operational performance with data‑driven maintenance strategies from ATS. Let’s talk.
References
Lyons, S., Simpson, E., Anderson, D., & Bellin, J. (2025, March 13). Customer service on the brink. https://www.accenture.com/us-en/insights/song/customer-service-on-the-brink
