In manufacturing, higher throughput means greater efficiency, faster production, and stronger ROI. But what exactly is manufacturing throughput—and how does it differ from capacity or production volume? With so many metrics to manage—such as total effective equipment performance (TEEP), overall equipment effectiveness (OEE), lead time, cycle time, and more—it’s easy to get bogged down in the details.
Thankfully, throughput is a straightforward concept: It’s the rate at which systems produce finished goods over a set time period. In other words, it’s a measure of how much you can produce and how quickly.
In this piece, we’ll break down the basics of manufacturing throughput, explore the factors that impact throughput, and offer actionable advice to improve overall throughput without sacrificing quality or operational efficiency.
Calculating throughput in manufacturing
Throughput is measured as output over time, using this formula:
Throughput = Total Units Produced / Time Period
For example, if you produce 500 units every 30 minutes, your throughput is 16.67 units/minute. While production throughput volumes are always measured in units, time can be measured in minutes, hours, days, weeks, or months. The time unit used in the calculation is the same as the unit in the answer. Consider a company looking to measure daily throughput using weekly product totals. If the calculation is written as follows:
10,000 units / 1 week
The answer will be 10,000 units per week, which doesn’t offer any insight. Instead, the formula needs to look like this:
10,000 / 7 days
This yields a usable result of 1428.5 units/day.
Throughput vs. capacity and production volume
While similar, throughput is different than both capacity and production volume.
Capacity is the maximum possible output of a system over time. This is the total number of units your production line could deliver if it ran 24/7/365 and required no maintenance. Capacity is a useful way to compare current throughput with maximum production volumes and identify areas for improvement.
Production volume, meanwhile, is simply a measure of total output. It is not rate-based, meaning that if you produce 10,000 units per week, your production volume will be 10,000.
Key factors that influence throughput
Both internal and external factors can influence throughput. Some of these factors affect all systems equally, causing widespread production slowdowns. Others are what are known as bottlenecks—they impact a specific component of the production process, leading to a material backup behind the bottleneck and reduced production efficiency downstream.
Identifying and addressing these factors is key to achieving high throughput. Some of the most common include:
- Equipment uptime and reliability: Unexpected downtime caused by unreliable machines can reduce throughput across your production line. These equipment issues may be widespread or localized to a bottleneck. For example, power outages due to faulty electrical grids impact your entire production line and reduce throughput. Failure of a single piece of critical equipment, such as a packaging machine, meanwhile, can create a bottleneck that slows production to a crawl.
- Workforce skill and availability: If staff haven’t been trained on new machinery, throughput often decreases due to errors or additional time required to determine next steps. Companies can also experience issues with specialized machinery. For example, if experienced staff retire, younger employees may not have the same ability to manage legacy equipment.
- Process efficiency and automation levels: A lack of automation or the existence of redundant processes can cause widespread throughput reductions. For example, multiple quality checks that address the same potential concern can lead to wasted time, especially if these checks can be automated with digital platforms.
- Raw material availability and supplier performance: Issues with material availability or supply chain management can lead to a bottleneck, even before your production line is up and running. In this case, reassessment of suppliers and supplier agreements may be required.
- Maintenance practices: Reactive maintenance waits until machines break to act. Preventive and proactive maintenance, meanwhile, look to solve issues before they occur. Reactive maintenance can lead to unexpected downtime and increased inspection time, with no clear timeline for service restoration since teams need to identify issues, determine the right repair approach, and source the necessary parts.
- Layout inefficiencies: Confusing layouts and cramped spaces can negatively impact throughput. For example, if staff must move from station to station but there is no direct path, the result is wasted time and reduced throughput.
- Poor quality: One solution for throughput is increased speed, but this comes with a caveat: quality may suffer. If products fail quality reviews, they must be either thrown away or reworked, both of which reduce total throughput.
Of course, it’s not enough to simply recognize these challenges. Conducting widespread issue identification and bottleneck analysis is critical to understanding where issues are occurring, which systems they are affecting, and most importantly, why they are happening.
How to improve throughput without sacrificing quality
There are multiple ways to improve throughput, but not all are beneficial to your business. For example, you could choose to reduce or eliminate product testing and verification during the production process. While this will lead to more products being produced, it can also lead to reduced quality, which in turn requires additional time for rework—time that could otherwise be spent producing new products.
As a result, it’s important to implement throughput strategies that don’t sacrifice quality for speed, such as:
- Implementing lean manufacturing principles: Lean manufacturing processes that focus on reducing production line waste and streamlining workflows are a good starting point for improved throughput. Best bet? Begin with a Gemba walk—have managers and C-suite executives explore the production floor and talk to staff directly to discover where processes can be streamlined. It’s also worth implementing MRO inventory optimization to ensure staff have the right parts and components on hand to fix equipment as quickly as possible.
- Deploying CMMS solutions or preventative maintenance procedures: Tools such as computerized maintenance management systems (CMMS) combined with preventive maintenance procedures can provide companies with better visibility into potential bottlenecks.
- Creating plans to train and cross-skill employees: The more staff who know how to use critical machinery, the better. By providing comprehensive training along with opportunities for cross-skilling, companies can create a safeguard against possible bottlenecks.
- Monitoring machine performance in real-time: Throughput time issues don’t wait until you’re ready—instead, they can happen seemingly out of the blue. Real-time machine performance monitoring helps teams identify and address potential risks before they become production line issues.
- Aligning production schedules with demand: One factor that limits throughput yield is idle time. Machines that are powered on but not actively producing products reduce overall throughput. Align production schedules with current product demand to reduce idle time.
- Streamlining quality assurance processes: While quality assurance is essential, process optimization can help streamline these processes to reduce redundant checks and better integrate QA operations into production lines.
The role of maintenance and technology in maximizing throughput
Technology provides key throughput data, which in turn helps identify optimal maintenance strategies. Common technologies used to acquire this data include Industrial Internet of Things (IIoT) sensors. These sensors can be wired or wireless, and may be placed inside, directly on, or nearby key production machinery. Sensors may be used to measure key metrics such as temperature, vibration, friction, noise, or deviations in typical movement patterns or output cycles. This data is then collected and compiled into solutions such as CMMS or MES tools.
Next is the use of machine learning to analyze this data and provide actionable insights. Machine learning (ML) algorithms underpin both existing analytics solutions and the evolving technology of AI—they represent the processes that make it possible for AI tools to “think” and improve over time. By analyzing this data, ML-enabled tools can identify changes in machine conditions or behaviors, forecast the likelihood of specific risks, and suggest ways to reduce these risks, such as preventative maintenance or modifications to current production processes.
Analysis of downtime data also sets the stage for root cause identification. This is a critical component of overall throughput improvement. Consider a machine that regularly experiences sudden temperature spikes tied to the failure of a specific part. Replacing the part eliminates the immediate issue, but the fix is only temporary, meaning equipment must be regularly taken offline.
Root cause analysis, meanwhile, can help pinpoint the cause of failure. For example, root cause analysis might determine that input pipes are slightly too narrow in diameter, in turn causing excess heat. Although removing and replacing these pipes is initially more time and resource-intensive than addressing the symptom, eliminating the root cause leads to reduced downtime and improved throughput in the long run.
Measuring and tracking throughput over time
Throughput isn’t a static metric. It can change over time in response to factors such as the deployment of new machinery, the onboarding of new staff, or modifications to production targets and quality expectations. As a result, it’s critical to both measure and track this data over time to ensure your goals for throughput are realistic and to identify opportunities for improvement.
Four best practices can help streamline metric management.
1. Establish benchmarks: Measure your current throughput, compare it to your ideal value, and then design a plan that includes step-by-step benchmarks to help steadily increase throughput.
2. Track KPIs: Track key performance indicators such as OEE, TEEP, cycle rate, and lead time.
3. Use dashboards and visualization tools: Use tools that let you see what’s happening across your production line in real time. Both digital dashboards and process virtualization solutions are useful in this approach.
4. Analyze historical data: Finally, use what’s happened before to inform what comes next. By analyzing historical data, teams can spot common bottleneck trends and pinpoint opportunities for improvement.
Improve your throughput with strategic planning
Better throughput means more products made, more quickly—and with fewer quality issues. This makes throughput optimization a critical component of sustained ROI, reduced waste, and improved production line performance.
To identify opportunities and take effective action, however, strategic planning is essential. This starts with the measurement of current throughput rates to establish a baseline, followed by the collection and analysis of performance, quality, and output data to determine where throughput may be falling short.
This sets the stage for proactive maintenance and continuous improvement. Equipped with root-cause data, maintenance teams can design repair and replacement schedules that anticipate rather than respond to equipment concerns. This reduces the risk of unexpected downtime, in turn increasing total throughput. The use of technology—including CMMS, MES, dashboards, and visualizations—helps create a roadmap for continuous improvement that links throughput with other key metrics, including TEEP, OEE, lead time, and cycle time.
Bottom line? The higher your throughput, the higher your manufacturing efficiency and ROI. By taking a strategic approach to analysis and optimization, your teams are better prepared to capture short-term benefits and drive long-term changes.
