Tracking production in manufacturing is an essential component of production excellence. The problem lies in which metrics should be taken into account to trace the required strategies for the line of work.
This article is a guide on the different manufacturing KPIs. Which are they, why are they relevant, and how to measure them. Also, we will explain the usage of Manufacturing KPI Dashboards and how production monitoring software can convert a monitoring process into a culture of production excellence.
What are Manufacturing KPIs
Key Performance Indicators (KPIs) are well-defined, quantifiable metrics that indicate performance criteria. They provide vital data relevant to monitor, analyzing and optimizing manufacturing processes in terms of quantity, quality, costs, time, etc.
On their own, they are just indicators that some people understand in detail, hence the importance of aligning KPIs with production goals and objectives. Once operators are familiarized with the different KPIs involved in production, they can make decisions based on facts, aiming for the best quality of products with the available resources.
Which are the Most Important Manufacturing KPIs of 2023 and Their Formulas
We have selected a list of manufacturing KPIs that can contribute to significant changes in terms of production when optimizing processes. Each element on this list of KPIs contains a description of its usage and calculation formula.
KPIs for Improving Production and Efficiency
Production volume measures the number of units manufactured within a determined time frame. It is one of the core KPIs to benchmark production efficiency.
Production Volume = Total count of products manufactured during a specified time
Production downtime measures the total time a shop floor’s production lines are idle. It comprehends planned and unplanned stops.
Production Downtime = Sum of total downtime during a time frame
Unscheduled downtime measures the total time spent on idle due to reliability, procedure, or breakage issues. Causes can be linked to machinery or operators’ skills.
Unscheduled Downtime = Sum of total unplanned downtime during a time frame
Number of Stops
Number of stops measures the total count of stops during a shift. Further information on stop causes can help us spot any inefficiency to be fixed.
Overall Equipment Effectiveness (OEE)
OEE is a metric used to monitor, evaluate, and improve the effectiveness of a production process, expressed in percentage. Through OEE, we can measure equipment utilization rate, which helps factories address which areas require attention.
OEE = Availability x Performance x Quality
Overall Operations Effectiveness (OOE)
OOE is similar to OEE, with a difference in the Availability measurement. Unlike OEE, the Overall Operations Effectiveness considers the time spent on maintenance tasks.
OOE = Availability x Performance x Quality
Throughput rate measures the volume of production completed during a time frame. It is often misplaced in use as a synonym to Lead Time. Compared with Cycle Time, it is intended to evaluate processes, as Throughput Time helps identify bottlenecks.
Throughput Rate = Total number of good units produced / Flow Time
Where Flow Time stands for the sum of Processing Time, Inspection Time, Move Time, and Queue Time. Keep in mind that throughput rate only counts right-first-time produced units.
Total Effective Equipment Performance (TEEP)
This KPI is identical in formula to both OEE and OOE. TEEP intends to evaluate the maximum effective equipment performance if machinery runs 24/7, all year long, and always under right-first-time production.
TEEP = Availability x Performance x Quality
In TEEP, both Availability and Quality are counted as 100%.
Capacity Utilization measures how much of the shop floor’s total capacity is in use. This KPI is used to analyze growth opportunities and also to assess efficiency. It is expressed in percentage.
Capacity Utilization = (Total capacity used during a time frame / Total available production capacity) x 100
Cycle time measures the amount of time it takes to produce a unit from start to end. Unlike Throughput Rate, it does not consider queues in the calculation.
Cycle Time = Net production time / Number of units produced
Another formula for this is:
Cycle Time = Process End Time - Process Start Time
Lead time measures the total time required for customers to receive orders from the moment they are placed. Companies usually consider the buffer - inventory of products available - instead of the actual time required to produce a unit (unless we talk about custom-made products).
Lead Time = Order process time + Production lead time + Delivery time
Takt time is a metric that determines how fast you ought to complete a product to meet consumer demand. It is a core KPI, much like OEE, Throughput Time, and Cycle Time, as it helps us optimize the production capacity to meet the demand without overloading storage capacity.
Takt Time = Total Available Production Time / Average Customer Demand
Changeover measures the time required for a product line to transition from working on one product to another. This time involves tasks like identifying specific tools or machine parts a product needs for fabrication, and removal of unrequested parts.
Changeover time = Net Available Time - Production Time
Reducing changeover time is essential to maximize production capacity.
KPIs on Cost Reduction and Improving Profits
Right First Time (RFT)
RFT is an important metric to acknowledge for OEE calculation in terms of quality. It means the total count of products that were produced in acceptable conditions the first time they were manufactured. The units that don’t meet this criterion are known as scrap or reworks, depending on their degree of failure.
Net Operating Profit
Net operating profit measures the remaining money from extracting the revenue and operating expenses, interest and taxes. It is an indicator of how much a factory makes, and can be calculated overall or per product line.
Net Operating Profit = (Revenue - Operating expenses) - Interest and taxes
Energy Cost per Unit
Energy cost per unit measures how much energy is required to manufacture one unit. This KPI is critical for factories moving towards sustainable production, and its monitoring can considerably reduce operating expenses.
Energy Cost Per Unit = Sum of energy costs / Number of units manufactured
Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA)
If we are looking to improve revenue, EBITDA is a measurement used to compare companies’ net income. Investors often use this metric to evaluate a company’s worth in industries with extensive assets in equipment, building, and land value.
EBITDA = Net income + Interest + Taxes + Depreciation + Amortization
EBITDA removes the impact of debt financing, depreciation, and taxes, meaning that you get the idea of the cash produced prior to paying its operational, stakeholders, and governmental expenses. Managers use EBITDA to understand the company’s cash flow before important financial decisions.
Overtime rate measures employees' extra working hours to cover beyond the shifts. It is an important metric to compare with the Throughput Rate to analyze flaws in production processes. It is expressed in percentage.
When tactics intended to automate processes or reduce stop causes are in place, companies can significantly reduce overtime rates, reducing production costs from the human capital side.
Overtime Rate = (Overtime hours / Total hours worked) x 100
KPIs on Quality Improvement
Scrap Material Value
This KPI represents the monetary value of scrap, minus its disposal cost. Scrap is typically sold as it cannot be reworked into new pieces.
Scrap Material Value = Money earned on disposal of scrap material - Disposal cost
Yield measures the overall volume of products produced compared to the input of raw materials. It does not take into account manufacturing inefficiencies.
Yield = (Actual count of products manufactured / Theoretical max production based on raw material input) x 100
First Time Yield
Unlike the previous related KPI, First Time Yield represents the total count of non-defective products in contrast with the total production. It is represented as a ratio.
First Time Yield = Total count of non-defective units / Total count of manufactured products
Using First Time Yield as an indicator helps to study the impact of material quality or equipment performance.
Rate of Return (ROR) & Rate of Consumer Reject (RCR)
Two definitions of ROR are relevant to manufacturing. The first one, the typical ROR definition, measures capital expenditure performance over time. Expressed in percentage, this Rate of Return evaluates profits or losses from an investment done over a period.
Rate of Return (ROR) = (Current value - initial value / initial value) x 100
There is, however, another way to define the Rate of Return in manufacturing, which we shall call the Rate of Consumer Reject (RCR). This metric measures the number of items sent back to a factory, called Consumer Rejects. RCR can also be found labeled as ROR, which often leads to misunderstandings.
Rate of Consumer Reject (RCR) = Number of rejected units per batch / Number of produced units per batch
KPIs on Maintenance
Mean Time Between Failure (MTBF)
MTBF measures the average time between machinery failure and is an important metric to evaluate reliability in our production processes.
MTBF = Operating time in hours / Number of failures
Its measurement heavily depends on the start/stop cycles, ambient conditions, servicing, etc.
Percentage Maintenance Planned (PMP)
PMP compares the total amount of hours an organization spends in repairing and maintenance tasks with the scheduled time for those tasks. This KPI helps to understand the time required to properly perform maintenance tasks.
Percentage Planned Maintenance = (Number of planned maintenance hours / Number of total maintenance hours) x 100
Mean Downtime (MDT)
Our last metric is Mean Downtime (MTD), which represents the time required to repair a failure after a breakdown.
Mean Downtime = Total downtime / Number of downtime events
How Can Companies Use KPIs on a Daily Work Basis
KPIs are a powerful tool for evaluating performance and where a company stands in terms of goals to accomplish. These metrics are used across all levels, from managers to defining strategies and investments and developing new product lines, to operators benchmarking performance for machinery and their own skills.
Manufacturing KPIs can empower employees. With the proper metrics to address their performance, they can question the tactics and knowledge required for operating machinery to excellence, helping them become better professionals over time.
Still, the most critical aspect when working with KPIs is counting on reliable data that can be analyzed. We shall talk more about it in the next section.
How to Track Manufacturing KPIs
Before tracking KPIs, companies have to pick which metrics they intend to track and by which methods. Nowadays, it is a common practice to rely on the services of manufacturing monitoring software, given its practicality and ease of access to data at any moment of the day.
Using manual methods to track KPIs, especially Quality KPIs, involves the risk of human errors, observer bias, and researcher bias, among other related data inaccuracies. In such cases, the reiterative task of manually counting defective parts can lead to underestimations in the scrap count. Energy metering is not accurate when done by manual methods, and that’s just a couple of reasons to opt for 4.0 industry technologies for this purpose.
When collected by software solutions, data collection and manipulation brings new possibilities to research. Time consumption for the data collection process is significantly reduced compared to manual processes. The extra advantage is to count on security-proofed backup methods for your information, allowing tier-categorized access to the people involved in different project stages.
What are Manufacturing KPI Dashboards
Now that we discussed the important KPIs, their importance in decision-making, and how to collect them, it is time to mention how to visualize these metrics.
A manufacturing KPI dashboard is a tool included in production monitoring software to visualize KPI data in real time. These dashboards are linked to IoT sensors for information retrieval, and they combine different tools for live time analysis, reporting, escalating problems for appropriate troubleshooting, financial tools, and more. Pre-built report tools help operators quickly address stop causes at different points of the product line, or to input information relevant to preventive maintenance tasks.
There are different types of manufacturing KPI dashboards, which we shall categorize by their main mean of usage.
Production Performance Dashboards
This is the typical dashboard you come across on the shop floor. It displays real-time information about selected metrics linked to production, such as:
Number of stops
Average stop length
Number of produced parts
Below we can see an example of a production performance dashboard by Blackbird.
Manufacturing Quality Dashboards
Often used as a tool to evaluate Quality in OEE in deeper detail. These dashboards track defective production numbers against acceptable quality production, but also may bring information on the quality of raw material fed to the machines.
Common KPIs tracked in manufacturing quality dashboards:
First Time Yield
Rate of Return
Manufacturing Cost Management Dashboards
Used mostly by product managers and the company’s financial area, these dashboards display production cost KPIs in real-time and profitability. Examples of the metrics they can track:
Net Operation Profit
Lean Manufacturing Dashboards
Companies that adopt lean methodologies ought to work with this kind of dashboard for proper goal tracking. Lean manufacturing dashboards can quickly bring insights, in the shape of reports, of potential growth opportunities.
Typical metrics tracked by lean manufacturing dashboards:
Energy Cost Per Unit
How Does Blackbird Data Help Companies Understand Their KPIs
Software solutions like Blackbird help companies not only to track KPI data and visualize it but also provide an extensive range of services such as:
Accurate data collection available 24/7: Data collection is handled by our Factbird sensors, then synced to the Cloud, and users can operate on them via desktop, dashboards, mobile, and tablets. This information is secured through SSL encryption, so rest assured all sensitive data is protected.
Data consolidation in reports: Gone are the days when operators and managers had to extract information into Excel sheets manually. Our Blackbird application instantly creates reports from selected time frames, which are easy to distribute among team members and stakeholders.
Insights on KPIs: By pairing the Factbird sensors with tools like Factbird View, the numbers displayed on KPI dashboards come to life thanks to accurate video footage, which enables users to spot the reasons behind indicators. Identifying bottlenecks or defects in the product line has never been this easy.
Interaction with other platforms: We acknowledge the other software solutions available in the market for data analysis, such as Power BI. Blackbird offers interaction with other existing technologies, such as PLCs, ERPs, and OPCs, allowing users to build complex manufacturing tech stacks according to their needs.
By working with instantly-retrievable data, operators don’t have to rely on gut feelings to troubleshoot performance issues. Decisions are now taken over facts, which builds confidence for operators and unifies the organization under a continuous development mindset.
Each piece of data retrieved can be subjected to real-time analysis, helping factories evaluate processes and performance and seek methodologies that allow the best production numbers out of the machinery at any given time.
Learning to work with the proper set of KPIs for your company is the first step toward production rate success. Management can shape decisions based on reliable information at any given moment, making it possible to adjust strategy goals out on the information presented by these indicators.