Manufacturers collect more operational data than they may realize.
Production orders, machine run times, labor hours, defect records, inventory levels, maintenance logs, and financial data are often already sitting inside ERP systems, manufacturing execution systems, quality platforms, spreadsheets, and reporting tools. But what should you do with all of those numbers?
Production efficiency KPIs give manufacturers a view of what is happening on the plant floor and how those activities affect profitability. The right KPIs can help uncover bottlenecks, reduce waste, improve scheduling, manage labor costs, and support continuous improvement.
The most useful ones are tied to a specific business objective and built from data sources that are consistent and reliable.
Before building new reports or investing in another system, take inventory of the data you already collect. Common sources include:
| ERP Systems | Include production orders, material usage, labor costs, inventory levels, sales orders, and financial data. |
| Manufacturing Execution Systems | Include production orders, material usage, labor costs, inventory levels, sales orders, and financial data. |
| Quality Management Systems | Contain inspection results, defect rates, scrap records, and rework data. |
| Maintenance Systems | Where equipment downtime, repair history, maintenance schedules, and service records are tracked. |
| Timekeeping and Labor Tracking Tools | Provide employee hours, overtime, shift data, and labor allocation. |
| Inventory Management Systems | Show raw materials, work-in-process inventory, finished goods, and usage trends. |
Once you understand what information is available, you can begin connecting that data to KPIs that reflect real production performance.
Overall Equipment Effectiveness, or OEE, gives manufacturers a broad view of how well equipment is being used. It combines three areas: availability, performance, and quality.
The data typically comes from machine downtime logs, maintenance records, MES data, production tracking systems, and quality records. For example, scheduled production time, unplanned downtime, cycle time, actual units produced, good units, and defective units all support the OEE calculation.
OEE matters because it can reveal hidden production losses. A machine may appear busy, but if it is running slower than expected or producing too many defective parts, it’s not performing as well as it should. Tracking OEE helps manufacturers identify where productivity is being lost and where improvement efforts should begin.
Production throughput measures how many units are produced during a specific period, such as a shift, day, week, or month. This KPI is especially useful for understanding whether production capacity aligns with customer demand.
The data usually comes from:
Examples include units completed per shift, daily production totals, weekly output volumes, planned output, and actual output. When throughput falls below expectations, it may point to staffing issues, machine constraints, material shortages, or inefficient workflows.
This measures the percentage of products manufactured correctly the first time, without needing rework and connects production activity directly to quality performance.
Data sources may include quality control records, inspection reports, MES data, and production logs. Manufacturers can compare total units produced against units that pass inspection the first time.
A low First Pass Yield can signal process variation, training gaps, equipment issues, or material problems. Improving this metric can reduce rework costs, lower waste, and support stronger customer satisfaction.
Scrap rate measures the percentage of materials or products that cannot be sold or used because of defects. For manufacturers, this KPI has a direct financial impact because scrap represents wasted material, labor, machine time, and overhead.
The data often comes from ERP systems, inventory management software, scrap logs, and production reports. Examples include material issued to production, material consumed, pounds scrapped, and defective units discarded.
Tracking scrap rate can help manufacturers answer important questions, such as:
By monitoring scrap trends, manufacturers can target waste reduction efforts more effectively.
Machine downtime percentage measures the amount of production time lost due to equipment being unavailable and is useful for understanding equipment reliability and its effect on production schedules.
Manufacturers can use downtime data from MES platforms, maintenance software, equipment sensors, production schedules, and ERP systems. Useful data points include downtime events, outage duration, downtime reasons, scheduled operating hours, and available production hours.
This KPI helps maintenance and operations teams move from reactive repairs to more proactive planning. If one machine is responsible for a high percentage of lost time, leadership can evaluate whether preventive maintenance, operator training, spare parts planning, or capital investment is needed.
Labor productivity measures output generated per labor hour worked and helps manufacturers understand how efficiently labor is being used across shifts, departments, or production lines.
The data often comes from payroll systems, timekeeping software, labor tracking tools, production reports, and MES platforms. Manufacturers may compare hours worked, overtime hours, shift data, units produced, or revenue generated.
Labor productivity should be evaluated carefully. A decline doesn’t always mean employees are underperforming. It may reflect poor scheduling, machine downtime, material shortages, unclear work instructions, or inefficient plant layout. The value of this KPI comes from using it as a starting point for deeper analysis.
Inventory turnover measures how efficiently inventory is used and replenished. While it is often viewed as a financial metric, it can also reveal production planning and operational issues.
This KPI typically uses data from ERP systems, inventory management software, accounting systems, and financial modules. Examples include average inventory levels, raw material inventory, finished goods inventory, cost of goods sold, material costs, production costs, and sales data.
A low inventory turnover rate may point to excess stock, slow-moving finished goods, inaccurate demand forecasts, or purchasing practices that tie up cash. A very high rate may suggest stockout risk or insufficient raw materials to support production.
This measures the percentage of production orders completed according to schedule and connects production performance to customer delivery expectations.
Data sources may include ERP systems, production planning software, MES platforms, and production reports. Planned completion dates, production deadlines, actual completion dates, and delay reasons are all useful inputs.
When this KPI is tracked consistently, manufacturers can identify whether delays are caused by equipment, labor, materials, quality issues, or planning assumptions. It also helps improve scheduling accuracy over time.
To get the most value from production efficiency KPIs, manufacturers should keep a few practices in mind:
Manufacturers do not need to start from scratch to gain better operational insight. In many cases, the data is already available. By connecting ERP, MES, quality, maintenance, labor, inventory, and financial data to the right KPIs, manufacturers can turn everyday information into better decisions.
For companies that are collecting data but struggling to use it, an accounting and business advisory partner can help identify the right KPIs, validate data accuracy, and build reporting systems that support both operational and financial performance.
© 2026 SVA Certified Public Accountants