Quality Metrics in Manufacturing

Manufacturers with complex processes and government contractors in the manufacturing sector must ensure quality to remain competitive and adhere to rapidly evolving industry regulations.

Real-time quality metrics in manufacturing provide much-needed visibility into production quality issues and help prevent future adverse occurrences. These measurements or performance indicators are an effective way of introducing transparency and accountability into an organization. With consistent monitoring of quality metrics in manufacturing, executives and various stakeholders can get the insight needed to make data-driven decisions and gain better control over the manufacturing process to lower overall production costs, deliver high-quality products and practice continuous improvement.

Organizations should work to define and optimize quality metrics in manufacturing to support operational objectives and exceed customer expectations. Manufacturers can leverage the power of this data to establish benchmarks for repeatable quality outcomes.

Manufacturing is a dynamic environment that requires consistent monitoring. Quality metrics are essential to manufacturing success and provide the instant intelligence necessary for optimal production. Quality metrics quantify how a manufacturer’s customer perceives its experience with the organization.

Examples of foundational quality metrics that complex manufacturers and government contractors should consider in enhancing overall customer experience and company reputation are as follows:

On-Time Delivery Rate

The On-Time Delivery Rate tracks the percentage of goods delivered on time as promised, complete without error. This quality metric reflects the response rate and significantly impacts customer satisfaction.

To calculate, gather the number of orders delivered on or before the promised date and the number of orders received during that same period. Divide On-time Orders by Total Orders and multiply by 100 to get the On-Time Delivery Rate.

On-Time Delivery Rate = (On-time orders / Total orders) x 100

A high On-Time Delivery Rate is an indicator that a company can consistently meet its delivery commitments to its customers. On-time Delivery Rate can also be a useful quality metric in spotlighting supply chain quality issues.

Customer Satisfaction Rate

The Customer Satisfaction Rate tracks the percentage of goods delivered against customer complaints and returns. This quality metric or rated response is another indicator of reliability and can be used to help identify and correct recurring problems that are visible to your customer. Manufacturers can gather customized feedback from customers through surveys, interviews, or product reviews.

Supplier Quality

This metric assesses the quality of materials or parts supplied by suppliers and third-party vendors. Ensuring high supplier quality is essential to maintain the overall product quality.

Production quality metrics focus on the manufacturing process. They assess how well a product or component conforms to the intended design standard. With an informed view into manufacturing quality, issues may be addressed at crucial points throughout the production process, which helps mitigate risk and reduce costs.

What is the Meaning of FPY?

First Pass Yield, commonly referred to by manufacturers as FPY, is a quality KPI in manufacturing that determines the usable parts or components in a process.

To calculate FPY, divide the number of acceptable units without rework that pass the quality inspection process, by the number of units that were part of that process. Multiply by 100 to get the FPY percentage.

FPY = (Acceptable units / Total units) x 100

First Pass Yield is a good indicator of waste elimination – a key priority for continuous improvement. A high FPY percentage suggests a successful production run resulting in products that meet the intended quality standards and is a key indicator in assessing overall manufacturing performance. Additionally, higher FPY values demonstrate production cost efficiency which is an important contributor to company profitability.

Quality metrics in manufacturing are critical in achieving operational excellence and superior product quality. With predictive measurements in place, companies can collect data in real-time to track against a defined set of standards. This data can then be used to identify quality trends and improve production performance to meet customer demands.

Scrap Rate

Scrap Rate very simply measures the percentage of materials (“the scrap”) that does not make it into the final product due to defects or errors. To calculate the Scrap Rate in manufacturing, choose a period such as a month or quarter. Here is the equation:

% Scrap = (# Scrapped materials / The material intake (or usable units)) x 100

In the case of scrap, the lower the percentage, the better. This production quality metric represents manufacturing efficiency and is a good indicator of the quality control process in place at an organization.

Rework Rate

The Rework Rate encompasses the entire effort to correct a defective, failed, or nonconforming item during or after an inspection. Disassembly, repair, or replacement of an item are all accounted for in the Rework Rate calculation.

One of the most common methods to calculate the Rework Rate is based on the number of units that need to be reworked and the total number of units manufactured. Select a timeframe, divide the number of units that required rework by the number of units produced and multiply by 100 to get the Rework Rate.

Rework Rate = (# Units that require rework / Total units) x 100

Rework significantly impacts cost and schedule and is a production quality metric that should be monitored closely to improve performance.

Manufacturing performance metrics provide the data to optimize manufacturing operations and streamline production processes. By keeping a close eye on these measurements, manufacturers can adjust operations that will reduce downtime and improve overall product quality.

Work-in-Process (WIP)

Work-in-Process (WIP) refers to components or goods that are not ready for completion. The items are either still waiting to be built, or they are being held for further processing. This manufacturing performance metric accounts for the goods that are still on the factory floor. WIP costs including raw materials, labor and overhead are accounted for as inventory assets.

To calculate WIP, take the value of partially completed goods in process at the beginning of the accounting period and divide it by production costs incurred such as materials, labor and overhead. This will illustrate the value of Work-in-Process at a specific point in time.

Work-in-Process (WIP) = Value of partially completed goods / Production costs incurred

Companies should work to keep the Work-in-Process at optimal levels to efficiently utilize manufacturing resources, keep the inventory lean and reduce costs.

Throughput

Throughput is a term commonly used in manufacturing that measures the number of products or components produced within a given period.

To calculate, take the number of units and divide it by the number of days.

Throughput = # Units / # Days

Throughput is a valuable manufacturing performance metric that assesses the overall efficiency of the production process.

Cycle Time

Cycle time is an important factor used in manufacturing to calculate the amount of time it takes to fully complete one full cycle of an operation. Cycle Time represents the total time it takes to manufacture a product or component from start to finish.

To determine an accurate cycle time, take the total time spent on production in hours, excluding downtime and maintenance and divide it by the number of total units produced.

Cycle time = Time spent on production / Total units produced

An efficient quality process reduces cycle time and leads to improved productivity, higher customer satisfaction and increased competitiveness.

Overall Equipment Effectiveness (OEE)

Overall Equipment Effectiveness (OEE) is a manufacturing performance metric that measures the complete collection of manufacturing assets. It provides insight into how well operations are performing, taking into consideration the availability, performance and quality of manufacturing equipment.

To calculate Overall Equipment Effectiveness, three factors must be considered: Availability, performance and quality.

First, calculate Availability by dividing run time or actual time equipment is in operation, by planned or scheduled production time.

Availability = Run time / Planned production time

Then calculate Performance by multiplying the ideal cycle time by the total count, divided by run time.

Performance = (Ideal cycle time x Total count) / Run time

Then calculate Quality by dividing the percentage of defect-free products by total count.

Quality = % Defect-free products / Total products

Multiply these three factors together to get OEE, a comprehensive asset effectiveness score.

Overall Equipment Effectiveness = Availability x Performance x Quality

Mean Time Between Failure (MBTF)

Mean Time Between Failure (MTBF) is defined as the average time between equipment failures. This manufacturing performance metric serves as a gauge for the reliability of a particular equipment or component and provides actionable intelligence that will help prevent problems from reoccurring.

To calculate, determine the total operating time for a particular piece of equipment and divide by the number of equipment failures during that particular time period. The result shows the number of hours expected before encountering the next equipment failure.

Mean Time Between Failure (MBTF) = Total operating time / # Equipment failures

MTBF is a solid reliability indicator of how shop floor equipment is performing during the production process.

Quality Key Performance Indicators (KPIs) in manufacturing are important benchmarks used to assess operational performance and are critical to the quality control process. Establishing a set of quality KPIs in manufacturing is considered best practice and is a reliable way to identify areas for product improvement.

Let's examine a few examples of quality KPIs in manufacturing to understand how they can deliver valuable intelligence to support quality assurance efforts:

Cost of Poor Quality (COPQ)

Understanding the COPQ is critical for manufacturers as it is a quality KPI in manufacturing that is not always given the attention it deserves, due to which, can result in missed opportunities for cost savings and quality improvements. It is important to an organization’s production strategy and can make a significant impact on manufacturing profitability.

There are several factors that contribute to the COPQ.

• Internal failure costs or direct costs occur within an organization and can include costs such as scrap or rework, retesting and downtime.
• External or indirect costs can be issues discovered by customers post-delivery, warranty claims, or returns.
• Appraisal costs include expenses related to product quality evaluation, testing and audit.
• Prevention costs are investments made to mitigate risk such as training, process improvement, quality management solutions and planning.

Add Internal Failure Costs, External Failure Costs, Appraisal Costs and Prevention Costs to find the Cost of Poor Quality. COPQ is essential to the practice of continuous improvement.

COPQ = Internal failure costs + External failure costs + Appraisal costs + Prevention costs

High COPQ indicates that there are significant quality-related issues in a manufacturing process.

A supplier audit provides a comprehensive look at supply chain performance to identify areas of improvement and best practices. Suppliers can have a significant effect on the cost of quality. Many of the factors that contribute to the Cost of Poor Quality stem from supplier-related activities. Manufacturers can then work with a supplier to improve quality or choose to pursue cost recovery through supplier charge-backs to recover the cost of faulty products.

Supplier Defect Rate

The Supplier Defect Rate is a quality KPI in manufacturing that measures the number of supplier-provided items within a shipment or batch that were defective. This KPI is an essential evaluation of an individual supplier and if kept in check, can reduce the need for rework and prevent defective materials from entering a manufacturer’s supply chain. Manufacturers should establish defect thresholds that align with their quality standards to improve outcomes.

To calculate Supplier Defect Rate, take the number of units defective divided by the number of units supplied and multiply by 100 for the percentage.

Supplier Defect Rate = (# Defective units / Total units) x 100

Non-conformance Rate

The non-conformance rate is a measure of the proportion of non-conforming or defective items or processes within a specific sample or over a given period. To calculate the non-conformance rate, the following formula is typically applied:

Non-Conformance Rate = (# Non-conforming items or processes / Total items or processes inspected) x 100

Reducing the non-conformance rate is a common goal in quality management and process improvement efforts.

Quality metrics in manufacturing are critical in achieving operational excellence and superior product quality. With predictive measurements in place, companies can collect data in real-time to track against a defined set of standards. This data can then be used to identify quality trends and improve production performance to meet customer demands.