First pass yield is a critical quality metric that most manufacturers keep track of. This straightforward KPI allows manufacturers to quickly and easily gauge product quality and process efficiency at a high level. It does this by identifying the quantity of defects, scrap, and rework originating from a particular production process relative to their overall productive output.

Manufacturing businesses are particularly interested in first pass yield because it helps supervisors identify sources of waste, allowing the company to allocate resources more efficiently.

In this post, we’ll explore first pass yield and how it offers visibility into a manufacturer’s productivity, enabling businesses to drive continuous improvement across their operations.

What is first pass yield?

First pass yield (FPY) is a quality metric that refers to the number of units produced that meet certain quality standards divided by the total number of units fed into the production process over a specific period of time. Also known as throughput yield, this key metric provides visibility into the entire production process, enabling manufacturers to assess their overall efficiency and reduce waste.

Additionally, the financial loss incurred due to wasted material, time, and labor is compounded by the waste created through rework of defective products.

Therefore, it’s important for manufacturers to incorporate first pass yield into their quality management processes. After all, improving this metric results in more effective resource utilization, leading to faster order completion, customer satisfaction, and profitability.

How to calculate first pass yield

Calculating first pass yield is relatively straightforward. It involves dividing the number of items meeting necessary quality standards by the total number of items that began the process. Therefore, FPY is calculated as follows:

First pass yield = number of quality products ÷ total number of units produced

In many instances, first pass yield is denoted as a percentage by multiplying by 100.

Let’s review an example of how this calculation works in a real-world setting:

In a metal works company, a production line molds, cuts, and paints 100 corrugated iron sheets daily. However, only 95 sheets meet the required quality specifications. And of these, 4 require rework, leaving 91 corrugated iron sheets that meet the needed quality standards.

FPY = 91 ÷ 100

FPY = 0.91

In this example, first pass yield is 91%.

How to improve first pass yield

As discussed earlier, calculating first pass yield provides manufacturers with a high-level understanding of their productive output. However, this metric is just a tiny part of the overarching quality improvement efforts, making it dependent on other manufacturing performance indicators.

Improving first pass yield involves refining some other facets of the manufacturing operation. This improvement entails:

Source high-quality materials: Raw material quality significantly affects first pass yield. If a manufacturer uses low-quality materials, there’s a higher chance of creating defective products. This leads to more rejects, scrap, and rework from each production run.

Therefore, manufacturers need to partner with reputable suppliers for a steady source of high-quality materials for their production efforts.

Implement regular preventive maintenance: Faulty equipment on the shop floor can result in defective products or items requiring rework. In addition, non-functional equipment puts production off schedule, delaying the completion of customers’ orders.

To prevent this, manufacturing businesses must carry out regular equipment maintenance to keep the line running. Furthermore, manufacturers should opt for more modern techniques like preventive maintenance.

This involves using analytics to predict equipment health, allowing manufacturers to avoid potential breakdowns.

Collect accurate real-time data: Modern manufacturing businesses are significantly more complex, necessitating more holistic, real-time visibility into their operations.

Utilizing powerful digital tools and leveraging the Industrial Internet of Things (IIoT) provides accurate real-time production data from the shop floor equipment. Analyzing this data shows machinery performance and recommends preventive measures before the equipment breaks down.

Furthermore, real-time data allows supervisors and managers identify inefficiencies across different areas of the business and more quickly respond with corrective action.

Improve employee and operator training: In many instances, employees might perform their roles inefficiently, resulting in low first pass yield. Alternatively, said employees might lack sufficient knowledge, skills, and direction in executing their duties.

Manufacturers can solve this by evaluating employee performance and implementing improved training programs to increase operator productivity.

Automate standardized and repetitive work: Direct human involvement can be inefficient in some manufacturing processes. For instance, efficiency and quality are reduced when workers do repetitive tasks, leading to low throughput yield.

Manufacturers can increase FPY by automating this manual, repetitive work. This frees up personnel for deployment to more complex, cognitive-centered tasks.

Using Tulip to improve your quality management processes

Tulip is used by manufacturers across a wide range of industries to track and visualize product quality data.

Using out no-code platform, businesses are able to integrate machines, sensors, and IoT devices to collect real-time data and track activities across the entire production lifecycle.

Many of our customers leverage our platform across their quality management activities to conduct in-line quality inspections as a part or product flows across production lines.

With real-time operator feedback, supervisors are able to quickly understand what types of defects are being flagged at which stage of production, creating a seamless feedback loop that results in quicker intervention and corrective action.

Real-world case study

One of Tulip's customers in the medical device industry builds custom implantable devices.

Each order requires a unique combination of steps to properly fulfill. They use Tulip in the last step before sending the product to the customer.

Here, it’s critical to ensure 100% right-the-first-time design and to eliminate any shipping errors. The prep-to-ship process is incredibly complex. Each component is custom-made, and there are millions of possible step combinations with each process being completely unique.

Prior to Tulip, operators required 6 months of dedicated training and were still prone to human error. Furthermore, prior to Tulip, there was no ground truth about what exactly got shipped to a customer. For example, if a small screw was missing when the customer unpackaged the product, there was no way of determining whether it was lost during the unpacking or if it was simply left out of the kit.

Tulip integrates with their back-end system to provide step-by-step instructions that are unique for each assembly. This eliminates the need for expensive operator training. As one operator says, “you just can’t make a mistake. All the information you need is right here.”

Tulip’s pick-to-light capabilities illuminate the appropriate bin at the appropriate time, effectively error-proofing the process. When an operator completes a kitting, Tulip captures a photo of the final product and associates it with that order. With Tulip, they have a definite record of exactly what was shipped to whom.

Since implementing Tulip, the manufacturer has not experienced a single incorrect shipment. Now, a new operator can fill production orders, unsupervised, on the first day. This further helps the medical device company by reducing regulatory compliance issues associated with sending the wrong shipment to the wrong customer. Furthermore, when a customer calls to report a missing screw (or other peripheral components), the manufacturer is now able to pull up the photo of that specific order in real-time to confirm whether it was packaged correctly.

If you’re interested in learning how you can use Tulip to track and measure key quality metrics across your operations, reach out to a member of our team today!

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