First pass yield improvement is a constant challenge for low volume discrete manufacturing lines. Tulip’s digital operator guidance systems, inline quality reporting, and real-time performance dashboards can help.
Low volume discrete manufacturers often struggle to maintain a high first pass yield in their operations. These lines are characterized by a high variability of product SKUs – sometimes built to order – that often involve complex manual workflows. When the product attributes change, the process for accomplishing the work has to flex accordingly. This makes typical methods to ensure high first pass yield, like implementing standard process, incorporating regular quality checks, and continuously monitoring line performance, a challenge. Simple solutions like providing standard work instructions, or offering operators an image of a “golden part” to reference when conducting a quality inspection, often fail to match the reality of these operations. The result is that each operator is forced to rely on memory. Over time, each operator develops their own tricks to get the job done. This propagates process variability, which causes yield consistency to suffer.
Tulip is a no-code MFG app platform that enables manufacturing engineers to build apps to guide manual operations and collect feedback in real time. This ability is particularly relevant in the context of low-volume high mix environments.
An example workflow might look something like this: an associate scans a work order with a barcode connected to their Tulip App. The information in the barcode scan is used to route them to the appropriate set of digital work instructions. The associate follows the steps for that particular assembly. Since the information needed to accomplish the work is provided through a digital app interface, the appropriate video or photo references can be related to the context of that particular product workflow. If a quality defect is detected the associate can report it immediately from within the app using a digital form. If necessary, they can include additional context by including a picture from a doc camera attached to the touch screen.
The whole process takes only a few minutes. Teams can view the real-time status of their key performance indicators via another app in the form of an interactive performance dashboard. Ultimately, these viewable performance metrics push accountability to the shop floor.
Case Study: Medical Device Manufacturer
One medical device manufacturer that runs Tulip on their prep-to-ship and final QA lines has a similar process. The manufacturer builds custom implantable medical devices. Each order requires a unique combination of steps to properly fulfill. They use Tulip on the last step before sending the product to the customer. Here, it’s critical to ensure 100% right 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. Each process is 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 illuminates 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 component), the manufacturer is now able to pull up the photo of that specific order in real-time to confirm whether it was packaged correctly.
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