Masimo is a global medical technology company that develops and produces a wide array of industry-leading monitoring technologies, including innovative measurements, sensors, and patient monitors. Powered by the Masimo Hospital Automation™ platform, Masimo connectivity, automation, and telehealth and telemonitoring solutions are improving and automating care delivery both in the hospital and beyond. In addition, Masimo is home to an expanding array of consumer health and wellness solutions like the Masimo W1™ health tracking watch and Stork™ baby monitor, as well as sound devices from legendary audio brands like Bowers & Wilkins® and Denon®.
The Challenge
With the expansion into consumer health and wellness products, the team at Masimo strived to improve the aesthetics and sturdiness of the packaging of their products. Specifically, the design team wanted to include a plastic shrink wrap around the box to provide a sleek look and feel to the product while also protecting the box during shipping.
Shrink wrapping is a common packaging technique in the consumer electronics industry, but Masimo had never performed this process, and had no internal talent or experience in developing a shrink wrap process. Manual processes proved costly and error-prone, so the process engineering team sought out to implement a fully automated shrink wrap process to increase output and ensure consistency.
The requirements for shrink wrap packaging are:
- Shrink the plastic wrap without any cosmetic defects such as tears, wrinkles, and voids.
- Minimize the size of “dog ears,” which are excessive triangular pieces of plastic that degrade the cosmetic quality of the product and make it more difficult to open, to a maximum length of 0.060.”
- Minimize the amount of material used to achieve (1) and (2).
The heat shrinking equipment that Masimo purchased consists of two units that operate sequentially: the first unit wraps the heat shrinking material around the product package, while the second unit shrinks the wrapping. The first unit comprises three numeric factors that control the amount of material used:
- The bag length.
- The film advance rate.
- The depth of the table which defines the wrap length.
The second unit heats and shrinks the plastic and consists of two numeric factors:
- Sealing time.
- Sealing temperature.
How Minitab Helped
Minitab Statistical Software helped characterize and optimize the heat shrink process.
The first step was to use Design of Experiments (DOE) to build a process model relating the heat shrink equipment parameters to the heat shrink output. The factors studied were the five described above, and the response was the dog ear length. There were limited materials available for this study, so the process engineering team consulted the Minitab Support web pages and decided on a “Definitive Screening Design.” The Minitab Support section provided detailed, easy-to-understand information on how to choose a DOE design while also detailing the rigorous mathematics involved in the statistical calculations.
Figure 1 summarizes the statistical output provided by Minitab. Much to the team’s surprise, no factors were significant at 95% confidence (p > 0.05) and the R-sq(pred) was negative, meaning there was no predictive power of the model.
This indicated that there was possibly too much noise in the data caused by the measurement system. The team suspected that measurement system variation, due to differences in how multiple inspectors performed the dog ear length measurement, was causing issues.
Figure 1
They consulted Minitab Support pages to identify Gage R&R as the appropriate tool to use to conduct a Measurement System Analysis. Gage R&R quantifies how the measurement system variation is attributed to the repeatability and reproducibility of the inspectors as well as the variation due to the parts themselves. Minitab was used to create a Crossed Gage R&R study with two inspectors, 10 parts, and two measurement replicates. The results summarized in Figure 2 show excessive repeatability error, especially for part numbers 1, 2, 6 and 8.
Figure 2
Thanks to these insights, the process engineering team investigated the measurement method and developed a fixture and set of instructions, then repeated the Gage R&R with the improved measurement method. Figure 3 demonstrates a significant improvement in the repeatability error. Although part numbers 6 and 8 still exhibited some repeatability issues, the team concluded that the overall improvement was sufficient to proceed.
Figure 3
Prior to executing a second DOE, the team performed additional feasibility studies and reviewed these results with the heat-sealing equipment vendor. The vendor recommended a fixed sealing temperature and sealing time, and to vary the total energy applied to the plastic by varying the oven conveyer speed. The second DOE was a full factorial studying the following factors:
- Bag length
- Film advance rate
- Table depth
- Oven speed
Figure 4 summarizes the results. The model showed significant improvement over the initial DOE due to the improved measurement system. The main effects of the bag length, film advance rate, and table depth were all significant at 95% confidence (p < 0.05), as well as various interactions including the oven speed. Without the power of Minitab’s DOE analysis, these important interactions may have been overlooked resulting in a sub-optimal process model.
Figure 4
Now that the team was confident in their process model Minitab’s Response Optimizer was used to identify the optimal factor settings to target a dog ear length of 0.060” as shown in Figure 5.
Figure 5
Additional runs were performed at the optimized settings to confirm short-term repeatability of the heat shrink process. Once confirmed, the team drafted operational qualification (OQ) and performance qualification (PQ) protocols and formally validated the process, successfully implementing the automated heat shrink equipment and realizing significant cost savings and improved product quality.
Result
The use of Minitab was instrumental in implementing the automated heat shrink process. The Minitab Support web pages were extremely helpful in identifying the DOE design and interpreting the results. This interpretation prompted the team to investigate the measurement method using Minitab’s Crossed Gage R&R tool. The detailed statistical output and graphics correctly pointed the team to identify a method to reduce repeatability error, and a second gage R&R confirmed the improvements. Lastly, Minitab’s factorial design and Response Optimization tools helped identify the optimal parameter settings.
Implementing and optimizing fully automated heat shrinking equipment with the assistance of Minitab helped eliminate inconsistencies in packaging caused by operator variability. Moreover, it led to increased output volume and has the potential for cost savings by streamlining the production process and reducing manual labor.
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Overview
- Founded in 1989
- Headquartered in Irvine, California
- Specializes in health technology and consumer electronics
Challenge
Automate and optimize their shrink wrap packaging process to enhance aesthetics and consistency, specifically through the removal of “dog ears” from their boxes.
Products Used
Minitab® Statistical Software
Results
- Improved measurement system accuracy using Gage R&R analysis
- Automated shrink wrap process successfully
- Achieved target dog ear length, enhancing packaging quality