In the first part of our interview series with Injection Molding industry experts Andreas and Björn, we explored the role of data-driven decision-making in optimizing the injection molding process. If you missed that conversation, you can check it out here.
Now, we dive deeper into how Minitab helps companies tackle real-world challenges in injection molding—from reducing scrap rates to speeding up production without sacrificing quality. In this second part, Andreas and Björn share specific examples and reveal what truly matters to professionals looking to streamline their processes.
| Björn Noreik is a Minitab Certified Trainer and Lean Six Sigma Master Black Belt with over 25 years of experience optimizing manufacturing processes and products across various industries, including automotive, electronics, pharmaceuticals, and plastics. As an expert in statistical tools and machine learning approaches, Björn helps project teams enhance quality and efficiency through methodologies like Design of Experiments (DoE). He also serves on the board of the European Six Sigma Club Germany (ESSC-D) and is a jury member for the German Six Sigma Award. | |
| Andreas Thümmel is a professor for applied mathematics in Darmstadt and an experienced consultant, offering training, workshops and project work to his clients. For this in 2009 he founded a company to provide this in the market. His experience is based in analyzing and optimization of complex economic and industrial data in various industries including materials science, aerospace industry, automotive, electronics, pharmaceutical, bioscience and plastics industries. His main focus is related to research, development and support based on statistical and data science approaches to improve the related developments and processes for his partners and clients. |
Andreas:
Supplier inconsistencies, especially with the growing use of recycled materials, are increasingly challenging. These materials naturally vary in behavior, complicating production processes. Statistical tools like SPC and regression analysis are crucial in managing this variability. Design of Experiments (DoE) further aids in understanding and adjusting to these material inconsistencies, which is particularly important for complex processes like foamed materials and 3D-printed parts.
Björn
Relying on your suppliers is key, but even small changes in raw material formulations can lead to noticeable shifts in product quality. While it’s impossible to test for every potential issue, using SPC control charts can help identify when something is off, even if the cause isn't immediately clear. For instance, an increase in variance might indicate a change in raw material quality. When a pattern appears, you can investigate further to determine if the variation will impact the next stages of production or the final customer.
SPC tools allow manufacturers to catch issues before products reach customers. However, these charts don't tell you why a change has occurred—they simply highlight that something is different. Once you detect the variance, you need to dig deeper into the process or material to find the root cause. This method allows manufacturers to spot trends—like reduced pressure in the process—and adjust accordingly, ensuring product quality remains high. The control chart becomes a sorting tool, helping you identify potential defects and prevent faulty products from leaving the factory before you fully understand the underlying problem.
Andreas:
Yeah, of course. We have many real-world examples, though some of them are confidential due to non-disclosure agreements with certain companies. However, we also have research projects. I’m involved with the University of Darmstadt, and we have bachelor’s and master’s theses, as well as other research initiatives, where data analysis has become a key component. The Institute of Plastics Technology in Darmstadt has been increasingly focused on integrating data science, computer science, and database management into their research projects.
Historically, research primarily revolved around material investigations, but now we see the value in leveraging data science. This requires collaborative teams—computer science experts, engineers, statisticians—working together. The key to success is combining these disciplines to gain actionable insights. Though the specifics of real-world examples may be restricted, we’ve seen significant success in these research activities by applying tools like SPC, regression analysis, and Design of Experiments (DoE).
Björn:
A real-world example, without naming the customer, involved a company facing typical injection molding challenges—shrinkage, surface defects, and short shots where the mold cavity isn’t fully filled. These issues are common, and while they seem straightforward, finding the root cause can be complex. They tried various approaches—adjusting pressures, temperatures, and injection channels—but couldn’t pinpoint the issue.
At this stage, they turned to Minitab for a more systematic, data-driven approach. Using tools like Minitab Workspace and DFSS (Design for Six Sigma), they mapped out the project and identified the critical factors impacting short shots. A simple factorial DoE with center points revealed interactions between plastic temperature and tool temperature that they hadn't previously considered. Within a short time, they reduced the short shot rate to zero.
This is a classic example where Minitab enabled them to move beyond the trial-and-error method and adopt a structured approach, leading to quick, effective results. Short shots—when a product like a pipe is only 70 or 80% filled instead of the full 100%—are a widespread issue, often caused by insufficient temperature or pressure. With Minitab, they could easily visualize and optimize the factors, solving a problem they had been living with for years.
Björn:
It's not just about making things cheaper; it's about increasing speed. I remember a customer who had their molding process under good control and could confidently tell their clients, “I can produce any quantity you need in a short time frame.” For instance, whether they need 80 parts or 10,000 parts, it's no problem. The key is speed and flexibility. This company could reset their machines multiple times a day—sometimes up to 3 or 4 times—to produce small parts, allowing them to meet exact customer demands. This ability to quickly adapt gives them high flexibility and a competitive edge when they’re close to their customers’ plants.
Andreas:
Time is money, right? The faster you are, the more profitable you become. Often, when Björn and I visit a company, it’s because they have a specific problem to solve. We're usually part of a task force or problem-solving team. In these cases, cost isn’t even the main concern. The goal is to provide a fast solution. When faced with the choice between faster or cheaper, the answer is clear: faster is better. And it’s not just about speed—it’s also about creating safer, more robust solutions. In the end, any claims we make in our presentations need to be backed by data, often through hypothesis tests or other statistical methods to prove that the solution is valid and effective.
The injection molding industry is driven by the need for speed, precision, and adaptability. As Andreas and Björn highlight, success often comes down to solving problems quickly and implementing robust solutions that are backed by data. Minitab's powerful tools not only help professionals identify and resolve complex issues but also allow them to stay ahead in an ever-evolving industry. Whether reducing scrap rates or optimizing production speed, Minitab is a crucial partner for injection molding companies seeking continuous improvement.
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