To improve quality in manufacturing, plant managers need to identify the root causes of their quality issues. Technology enables you to analyze your data at the deepest level, identifying previously untapped areas of improvement. And this empowers you to unlock greater levels of efficiency and quality control in your operation as you embrace lean manufacturing.
Operations are exploring tools to manage their own specific quality goals and challenges. Plant managers may consider plug-and-play technology because they offer the fastest time to value, but they’re often unsure if a plug-and-play technology system can be integrated and customized to meet the specific needs of an operation. The goal is to implement technology that improves quality while also maximizing efficiency.
When some quality issues arise, there can also be a considerable amount of shop floor activity to deal with defective products. Such issues not only increase working hours, they also waste opportunity costs.
When dealing with a quality defect issue, impacts include cancelled orders, dissatisfied customers and reputational damage, as well as the potential loss of competitive edge. Reducing waste during the end-to-end production cycle is critical for lean manufacturing, which is why quality control is such a vital component of any efficiency strategy.
While there’s no one-size-fits-all approach to improving quality in manufacturing, there is a simple roadmap you can use to plan your individual strategy. Every operation is different, but when using technology for quality control, this strategy can guide operations to maximum efficiency with minimal disruption.
The following is a list of five key stages to consider.
Before you implement new technology, review your existing workflow. Ask the following questions as part of your discussion:
Once you have identified your quality benchmarks and desired margins for improvement, you can start looking at how data can help you reach your goals. If the devices in your factory are not yet connected to one single database, that is the first step. Once you have a single master database, you can start using predictive quality and data analytics to drive lean manufacturing and quality control.
After reviewing your processes and applying technology to collect and analyze your data, you can start using data-led insights to assess which processes are adding value and which are valueless. This analysis not only prioritizes your best areas of improvement; it also helps you plot your individual roadmap.
In the early stages of your digital transformation journey, improvements can be made to reduce unplanned machine downtime by analyzing temperature and vibration. With the right technology, it only takes approximately 60 days of data to start zoning in on these improvements. Operations can potentially earn very quick wins by engaging predictive maintenance and addressing quality issues that result from malfunctioning machines.
With data-led technology, there is the opportunity to see a significant increase on annual cost improvements. Results differ greatly between operations, but in some cases, we’ve seen manufacturers experience results that increased first-pass yield by 25% and increased engineer productivity hours by 30%. Simply layering technology over a factory’s existing machines and devices generated massive results—a tremendously exciting development for manufacturers.
The human element of digital transformation is a critical success factor, so it’s key to invest time in regular training and discussion, as well as mastermind groups to explore innovation and development.
It’s vital to set clearly defined goals and responsibilities both as a team and as individuals. Scheduling regular team meetings to assess the progress of your technology-led projects and participate in ongoing training will help keep momentum on track.
The conversation around digital transformation and the Internet of Things is typically focused on technology, but the reality is that people are just as key to the success of a manufacturer’s digital transformation journey. That’s why training should be prioritized alongside the tech. For optimal efficiency and adoption, people need to understand how technology is used throughout the end-to-end manufacturing process.
In order to keep improving quality in manufacturing while maximizing efficiency, it is essential to understand and benchmark what ‘success’ looks like. By setting clear productivity and quality goals for you and your team, you can start small and scale up, setting small and incremental targets against energy, production time, materials, quality control, working hours and labor costs. You can measure these goals across your preferred time frame using Oden’s technology and get a clear overview of production costs and progress.
Productivity and quality goals illustrate the ongoing status and, most importantly, the purpose of your plant’s digital transformation to everyone from the shop floor to the offices.
If you can cut waste from your production run, then you can increase margins and improve your supply chain management. Technology can play a huge role here, enabling you to find new opportunities for waste reduction in the production process. Technology also gives you greater visibility over your entire supply chain, so you can manage supply issues and adjust your production schedule accordingly. Supply chain visibility proved to be particularly valuable for manufacturers during the COVID-19 pandemic, allowing operations to work in a more agile way around complex supply chain disruption.
Improving quality in manufacturing is a journey. It begins when you design the product with efficiency in mind from the very start.
Let’s briefly recap the 5 key stages to improve quality:
Once you’ve evaluated your processes, you’re ready to set your quality benchmarks and use technology to measure and provide data on the fastest, most efficient way to meet your quality benchmark. There are multiple factors, such as performance benchmarks around material cost and quality, staff hours, energy, and time to market. At Oden, we highlight the value of simplifying operators’ jobs using Process AI, which combines the power of prescriptive performance analytics with predictive quality to lower manufacturing input costs while ensuring good quality.
Improving quality in manufacturing and meeting your efficiency goals is challenging without the right technology. If you rely on the expertise of certain staff members instead of leveraging data, you need those specific people to be available whenever you need them, and that’s not always possible. Many manufacturers have experienced some challenges around this issue during the COVID-19 pandemic. Leveraging data creates resilience in your workforce, enabling you to better manage issues such as staff shortages and turnover, and crisis situations that require your operation to pivot quickly.
One of the most exciting aspects of Oden’s plug-and-play technology lies in its ease of use. Without purchasing new machines or investing in additional talent, such as data scientists and engineers, you can make gains simply by enhancing your existing operation with the right technology – it’s the lean manufacturing dream.