Across industry, Industry 4.0 and the Internet of Things (IoT) is reshaping manufacturing. Things that were previously too expensive, time consuming, or simply impossible are all becoming realities with IoT strategies.
IoT projects allow more connectedness between machine systems and disparate legacy programs. This leads to gains in areas across the plant, from productivity, to logistics, to maintenance, and more.
One area that may not be immediately associated with IoT is product quality. After some consideration however, product quality surely can be enhanced by IoT projects, be it directly or indirectly. Keep reading to find out more.
One clearly understood feature of IoT projects is the increase in data made available to the organization. By adding more sensors in the field, real time data will be generated. This data can be made available throughout all levels of the organization.
This may seem like a simple idea, but improving data availability is a huge key to unlocking your plant’s full potential. Think about your existing data and systems. Is it costly to access your data?
For example, is some of your data on a paper log in a file cabinet? Does it require a human to pore through screens or manually input the data? Though these costs are not always quantifiable, they make data accessibility prohibitive.
IoT projects should address this by making data more available to analysts. This obviously includes the Quality Department, who can utilize this data in their investigations. In the same fashion, having more good data can be used during your reliability engineering efforts and could hold additional clues to how to improve product quality.
Upstream & Downstream Effect Tracking
When a quality event occurs, it can sometimes take a lot of effort to determine the root cause. This is where IoT projects can assist. When more data is accessible, understanding of the process is augmented and end-value analysis can be completed faster.
The idea is to make information about the process more obtainable. Thus, it becomes easier for your staff to run analyses using that information.
Over time, increased data flow from IoT strategies will likely result in a deeper understanding of the process. It should become clear which process indicators could lead to potential downstream quality problems. This analysis can be further accelerated with AI and machine learning software.
This idea also works downstream in product storage and delivery. For example, many products my degrade under high or low temperature conditions. These conditions could potentially be tracked and saved, so that product quality becomes more traceable.
Equipment Operation and Maintenance
One of the most straightforward applications of IoT is in measuring equipment operation. Through tracking key operating conditions, you can get a sense of when a machine’s performance starts to degrade. And when this happens, you can be on alert for quality issues as you prepare for the maintenance or repair to the machine.
Equipment breakdowns contribute to quality problems, often before the breakdown actually occurs. However, if your plant is simply running equipment until failure, you will likely have more worries than product quality. The issues could be numerous: downtime, schedule adjustments, spare parts, overtime, safety hazards, and more. That’s why it’s so beneficial to use strategies like IoT to cut down on equipment malfunctions.
But before a breakdown, IoT and institutional know-how will give your team insights about the equipment that could be hard to discern by a person alone. Small shifts in performance could lead to further problems down the line. Tracking these shifts digitally takes away the guesswork. It puts the power into people’s hands, so that they can make informed decisions about their processes.
As an example, maybe you want to track a critical pump in the process. You could add vibration sensor, a current meter, and a temperature sensor to the pump, tracking each. A few months later, your technician notices the current draw starting to move off its normal reading. He investigates further and finds an impeller has broken off, causing fluctuations in current as the pump attempts to keep up with needed cooling rates. He schedules a repair which is worked into the production schedule.
This whole scenario avoids the later breakdown, which causes potential headaches, including quality issues. Maybe a reduced cooling rate raises the temperature of the process slightly, which affects the end product negatively. By catching the problem early, the process is spared from several problems.
Lastly, IoT infrastructure and thinking can lead to many future breakthroughs. Once the barrier for entry is lowered, additions can be made rather easily.
Want to error-proof the process? Use IoT to add limit sensors on key valves or switches which detect when specific actions are occurring. A wireless infrastructure allows for rapid addition of more devices.
Part of this enablement occurs through a change in thinking on the plant floor. As the operators on the line start to see the benefits the IoT can bring, they may start to add their suggestions on how to further improve the data collection and aggregation.
Just as IoT projects are driving improvements in productivity and safety, they are also enhancing product quality. There are several reasons why this happening. IoT improves data availability, which allows for better downstream understanding of process changes. It augments equipment performance and finds problems before they happen, which saves the plant from quality mishaps due to equipment malfunction. Finally, it enables further improvement as the plant becomes accustomed to IoT as a whole. Though there are some risks in implementing IoT, it can pay off in product quality, and many other areas.
About the author
: Bryan Christiansen is the founder and CEO of Limble CMMS
. Limble is a modern, easy to use mobile CMMS software that takes the stress and chaos out of maintenance by helping managers organize, automate, and streamline their maintenance operations.
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