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Leveraging IoT in Manufacturing

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Leveraging IoT in Manufacturing

Want to learn more about how IoT can revolutionize the manufacturing industry? Check out this post to learn more about IoT processes in factories.

· IoT Zone ·
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A digital wave has swept across organizations from all functional areas in the last few years — from manufacturing to planning to finance and budgeting. It has the potential to create a competitive advantage by providing critical information about consumer behavior that helps optimize business processes. By facilitating automation and tightly integrating multiple business functions, the term "digital" has multiple warriors: automation, big data, IoT, Virtual Reality (VR), etc. IoT, with big-data capabilities, holds a lot of promise, especially in the manufacturing realm. This article attempts to briefly explain the technical role of IoT in manufacturing processes.

First and foremost, what is an Internet of Things (IoT) platform? At a high level, it’s a “packaged big-data” platform that can extract a large amount of data from virtually any source. This, in turn, helps optimize business processes and reduce costs leading to a competitive advantage for organizations. Let’s analyze how it can be effective in the manufacturing area.

First, we need to address how it is different from traditional, analytical Business Intelligence (BI) tools. Basically, it has a few distinct advantages:

Ability to Extract Data From Equipment and Devices

Traditional Business Intelligence (BI) relies on extracting and analyzing data from systems and files. More often than not, it’s not real-time data. IoT platforms have the capability to extract data from equipment as well as devices. When equipment is operating on the shop floor, they generate large volumes of data. Much of these data relate to machine parameters, such as temperature, pressure, inlet valve readings, etc. Before the advent of IoT, much of these data went “unnoticed.” With IoT platforms expanding, factories could extract the data generated by equipment and devices to analyze for efficiency improvements.

Leveraging Edge Analytics to Analyze a Large Volume of “Unstructured” Data

Often times, data generated by equipment and devices are “unstructured.” Loosely speaking, un-structured data essentially means that they are not in the form of records separated by delimiters. They are free texts with hardly any pattern. Using traditional BI, it’s almost impossible to read the data and make sense out of it. Whereas, IoT platforms have the ability to read and analyze data on its own.

Ability to Perform Real-Time Analysis

As outlined above, current BI applications generate reports periodically. In other words, contents of reports are refreshed after a certain interval, e.g. after a few hours or sometimes even after days. Modern factories need to respond to changing market scenarios quickly. Hence, their decision making depends on real-time or near real-time reporting and analysis. IoT platforms empower factories with a real-time report feed.

High-Level IoT Architecture

The following diagram represents a high-level IoT architecture in a manufacturing environment. It may be noted that this is illustrative in nature; every organization will develop their own architecture.

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Let’s look at a few use cases in manufacturing;

Predictive Analytics to Minimize Downtime

It is critical for factories to meet the production target, which is, in turn, dependent on uptime. Hence, it’s important for factories to avoid unplanned downtime. Plants do perform “Preventive Maintenance” to limit equipment breakdowns. However, preventive maintenance, by nature is more periodical and doesn’t factor into warning signs from equipment. With an IoT platform, data generated by machines can be collected and analyzed using the analytical engine. This analysis can identify patterns in machine parameters just before a breakdown. Going forward, whenever the same pattern is observed, plants can do “predictive maintenance” to avoid machine break down. There may be new scenarios that this pattern or algorithm may not be able to predict, but it can prevent many breakdowns. This will also help reduce unplanned downtime.

Quality Management

In this era of intense competition, maintaining the highest level of product quality is key to beating the competition. Assembly lines produce thousands of units every day. They go through a robust testing process before being shipped out. However, there are instances when defects escape tests. Factories learn about these defects only after customers log complaints. There have been multiple instances when products have been recalled due to defects. With IoT, plants can collect a large volume of manufacturing and testing data in real-time. It will then be worked on by an advanced analytics module in the IoT platform to match the pattern with the ones collected from defective products. Whenever the patterns are similar or the same, the lot in question can be sent back to the factory to undergo further testing. This will help preempt the defects and fix them before customers log any complaints. Even in cases when defects are analyzed post-customer complaint, it’s challenging in the existing analytics framework to nail down an exact lot of the materials used in them. This is because the volume of data is so large that traditional analytics platforms may not be able to effectively handle them. IoT platforms specialize in analyzing a large volume of data, hence they can provide more accurate analysis as to where the defect originated.

Optimization

Plants consume many resources, such as raw materials, energy, water, etc. There are two ways IoT can help optimize this. First, in a process manufacturing environment, a given amount of chemical is used to produce multiple lots of finished products. The amount consumed is not uniform all the time. IoT can be of help, in this case. Connected devices can collect real-time consumption data directly from measuring instruments in equipment and analyze them over hundreds of lots. They can then point to possible anomalies, which might be responsible for overconsumption in some cases. Fixing this can help save money for the factories. Similarly, IoT platforms can collect and analyze energy consumption data of equipment, air-conditioning system, lighting systems, and perform complex what-if analysis and suggest an optimum usage pattern.

Conclusion

IoT holds a lot of promise in the manufacturing area, however, it’s not a quick fix solution. It involves significant investment in terms of budget and manpower. Many times, payback is not “attractive.” Hence, it’s essential that companies have a longer-term view and an appetite to make a significant investment, which may not yield tangible results in short term. It may be called out that a realistic approach will be able to start small and gradually increase the scope to include multiple functional areas and business processes.

Topics:
connected devices, data, data analytics, iot, manufacturing, smart factory

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