The Industrial Internet of Things (IIoT) asserts that “smart” machines can be faster, more accurate and more reliable than humans when it comes to capturing and communicating data. In the manufacturing environment, this means identifying sooner the inefficiencies and problems that can waste time and resources, and lead to costly recalls and warranty claims.
But how do you efficiently and cost-effectively manage all the data generated by the processes and test stations on a modern manufacturing line?
In discrete manufacturing industries like automotive, off-highway or even medical, a line can include dozens, even hundreds, of stations. Each one generates reams of data per shift, be it scalars, digital process signatures or machine vision images with their related datasets.
Manufacturers can no longer afford to leave this data trapped in silos. It must be collected into a central database where it can be correlated, studied and visualized, by the serial number of the individual part or assembly.
The falling cost of sensors and data acquisition systems, network topology and throughput, and multi-terabyte class storage make collecting all these datasets from a line very feasible. Powerful and advanced off-the-shelf analytical tools make it easy to integrate, correlate and analyze all this data together, for rapid trending and root cause analysis. Quality engineers can then find trends and patterns that reveal the how and why of decreases in yield. This applies to any controlled process—from press fitting and leak test to rundown, crimping, welding and dispensing.
A part failure can easily be distinguished from a test malfunction. The quality team can spot anomalies that require further investigation, pinpoint where problems occur during a process, and optimize test stations by understanding how to shorten the test. A few dozen defective units can be tracked down without having to scrap, rework or recall thousands.
This is the essence of IIoT.
OEM struggles with production downtime
Take the example of a manufacturer of agricultural machinery that struggled to make effective use of its production data. Its team understood the value of levering its data, but lacked a consistent and centralized means of collection, storage and retrieval.
Scalar pass/fail data from end-of-line engine hot test cells would end up in one silo, entered manually and indexed by time and date stamp. Further up the line, some process stations, such as torquing for bolts, collected full process signatures, indexed by serial number, but this data ended up trapped in a different silo.
These silos included a self-built SQL database as well as vendor-specific databases that lacked the functionality or connectivity to quickly pull full birth history for a part or assembly by serial number. The data wasn’t lost, but any exercise at retrieval and analysis to address an issue was a search for the proverbial needle in a haystack that required custom query tools.
The entire global operation suffered from a mashup of databases and data retrieval systems. Each plant operated with its own standards, processes and metrics for quality management. A quality engineer at a plant in Mexico could do nothing to help their counterpart in France who had an issue with a comparable machine or line because there was no standardization across the enterprise.
Weeks lost due to safety fears
When a product came back from the field due to a customer complaint or warranty issue, it routinely took as long as a week to retrieve all the related data scattered across the plant.
The result? A lengthy feedback loop to trace the root cause and scope of a quality issue. This created uncertainty and lengthy production delays because the manufacturer didn’t want to take the risk of continuing to ship what could be defective products. In one example, a faulty gear system caused high-risk issues for customers in the field. Full production was halted until the cause of this defect could be found and addressed. That took several weeks.
Though such disruptions have an obvious impact on revenue and profitability, the greater concern for this manufacturer was the public relations impact on its image. This is a premium brand with a quality reputation that justifies a higher retail price than its competitors.
Better data analysis tools were needed to shorten the time to insight between the presence of a quality issue, awareness and resolution.
The manufacturer turned to a vendor that specialized in data management and manufacturing analytics.
The vendor took all the manufacturer’s disparate forms of process and test data from the line and converted it into formats that could be uploaded into its own centralized database. Data was no longer trapped in silos. The manufacturer’s quality teams were provided with the tools and know-how to develop a suite of algorithms to quickly search, retrieve and correlate data from this single centralized repository for rapid root cause analysis.
Production and quality issues that once took days or weeks to identify and address can now be resolved in minutes.
The vendor’s analytics tools have allowed the manufacturer to quickly drill into its data and analyze the impact of design changes, improve quality checks and report on metrics.
All this has been achieved from the data the manufacturer already collected—it just needed the right tools to unlock its potential. These tools were readily available from a third-party vendor and at a desirable price point.
Improving quality, systemwide
The manufacturer is now adopting the vendor’s data management system as a standardized quality platform across its engine and powertrain units at four plants in North America and Europe.
Additional work is being done to intensify the value of this investment by increasing the number and types of data collected from the line—automatic valve lash stations, torque tools and leak testing for engine block fuel, oil and coolant cavities.
It’s important to note that, in this use case, the vendor’s tools are flexible and agnostic—they can interface with and ingest data from other third-party process and test station equipment and operating systems. The manufacturer could elevate the return on its existing technology investments from other vendors—a costly rip and replace was not necessary.
The concept of IIoT, along with Industry 4.0 and Manufacturing 4.0, are no longer futuristic “hope to achieve some day” concepts. They are redefining the competitive landscape of global manufacturing today.
Manufacturers can chart a reasonable upgrade path using proven data collection and analysis tools that can collect and correlate all the data from the plant floor. For relatively modest investments, they can realize substantial gains in quality, efficiency and profitability.
If you want to benefit from rather than be sidelined by IIoT, it’s time to elevate the conversation about how to make the most of your data.
This article is provided by Sciemetric, a supplier of products that provide visibility into assembly line processes to optimize yield, boost quality and drive down costs. For more information, visit Sciemetric at www.sciemetric.com.