ESCAPING DATA ISLAND How manufacturers are going beyond data collection to drive operational improvements
T
he proliferation of sensors, programmable logic controllers (PLCs) and countless other advancements in Internet of Things technologies has created a massive trove of data for manufacturers to access.
Control. “I’m doing something that’s adding no value to my organization and adding no value to the customer. If I’m just collecting data and not using it to impact my business, then I’m wasting my time and resources.
However, without the proper tools, systems and training in place, this wealth of information can largely go untapped, creating a drag on an organization rather than improving efficiencies, increasing quality and driving value.
“You don’t realize the impact or ROI on these investments until you use the data to make changes in your organization.”
“You can collect all the data in the world, but unless you’re using that data, it’s overprocessing, as they would say in manufacturing,” said Michael McGray, a senior sales engineer with Zeeland-based Industrial
“You can collect all the data in the world, but unless you’re using that data, it’s overprocessing … (You’re) doing something that’s adding no value to my organization and adding no value to the customer.” —Michael McGray, senior sales engineer at Industrial Control 8
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Data collection is nothing new. Supervisory Control and Data Acquisition (SCADA) systems have been around for decades. However, in today’s technically driven manufacturing environment, it’s no longer enough to simply collect data. SCADA has since evolved into a larger umbrella including data storage, visualization and analysis in which myriad tools, information systems and software are used to drive process improvements on the shop floor. Yet many manufacturers are “deadheading” the data they already collect, leaving it on their various machines or stuck in “shadow” databases they don’t readily access, McGray said. In his work, McGray points to numerous examples where an operator manually records the number of pieces a machine is producing. A supervisor then comes around, evaluates the production rate and determines if the operator and machine are on task. This amounts to a manual
intervention, ultimately decreasing efficiency, according to McGray. The machine’s sensors are already conducting a piece count along with other key parameters such as the RPM of the tool. This data should then be exported to a database where it can be viewed immediately by the operator and analyzed and benchmarked later by production managers and other executives. “Unless people are looking at the data, it’s still not helping at all and you’re still going to have the same old problems,” McGray said. “That’s what this has really been about: getting this data off these islands and into an environment that brings transparency to it.”
IMPLEMENTATION REALITIES In practice, expanding beyond data collection into storage, visualization and analysis can prove challenging for manufacturers, particularly smaller and mid-size companies that may lack the resources and expertise to properly implement the systems. One of the primary challenges comes from making all the different technologies — sensors, PLCs, robotics, machines and other tech — interface in a cohesive manner. These technologies may all operate under different industrial protocols or “languages” and require third-party tools and software to translate them through a company’s ERP or MES system, according to Gray. Once all the different pieces of technology around the shop floor are communicating in the same language, manufacturers need to store that data in a database such as SQL or Oracle. From there, they should deploy a data visualization tool, like PowerBi, to properly analyze and act on the data. The issue is that these are often complex and expensive systems, outside the budgets for many small and mid-size manufacturers. When helping manufacturers implement these systems, McGray likes to begin VISIT ADVANCEDMANUFACTURINGEXPO.COM
