I’ve got a great deal for ya. I’ll sell you this high-end laptop for $5. Amazing, right? But the power cord — that’ll cost you $3,000.
It’s an old trope, but there’s a lesson to be learned. A product is only as good as the sum of all its parts. What good is an exotic European sports car if the tires have no air? Why bother with a 4K monitor if you’re only using it to play back your old VHS tapes from the ’90s? In manufacturing, why would you gather petabytes of data without a way to contextualize it all?
The Rise Of A Data Swamp
Manufacturers should be well past seeking ways to collect data. The industrial internet of things (IIoT) makes possible the connectivity of each machine, device, and person on the factory floor. Each of these numerous sources now has a hose connected to it siphoning rivers of information back to your central repository. But we shouldn’t all start shaking hands and congratulating each other on a job well done just yet. Now that we’ve got the data lake, how can we drink from it? Certainly, you didn’t construct this vast reservoir only to cup your hands for a few small sips at a time.
The promise of smart manufacturing and Industry 4.0 isn’t to collect data for the sake of having it. The real magic is extracting great amounts of intelligence from it to improve yield, quality, safety, and compliance. To make sense of and tap into all this great information, you must seek to contextualize it.
Beyond Connecting And Collecting Raw Data
Contextualization boils down to analysis and action that optimizes and enhances processes. This is where the true magic of all those fancy sensors becomes tangible in the form of auto-adapting processes and high-volume production with minimal human intervention. It’s where we start unearthing deeper, non-obvious insights into anomalies and events that seem otherwise random and unpredictable without this new layer of rich, multi-dimensional context. Now you finally may be able to eliminate downtime and operate as lean as possible with a minimal amount of inventory.
By moving beyond connecting and collecting raw data, you begin to extract real benefits. You not only begin to lower costs significantly, but that value arrives much faster upon implementation. Achieving complete understanding of your data liberates silos, drives business, and unearths new competitive advantages.
Contextualizing Manufacturing Data
ThinkIQ’s Manufacturing Digital Transformation SaaS provides a fact-based granular, data-centric contextualized view of material flows and related provenance attribute data. As you would expect, this contextualized farm-to-fork visibility unlocks access to unprecedented traceability and insight into actions that improve yield, quality, safety, and compliance while reducing waste and environmental impact. Other digital manufacturing techniques fall short because they lack the power to contextualize the data, and thus cannot accommodate hyper shifts in demand or supply sourcing. They rely too heavily on equipment-centric views, which results in trillions of dollars of waste and underperforming assets on top of billions of dollars in warranty reserves for quality and safety issues.
The fact-based granular, data-centric contextualized view of material flows powered by ThinkIQ can improve global manufacturing yield by 3 percent to 5 percent, and save tens of millions of dollars by identifying waste and underperforming assets.
For example, the ThinkIQ platform coupled with a ThinkIQ process model successfully integrated legacy manufacturing systems and provided critical manufacturing data context and analysis for McCain Foods, a multinational frozen food company based in Canada. This deployment demonstrated that an analytical approach to quality control would significantly exceed legacy manufacturing systems. In fact, ThinkIQ outperformed its initial 3 percent yield target to achieve an 8 percent improvement.
Another success occurred when ThinkIQ improved quality and safety at General Mills by virtually eliminating recalls, doubling yield to 90 percent, and reaping more than $40 million in operational savings in the first two years alone. As one senior technology executive put it, “This new approach wasn’t about connecting data sources but instead modeling and semantic technology to achieve business outcomes immediately.”
So, if you find yourself drowning in a data lake of your own making, dreaming of the highly adaptive, automated environment you envisioned when you first endeavored into smart manufacturing, your previous efforts having failed to deliver on expected value, we can help. A friendly ThinkIQ expert is standing by to help you pull all the context you need out of your data. You can also start by downloading our eBook titled "Advanced Material Traceability Revolutionizes Digital Transformation"
