Modern problems require modern solutions. You wouldn’t have your Tesla Model X serviced by a blacksmith. Nor would you get your Wi-Fi network installed by a an old-timey switchboard operator. It stands to reason, then, that in matters of mitigating risk in the supply chain brought on by unique disruptions such as a global pandemic, companies can’t solely rely on technology and protocols that were acceptable in a pre-pandemic world.
The supply chain is still playing catch-up against unprecedented, unforeseen obstacles. To wit, the average global supply chain disruption costs organizations $228 million. Forty percent of C-suite executives report a marked tarnishing of their brand image due to disrupted supply chains. Sixty-one percent agree that achieving greater resiliency in their supply chains is more important than speed and efficiency. Fifty-four percent believe significant changes must happen to curtail disruption in the next five years.
Like the world-view of a first-time parent, consumers see the everything differently now, and it will never be the same again. Manufacturers would be wise to do the same. Resiliency through a data transformation is crucial to navigating the choppy waters of ahead.
Demand has become extremely volatile. Perhaps more alarming, despite every new obstacle, consumer expectations have also exploded — including next-day shipping, real-time tracking, and dead-accurate predictions months in advance. It may seem impossible to keep up, let alone exceed expectations.
However, modern solutions do exist to help alleviate these modern problems.
Data and the handling of it is critical to survival. It’s no longer enough to have sensor technology on equipment. How do you harness it and use it to make better decisions on a macro level? It can feel like too much data. A small team of people couldn’t possibly take in all data generated by the supply chain and make a rational decision that takes every factor into account.
That’s where Industry 4.0 smart manufacturing comes into play. Industry 4.0 builds upon the progress of the last Industrial Revolution by tapping into the data generated by internet of things (IoT) sensors, cloud computing, and artificial intelligence (AI) and making autonomous decisions. Guided by smart data collection and analysis, manufacturing equipment has become self-sensing, self-acting, and interconnected in real time. Instead of relying on machine operators, engineers, and those on the shop floor to meet and discuss solutions and reengineering designs, intelligent systems consume every bit of information and arrive at rapid, accurate decision-making, greater efficiency, and resiliency in the face of disruption.
The Writing Is On The Wall
Before the pandemic, 75 percent of large manufacturers were looking to update supply chain operations using IoT and analytics-based situational awareness, but now 90 percent of all manufacturing supply chains will have invested in the technology and business processes necessary for true resiliency.
Demand forecasting and predictive analytics made possible through machine learning (ML) help close the gap of uncertainty by recognizing patterns and anticipating changes.
One such breakthrough in Industry 4.0 smart manufacturing is the digital transformation of manufacturing data. Using a fact-based granular, data-centric contextualized view of material flows and related providence attribute data, a digital transformational intelligence platform, such as that built by ThinkIQ, companies can gain access to unprecedented material traceability and insight into ways to improve yield, quality, safety, compliance, and brand confidence while reducing waste and environmental impact. And it’s all made possible by collecting data across the value chain from existing IoT sensors and business systems.
ThinkIQ’s approach consists of an analytics and execution engine that includes powerful scripting using Anaconda Python, a distributed engine that scales as needed, and AI and ML with access to industry-leading libraries. Altogether, any data can be converted through pre- and post-processing — data pulled from IoT, shop floor sensors including PLCs and historians, ERPs, MES, and other enterprise systems. This continuous intelligence offers granularity through time-series data that presents attributes at the individual-equipment and unit-of-material level.
In some applications, the ThinkIQ platform, a ThinkIQ mobility extension, and the integration of a customer legacy quality system has enabled standardization and validation of raw material data from farm to finished goods. Such a setup facilitates optimal raw material production scheduling, traceability analysis, and performance optimization from raw material to finished goods, simplifying the achievement of contractual requirements.
Such efficiencies represent the first steps into greater resilience throughout the supply chain, optimal risk mitigation, and data transformation in line with the current manufacturing climate.
Are you risk-averse? Ready for true resiliency? Let’s talk about transforming your data to not only mitigate risk but open up a competitive advantage as well. Talk to a ThinkIQ expert today to learn more. We also have a new selection guide eBook to help you better understand the questions you should be asking. Download your copy today.