Manufacturers that are turning to predictive analytics models are hoping to identify faults and component failures better and pre-empt issues before they escalate – a level of aspired innovation that runs through the heart of the much-lauded smart factory.
We say “aspired” because, for the most part, a factory floor in which IoT-enabled machines and systems can self-diagnose and auto-adjust exists only at a very nascent stage. The reality is that most manufacturing plants are still facing with the fundamental, day-to-day challenges of merely knowing where their data reside, finding the right data, filtering out the noise, and moving it from one system to another.
At its core, a smart factory is a highly connected operation. Machine learning capabilities that fuel predictive analytics depend on a consistent flow of data from the factory floor to support the agility and insight that feeds every digital improvement tweak.
And yet, many operators are failing to achieve even the most basic data connections across the factory floor. Bulky, expensive legacy systems and the volume and the growing number of data sources are all culprits. Lack of machine connectivity results in human errors. Data bottlenecks, silos, and delays compromise the efficiency of basic daily operations, leading to order delays that undermine supply chain productivity.
Establishing a solid foundation of data logistics that leads to fast retrieval, stringent cleansing and transformation of data is emerging from under the radar to become the standard of digital infrastructure. The focus should fall on standardizing, simplifying and centralizing the processes around data transfer and integration with automation that speeds up its retrieval from back-end systems and reduces tedious human workloads and costly errors.
Take the retrieval and transfer of order processing information that needs to be shared quickly and securely around multiple sites in a tire factory environment. We still see instances of seemingly successful and established players relying on their workers to manually re-enter information and fulfillment orders from system to system. With so many manual processes in play, data errors and inaccuracy naturally occur. These types of manual processes not only impact efficiency but overall morale. The turnover rate is especially high among employees engaged in repeatable, tedious work.
The onus falls on solutions that bring greater consistency, visibility, and easily understood usability of data at scale. HULFT’s data logistics platform has been adopted in critical manufacturing systems all over the world. We’ve worked with some of the largest manufacturers, helping them to eliminate inefficiencies within their supply chain network by automating extraction, transformation, and
The result is a manufacturing plant that can look towards Industry 4.0 and capitalize on an era of real-time operational intelligence to respond to the unplanned ebbs and flows of the factory and be truly smart.
HULFT is working with many manufacturing customers that are trying to understand how they can begin to capture the potential of digitization. Setting the right data infrastructure foundation is critical to successfully leverage emerging industrial applications. Email us at firstname.lastname@example.org and let’s talk about how HULFT can help you find the levers that will set you on the path to success.