The Fourth Industrial Revolution—Industry 4.0—is well underway, transforming manufacturing with smart technologies like machine learning, generative AI, IoT, and advanced data analytics. These technologies depend on data, and today’s factories are spewing it out at unprecedented rates.
Yet by itself, industrial data has limited value. Obtaining actionable insights from it requires deep knowledge of its history and context. Legacy records contain a treasure trove of history and insights, but they’re typically stored in older systems like paper files, tape drives, floppy disks, or microfiche. That makes them inaccessible to digital tools—and leaves many manufacturers stuck in Industry 3.9.
Digitizing legacy records gives AI systems a comprehensive picture of factory operations over time. By learning how company-specific processes work, they can deliver customized recommendations to improve efficiency and cost-effectiveness. That’s a powerful advantage over competitors relying on trial and error to make progress.
To understand why digitizing legacy physical records is so important and learn how get it done, read on.
Legacy systems contain years of operational data, maintenance logs, and production histories. Analyzing this information can shed light on a host of trends, from machine performance to market shifts and customer preferences. Without institutional knowledge, it’s hard for managers to understand challenges and make sound business decisions.
Digitizing legacy records enables factory managers to preserve valuable institutional knowledge without depending on outdated technology. Once they’re in digital format, past records can be analyzed and put to work with sophisticated modern tools. Instead of being neglected and wasting valuable space, they become strategic assets that can optimize operations throughout the factory.
AI and large language models are notorious data hogs. That’s because the more historical data they train on, the more accurate and capable they become.
For example, by analyzing machine data over time, AI algorithms can predict failure before machine parts break down. IoT systems can send automated alerts to technicians and schedule appointments for repairs during off-hours, minimizing downtime.
Legacy data also makes it easier for AI algorithms to spot bottlenecks and inefficient processes. Generative AI systems can suggest changes to improve throughput, modeling expected results against past operational data.
On assembly lines, detailed product information stored in old records improves quality assurance checks, enabling computer vision systems to better spot defective items.
Outside the factory floor, analyzing past sales data helps manufacturers predict future demand. With a better grip on upcoming orders, they can implement “just in time” production schedules, increasing efficiency and lowering warehouse storage costs. Algorithms can also analyze historical data about delivery routes, road conditions, and weather to improve logistics planning, leading to fewer delays and happier customers.
Join us for the final session of the See Clearly webinar series, where we’ll explore how to future-proof your information governance program. From AI and automation to secure data migration and emerging industry trends, this session will help you build…
To put your past records to work in modern systems, you need a sound implementation strategy. Here are seven essential steps to help you get started.
Why let your valuable legacy data sit idle? By making it accessible, usable, and secure, you will unlock the full potential of Industry 4.0 technologies. Gaining an early foothold in the digital world enables you to optimize operations, save money, and release innovative products ahead of competitors.
Once you realize the benefits of advanced technologies, you’ll never look back. Start your digital transformation today, using this eBook as your guide to a brighter, smarter future. Grab your copy today!
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