Modern tools and equipment are built for the Internet of Things (IoT), offering easy, plug-and-play connectivity options to share and use data across the entire manufacturing process. However, most manufacturing plants still contain assets that pre-date these innovations, making full IoT deployment a challenge. Nevertheless, manufacturers that fail to integrate these legacy systems do so at their peril. When unlocked, the quantity and value of data produced by older equipment and systems contribute to an IoT-based system that can transform not only operations, but the entire business.
The data within legacy equipment is relatively untapped. Siemens estimates that only 3% of the world’s machines within plants are monitored because so much of the world’s industrial capacity involves outdated infrastructure—much of it decades old with limited digital electronics or communications features, if any.1 However, by fluidly connecting these legacy assets, operational and performance data can open the door to meaningful insights. Data processed into one centralized system allows operations teams, business analysts
, and data scientists to discover valuable and actionable insights.
Consider the value gained from a single shift to more effective maintenance practices—namely predictive maintenance—which is made possible when you connect legacy systems to IoT-based operating systems. The financial benefits are significant considering the ARC Advisory Group estimates that the cost of unplanned downtime is ten times that of planned downtime.2
Turning data into predictive maintenance solutions
Hidden within older, legacy production equipment and systems are the data that power predictive maintenance. Variables that indicate the health of the equipment include levels of vibration, temperature, cycles, load, pressure, etc. By capturing such data from legacy systems and connecting it to an IoT-based predictive maintenance solution, manufacturers will enable early detection of asset defects and other conditions that can lead to faults. In other words, the solution will proactively identify when a breakdown or failure may occur so that it can be avoided, which will:
- Eliminate frequent breakdowns that require expensive repairs and costly downtime
- Increase efficiency and ensure consistent performance to improve production yields
- Strengthen a competitive position versus smaller, digitalized organizations that threaten to disrupt the industry
- Extend the life of aging assets
Capturing production and operations data from legacy systems serves as the foundation for digital manufacturing success, which culminates in closed-loop innovation with end-to-end digital twins. Without data from legacy systems, a production digital twin, which is a cross-domain virtual model that accurately represents a production process, cannot be created.
By combining the detailed information from all systems—legacy and new—into an industrial IoT platform and connecting it to high-fidelity digital twin models, companies will close the loop through product ideation, realization, and utilization to seamlessly integrate operational data throughout the value chain. This consistent digital thread will enable companies to speed up development, optimize manufacturing, and improve products for the next version or iteration with real-time insights.
If you want to capitalize on the power of digital manufacturing, ignoring legacy assets is not an option.
To learn how to effectively pull data from legacy systems, read this white paper.
This content was developed together with Machine Design.
 Siemens, Things Making Things: How Equipment Makers Can Use MindSphere to Unleash New Business Models, 2018.
 ARC Advisory Group, “Reducing Unplanned Downtime and Helping Future-proof Automation System Assets,” August 3, 2016.