Internet of Things
NAVIGATING CHALLENGES IN IMPLEMENTATION
Despite the transformative potential of predictive maintenance, manufacturers often face hurdles when adopting IIoT-enabled solutions. Key challenges include:
1. Data Overload and Management: IIoT generates massive amounts of data, often overwhelming traditional systems. The solution lies in edge computing, which processes data locally, reducing latency and ensuring real-time insights. Additionally, scalable data platforms such as MOM aggregate and contextualise this data, turning it into actionable intelligence.
2. Integration Complexities: Many manufacturers operate legacy systems that don’t easily integrate with modern IIoT technologies. MOM bridges this gap by creating a unified architecture that connects operational technology (OT) with IT systems, ensuring seamless data flow across the enterprise.
3. Skills Shortages: The shift to predictive maintenance requires both IT and OT expertise, which many manufacturers lack. Cross-functional training programs and targeted recruitment efforts can help bridge this gap. Tools also support this transition by providing intuitive tools and platforms that simplify adoption.
4. Cybersecurity Risks: As IIoT expands, so do potential vulnerabilities. Manufacturers must implement robust security protocols, including encryption, regular audits, and compliance with industry standards to safeguard their systems.
SUSTAINABILITY AT THE CORE OF PREDICTIVE MAINTENANCE Predictive maintenance contributes to sustainability by reducing waste, optimising resource use, and minimising energy consumption. MOM solutions integrate sustainability metrics into core operations, enabling manufacturers to track, measure, and improve their environmental performance. The benefits of enhanced sustainability practices in predictive maintenance include:
Energy Optimisation: Real-time monitoring and predictive insights reduce unnecessary energy usage by aligning production schedules with energy-efficient practices.
Waste Reduction: Predictive algorithms identify inefficiencies, enabling manufacturers to reduce scrap and recycle materials effectively.
Circular Economy Support: MOM supports closed-loop systems, allowing manufacturers to reuse materials and minimise landfill contributions.
Lifecycle Assessments: Simulate environmental impacts with virtual twins across a product’s lifecycle, enabling manufacturers to make informed decisions about design and production.
Real-World Impact: For example, manufacturers using MOM have reported up to a 25 per cent reduction in their environmental footprint by leveraging IIoT for sustainability initiatives. These savings stem not only from operational efficiencies but also from smarter logistics and material sourcing strategies.
A VISION FOR THE FUTURE Predictive maintenance and virtual twins are no longer future aspirations. They are necessities for manufacturers who want to stay competitive. Manufacturing and Operations solutions exemplify how these technologies can be harnessed to drive efficiency, resilience, and sustainability. By embracing these innovations, manufacturers can ensure their operations are not only prepared for today’s challenges but also poised to lead in the smart factory revolution.
Bottom line: the factory of the future is not a concept; it is a reality taking shape today. With tools like predictive maintenance and virtual twins, the possibilities are limitless.
DELMIA
www.3ds.com/products/delmia Instrumentation Monthly November 2025 15
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