There is no question that each generation of technology is different from the last. In this sense, many would think that modern industrial facilities would rely on the same types of newer, more advanced equipment across the board. However, this is not the case.
In an industry where many industries utilize systems that are patched together with equipment and interfaces from technologies that stretch back to the 1980s and 90s, companies of all types and sizes are forced to rely on infrastructure that requires different levels of maintenance, the availability of parts, as well as communication protocols and failure models that may not coexist with modern machinery.
According to Premanand Jothilingam, an industrial automation engineer whose work focuses on control system asset management, this layered reality is often underestimated. He has noted in industry commentary that legacy systems frequently persist not because they are optimal, but because they are deeply embedded in production workflows and difficult to unwind without structured planning.
While traditional industrial maintenance often follows a “fix it when it breaks” or “fix it on a schedule” mentality, it is important to recognize that some technologies may not be designed to last in the long run. With newer, more advanced systems and technologies, managers and business owners can now leverage these devices and tools to keep their businesses operational in the long term, without risking downtime from maintenance or other issues.
A shift to predictive maintenance
Understanding how and when technology may fail is a key factor in long-term operational planning. Jothilingam’s work in industrial control system reliability has emphasized combining on-site inspections, historical performance data, and predictive modeling to identify assets that carry disproportionate risk within a plant environment. Rather than treating all equipment equally, this approach focuses attention on systems whose failure would have the greatest operational impact.
Jothilingam isn’t the only one who believes in these types of failsafes, however. Many believe that businesses will eventually have to forego traditional, outdated legacy systems to keep up with the demands of the modern digital world.
Industry research suggests that replacing legacy applications has been among the most consistent investment priorities for enterprises. While legacy systems are most often found in the healthcare and financial services industries, they are also incredibly common in manufacturing. Unfortunately, they are also incredibly expensive. Organizations spend large portions of their IT budgets maintaining legacy systems. This includes not only maintenance costs but also hardware and software upgrades and licensing fees. As Jothilingam has observed in professional discussions, these costs can obscure the longer-term risk exposure created when outdated systems remain critical to production.
Adjusting technologies with life cycle optimization
Life Cycle Optimization (LCO) is the strategic process of maximizing the value, efficiency, and sustainability of extending a business’s technology throughout the life of a product, service, or system. In the industrial industry, this mainly focuses on integrating predictive analytics to anticipate end-of-life indicators in systems.
Jothilingam has written and spoken about the importance of balancing maintenance, retrofitting, and planned replacement, noting that extending asset life does not necessarily mean avoiding upgrades, but timing them more deliberately. By mapping assets across their life-cycle stages, organizations can reduce premature failures while avoiding unnecessary capital expenditure.
While regular maintenance may extend a technology or system’s practicality throughout its lifespan, businesses should eventually aim to retrofit new technology and incorporate replacement strategies, especially those that rely solely on reactive repair of systems, products, and tools. By understanding the life-cycle stages of industrial assets, organizations can make informed decisions to prevent premature equipment failure, ultimately resulting in fewer losses.
However, monitoring these systems is no longer purely a human task. With advancements in IoT and system-level monitoring, some of which can be enabled by artificial intelligence, businesses can use real-time data from the environment, along with AI analysis, to identify issues and fix them before they fail or run suboptimally. According to Jothilingam, this data-driven approach supports more consistent decision-making, particularly in facilities managing a mix of legacy and modern systems.
Testing scenarios and predicting failures with digital twin technology
The innovation behind digital twin technology for industrial asset management has helped engineers to test a range of scenarios, not only predicting failures but also optimizing maintenance strategies without interrupting production.
In industry coverage of his work, Jothilingam has highlighted digital twins as a practical planning tool rather than a replacement for engineering judgment, emphasizing that their usefulness depends on the quality and relevance of the underlying data. Unlike traditional simulations, digital twins incorporate real-time inputs, which can improve the accuracy of predictive models when implemented carefully.
Despite its advanced technology, digital twinning relies heavily on the accuracy of the data used in designing its models, which is why it is crucial that the data supplied is accurate to achieve unbiased results.
The answer to lifespan lies in methodology
While the technical prowess of new technologies may fuel a business, it is the methodology engineers use to maintain them that determines their lifespan. By shifting maintenance from reactive functions to strategic, data-driven actions, businesses can better understand what their technology is doing, anticipate issues, and fix them promptly.
As Premanand Jothilingam has consistently noted in professional forums, extending system lifespan is less about individual tools and more about the frameworks used to assess risk, prioritize action, and align engineering decisions with operational goals.
While modern companies are undoubtedly making strides in incorporating new technologies into their operations, knowing how to replace legacy systems effectively is an important part of the business process. With staffing challenges ever present and the high maintenance of IT systems burdening companies with resourcing challenges, it is now more important than ever to invest in future efficiency, especially as AI and other cloud and SaaS models become more dominant.
