How old can infrastructure realistically be? When does it make sense to modernize? The “bathtub” is a hot topic in the industry, representing the costs—and sometimes error rates—of assets over time. Equipment is built, experiences costly “teething troubles,” then works reliably for a while, and finally becomes increasingly maintenance-intensive as it ages. Much like people: while some are ready to stay home by 60, others are happily biking at 90.
The Bathtub Discussion Is Complex
Unlike a relaxing bath at home, the “bathtub” in infrastructure is rarely comforting, as the end is always looming. And in infrastructure, the end of the bathtub’s life approaches so closely that many operators console themselves with the thought that the famous “bathtub curve” is, first, just a theoretical construct, and second, while we know its beginning, we never truly know its end. Besides, the end has never actually been reached in practice—thanks to replacement investments!
The debate around this topic is complex and often academic. There are many vendors in the market who promise miracles, ranging from IT providers to university professors. Some approaches are indeed good, but many others are from charlatans who might be better off reading palms at a fair. Many infrastructure operators are—understandably—put off by this.
Understanding Infrastructure Aging More Deeply
On the other hand, it’s crucial to get to know your assets better, understand aging more precisely, assess failure risks accurately, and determine the extent of potential damage. Planning methods like condition-based, risk-based, or value-oriented maintenance offer substantial savings while also enhancing quality and performance.
Since no sustainable infrastructure management is conceivable without aging models, I work with various models, constantly exchanging ideas with manufacturers, universities, service providers, and users. No model fully satisfies me. To make sound investment and maintenance decisions in the future, it’s essential to combine different aging perspectives.
Technical Age
From a technical standpoint, much of our infrastructure is nearing “retirement.” Many assets from the massive development period between the 1960s and 1980s are now in the late phase of their technical and economic lifespan. Projecting replacement costs with asset-specific price indexes reveals required investments that are significantly higher than current book values—a fact few dispute.
Technological Age
Of course, assets can be usefully operated for a long time. New isn’t always better. The asset manager of a canal system in central England told me about centuries-old assets, which now mainly serve heritage preservation rather than traffic. On the other hand, many aging models fail to account for the lost savings if a switch to improved technology occurs either too early or too late.
Demand-Aging of Infrastructure
Amid World War II, early concepts for European reconstruction emerged, guiding real plans until the late 1950s. Today, few still endorse car-oriented city planning. New structures arose with the prosperity of society—like the boom in suburban housing developments. Our infrastructure is geared towards this suburban sprawl and must now adapt to growing urban migration. Meanwhile, individual needs are shifting. We want green, decentralized energy, ever more communication, and faster travel.
Many aging models overlook the “aging of demand fulfillment.” Even technically “new” assets can quickly become outdated if they perpetuate old structures. If we were to plan infrastructure based on today’s needs from scratch, it would likely be better and cheaper. These structural costs can shift the bathtub’s end point to the left or right.
Operational-Aging
The roles of skilled workers and technicians have changed significantly—and the “bathtub’s end” is just as invisible as the end of these roles’ evolution. Today’s training emphasizes a comprehensive technical understanding and IT integration. No technician goes out without a laptop or tablet. Today’s maintenance relies more on replacement than repair of individual components. The more unfamiliar older assets are to today’s technicians, the higher the maintenance effort. These costs belong in the bathtub.
Maturity of Knowledge
Engineers love numbers, and rightly so. Most aging models are based on demographic information about assets: year of manufacture, number of failures, temperature, and hours of use. But the best aging models don’t reside in our IT systems but in the experience of skilled workers and technicians. They understand “their” equipment in the same way that, as a student, I knew whether my old car would start just by looking at the weather. Going forward, these valuable experiences must play a much larger role in the development of aging models. My team and I are working intensively on structurally evaluating this wealth of practical knowledge.
The Challenge of Regulation
Many regulatory models set the wrong incentives. As reasonable as incentive regulation is, its implementation often works counterproductively. In the German energy market, investment and maintenance costs are assessed differently. Simply put, German network operators live only off their capital, not the good maintenance by their technicians. There’s an incentive to make replacement investments as early as possible. That’s not right. No one in their private life would replace a light bulb just because it surpassed its estimated lifespan. Aging models must be incorporated into regulation.
In my view, a better understanding of one’s own assets is essential for modern infrastructure management. How capable is my asset fleet, and how well does it meet today’s and future demands? Without aging models in the form of a bathtub, significant cost reductions or performance improvements in infrastructure are unattainable.
Many models are very promising. However, it always depends on how you apply these models in real life and in the specific context. Since 2011, my company, Meliorate, has been helping infrastructure owners and operators address these critical issues.
Author Oliver Förster