Quanta Technology Blog
The Risks of Anticipating Technology Advance, or Not
Posted on: Jun 16, 2015
In early 1953, my grandfather bought a new Cadillac Coupe DeVille. I was a young child at the time and thought that it was the most wondrous thing I had ever seen, full of magical features like power windows, a signal seeking AM radio, and perhaps most spectacularly, air conditioning. My grandfather was smitten with those features, too, but talked mostly about the car's power – power brakes, power steering, and about as much horsepower under the hood as in any car available. The car was so advanced, he told the whole family, that he had no doubt he would be keeping it at least ten years, so it was a good investment, costly as it had been. Yet a year and a half later it was gone, replaced by a ’55 model.
What my grandfather had not anticipated was the pace of progress. Yes, his Cadillac had air conditioning, but that AC unit was located in the trunk and blew air into the back seat, so it took a long time to cool the front seat passengers on a hot day. By 1955, Cadillacs had more powerful air conditioners built under the hood and blew air out the dashboard right at the driver. The newer cars' power windows were no better, but it had a signal seeking AM-FM radio, the power brakes and steering were considerably upgraded, and it had a lot more horsepower. Most visibly, '55s had wrap-around windshields, arguably giving better visibility, but indisputably meaning the '53 model wore its obsolete status right out front for all to see — it looked as outmoded as it was.
My grandfather kept that 1955 Cadillac nearly twelve years. I was old enough to drive it a few times before he traded it on a 1966 model. "Cadillac progress" continued during that time, but at a slower pace, and the 1955 performance and features proved satisfactory to him for all that time.
Electric and gas utilities sometimes find themselves in the same situation as my grandfather had. They invest in a new technology like remote meter reading, digital automation or smart distribution controllers, only to find it is eclipsed within only a few years by newer systems. This puts a company in a dilemma: it faces years of operating with equipment performing at a now sub-standard level, or investing in newer systems and stranding costs it incurred only a few years earlier.
In these situations, an only slightly newer version or technology provides much better performance. While there are management tools and accepted business methodologies to sort out "what is the best thing to do now?" when this happens, executives know the best option is to never get into that situation in the first place. The issue is not that the older technology won't continue to work. That 1953 Cadillac continued to do what 1953 Cadillacs do for many years after my grandfather sold it. But for all that time its performance was noticeably below that of a car just two years newer. The progress curve for new cars was very steep in the decade after World War II, and the two-year period between those cars came right at its end. In the same way, the earliest automated meter reading systems work today just as well as they did when first installed. But improved systems from just a few years later gather data faster with greater precision and more operational flexibility. First-generation smart controllers still do what they did when new, but next-generation ones do more, quicker, have a higher IQ and make a utility that much more competitive. Thus, one challenge that should be addressed when considering any technology-based investment, whether one is a grandfather buying a new car or a utility about to spend eight figures on a new smart system, is "what is the pace of this technology's continued advancement?" Ideally one wants to buy when the technology has almost matured. Available equipment and software work well, and the likelihood that further advances will quickly obsolete today's system is rather small. While progress will likely continue, it will not make such a big difference to the value and competitiveness the utility needs. Tools exist to help determine if, when, how and even why a new system or invention may soon be eclipsed by even greater performance, or that buying now is likely to provide years of competitive performance before needing to be replaced, not because it is obsolete, but because it is worn out. Such technological forecasting is, at best, an inexact science, as any and all forecasting fields are. But it is a science, with rules, proven methodologies, and known and calculable limits to its accuracy. Despite this, some utilities never or rarely use it when facing major decisions, even though they use quantitative engineering economics and decision-making methods of similar intricacy when evaluating the "money side" of those decisions.
Figure 1: The Gompertz, or S curve, has proven to be the trend of development in most emerging technologies as they mature over time. Typically a technology forecasting study determines expected development trends for several relevant metrics related to the new invention or system, each a form of this curve.
Much of technological decision-making revolves around the Gompertz, or "S" curve (Figure 1), which both theory and historical precedent in hundreds of industries and sciences show is nearly always the expected trend for any new technology-rich development. The plot's vertical axis is always a relevant performance metric — the top speed or fuel economy of airliners, the data-transfer rate of remote equipment, the efficiency of a load reduction system, etc. — and the horizontal axis is always time, usually measured in years.
Aspects of the curve's shape for a particular technology — the eventual asymptotic level of performance that will be achieved, the maximum rate of increase over time (at the middle inflection point in the middle if the curve is symmetrical) and where current technology is on the curve, etc. — are determined by analysis of the physical laws and properties involved. Forecast accuracy depends on how well the basic physics or system interactions are understood and analyzed.
Generally, the end point of applied technology forecasting is converting such basic metric analysis, perhaps involving more than one key performance indicator, into a curve in which the vertical axis is the net benefit for the utility (Figure 2). Using that, the long-term value of buying now vs. waiting one or more years for a higher-performing technology, can be made using standard utility economics and business decision-making methods.
Figure 2: While the basic forecast of a technology is done based on metrics specific to its nature (Figure1), generally those are converted to benefit/cost for decision-making on when to buy. Here, waiting five years (orange dot) is likely to get the utility only a small, if noticeable, performance gain over waiting just two years (green dot), a period during which analysis has determined the utility can expect to see the bulk of potential improvement over present systems. Standard time-value-of-money methods can then be used to determine which of the three cases makes the most sense for the utility.
Pilot programs to test a new or emerging technology don't really address this "will it become obsolete question" directly. Instead, they show if the candidate equipment or system works, and how well it fits the utility and its needs. That is very useful information, but not particularly illuminating on the question of if, when, and how much that existing equipment or system will be eclipsed by further development.
But in many cases, pilot test programs answer that question indirectly: during a several-year pilot period, the technology improves considerably, providing better information on just when it is likely to mature and how good it will be then — meaning that if the utility buys after the program, it now invests in a more capable system. As a result, generally, it is best to do pilot programs rather early on when a technology is emerging, even if the expected and ultimate decision is to wait a bit, so that the utility is poised to buy in a timely and informed manner, at the earliest point where the new technology is ready.
While people mostly remember the cases where a utility, or a grandfather, bought much too early, a person or company can wait too long, too, foregoing years of potential benefits by sitting on the fence. Technology forecasting cannot always answer the timing question with great specificity (buy in the 3rd quarter of next year), but it generally gives a clear indication of when it's foolish to buy or when to wait a few years (best to wait two or more years), or when it is time (buy now or within the next year). It's not the perfect answer, but it's an answer that could have saved my grandfather some money and frustration, and it can help utilities and other heavy technology-dependent industries stay more competitive.
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