Field service has historically contributed significantly to the bottom line of many industrial OEMs. In fact, at least since the installed base of plant and machinery surpassed annual product sales in the 1990s (in western industrial markets), industrial field service has been an important contributor to the economy as a whole. Perhaps surprisingly however, we have scant data for this, since economic statistics, both government and private, do not capture field services turn-over in any useful aggregate form and few companies break out service revenues in financial reports. Nevertheless, given the size of industrial installed bases (measured in $ trillions) and given that field service is a meaningful fraction of that, we can discern that this is a large business. But lack of figures necessarily means that, aside from anecdotal evidence, we don’t really know whether productivity, in any form it is measured, is actually improving or not, e.g. through the implementation of technology, in particular field service management systems, which have exploded in both number and new features in the last few years.
But the problem of productivity in field service is not only one of missing numbers. It is also conceptual. For example, manufacturing productivity can be captured by dividing the value (or volume) of outputs (products) by the value of inputs (resources such as labor or capital) required to produce them. But in service, the value of a repair (measured as the revenue generated for the service provider) is only partially meaningful and only in the short term. A repair averted, for example through the prevention of failure, is economically more valuable, though it actually indicates a drop in the service provider’s revenue (depending on how it is priced and sold) and conventional productivity metrics (in the absence of resource adjustment).
In the context of how technology or digitization impacts business and services in particular, much has been written about how it is creating an “everything-as-a-service” or an “outcome-based” economy -with some justification, also in this blog. Providing customers with the usage or the outcome of a productive asset rather than the asset itself is a fundamental shift in the prevailing business paradigm. The auto industry is starting to feel what it means and is in the process of being disrupted. This has implications for how we should measure productivity. If, for example, “outcome” is defined as uptime, the relevant output for productivity measurement would then be uptime rather than repair. And service providers will need to find ways to monetize failure prevention or uptime (not easy in practice) and/or shrink the resources required to achieve them. Uptime, of course, has two components: failure prevention and reduction of downtime once a failure has happened. The first is mainly a function of knowledge, the second of logistics and it underscores a shift in the nature of service business from logistics to knowledge as a result of digitization. This has implications for investment priorities (e.g. logistics v knowledge tools) and strategy.
But it has also significant operational implications. Consider, for example, machine learning based predictive analytics in maintenance service (on the basis of IoT infrastructure). Over time it will supplant (automate) conventional condition monitoring and help to avert failures. At this point we don’t yet know what the prevailing business model will be for providing this application, nor whether the providers will be OEMs or digitally native specialist third parties disrupting the field service market, however a time-based contract for a class of equipment (basic fee plus bonus/penalty service level agreement, e.g. based on uptime objectives) seems plausible (somewhat akin to insurance), including or excluding the actual physical repair when needed. As this offering is software-based, its characteristics are high fixed and low variable (marginal) costs. The price, therefore, will tend to be low and decreasing. Revenue will be driven by volume (and good outcomes by data volumes) and, in the absence of monopolies, it is highly unlikely that OEMs will be able to replace the lost revenue through fewer repairs (fewer failures) through the income from uptime fees. This will drive a need to restructure and shrink the on-hand field service resources -which of course may well come into conflict with the need for timely availability when required or risk damaging customer relations.
But again, technology may offer solutions here. Just as platform approaches have enabled the sharing economy (Uber, Lyft and Airbnb in B2C-but also many others, including of B2B capital assets, such as garbage trucks, tractors, construction equipment, and even aircraft engines), it is now enabling shared field services and engineering, which can help optimize utilization and manage peaks and troughs in service demand. Admittedly it may not be suitable for all the work, particularly where complex machinery is concerned, and highly qualified or experienced engineers are needed. But analyzing frequencies of problem type occurrences may reveal that a partial outsourcing of field services is possible, providing certain standards or quality criteria are met. And, of course, applications like Augmented Reality (AR) can play an important supporting role. In fact, enabling (partial) outsourcing of field services to either specialist contractors or, as part of the “gig economy”, sourcing field service capacity through platforms, such as FieldEngineer.com, maybe one of Augmented Reality’s (AR) killer Apps. In our recent survey of AR implementation by industrial companies in field services, one interesting insight provided by a services contractor was that they expect AR to allow them to lower costs by increasing the ratio of technicians to engineers, i.e. by reducing the number of engineers required to perform a given volume of work and substituting technicians. Engineers would use AR to support less qualified or experienced technicians in the field.
All this will take some time to materialize, but it will be quicker than most people think. The progress of digital technology is after all exponential. And the lessons here are threefold:
- Look at the bigger picture and a few years down the road. Understand not just the technology, but the implications of the technology.
- For service business in general and field service in particular, the impact of digital technology will be not only in terms of business models (servitization, solutions, outcomes), but also in terms of operations and operating modes. The latter will be just as radical as the former.
- The nature of digital business (high fixed, low variable costs) means strong drive and pressure to add new customers quickly. Therefore first mover advantage is very important. Do not get left behind. It is difficult to catch up if you do.
Augmented Reality in Service: Ready for Prime Time? A Si2 Partners Survey and Management Report. You can download the flyer (summary) or purchase the full report at the Si2 Hubshop
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