In conferences and seminars there is a steady flow of industrial companies showcasing their digitization efforts. While many, for diverse reasons, still have difficulty implementing or committing to digitization in full, almost all expect substantial benefits -not only in productivity and operational efficiency, but also in terms of top- and bottom-line growth. That latter assertion however, slightly smacks of illusory superiority syndrome, where individuals (or companies) overestimate their own capabilities relative to others. For example, in driving skills surveys large majorities of drivers routinely rate themselves better than average.
For digitization to boost growth for everybody it must unleash a sustained demand and market expansion (volumes). But that is only a necessary not a sufficient condition. In addition, the expansion must be such that incumbents are somehow protected from new entrants and can resist digitization induced price pressures. This is not likely and so the actual results of industrial digitization will most probably be an assortment of winners and losers. Furthermore, realistically it is unclear whether industrial digitization will indeed drive revenue growth for incumbent vendors even on average.
This is because a primary expected result of digitization is dematerialization (achieving more or better outcomes with less resources) which almost always (in competitive markets) results in lower prices, in some cases close to zero prices. This leaves users/consumers with a surplus which they can either use to consume more of the same or something else (e.g. a new or different product or service) or save (which then translates into investment through the markets, probably in further digitization). It follows then that to grow the combined volume effect (up) must be greater than the price effect (down), while understanding that the most likely effect on volume is actually new products and services.
For example, it is unlikely that digitization of, say, wind turbines (in the sense that they become smart connected products), will drive a higher demand for wind turbines. Nor does it seem likely that digitization will drive an overall increased demand for wind power services, though it will cause a shift in the service-mix: smart services (e.g. data analytics based prognostics and optimization) will displace traditional services (e.g. repairs due to failures). As smart services are immaterial and have low marginal or variable cost (high fixed cost) their price will be low in competitive markets (i.e. in the absence of monopolistic structures – admittedly a caveat). The net effect will probably be a decrease in revenue for service vendors, eventhough the “value” of the service (e.g. as measured in production throughput (MWh) per $ value of input) will be much higher (increased user surplus). Of course, vendors do count on their ability to push through robust prices for smart services based on a perceived installed base advantage. However, smart services, in contrast to conventional services, are based on open infrastructural technologies to which anyone (eventually) has access, thus conveying limited sustainable competitive advantage. Price pressures therefore are likely to be considerable.
 By improving the efficiency of turbines, digitization may in fact make some marginal projects viable and therefore increase demand for wind turbines. However, demand for energy assets is constrained by total energy demand, which will probably decrease due to digitization. Therefore, any increase in demand for wind turbines will be accompanied by a decrease in demand for competing energy assets.
In fact, the history of digitization and the associated dematerialization in various sectors is a warning for industrial / manufacturing based vendors. In many cases, far from boosting revenue growth, digitization has had the effect of boosting volume growth while shrinking prices to the extent that the total effect has been negative. A good illustration of dematerialization is that current regular smartphones (price $300-$600) offer features that cost > US$ 900,000 (in current dollars) at the time of their launch (example from 2011).
In the music industry, digitally induced dematerialization famously upended and reduced the monetary value of the market in spite of explosive volume growth.
US recorded music sales 2000-2013 (Source: Music Business Research)
While piracy (illegal downloads – a side effect of digitization) has been a significant contributor to the 50% drop in revenues over the period, there were also other factors at play: Digital collapsed costs by removing the physical medium, so prices came down. With the introduction of iTunes and similar services, consumers were no longer obliged to buy complete albums (whose sales dropped by >70%), but could focus on individual tracks (drastically reducing perceived cost) whose sales increased disproportionately, but by far not enough to arrest the overall decline. Only in 2015 with the rise of streaming (a business model which reduces the incentives for piracy) and smartphones achieving global ubiquitous status did the industry manage to grow again on a global level (after 20 years!), though with most of the growth coming from emerging markets catching up. In 2014 music from digital channels overtook music from physical channels and it is expected that in 2016 streaming overtook downloads.
Source: McKinsey & Co, IFPI
Importantly, the structure of the market was also disrupted: Platforms distributing music have claimed a significant part (25-35%) of the revenue pool. Winner-take-all effects (augmented by current recommendation algorithms guiding people to what others already have chosen) are extremely pronounced. According to the Economist (citing Nielsen), 96% of 8.7 million tracks that sold at least one copy in the US in 2016, sold less than 100 copies and 40% sold just the one. At Spotify 20% of tracks don’t get played at all, but the top 1000 tracks (out of 20 million) accounted for 23% of streams in 2016. As a result numerous intermediaries have gone out of business and even large labels are dealing with significant margin pressures. But simultaneously the number of artists supported (making a living) has increased, according to one report by over 570%, as sufficient consumers embrace niche (long tail) products (presumably at lower prices). Some innovative artists and new entrants seem to be taking sales and marketing in their own hands and new business models are emerging. For example, artists can use data analytics to better understand demand patterns and consumer preferences and organize their own live performances where they can have the most impact. A London based live music site called Songkick has developed an “On-Demand” performance concept called Detour, a crowdfunding site that lets consumers place advance orders for tickets for a potential concert. This takes the risk out of booking venues in far-off places for musicians and consumers are only charged if the concert goes ahead.
Dematerialization does not stop with the music industry. Consider the chemical photography disruption which devastated Kodak and the entire film processing industry or the printing industry which is still in the early stages.
It is possible to argue that the industries mentioned above are “information based” and the “end product” was digitized. In most other industries this is not the case. But while a product may remain essentially physical, elements of the value creating process may be at least partly digitized (and hence dematerialized) as may its usage, which again affects the revenue pools, both their overall size and distribution, as well as the profitability and structure of the industry.
In automotive, smart connected cars eventually means “sharing” (car sharing or ride hailing) and ultimately self-driving. This translates into a massive drop in direct and indirect transportation costs (driving, fuel/energy, pollution, congestion, accidents, repairs, insurance, parking, etc) for consumers (and associated revenues for vendors) for the same outcome. At the same time it leads to increased utilization of car assets on the aggregate (ratio of km to assets) reducing deadweight costs and driving dematerialization. The simultaneous emergence of digitization and electrification reinforces the dematerialization effect, as electric vehicles have far fewer needs for spare parts and service, while battery and fuel (wind and solar energy) costs are dropping fast. Eventually significantly fewer cars may be required (though cars may take share from conventional public transport) as well as different types of cars. Automobile manufacturers have already started to rethink their business models, particularly around mobility-as-a-service. But depending on how this is effected it requires massive initial investments in software, support infrastructure and the balance sheet. It is unlikely that all existing auto-makers will be able to pull this off, so industry consolidation is likely. At the same time companies like Uber (ride hailing) have a significant head start. Though cash hungry due to large growth investments, their valuations top or rival those of automobile manufacturers. Perhaps investors expect that today’s ride hailing franchises may evolve into platform mobility/transport service providers, opening their platforms to third parties (through apps) while designing optimized platform products (Apple, Google, Amazon examples from another industry).
If we now consider other manufacturing and capital goods engineering industries there are quite a few parallels. One of the prime purposes of the Industrial Internet of Things combined with advanced data analytics and machine learning is to drive productivity by maximizing machine uptime and optimizing performance along and across processes, by, for example implementing, prognostics to optimize (and reduce) service and maintenance interventions. This will reduce costs and improve productivity for customers, but for many (most?) vendors it will mean cannibalization of service business and a net reduction in revenues as technology replaces labor and physical parts. Technologies such as 3D printing and augmented reality will have reinforcing effects as transportation, inventory, labor and risk are taken out of the value creating process, while 3D printing may also reduce material usage and improve a product’s inherent characteristics and robustness. It doesn’t stop there. Technology transforms productive assets of all types into cyber-physical systems. Asset and resource sharing business models are therefore not hard to imagine and there are now numerous examples in many markets, such as earthmoving and construction equipment or tractors, leading to similar utilization effects as with cars and consequentially the potential of sales volume reductions for vendors over time. This of course can spread to non-mobile assets as well. For example, Electrolux floated an idea of sharing washing machines which was slightly ridiculed for B2C applications, but how about industrial washers, where capacity utilization may be optimized and flexibility increased or platforms where repair workshops of all types share capacity.
Going even further, the idea of “collaborative production” is gaining traction as “makers” gain access to productive capacities via microfactories such as Techshop or Firstbuild, the latter backed by GE Appliances as a way to engage outside talent. And as factories digitize and production becomes faster, easier and more flexible, it is entirely plausible that such forms of “making” will expand to whole new levels (textbook disruption in the sense of Clayton Christensen). Undoubtedly the economics of collaborative production are different than, say, car sharing simply because companies plan to run manufacturing assets to full utilization and so any utilization effects from sharing will be comparatively smaller. But micro-factories are more about providing shared facilities and resources to people who otherwise would not have them, reducing barriers to entry and allowing them to enter the market, potentially displacing existing capacity much like Uber and Airbnb have done to taxis and hotels respectively. And these are not trivial losses. For example, a study for the Hotel Association for New York City estimated that local hotels currently lose $2.1 billion in annual revenue as a result of Airbnb displacement and that is without counting the impact on hotel prices. In cities such as Los Angeles, New York or Boston taxi industry revenue declines have exceeded 30% in recent years.
Manufacturing-as-a-service is therefore not a far-fetched idea. Starting upstream with design and engineering, Dassault Systemes has developed a platform that connects users of its CAD software and others with manufacturers who can accept designs, quote a price, manufacture and ship a product prototype. The platform, MySolidWorks, now has an ecosystem of almost 100 CNC milling, injection molding, 3D printing and sheet metal manufacturing companies. A similar project at the University of Michigan aims to match thousands of small to mid-sized manufacturers that have underutilized equipment, with the customers who need small production volumes for their prototyping or business purposes with research focused on various back-end functionalities such as optimization algorithms and data handling procedures. The framework is expected to get the production request from the customer, collect quotes from the suppliers about the desired manufacturing processes, and propose optimal and near optimal solutions to the customer according to given constraints for the production such as cost, time and quality. An EU FP7 R&D project called ManuCloud is essentially trying to do something very similar on an international scale and in Germany a company of the Innovation Alliance network has proposed modular, mobile micro-factories as service add-ons to mainstream factories (charging by usage) for, e.g. heat treatment of aluminum components, which currently are shipped over large distances to centralized large facilities.
But possibly the greatest dematerialization effect will come from new robotics technology. Collaborative robots (manufacturing robots, drones etc) today account for 5% of global robot shipments, but by some estimates are growing at 75% p.a., a highly disruptive growth rate. Industry analysts expect the percentage of tasks handled by robots to grow from 8% to 26% over the next 5 years. Costs associated with manufacturing robots are also coming down fast and are currently by some estimates at around US $4 / hour and payback periods of <1.5 years, putting 90% of manufacturing industry that traditionally has been out of scope of robotics within range. This is augmented by very steep learning curves and productivity growth rates. While there is huge potential for companies to program or train their robots to perform more and more applications in one plant, the potential through knowledge transfer to robots in other plants is possibly even greater. And such machine-to-machine knowledge sharing will make it easier for companies to transfer production or switch from the production of one product to another without the substantial investments in people, training, set-up and related costs traditional manufacturing requires. What is more this knowledge may be something that could be available on market places, just as legal, medical or consulting advice can be bought and sold on the internet today. For example, Robo Brain is a large-scale computational system developed by Stanford University researchers that learns from publicly available Internet resources, computer simulations, and real-life robot trials (structured and unstructured data). It accumulates everything robotics into a comprehensive and interconnected knowledge base and makes it available to others. Essentially it is a knowledge engine for robots and as capabilities develop and mature such concepts will probably be commercialized. Companies would then be able to purchase, download and implement robotic capabilities directly into production processes. The implications for productivity and cost reductions are staggering.
As in other industries dematerialization induced through digitization in manufacturing will cause prices to drop significantly. As value shifts from processes to technology, algorithms and data (we have not talked much about data here; we’ll leave that for another post), entry barriers will drop, new companies will enter previously inaccessible markets and market structures will change.
A key question of course is what should incumbents do to defend market positions. There are no easy answers to that, but we’ll tackle this issue as well as after sales service related issues in following posts.
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