The pandemic is a  humanitarian disaster. And it precipitates tectonic shifts in the global economy. For service businesses, using technology to provide uptime at a fraction of its hereto cost is the way forward. 


A recent study by researchers at MIT put the global COVID-19 infection rate at roughly 12 times reported figures, depending on the country, for the roughly 100 countries with somewhat reliable figures. That would put infections at roughly 120 million globally by extrapolating to the rest of the world. The infection fatality rate is estimated at approximately 0.65%, but heavily skewed towards older cohorts where it rises up to 10%. So probably 800,000 people have already died of this pandemic excluding collateral damage, that is all the thousands who succumbed to other diseases that could not be treated because hospitals were overwhelmed.

Of course, it does not end here. The pandemic is raging in Latin America, South Asia, the Middle East, and most of Africa where many local health systems have effectively collapsed. With healthcare supply marginal or even irrelevant the natural mortality rate of the virus is the limiting factor -a situation eerily reminiscent of 1918 and another pandemic. In the US, a misguided early relaxation of restrictions has allowed cases to soar to new records daily in many states in the South and Southwest, while in most states cases are rising faster than testing. Europe also took a probably premature victory lap in early June and now it appears to be facing an uptick again -though from a far lower baseline. Only East Asia with the SARS experience of 2003 still fresh has there been a consistent and sustained clampdown on the disease with low case numbers and even lower death rates (with the exceptions of Indonesia and the Philippines; Possibly also due to a less virulent strain of virus). A consensus is starting to emerge among experts that either a second wave will hit in the fall which would coincide with flu season making it very dangerous or that the current wave will be sustained with similar effect. In the 1918 flu pandemic, the second wave (out of three), also starting in the fall, was the longest and worst in terms of casualties.

However, on a hopeful note, and in contrast to the political and socio-economic response which has been largely inept, inconsistent and ad-hoc internationally (with the US abdicating its traditional leadership role) as well as full of contradictions (e.g. U-turns on masks, hydroxychloroquine), the scientific and clinical response is fascinating to observe. It resembles a gigantic, distributed, self-organizing, self-correcting Manhattan project at warp speed. Millions of scientists in academic and corporate labs and hospitals around the world are cooperating in virtual networks that emerge spontaneously with little top-down coordination, sharing hypotheses and ideas, research and treatment results, therapy experiences and protocols, viral genomes, host genetic information, and much more in real-time -building towards defeating the virus. It is rather like witnessing the collective immune system of humanity at work.

Within months, mortality has come down significantly. In New York, COVID-19 ICU mortality rates appear to have halved while in England hospitalization mortality has reduced from 6% to 1.5%. Many factors contribute to this but learning curve effects and better knowledge through collaboration are among them. Some existing drugs have proven at least partially effective through big clinical trials conducted with unprecedented rapidity and have been repurposed to treat the disease -though they are not silver bullets. But hundreds of trials, including of new drugs are underway, and the WHO lists over 200 vaccine candidates under development or trials, with at least four in large Phase II/III trials.

The pandemic will not end until either an effective curative or preventive treatment or a vaccine is available. We are a long way from any kind of natural herd immunity which probably requires at least 40% of the population becoming infected (it is even possible that herd immunity is not achievable at all given the short time antibodies appear to last in people who have been infected and have recovered). And while the pandemic is in progress economies will not function normally. Sectors with high contact intensity -most visibly travel and tourism, bricks and mortar retail, mass sports, events, restaurants, education, warehouses, meat processing plants, large open office spaces – will contract sharply, while those that enable low contact intensity -from cloud computing and online services to e-commerce or packaging will expand.


Given all this (for example, travel and tourism contribute >10% to global GDP), the IMF, factoring in the lockdowns and the temporary inhibition of both demand and supply as well as world trade, projected in June an unprecedented 4.9% decline in world GDP for 2020, a negative change of 1.9 percentage points (pp) from its April projection which was at -3%. This, however, does not include a second wave hit. The OECD projected a -6% recession for 2020 in a single wave and -7.6% in a double wave scenario. For better understanding, these numbers amount to roughly US $7 trillion in lost output and tens of millions of jobs. And they assume that the pandemic will always be under sufficient control to allow economies to operate, no new lockdowns will be necessary and that governments and central banks will support economies with massive cash transfers, bond buying, and almost unlimited credit facilities. How optimistic that is can be seen now in the US. In reality, nobody knows what will happen, particularly over the medium and longer-term. Not so much because the duration of the pandemic is unclear or what the impact of lockdowns will be -that can be modeled. The problem is that it is impossible to know at present either how permanent emerging changes in economic circumstances and behavior will be or how the dynamics of government policies will evolve: Will countries take on huge amounts of additional debt to support the unemployed and consumption/demand for the duration? Will they raise taxes to repay debt and/or fight high levels of inequality or will they try to spend their way out of the crisis? Will they support all failing sectors? How will the immense concentration of market power, particularly in the tech industry, affect the resilience of economic systems and industrial sectors everywhere? Will globalization come to a halt and will that lead to a massive reallocation of resources and capital, protectionism, a decline in world trade, and wholesale restructuring of supply chains? Has peak oil already passed and how will the pandemic impact climate change policies? Will inflation make a come- back? Will consumers and businesses permanently change their behavior and practices? How will the rapid rise of e-commerce change cities and real estate and will it permanently and substantially displace bricks and mortar retail? How will working from home affect corporate and economic productivity (the cornerstone of economic progress and wealth creation) and organizational behavior? How will declines in corporate investment combined with massive layoffs in many sectors affect the organizational capacity to compete and innovate? Most importantly, will the Washington Consensus with its focus on deregulation, markets, finance, and entrepreneurship (given the huge inequalities and environmental degradation it has produced -exposed sharply by the pandemic) come to an end? What will replace it and how fast will all this happen? We are undoubtedly at an inflection point in uncharted waters.

As answers to these questions and more are currently unavailable (they will drive socio-economic research for a long time to come), economic projections are currently hardly better than educated guesses. Still, companies need to navigate through the storm, protect their cashflows (their ability to survive), minimize losses, attract talent, and somehow look to the future. Some hereto pillars of the industrial world may need permanent downsizing -along the entire value chain: Travel, for example, has collapsed and may never return to pre-pandemic levels. This impacts not just airlines and cruise operators, many of which are either failing or being rescued by governments. Major equipment manufacturers like Boeing, Airbus, and Rolls Royce have begun restructuring operations and laying off thousands of workers. So are their suppliers -from subsystems and components all the way to raw materials. In automotive, the pandemic seems to be precipitating the faster than expected end to the internal combustion engine (ICE) and a massive restructuring of both the sector landscape and the main manufacturers. Tesla’s market capitalization is now at almost $180 billion taking it slightly higher than Toyota and making Volkswagen’s $76 billion and GM’s $35 billion seem paltry by comparison -though it still makes only a fraction of their number of cars. As electric motors have far fewer parts than ICEs they are far less labor-intensive to produce. In the world’s key automotive country, Germany, large layoffs in the sector are being announced every other day. Consequently, peak oil may have already passed. The oil price dropped from a high of $63/bbl in early January to roughly $38 now despite huge production cuts by OPEC and its associates. Market capitalizations of oil majors have halved in the last 6 months. And fossil power is looking increasingly wobbly intensifying the shake-up of both the power generation and utility markets and their supply chains: On April 20, solar PV plants in Germany broke all records producing over 32 GW of power. On that day solar PV produced 40% of Germany’s gross consumption, wind, and other renewables 38%. And to top that a recent analysis showed that renewables accounted for over 50% of Germany’s gross consumption in the first half of the year. One of the reasons for the off-season surge in solar were clear skies without smog during the German lockdown. So, it turns out electrification -whether of cars, industry or households- increases the effectiveness of solar by reducing smog particles in the air. Consumers may want more of this. And we are seeing only the tip of the iceberg.

Anecdotal evidence suggests that companies are already reacting to changes -trying hard to exit obvious sunset sectors and invest in new ones: German auto manufacturers are going all out on electric cars; Energy equipment suppliers are investing in renewable technology; So are oil companies. Some large organizations are rethinking leases of office buildings and all are trying to adjust capacities to expected future levels of demand. This is already causing a massive re-allocation of resources, including in R&D, and affecting millions of jobs. The process will not be easy: Downsizing in the middle of a pandemic is socially difficult and risks reputational damage; New markets often have different economics, dynamics, and bahaviors than old ones. They require time and learning curves. And when many try to enter at the same time, particularly during a recession, competitive intensity and price pressures increase. Yet it is inevitable for many.


As industrial landscapes transform in the wake of COVID-19 and economies and supply chains realign – where does that leave the service business?

Customers facing significant demand reductions for their own products and high market uncertainty will have to deal with declines in asset utilization and try to conserve cash. They will sharply reduce investment in machinery and equipment, and pressure suppliers on prices. Service revenue is primarily driven by the existing installed base (not new sales) so, at least initially, should be more resistant to pressure than product revenue, particularly if it is underpinned by an extensive contract base. But a good rule of thumb is to assume that every 10% reduction in industrial asset utilization leads to a corresponding 6% reduction in service revenue. As already mentioned, not all sectors are on the same trajectory. But for those facing substantial operational slowdowns, many service providers will need to reduce capacity. Grasping and acting on the need to reduce service capacity does not come naturally -both conceptually and practically: On the one hand, service revenue may be initially resilient but the (opportunity) cost of low utilization in service is much higher than in products. At the end of the day, products can be produced for inventory, services cannot -a lost hour is lost forever: service is perishable. On the other hand, however, a delayed product delivery may be inconvenient but it does not cause too much damage to a customer. But delayed service to critical equipment can have severe consequences for the customer and result in penalties and reputational harm for the supplier -with spill-over effects into its product business as well. Furthermore, the service business’s intellectual assets are (at present) embedded in people rather than in capital. Cutting capacity, therefore, directly impacts know-how and organizational capability. For these reasons and in the hope that business will pick up sooner rather than later service businesses have usually refrained from reducing capacity in downturns. And during the pandemic the social ramifications become more important than usual.

Nevertheless, the severity of the downturn and the squeeze on profits and cash flows, the uncertainty of how long it will last, and what the next day will look like make for an unprecedented situation. Postponing hard decisions on capacity and costs is dangerous for companies -even with the help of government cash and furloughs or reduced working hour facilities (though they do buy some time). The challenge is then how to reduce capacity and cost base while at the same time retaining the capability to deliver required outcomes, fulfill obligations towards customers, and retain flexibility for whatever comes next.

From past experience, the challenge can only be met through technology. Technology transforms the world when it succeeds in generating the required outcomes at a vastly lower cost. For example, prior to the printing machine, producing a book cost 208 days of average wages. After the printing machine, the same book cost 0.17 days of average wages -a reduction of 1200 times! Lighting a room costs today 400 times less than before Edison invented the light bulb -the impact was similar. And there were many other technologies that underwrote major changes by taking out the bulk of the cost of outcomes: The electric motor replaced steam engines in manufacturing for example, and was the key to large scale industrialization and the resulting increase in living standards. The smartphone combined with broadband Internet and 3G/4G has already made a big dent in society and the economy -by making many things free or even simply possible- and it is precisely just 13 years old (introduced on June 29, 2007): The Apps incorporated free in an Apple iPhone 5S already in 2013 would have cost $3.5 million to purchase in 1991 (from video-calling to GPS to music streaming to monitoring heart health). In service and maintenance, the computerization of Fast Fourier Transforms and data collection on PCs in the 1980’s revolutionized condition monitoring -which would have been otherwise prohibitively expensive, i.e. impossible, at any scale, and led directly to new approaches to asset maintenance and large declines in downtimes and associated costs in energy, process industries, and many other sectors.

In the current era of machine learning/AI, ubiquitous connectivity, cloud computing, big data, and ultracheap sensors (the Internet of Everything) we had seen indications of inflection points and early signs of large scale transformations already much before the pandemic. With a large premium now on low contact-intensity and the all-encompassing need to cut cost -not just to stay competitive but to survive- things will accelerate faster as companies harness technology not just to muddle through lockdowns but to radically transform their cost base while providing required outcomes.

For industrial service businesses, this means drastically bringing down the “cost of uptime”: How much does it cost to keep a customer’s equipment running in the way it should. And to succeed it is not sufficient just to adopt and deploy the technology. Companies must also change the way they do things. When manufacturers adopted electric motors in the early 1900’s they didn’t replace the steam engine as a prime mover with an equally big motor driving the whole plant with a huge shaft. They innovated with group drives (driving a few machines) and unit drives (driving individual machines) so as not to have to stop the entire plant when a drive failed and to provide flexibility in operating modes. They changed locations and layouts of plants to take advantage of all the technology’s characteristics and capabilities to improve throughput and productivity and reduce costs further. In a similar vein, wholescale deployment of digital technology in service must go hand-in-hand with bold organizational changes to augment and enhance the technology’s potential. And this applies to operating models just as much as to the way offerings are designed, packaged, priced, and sold.

If the goal is to offer low contact intensity, low-cost uptime, companies should consider, for example, whether it makes sense to completely servitize their offering and provide strictly outcomes instead of products. Many things then become easier through the elimination of interfaces, warped incentives, and transaction costs.

Another major consideration is whether sustaining a large field service workforce supports or hinders the adoption and deployment of technology and the transformation of work. There is an on-going debate about the impact of technology on total employment. But there is no doubt that technology can be substituted for human work (there are no people operating elevators any more and very few bank tellers). Therefore, in the era of the pandemic when things must happen rapidly, an important question is whether field service should be restructured before deploying technology or after. For example, companies could decide to keep only small numbers of key personnel whose work can be augmented by technology and release the rest into freelance pools operated by specialists utilizing them as necessary. There is a precedent for that from when oil companies in the North Sea (which has higher production costs than elsewhere) had to react to the oil price slump in the 1990s. They effectively pooled their technical workforces by outsourcing the bulk of technical work to subcontractors. Pooling has the advantage of reducing costs for participating companies by increasing the total utilization rate of the pool. This is important because large scale technology adoption and deployment require major upfront investment and create a fixed cost base (though cloud offerings based on usage ameliorate that). But the marginal (variable) cost of adding and supporting additional customers is very low. If companies must be cost leaders in the pandemic era they need to go down that road.

The argument against full digitization is that technology may not be ready to take on the task. Do machine learning predictive maintenance programs perform well enough? Is Augmented Reality technology robust enough and do the customers have internet connections in their plants? Can 3D-printing really print all the spare parts? The answer to these questions is probably not yet fully. But technological development accelerates and its best applications found the more it is used. So while it may not be fully ready (whatever that may really mean) it is ready enough to be put to use.   

Radical transformation with simultaneous technology adoption at high speed is risky and can easily fail. It requires an offensive market strategy to bring in new revenues quickly, strong leadership, good planning, and outstanding execution and perseverance.  It is not for the faint-hearted. But on the other hand, standing still in the era of COVID-19 when the world is changing at speed and at scale is riskier still.