How digitization is changing the competitive space in industrial after-sales services

Profitable long term growth comes from having the right people in the right place at the right time.’ Technology although important, usually plays a secondary role.

Digitization enables continuously increasing automation in business execution, operations, and decision making. As a matter of course, this not only opens up numerous opportunities for major improvements, for example in productivity, but, when certain thresholds are reached, fundamentally changes the nature of the business. It therefore also brings numerous risks, challenges, and threats, including competitive and disruptive threats, from both existing players operating new business models and/or from new, digitally native, entrants. Over the past 10-20 years, many companies, even whole industries, fallen by the wayside attest to the power of “digital”. And while the FAANGs (Facebook, Apple, Amazon, Netflix, Google) are flirting with trillion dollar valuations, other major companies in key industries are fighting for survival, including in automobiles or banking.  But the digitization impact is felt in subtler ways as well, for example in the way it is changing the ecosystem around capital goods and machinery OEMs, their customers and their (independent) dealers, distributors or service providers by starting to gnaw at a symbiotic relationship that has existed for decades.

One illustrative example here is Tesla, the electric vehicle, battery and solar power company, that right from the start did away with the dealer distribution and service paradigm for cars. In both the US and Europe, cars have been traditionally sold through dealers, a practice often even embedded in statutory (i.e. legal) prohibitions on direct manufacturer sales. It originally came about because car manufacturers needed many remote sales outlets and a B2C salesforce, an extensive service network, and, importantly, were pressured by investors to quickly move built inventories off their balance sheets. What better way to do this than through a loyal network (created over decades) of dealers, who were rewarded with the opportunity to profit, not just or necessarily from product sales, were competition is very intense and prices always under pressure, but from services, including maintenance and repairs, parts, insurance sales and financing. By some estimates, profits from services account for over 75% of total dealer profits. However, as its General Counsel explained at an FTC[1] hearing aiming to get direct sales prohibitions lifted in some US states, Tesla’s business doesn’t work like that. Its business model requires it, among other things, to have a direct relationship with its customers to inform, educate and convince them of the merits of electric cars. Furthermore, its marketing is direct to end-customers without traditional advertising (e.g. over social media). Tesla does not build inventories, so does not need to unload them; And, importantly, dealers cannot make much money from service as electric cars have far fewer service and parts requirements.  Now, what was not mentioned at the hearing is that Tesla has also other reasons to want to sell directly to end-customers, which are perhaps even more powerful. For example, with a direct sales strategy, Tesla can internalize all service revenue that is generated through its “installed base”; And a direct customer relationship is necessary for Tesla to collect sufficient data for product development towards the current holy grail of the automotive industry -self- driving cars with full autonomy. According to reports, by following this strategy, Tesla has now over 5 billion miles worth of driving data, far exceeding any other brand; Only Google’s Waymo comes close in self-driving mode[2].

On the service side, Tesla is developing or has already deployed a number of offerings, including a charging station network and a car sharing scheme. And, while it does not break down service revenues, analyst estimates put revenue share from after-sales services (maintenance, repair, upgrades) at between 5 and 8% -which is quite significant for a company whose product sales are growing so fast (from 3,000 car deliveries in 2012 to over 100,000 in 2017). But in addition to generating revenues, Tesla has the opportunity to shape the after-sales service market anyway it sees fit and to innovate quickly. For example, it provides over-the-air software upgrades, is developing an App that will get a technician to a stranded Tesla car quickly, with no paperwork and is working on smart remote diagnostics.

Tesla also offers different levels of extended warranties and service contracts and has partnered with hundreds, possibly thousands, of workshops for repairs to bodywork and replacement of parts and consumables. It is hard to imagine such rapid deployment of innovations or such a level of control over a product in the field would have been possible through a dealer network. And if the Tesla model proves successful for the electric vehicle market it will be imitated by others, indeed it already is. Electric vehicle sales are still only a small fraction of the total car market, 1.3 million units were delivered in 2017 (1.35% market share) globally. But growth rates have been above 50% p.a., and so the impact will be felt relatively quickly by dealers, as the vehicle population changes over time.

One critical upshot of digitization is the generation of gigantic data volumes (e.g. through the Internet of Things), which support cost-effective and sufficiently accurate predictions through Machine Learning algorithms. They, in turn, enable ubiquitous automation (autonomy) and tight control of processes. Another is connectivity and communication, enabling a degree of awareness of an environment in unprecedented granularity and timeliness. Both create the conditions for levels of performance not only of products, systems and production lines, but of entire supply chains -far beyond what is currently achievable.[3] It follows therefore that the ability to control industrial operations will be more important (add more value to customers) than individual product/system performance, features or quality. For most OEMs, this means a dramatic shift in the basis of competition. And it also means a battle for data. It, therefore, creates the imperative for direct and permanent customer relationships after the sale -at a far deeper level than what usually exists today. And, naturally, OEMs with lots of data will be better positioned than those with less data, who then risk commoditization of their offerings. This cascades along the supply chain. But the quest and battle for data also affect distribution and service profoundly. As with Tesla, this is not only about OEMs internalizing all after-sales service revenue. It is also, mainly, about their ability to compete in their primary markets, using data for product and operational improvements and about being able to servitize offerings (in terms of product-as-a-service to improve cost-effectiveness) or transforming products into platforms to dominate markets or market niches. In this kind of environment, it is not easy for dealers and/or independent service providers to protect their current positioning in the supply chain or ecosystem, nor to sustain the value-add they currently provide or the margins they receive. And for service providers, a change in positioning, for example, a role in a service/repair (shop) network managed by one or more OEMs, will probably mean significant pressure on pricing and margins. Of course, the longevity of industrial equipment and the very large legacy installed base, means that industrial dealers and service providers are looking at longer time frames for disruption than car dealers. But technological development is always accelerating, therefore the additional time span may be shorter than expected.

Realizing the opportunities and threats of digitization, many OEMs, in different industries, are making big plays on digital servitization. Winning, however, will not be easy, not only because of the competitive intensity between OEMs, but also because it is not yet clear to what extent customers, particularly large groups, will play along (ceding some control of operations may be unworkable) or whether major technology vendors (e.g. Google, Microsoft, IBM) will play a cooperative, supportive or directly competitive role. GE, incidentally, tried to answer these questions, almost by brute force and an investment of US$ 4 billion in building a digital portfolio, only to find that even for a company with a huge installed base, “going digital” is not easy. On July 30th, the Wall Street Journal reported that GE has hired investment banks to prepare the sale of its digital assets, a stark reversal of strategic direction. It remains, therefore, to be seen whether a strategy of building an in-house cutting-edge, stand-alone and open digital capability (in GE’s case, centered on its Predix platform) is at all feasible for industrial companies.

But the question also remains for independent service providers (ISPs) of how to react to shifting service strategies of OEMs as driven by digitization.  In their majority, ISPs are small or medium-sized companies, who individually have far more limited capacity to invest in digital capabilities. Individually that is, but perhaps not collectively. Service providers (more than dealers) have usually a good degree of independence from OEMs and often provide service for many brands and different types of equipment. And while the data they can collect is individually limited, it may be collectively big enough to rival even large OEMs -if it could be usefully aggregated. Collectively there is also far greater domain expertise and capacity to invest in data science and algorithmic innovations or to develop and market new offerings. This may be enough to moat and defend the business, perhaps in partnership with major digital players.

Creating and developing industrial data aggregator entities, essentially some form of platform, is a formidable undertaking, which faces many challenges and hurdles: financing, ownership, organization, rules, pricing, legal and privacy considerations to name just a few. On the other hand, such businesses would benefit from platform economics and network effects, with their value increasing faster as new participants join. Independent service providers (and OEMs) face tough battles ahead. Technology development is accelerating. The smart money is on those who move carefully, but quickly.


[1] US Federal Trade Commission

[2] Of course, not all of Tesla’s driving data is from self-driving. But the algorithms learn from human drivers, essentially predicting the right behaviour or action. Therefore all driving data are valuable.

[3] To illustrate with a car analogy, self-driving cars will not reduce accidents to very low levels just because they will drive better than humans, with less mistakes or faster reaction times; But also, because they will be far better aware and informed about their environment (e.g. what other cars within an extended radius are doing or if there is a jam behind a curve) in ways that humans cannot be. This will lead to cars being able to drive faster, without the need for traffic lights, unless they are to facilitate pedestrians. The entire operating system achieves then far higher performance levels.

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Deep dive into the industrial service business.

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Service Innovation for value-driven opportunities:

Facilitated by Professor Mairi McIntyre from the University of Warwick, the workshop explored service innovation processes that help us understand what makes our customers successful.

In particular, the Customer Value Iceberg principle goes beyond the typical Total Cost of Ownership view of the equipment world and explores how that equipment impacts the success of the business. It forces us to consider not only direct costs associated with usage of the equipment such but also indirect costs such as working capital and risks.

As an example, we looked at how MAN Truck UK used this method to develop services that went beyond the prevailing repairs, parts and maintenance to methods (through telematics and clever analytics) to monitor and improve the performance and  fuel consumption of their trucks. This approach helped grow their business by an order of magnitude over a number of years.

Mining Service Management Data to improve performance

We then took a deep dive into how Endress + Hauser have developed applications that can mine Service Management data to improve service performance:  

Thomas Fricke (Service Manager) and Enrico De Stasio (Head of Corporate Quality & Lean) facilitated a 3 hour discussion on their journey from idea to a real working application integrated into their Service processes. These were the key learning points that emerged:


In 2018 the Senior leadership concluded that to stay competitive they needed to do far more to consolidate their global service data into a “data lake’ that could be used to improve their own service processes and bring more value to customers. As a company they had already seen the value of organising data as over the past 20 years for every new system they already had a “digital twin” which held electronically all the data for that system in an organised fashion. Initially, it was basic Bill of Material data, but has since grown in sophistication. So a good start but they needed to go further, and the leadership team committed resources to do this.

  • The first try: The project initially focused on collecting and organising data from its global service operations into a data lake.  This first phase required the development of infrastructure, processes and applications that could analyse service report data and turn it into actionable intelligence. The initial goal was to make internal processes more efficient, and so improve the customer experience. E+H looked for patterns in the reports of service engineers that could:
    • Be used to improve the performance of Service through processes and individuals
    • Be used by other groups such as engineering to improve and enhance product quality.
  • Outcome: Eventhough progress was made in many areas, nevertheless, even using advanced statistical methods, they could not extract or deliver the value they had hoped   for from the data. They needed to look at something different.
  • Leveraging AI technologies: The Endress+Hauser team knew they needed to look for patterns in large data sets. They had the knowledge that self-learning technologies that are frequently termed as AI, could potentially help solve this problem. They teamed up with a local university and created a project to develop a ‘Proof of Concept’. This helped the project gain traction as the potential of the application they had created started to emerge. It was not an easy journey and required “courage to trust the outcomes, see them fail and then learn from the process”. However after about 18 months they were able to integrate the application into their normal working processes where every day they scan the service reports from around the world in different languages to identify common patterns in product problems, or anomalies in the local service team activities. This information is fed back to the appropriate service teams for action. The application also acts as a central hub where anyone in the organisation can access and interrogate service report data to improve performance and develop new value propositions.
  • Improvement:  The project does not stop there. It is now embedded in the service operations and used as a basic tool for continuous improvement. In effect, this has shifted the whole organization to be more aware of the value of their data.

Utilizing AI in B2B services

Regarding AI, our task was to uncover some of the myths and benefits for service businesses and the first task was to agree on what we really mean by AI among the participants. It took time, but we discovered that there are really two interpretations which makes the term rather confusing. The first is a generic term used by visionaries and AI professionals to describe a world of intelligent machines and applications. Important at a social & macroeconomic level, but perhaps not so useful for business operations -at least at a practical level. The second is an umbrella term for a group of technologies that are good at finding patterns in large data sets (machine learning, neural networks, big data, computer vision), that can interface with human beings (Natural Language Processing) and that mimic human intelligence through being based on self-learning algorithms. Understanding this second definition and how these technologies can be used to overcome real business challenges is where the immediate value of AI sits for today’s businesses. It was also clear that the implication of integrating these technologies into business processes will require leaders to look at the change management challenges for their teams and customers.

To understand options for moving ahead at a practical level we first looked briefly at Husky through an interview with CIO Jean-Christophe Wiltz to CIOnet where we learned that i) real business needs should tailored drive technology implementation, and ii) that before getting to AI technologies, there is a need to build the appropriate infrastructure in terms of database and data collection, and, most importantly, the need to be prepared to continually adapt this infrastructure as the business needs change.

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