Technology is servitizing the value paradigm. Time to change management thinking

As digitization servitizes business models it is also shifting the source of value -from products to data and information. For most product companies this is an existential threat. But for a few a big opportunity.

Almost 10 years ago, Nokia, then still the global leader in mobile phones, was busy undertaking project after project to improve its business, upgrade its product offering and strengthen its value proposition, particularly with its key customer segments. But its brand preference had been slipping fast in all its key markets. It took a new CEO, Stephen Elop, to issue in 2011 a now famous, but belated warning -“Nokia our platform is burning”- about how the Apple and Android ecosystems were taking over Nokia’s market by changing the smartphone value paradigm, converting a product into a platform. The rest is history.

As technology ever more rapidly disrupts industry after industry, many companies find themselves on a similar trajectory as Nokia. And many, like Nokia, either do not recognize the problem or, if they do, often choose not to confront it.

In a November 2014 survey by Accenture (CEO-Briefing: Competing in a Digital World. Accenture 2014), a management consultancy, C-suite executives were asked about their views on digitization. A slight majority (52%) expected that it will change or completely transform their industry. Even so, they cited operational efficiency (69%) and customer experience (61%) as the areas of highest importance for digital investments, with most (59%) focusing more on process efficiencies and cost-cutting than revenue generation. Such thinking, which has been confirmed in other studies since is surely paradoxical. If you assume your industry will be completely transformed how does it make sense to invest in incremental efficiencies or peripheral improvements rather than in understanding and mastering the upcoming transformation? Consider: In the survey, some participants gave examples of their own digitization efforts and an executive from a major car rental company described how it had recently launched digital signage at airport locations, providing customers with the latest flight, check-in, and other information, with the aim of improving the customer experience and, hopefully, strengthening customer loyalty and value. But while the company was busy on the periphery of its offering, customers were busy rejecting its core value proposition. Namely at the exact same time, a Certify.com sharing economy report was showing that “ride-hailing” companies (Uber, Lyft) were making extraordinary gains on both taxis and car rental in terms of market share with quarter on quarter growth rates topping 25%. Car rental had lost an extraordinary 11 percentage points in market share within 7 quarters. Furthermore, both Uber and Lyft scored significantly higher in terms of customer satisfaction relative to both their competitor classes.

Now it may be the turn of the auto industry as a whole: In a recent article in Automotive NewsBob Lutz, a former GM Vice Chairman and legendary product developer, described how within 20 years the same fleet and mobility service providers (now upgraded from simple ride-hailing Apps) will relegate automotive manufacturers to a role of commodity device producers and capture the lion’s share of profits generated in a new mobility industry. According to Lutz, value in automotive is now moving downstream driven by autonomous vehicle technology. The complete value paradigm for cars and mobility will change as the industry moves from a mainly B2C/ownership to a mainly B2B/utilization model[1]: “The transportation companies will be able to order modules of various sizes and types… But the performance will be the same for all because nobody will be passing anybody else on the highway. That is the death knell for companies such as BMW, Mercedes-Benz, and Audi. That kind of performance is not going to count anymore.”  And in almost uncanny timing, Uber announced just a few days later that it is buying 24,000 Volvo cars to kickstart its self-driving fleet. Lutz was by far not the first to predict this paradigm shift, which will have far-reaching repercussions for the wider economy and society as a whole. But he was the first industry insider to issue such a stark warning and few auto companies have taken the necessary strategic steps to deal with the disruption and even fewer have addressed the shift in the value paradigm[2] specifically, usually preferring to challenge the narrative (i.e. it probably won’t happen this way) instead.

What can be inferred from these cases? First, incremental, business-as-usual, thinking is not helpful in times of great disruption and burning platforms. It distracts management attention from the real problem, provides a false sense of comfort (“we are doing something”) and misallocates resources. Most managements (like most people) are far more comfortable looking for ways to improve the periphery rather than challenge the core (what a company, in its own mind, actually stands for), a kind of “head-in-the-sand” attitude.

Second, as we move towards an economy dominated by platforms, the sources of value are changing. It is now less about achieving outcomes through control of resources and sequential value chains. Rather it is about orchestrating resources and facilitating interactions and transactions in networks or, better, “ecosystems”, to maximize their value. It is less about the individual customer and more about the totality of customers (and suppliers), the presence of each making the value of the platform greater to all and attracting others. These so-called “network effects” cause platform businesses to scale to great sizes at speed. In the process, individual products or services lose in relative importance and run the risk of becoming commoditized.

Following the B2C proliferation of platforms in the past decade or so, we see this now also in B2B environments. But we are also beginning to see how platform markets can be reinforced by the IIoT –Industrial Internet of Things, which, as it turns out, is not just about efficiencies.

Sharing economy platforms are knocking hard on the door of the machinery industry by decoupling asset ownership from asset usage. In the process, they are driving industrial servitization and product commoditization. For example, a Boston firm, Cohealo, has launched a platform to time slice, share and optimize the usage of expensive hospital equipment such as MRI machines among healthcare facilities to increase utilization while reducing costs and waiting times. Smaller facilities that cannot justify their own investment can participate. The necessary logistics are provided as part of the service.  Similar platforms already exist for earthmoving equipment, tractors, and garbage trucks. Almost any asset can now be sliced, “platformized” and servitized, including manufacturing or service capacities, essentially the value chain itself: Dassault Systèmes has developed a platform that connects users of its CAD software and others with manufacturers which can accept designs, quote a price, manufacture, and ship a product prototype. The platform, MySolidWorks, now has an ecosystem of over 100 CNC milling, injection molding, 3D printing and sheet metal manufacturing companies. A “Production-as-a-Service” project at the University of Michigan, similarly aims to match thousands of small to mid-sized manufacturers that have underutilized equipment, with customers who need small production volumes. The system proposes optimal and near-optimal solutions according to given constraints for the production such as cost, time and quality. There are many other examples.

Just as intriguing is the potential reinforcement effect through the Industrial Internet of Things. The IIoT has been mainly understood as an efficiency and productivity engine for manufacturing or services. However, it can also be thought of as a market space for transactions, whether for over the web services (Apps) or products to support them. Essentially it functions to shift value from products to the data, knowledge, and services it is a conduit for. At the recent Copperberg AfterMarket Business Platform conference in Hamburg, a presentation by Caterpillar included the statement: “When Customers buy Caterpillar Products to get access to Caterpillar Services”. This hints at what can be expected but doesn’t go far enough. While companies will use IIoT-based services initially to support and then drive product sales, ultimately few, if any, will be able to resist the service revenue streams even if customers are not buying their products. And if they do, they will have difficulty competing with product-agnostic platform and service providers in collecting the necessary volumes of data needed to deliver strong outcomes. Value moves from the product to the platform and the service because the data and knowledge about and the ability to precisely control product operations becomes more important than the absolute or relative performance of different (competing) products. In other words, it may be more valuable to know when a product needs maintenance than to have a product that is slightly more robust or “better” than another one. Products can then be made to required cost-performance specifications determined by data and will tend to be quite similar. This describes a process of commoditization.

Some manufacturers, even smaller ones, are already trying to ride the bandwagon. For example, Netzsch, a medium-sized German engineering company and manufacturer of grinding and dispersing systems, developed a Web-based software that enables the remote monitoring and maintenance of all grinding and dispersing machines at a customer’s site (i.e. not just its own). As the company reports in a joint VDMA/McKinsey survey from 2016: “The system is independent of the installed machine or configuration and can easily be adapted. It is built in as standard…, but also offered as an optional add-on to all our customers. NETZSCH-Connect enables our customers to monitor the process data of their machines during operation and check the current production conditions… On request of our customers, we provide remote services and other process support. More and more of our customers have become NETZSCH-Connect users since we launched the system in 2013. The software has also had a positive impact on our hardware sales, as the ability to monitor machines is more and more important in order to optimize the processes.” – Customers buying Netzsch machines to get access to its services, Caterpillar’s objective coming true.

Netzsch and Caterpillar are of course not alone. Many other manufacturers have come up with similar systems and are experiencing similar customer reactions. But digitally native service and software providers are also forcefully entering this market. Services such as predictive maintenance based on machine learning and data analytics are now offered over the Web (Uptime-as-a-Service), either stand-alone or as part of broader IIoT implementations helping customers drastically reduce the cost of condition monitoring (by eliminating the people who carry it out), expand usage and reduce equipment downtimes. Of course, IIoT platforms would eventually cover all the processes and all the equipment. And it is not difficult to imagine IIoT platform vendors expanding vertically, offering customers integrated product/service packages, or horizontally, offering enhanced market place functionalities and Apps selling products on the platforms, as is already happening in the home IoT markets led by Amazon or Google Nest.

In the past 20+ years, “value” has become so central in management thinking and practice, it is now almost cliché.  A whole advisory industry has evolved to help managers understand in painful detail how their products add value relative to competitors. Value-based pricing (VBP), a method to decouple price from cost and charge customers based on the benefits (however these may be defined, from intangibles to profits) they receive, is an approach pursued by many, if with varying degrees of success. However, it should be now obvious that such value-based approaches work only within static competitive, market and technological frameworks, where all players are adhering to essentially the same value paradigm. But in a world of platforms, the validity of such approaches needs to be reexamined. After all, airlines and hotel chains were pioneers in applying revenue management and a form of value-based pricing. But that did not stop low budget airlines or platforms like Airbnb from disrupting their respective industries and then scaling their businesses by shifting customers to a different value paradigm: inexpensive, no-frills, but adequate experiences -and taking huge market share in the process.

The subtle emphasis in value-based approaches is “to provide more for more” and thereby justify premium pricing for better-expected outcomes. But the calculations are based on what is known at the time they are made and assume that the future will be like the past. Increasingly that may not be the case. In fact, technology is advancing in a way that causes dematerialization, – costs to disappear and consequently many prices to fall. How do you persuade a customer to buy an expensive product or service today, if she expects the cost to be lower tomorrow?

As the theory’s originator, Clayton Christensen, points out, disruption is about companies, often with fewer resources, capabilities or know-how, finding ways to be “good enough” at a lower cost for a sufficient number of customers:

Disruption describes a process whereby a smaller company with fewer resources is able to successfully challenge established incumbent businesses. Specifically, as incumbents focus on improving their products and services for their most demanding (and usually most profitable) customers, they exceed the needs of some segments and ignore the needs of others. Entrants that prove disruptive begin by successfully targeting those overlooked segments, gaining a foothold by delivering more-suitable functionality—frequently at a lower price. Incumbents, chasing higher profitability in more-demanding segments, tend not to respond vigorously. Entrants then move upmarket, delivering the performance that incumbents’ mainstream customers require, while preserving the advantages that drove their early success. When mainstream customers start adopting the entrants’ offerings in volume, disruption has occurred.

The technology advancements we are witnessing now are explosively increasing the disruption potential: First, by drastically reducing the amount of resources (costs) required to achieve desired outcomes (dematerialization) and second, by allowing customers to buy just what they need -not more or less, conveniently and cost-effectively, decoupling the asset from the usage and the outcome: Not the car, just transportation from A to B; Not the drill, just the hole in the wall. This effective process of servitization further enables the shedding of deadweight costs (through underutilization) while feeding back into resource reduction in a positive on-going loop. Prices drop rapidly across the board, value paradigms shift. It is time for managements to come to grips with the new reality and find ways to address it.     

 

[1] Of course, some customers might wish to own (and enjoy) their own self-driving cars. However, the economics speak against this as a general rule. If cars are self-driving, the cost of transportation drops substantially, as the driver is the main cost element. In addition, self-driving cars can have very high utilization rates reducing unit costs even further. If they are ubiquitous (and there is no reason to think they will not be, according to Lutz they will be made mandatory as they will be far safer), the economic argument for owning a self-driving car becomes weaker still. It can, therefore, be expected that most of the demand will be from fleet operators and mobility service providers.

[2] Traditionally cars have been sold and priced based on image and performance. Lutz’s argument is that in the future purchasing decisions will be far more utilitarian, more like for buses or passenger aircraft today.

This article was first published in Service in Industry Hub on 4/12/2018

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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:

Leadership

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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