Digital Servitization: Breaking down the technology conundrum into easy to manage chunks

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The increased accessibility of digital technologies is accelerating the shift from product to service led growth strategies.  The problem is that many leaders are confused by the jargon and unclear how to leverage these opportunities Intuitively they know they must do something or potentially face disruption, as they see the industrial world shifting in 3 major ways: (1) The growing awareness of the importance of data (2) The ease with which data can move through the Industrial Internet of (3) A growing trend by business leaders to make services a key part of their growth strategies. Successful companies starting along this ‘Digital Servitisation’ route, typically commence their journey with the following three basic steps:
  1. Discovery: Opening up their eyes to the possibilities
  2. Solutioning: Developing and piloting a tangible idea
  3. Pilot & Scale up: Service offer developed, pilot, validate business case, then scale


Understanding the potential impact of these trends on your business and developing a compelling vision is an important first step. A mistake many companies make is to start with technology first, creating platforms and offering services they assume the customer wants. If they started with the customer and industry need and then worked back to how they add value through technology and know-how, they are much more likely to be successful.  The Discovery phase can be facilitated by three simple methodologies to identify the profit pools that will pay for your investments; Value Mapping your customer and industry supply chain, examining your Points of Selling in the product life-cycle, and, finally, a review of the data you currently create and will/can create in the future.


Solutioning involves breaking the vision down into tangible projects and programmes that deliver something real. Although understanding customers enables us to quantify the opportunities and set priorities, figuring out where to focus a Digital Servitization strategy that flows across organizational silos is not so easy.  One way is to see the impact from two very distinct perspectives:
  1. Installed Base Digitization: That products and supporting operational infrastructures are designed to produce data that can be collected, analyzed and then monetized through service-based business models. Generally, technology is used in one of two ways:
    1. Technology in the product and company infrastructure that enables Digital Support, such as remote diagnostics or predictive maintenance.
    2. Capabilities and technologies in the organization that enable Data Analytics, such as machine learning, visual analytics, and business intelligence technologies.
  2. Process Digitization: The tools we use to manage our business back-office which sustain and improve margins /profits. Examples might be Service Management solutions, CRM and ERP. Generally, there are two aspects to consider in terms of system & process development
    1. To enable Customer Management, making customer data transparent and so breaking down silos.
    2. To enable Business Process Automation, so reducing cost and often leading to improved customer experience.

Combining Installed Base and Process Digitization: When products and infrastructure that collect, analyze and action data, are fully integrated with the back-office process, we can explore what new business models such as Digital Servitization can deliver in terms of value to customers.


Having identified the customer solutions and internal process improvements, it is time to execute and deliver the products and offering.  We require a business plan which defines Where we will target, with What, When and Who in the target organizations and How the delivery model will deliver excellence. This is a process in its own right and one which we call Customer Focused Business Development.  It involves working through a structured approach to customer segmentation, defining the service product portfolio that is relevant to specific customer profiles, the GoTo market or sales strategy that will be most effective, and the service delivery model that drives profitability.


Digital Servitization does not all have to be done at once, nor is it necessarily a linear process. An agile approach in small pilots or sprints that overcome specific hurdles is a good way to drive small incremental changes towards a larger goal.

In our experience, it is possible to run through these 3 phases between 3-6 months depending on the complexity and ambition of the business. The key to success is to use cross-functional teams with a breadth of expertise and experience coupled with a logical framework to cut through complexity. Don’t be put off by technology jargon, and if in doubt always come back to the customer value as your guiding light through the complexity of change.

Once you have developed your direction, execution of the transformation strategy is more akin to a major change programme. For more thoughts on this process you can read our articles on the Art of Driving Innovative Change and Self-learning solution focused mindset.

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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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