SLN Summit

 -30 November 2023-
NH Hotel Frankfurt Airport West, Germany.


Service Leaders Network

The Service Leaders Network builds on the exchange of experience and insights as well as collaboration between members in common projects to build stronger capabilities and knowledge among participants, drive performance, master challenges and solve problems.

Find out more about the Service Leaders Network

SLN Summit

For existing SLN members, the Summit is an opportunity to pick up on some of the discussions you started in earlier SLN events, to share experiences on current challenges and to define how you want the SLN to work and the topics of interest going forward.

For newcomers, it is an opportunity to understand how the SLN works, to gain insight into current challenges facing service leaders around Europe and beyond, to listen to and question experts and to network with colleagues.

Additionally, the goal of the Summit is for members to set the agenda for the Service Leaders network for the coming 12 months for Collaboration Projects and SLN Experience Exchanges.

Attendees have the opportunity to help co-design the services and operating process of the network, so that they ensure that it works best for them and their requirements.

The atmosphere is open, friendly and confidential and vigorous debate is encouraged.

The SLN Summit is held once per year. If you would like to receive more information or would like to register for the event please fill in the form below.

Participation fee: 500 EUR

Focus and Topics

This meeting will focus on:

  • How to use machine learning with vast amounts of data contained in field service reports to not only improve effectivesness and productivity of field services but also to improve product quality and and identify new customer needs and trends. The speaker is Enrico de Stasio, head of Corporate Quality, Lean and IT at Endress+Hauser. Enrico and his team have developed an interesting tool and methodology to do this.

  • How to articulate and integrate the Total Cost of Ownership (TCO) concept into service sales approaches and programs. Si2 has found that understanding TCO through the product lifecycle is increasingly recognized as a key capability service leaders must develop in order to drive sales and customer success. Nevertheless, many struggle with how to employ TCO arguments to improve sales outcomes. This facilitated discussion aiming to enable participants to actively develop their own ideas with peers and experts.

  • Other priorities for the next 12 months.


The Summit style will be a flexible & informal workshop format with the following proposed agenda. If you would like amendments or to include some specific points, please let us know.

8.30        Coffee and networking: chance to get acquainted to attendees and meet old friends

9.00        Welcome: Objective, attendee expectations and agenda

9.30        Discussion – what is on your minds. Brainstorm key challenges you need to address in the coming 12 months. Collect, review and group ideas

11.00      ‘Why and how to use Artifical Intelligence (AI) for daily analysis of service technicians’ reports’ – A presentation and discussion with Ing. Enrico De Stasio, Head of Corporate Quality, Lean and IT at Endress + Hauser.

Every day hundreds of service reports are generated by technicians that contain valuable data that can be used to improve product quality, identify new customer needs and trends and much more. Over the last few years, Enrico and his team have developed and implemented a service report and evaluation tool based on AI technologies. This is a great opportunity to hear from an expert how advanced analytics are being deployed to transform the use of data in service businesses.

12.00      Morning review / Plan for the afternoon

12.30      Lunch & Networking

13.45      Going beyond Total Cost of Ownership to deliver Customer Success

Si2 has found that understanding Total Cost of Ownership (TCO) through the Product Lifecycle is increasingly recognized as a key capability service leaders must develop in order to drive sales and customer success. However, may struggle to articulare the power of TCO and how to integrate it into their service value sales programs. This facilitated discussion will enable you to actively develop your own ideas with peers and experts.

14.30       Identify your Priorities: Based on the morning’s discussion identify your priorities for the next 12 months

15.00       How can the SLN help you with these challenges? What support can provide most value? How should it be structured given the experiences we have had with SLN Collaboration Projects and Experience exchanges?

15.30       Next steps and projects. Agree the program for the next 12 months

16.30       Report out and summarise actions

17.00       End

Register for the SLN Summit

Further information on the venue and the logistics will be sent to you after registration.

Service in Industry

Deep dive into the industrial service business.

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Service in Industry

Deep dive into the industrial service business.

Join our community to receive analysis, insight, news and more.
We will never share your data

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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