SLN Summit

 -05 June 2024-
Lufthansa Conference Center near Frankfurt, 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.

This Event will focus on: “Releasing the Value of your Service Data – A Practical Case Study with Smiths Detection”

Attendees will have the opportunity to have an in depth exchange of experiences and a deep dive with the global Service Director of Smiths Detection and his colleagues on how to release the value of data in service environments. Also attending will be Aquant, the company that designed and implemented the system Smiths is using.

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

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

We all know that Service businesses have huge amounts of information generated everyday and that it comes in many shapes and forms. This summit will look at a real case study of how advanced analytical technologies often associated with Artificial Intelligence applications, can help Service Managers turn unstructured data into into intelligence and actionable insights. Our cornerstone case study will be:

“How Smiths Detection Utilizes Service Data to Implement Proactive Product Strategies”

The discussion will range over:

  • How data analysis can be used to identify technical and product trends, as well as equipment quality and condition issues.

  • Developing and implementing proactive strategies for product improvement.

  • Establishing shared goals to enhance key KPIs (such as First Time Fix Rate, Time Between Visits, and Resolution Time) while securing stakeholder buy-in for the process.

  • Why Smiths chose to work with Aquant, a sophisticated analytics solutions application.

The experience exchange will include be Ben Sutton, Head of Global Service -Technical, Smiths Detection and Tim Burge, Director Aquant.


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.
Si2 will host an informal dinner on 4th June at 7pm at the Lufthansa for attendees who arrive the day before the event. It’s an opportunity to get to know the other attendees.
  • 8.30:   Welcome and first discussion on your priorities & expectations
  • 9.30:   Getting set up to work with Data – What challenges do you face?
    Tim Burge will present some 2024 Benchmark data on field Service followed by a facilitated interview, questions and discussion on how service teams can get ready with data to improve performance
  • 10.30:  Break
  • 11.00:  How Smiths Detection utilizes service data to implement proactive product strategies – with Ben Sutton, Smiths Detection
  • 12.00:  Morning review / Plan for the afternoon
  • 12.15:  Lunch & Network
  • 13.30:  Case Study review : Provide an opportunity for you to align the case study to your business challenges and requirements, through a series of breakout group brainstorms and plenary discussion
    • Use cases & value
    • Challenges and way forward
    • Priorities & next steps
  • 15.30:  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?
  • 16.00:  Next steps and projects (Agree the programme for the next 12 months?)
  • 16.30:  Report out and summarise Summit Actions
  • 17.00:  End

Complete this short form if you would like to attend and we will get back to you

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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