Bruker: Trusted Advisor Programme for Global Service

Increasing customer loyalty and contract penetration through more effective communication by Service People

Background

Since 2018, Si2 has worked on a programme with the Service Vice President of Bruker Nano, to improve the communication and value delivered by the global Service team. Over many years, this programme has evolved and developed to meet the needs of the business at that time.

Bruker Inc is a global $3bn manufacturer of diagnostic solutions for life sciences and silicon chip industries. The Service Global lead of the Bruker Nano Surfaces & Metrology division has been implementing a strategy to drive customer loyalty and value through the services that support it’s products. The service organisation reflects the science based nature of its clients, and has a policy of hiring highly qualified degree level engineers, often with a PhD

Business Challenge

To increase team engagement by empowering them with communication and customer management tools, with a goal to increase customer satisfaction and service

Solution

Si2 began the implementation of Trusted Advisor programme in Europe, North America and Asia. Initially, the goal was to support the regional service directors in developing behaviours that would help technical people foster collaboration and bring more value to the customer and hence the company.  Si2 first engaged with the managers to understand their specific challenges and in particular the context and examples of work situations that their team members found themselves in. From this we developed and implemented a 2-day workshop environment where team members discovered and understood for themselves:

  • What is a Trusted Advisor and Why the company was making this investment
  • The words and phrases of collaboration within their own culture which we call GREEN language
  • How to deliver the mindset for exceptional customer care – The 3 Fixes
  • An easy tool  for dealing with tricky situations – SAB

 

This workshop was NOT a training. There were no desks, no powerpoint or lectures.  Instead we created a learning environment where team members develop skills, methods and understanding through activities, small group discussions and role play. Their takeaway is knowledge, experience and a simple A5 laminated card, which they often keep for years!

4 months after the workshop we held a virtual refresh session with the participants, in which they reviewed and shared their real Trusted Advisor experiences.

In between we ran 1 on 1 coaching sessions for the managers on how to re-enforce the Trusted Advisor mindset, as well as the participants who wanted to further develop their capabilities.

The following year we set up another series of workshops to review, practice and build more advanced Trusted Advisor capabilities. And the following year we incorporated the Trusted Advisor development into the Regional Service Meeting environment, as well as a 12 month programme for the Global Service Leadership team.

Outcomes

The outcome has been very high engagement with 110+ participants whose satisfaction levels has been greater than 90%. This engagement has driven a commitment from the client to invest in a multi-year programme that since 2018 has enlarged in content, regions and teams. This development has reflected the specific needs of the different regions within the global service team.

The business outcome has been to dramatically increase contract penetration,  engagement of the service team.

Conclusions

The longevity of the programme has been due to it’s ability to flex in an engaging manner to the particular priorities of a team and a region, but within a common company wide trusted advisor mindset. At it’s basis is learning through doing and teamwork to influence culture. The basic principles of the tools and methods are easily converted and related to the different cultures found across a global organisation such as this.

Benefit of working with Si2

Si2 has acted as a catalyst for unleashing the inherent knowledge within the organisation. We have done this through listening, co-creating & delivering engaging workshops experiences, high levels of facilitation, as well as input based on our own extensive leadership experiences within a variety of service businesses.

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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.