Making your SERVICE Data Work For You

“I know the data I need, but obtaining it from the various systems in my business… what a nightmare!”

As a Service Leader, have you ever felt this? Of course you have! Any service leader worth their salt knows that having the right information in the right place at the right time is critical for success. This was abundantly clear at our last Service Leaders Network Summit, where there was intense interest in the experiences shared by colleagues from Endress+Hauser. They discussed their journey from leadership vision to the nitty-gritty of executing ideas. They shared their challenges with data collection, data cleaning, and analytics, as well as how they collaborated with third parties to overcome specific analytical hurdles using AI technologies such as Machine Learning. They also discussed the challenge of integrating an analytics solution into business processes, particularly those involving people. At no point was there a discussion about why, as that was apparent to all the business leaders present. They were much more interested in the how, especially how others had achieved it.

My observation is that service leaders are beginning to reach the right level of data maturity, enabling them to move away from AI buzzwords and focus on value. They recognize the vast reservoir of unstructured data and potential knowledge contained within service reports and management systems. They acknowledge the urgency of knowledge transfer, as many face a skills shortage due to retiring service staff. They understand that if they could manage the Bill of Material of their installed base and equipment locations, they could deliver more value to both customers and their own businesses.

Perhaps this shift towards a focus on value over the inundation of marketing with AI terminology is also true for solution providers. They too are becoming more value-focused while recognizing the need to develop capabilities to use advanced analytics methods where appropriate. Moreover, the availability of more “off-the-shelf” analytics solutions providing access to advanced mathematics and computing power is facilitating this transition.

What’s particularly interesting is how the implementation of these projects depends so much on a company’s context—its mindset, competitive environment, existing system complexity, and geographical spread. This extensive list underscores the importance of listening to and exploring as many use cases as possible for successful execution. This isn’t about finding a benchmark; there’s no one-size-fits-all solution. Instead, it’s about understanding the parameters necessary for success.

With this in mind, we’re hosting another SLN Summit on June 5th, where we’ll explore with Smith Detection their journey of turning unstructured data into structured intelligence and actionable insights. This won’t be an ordinary event; the audience will be small, perhaps 10-15 experienced service professionals, who will have the opportunity to delve deeply into the application over a full day. To provide a different perspective, the technology provider of the solution, Aquant, will also participate in the discussion. Understanding the supplier perspective is crucial to grasping the ecosystem of partners required to access the capabilities and insights needed at any given time. For instance, the readiness of an organization’s data architecture is an important consideration when planning transformation.

With this diverse mix of backgrounds and experiences, along with the opportunity for collaboration with colleagues after the event, all attendees will walk away with valuable insights that can make a real difference to them.If you’d like to learn more about this topic and the event, you can use this link to Si2Partners to view the agenda and register.

Service in Industry

Deep dive into the industrial service business.

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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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