Legal / Impressum

Responsible in the sense of Section 5 of the German Teleservices Act (TMG):

Verantwortlich nach §5 Telemediengesetz (TMG): 

Si2 GmbH
Neuberg 6
65193
Wiesbaden
Germany

Phone: +49 611 2712694‬ 

Email: [email protected] 

Registered seat / Sitz: Wiesbaden
Commercial register / Handelsregister: Local Court / Amtsgericht Wiesbaden, HRB 35641
Managing Directors / Geschäftsführer:  Nick Frank, Peter Maier, Harald Wassermann
VAT ID no. / USt-Id.-Nr.: DE 32565212

Responsible according to Section 18, Paragraph 2 of the State Media Treaty (MStV): Peter Maier 

Verantwortlich nach Abs. 18, §2 Medienstaatsvertrag (MStV): Peter Maier

All rights reserved. Texts, images, graphics, sound, animations and videos and their arrangement on the this website, are protected by copyright and other protective laws. The content of this website must not be copied, distributed, altered, or made available to third parties for commercial purposes. Some website pages also contain images copyrighted by third parties.

Publications on this website  only serve to provide information, are non-binding and do not constitute advice. Si2 GmbH does not accept any liability for the up-to-dateness, accuracy and completeness of the content provided on this website. The same applies to the contents of external websites to which this website directly or indirectly refers by means of hyperlinks and over which Si2 GmbH has no influence.

Alle Rechte vorbehalten. Texte, Bilder, Grafiken, Ton, Animationen und Videos sowie deren Anordnung auf dieser Webseite unterliegen dem Schutz des Urheberrechts und anderer Schutzgesetze. Der Inhalt dieser Webseite darf nicht zu kommerziellen Zwecken kopiert, verbreitet, verändert oder Dritten zugänglich gemacht werden. Einige Seiten enthalten zudem Bilder, die dem Urheberrecht Dritter unterliegen.

Die Veröffentlichungen auf dieser Webseite dienen lediglich der Information, sind unverbindlich und stellen keine Beratung dar. Die Si2 GmbH übernimmt keine Haftung für die Aktualität, Richtigkeit und Vollständigkeit der auf dieser Webseite bereitgestellten Inhalte. Dies gilt ebenso für Inhalte externer Webseiten, auf die diese Webseite direkt oder indirekt durch Hyperlinks verweist und auf welche die Si2 GmbH keinen Einfluss hat.

 

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 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:

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.