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Smart Service Innovation for Adapting to the Pandemic Situation – Successful Smart Services Summit 2021

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By Jürg Meierhofer

On October 22, the expert group Smart Services welcomed worldwide top experts to the fourth Smart Services Summit. The focus was on how Smart Services allow firms to adapt in the COVID-19 pandemic. Examples of remote and collaborative working have created new forms of co-delivery where customers are integrated into the service processes. Such a change requires a mindset change for more traditional firms as the service model migrates from ‘do it for you’ to ‘do it yourself’ or some mix of ‘do it together’. Considering service science, the switch makes perfect sense as it means that the full set of resources within the ecosystem are now being used rather than only a part. Services can be delivered faster and at lower costs with the support of new technologies and when working with the customer in a co-delivery mode. The changes are leading to new value propositions and business models today and will lead to an evolution in Smart Services in the future. The changes themselves must be understood, and we may need to consider new or different implementation and delivery models for Smart Services. These new working approaches may also requite use to re-evaluate both training and education.

Across the papers and presentations, it became apparent that digital service innovation has substantially changed and accelerated since the start of the pandemic. Customer needs and service processes have undergone dramatic disruption, which is still ongoing. A common thread throughout all the papers was the concept of the ecosystem thinking, which was discussed from a wide field of perspectives and in a comprehensive way. In line with the concept of the Service-Dominant Logic, the needs of the different actors in the ecosystem need to be identified and integrated into the design of the services and the integration of the various resources in the ecosystem. The ecosystem perspective not only integrates the different human actors, but also technological, digital resources.

Innovation through intensive collaboration allows to switch different perspectives and innovation approaches. This results in seamless value propositions and solutions for the beneficiary actors, which is a necessary prerequisite for economic value creation. Well-designed service experiences based on a consequentially customer-centric view and approach are thus at the basis of value creation.

This transition to digital service innovation in ecosystems requires not only fundamental changes of the technological platforms. In particular, collaboration across actors, organizations, and industry requires a new level of trust, culture, skills, marketing approaches and innovation frameworks.

Many thanks to all those who spoke at, and attended, the Smart Service Summit. A big thinks to IBM, data innovation alliance, ZHAW Zurich University of Applied Sciences and Lucerne University of Applied Sciences and Arts for supporting the event.

data innovation alliance at the AI+X Summit

The ETH AI Center celebrated its first birthday on October 15, 2021, at the AI+X Summit and the data innovation alliance was there to congratulate and to join the inspiring crowd. The day started with workshops.

David Sturzenegger and Stefan Deml from Decentriq organized one of the workshops on „Privacy-preserving analytics and ML“ in the name of the alliance.

It was our first in-person workshop again, and such a great experience for us. We gave an overview of various privacy-enhancing technologies (PETs) to a very engaged and diverse audience of about 30 people. We had in-depth discussions about the use-cases that PETs could unlock, and also presented about Decentriq’s data clean rooms and our use of confidential computing. Our product certainly generated a lot of follow up interest, especially from those who wanted to reach out to demo the platform. We were also joined by a guest speaker from Hewlett Packard who spoke about „Swarm Learning“.

David Sturzenegger, Stefan Deml

Melanie Geiger from the data innovation alliance office attended the workshop about AI + Industry & Manufacturing led by Olga Fink from ETH. The overall goal of the workshop was to identify the next research topics. Small groups with representatives from manufacturing companies mixed with researchers discussed the challenges and opportunities of predictive maintenance, quality control, optimization and computer vision. We identified research topics such as more generalizable predictive maintenance methods that work for multiple machines or even multiple manufacturing companies. But we also realized that some challenges are more on the operational side or applied research like in the integration of the method into the whole manufacturing process and closing the feedback loop.

In the evening the exhibition and the program on the main stage attracted 1000 participants. We had many interesting discussions at our booth with a wonderful mix of students, entrepreneurs, researchers, and people from the industry. Of course, we also saw many familiar faces and due to the 3G policy, we got back some „normality“.

Spatial Data Analytics at the
#wetechtogether conference, 02.10.2021

By Nicolas Lenz, Litix Applied Data Science

Spatial Data was a workshop topic at the #wetechtogether conference which took place on October 2. The data innovation alliance, its member Litix and WiMLDS Zurich sponsored and organized two workshops entitled „Jump into Geodata“. The participants learned how to access Swiss geoservices with Python and how to use them for the presentation and analysis of geodata. We would like to thank the participants for their attendance and the lively discussions in the workshops. We hope that this will lead to new innovative geospatial projects in the future!

Innovative ideas around geodata are indeed welcome, since Spatial Data Analytics is one of four focus topics in the Databooster initiative. Project ideas related to geospatial data have increased chances of receiving Databooster support!

Being part of the #wetechtogether conference was a great experience for data innovation alliance and Litix. We support the main goal of the conference to empower people to bring more diversity into tech. We are already looking forward to the 2022 edition!

Visualizing Swiss Open Government Data

Benjamin Wiederkehr, Interactive Things
June 28, 2021

Open Government Data has the power to transform how governments engage citizens. But looking at today’s open data platforms, we have to ask the question of how accessible, usable, and shareable open data for the majority of people truly is?

Turning a downloaded spreadsheet into an insightful visualization requires design expertise. Querying data via an API requires programming knowledge. Sending the link to a data source puts the burden to make sense of it onto the recipient. Users expect from open data to discover facts and tell stories, not to wade through spreadsheets in search of answers. Organizations aspire to provide open data to improve transparency and increase engagement, not to fill a complicated file cabinet with it. Future platforms must lower the barrier to access and bridge the gap to use open data for everyone.

This presentation shares our learnings from building such a platform: visualize.admin.ch. Commissioned and co-created by offices of the Swiss Federal Administration, we envision a new way to better serve citizens through linked open data: a self-service interface that empowers users to visualize open data based on smart defaults and design best practices. Furthermore, the service empowers the user to boost the reach of open data with options to share and embed these visualizations with proper attribution and reliable reproducibility. The audience learned all about the underlying design principles, the impact of participatory development methods, and the benefits of user-centric open data services.

Problem: What makes working with data so difficult?

  • Organizations maintain a variety of scattered data sources often locked in information silos.
  • Standard analytics software products might be powerful but are complicated to use for non-technical users.
  • Results from existing products are un-designed, un-responsive, and un-customizable.
  • Results from existing products can’t be easily shared in their interactive and dynamic form.

Solution: How does visualize.admin.ch solve these problems?

  • Access with confidence: Give users secure and regulated access to your data through our unified interface to search and browse the most up-to-date data sets independent of their original information silo.
  • Visualize for efficacy: Empower users to visualize your data with compelling charts and maps in our intuitive visualization editor that comes with smart defaults and design best practices built-in.
  • Share for impact: Boost the reach and engagement of your data with our flexible options to share and embed the visualizations with proper data source attribution and reliable reproducibility.

References:

The first „Battle of NLP Ideas“ @SwissText

It has become rather difficult over the last two years for professionals to meet and informally discuss topics in their fields. Databooster is about bringing research and industry together so that new innovative projects in different data-intensive fields can be born. But how can we stimulate this exchange? The NLP Focus Group of the Databooster initiative (with Focus Group Leaders Mark Cieliebak, Philipp Thomann, Natalia Korchagina) addressed this challenge by organizing the first „Battle of NLP Ideas“ workshop at the SwissText conference. The event was held on June 15, 2021 online.

About 50 NLP enthusiasts joined our virtual battle. The goal of this interactive session was to give stage to as many ideas as possible, multiplying the chances for the people with similar ideas to meet and possibly form an alliance. The final prize of the battle was a chance to turn your idea into an Innosuisse-funded project!

Our battle had several rounds. In the first round, the participants were split in small groups of 3 to 5 where everyone could pitch their idea to other member of the group in 10-20 seconds, and the most promising idea of each small group was selected by voting. In the next round, a small presentation of an idea was prepared by each group and then pitched in front of the members of other groups. The most promising ideas were discussed in more details with all participants. After three rounds and the final vote, we had our winning ideas:

• Fake-News detection
• Topic Segmentation and Classification in Multi-topic Reports
• Automatic Contract Generation

It has been an inspiring couple of hours. There is so much research potential and industry need in working NLP products in different domains, from journalism to ecology. To assist our winners in bringing their ideas to life, the focus group leaders organized follow-up meetings where the ideas were further developed, and their market potential was discussed in more details.

Fake News Detection API, capable to recognise fake news, has proven to be in huge demand. Three large Swiss media companies and two Swiss universities (FHNW and ZHAW) are discussing to collaborate. The plan is to start with an Innocheck which will be most likely followed by an Innosuisse application.

A first follow-up workshop on the Topic Segmentation idea showed that there is large interest from academia, but only limited significance to industry. Automatic contract generation did not receive enough support from the industrial partners after the first follow-up.

We wish all our participants to turn their ideas into real projects and keep our fingers crossed for the winners!

The next battle of NLP ideas will be fought at the upcoming SwissText conference in June2022. We are looking forward to seeing many fellow NLP enthusiasts joining the contest.

Expert Group “Blockchain Technology in Interorganisational Collaboration” meeting 29.04.21

The 12th meeting of the expert group “Blockchain Technology in Interorganisational Collaboration” took place over lunch on the 29th of April.

First, the members were informed about the opportunities of the innovation process of the databooster. The innovation process gives the benefit of exploring and testing ideas together with experts in the field. Moreover, one can get support for project funding. The iterative process involves different steps such as scouting for ideas, setting up a call, shaping and re-shaping the challenge and ultimately setting up a deep-dive workshop (https://databooster.ch/expertise/).

After these introductory remarks, the expert group hosted Daniel Rutishauser from inacta AG to give an overview on the new DLT law and future trends in the blockchain sector. Daniel presented inacta’s hypotheses to four key areas of the blockchain space: crypto assets, token economies, DLT solutions, and DLT base layer. In particular in the area of crypto assets, Daniel explained that Switzerland has a competitive advantage due to the new favorable DLT law and he expects that many of the future crypto assets will be issued and traded in Switzerland. Besides many other topics, the current hype around NFTs (non-fungible tokens) and its effect on the digital art market gave rise to much discussion among the experts. The members could not agree on the fundamentals for the immense prices that this new market has realised. Considering the number of open questions, NFTs might be a topic that deserves a meeting for itself.

The online meeting was concluded without an apéro, but with many new insights on the blockchain sector gained.

Service Lunch Smart Services: Pollux – Digital Alpine Twin

Marco Zgraggen, Geschäftsführer, Sisag AG, Daniel Pfiffner, Geschäftsführer, ProSim GmbH
Date of presentation: 16.03.21

Die Firmen Sisag AG, Remec AG und ProSim GmbH haben einen Bergbahnsimulator entwickelt, der es ermöglicht, Alpine Destinationen wie beispielsweise Skigebiete mit ihrer Infrastruktur in kurzer Zeit digital abzubilden und Entwicklungsmöglichkeiten zu testen und auszuwerten. Dabei geht es vor allem um Kapazität, Kosten des Betriebs und Verhalten der verschiedenen Gäste im Skigebiet.

Der Simulator hat dabei zwei Anwendungsgebiete. Einerseits ist dies die strategische Entwicklung der Bergbahngebiete. Dabei kann die Frage sein, was die ideale Dimensionierung eines zu ersetzenden Liftes ist und was die Auswirkungen der Dimensionierung auf das restliche Gebiet sind. Ebenso können neue Pistenführungen oder neue Anlagen und ihre Auswirkungen im Gebiet im Voraus getestet werden. Andererseits dient der Zwilling der operativen Entscheidungsunterstützung. Beispielsweise was passiert, wenn ich heute unter einer gewissen Anzahl Gäste eine weitere Piste öffne oder einen Lift schliesse, oder wie viele Kassen muss ich öffnen, damit die Wartezeit an der Talstation nicht zu gross wird.

1. What was the Challenge?

Es gab mehrere anspruchsvolle Entwicklungsschritte. Einerseits war es sicher die Zieldefinition des Projektes. Was sind die Fragestellungen, welche die Bergbahngebiete wirklich beschäftigen. Am Anfang wurde vor allem in Richtung Kapazitätsplanung entwickelt. Im Laufe des Projektes hat man festgestellt, dass die Kostenberechnungen ein ebenso wichtiger Teil für den Nutzen der Software ist.

Ein weiterer anspruchsvoller Schritt war, das Personenverhalten von verschiedenen Personengruppen im Gebiet abzubilden. Diese konnten durch agentenbasierte Simulation gut und generisch abgebildet werden.

2. By which Service-oriented Approach did we Solve it?

Der digitale Zwilling wird für jede Alpine Destination individuell gebaut und parametrisiert. Durch die vorgängige Entwicklung einer Bibliothek für Alpine Destinationen kann dies in kürzester Zeit realisiert werden. Die Software wird dem Betreiber anschliessend auf einer Plattform zur Verfügung gestellt.

3. What are our learnings?

Dies ist eine Umsetzung eines Digitalen Zwillings. Es wurde von der Zieldefinierung, über die Umsetzung, bis hin zu den Weiterentwicklungsmög

Ask the Experts

New structure, new logo, new concept: the expert group “Machine Learning Clinic” is a unique pool of expert knowledge. Our last meeting aimed to connect experts willing to share their knowledge with companies in need of expertise to push AI-projects forward. Despite the hype around AI and deep learning of the last years, only a few deployed solutions are running in industry. Why is this? What are the missing bricks? One of the missions of the ML-Clinic is to overcome this gap between lab and real-world applications.

During registration we identified needs and experts on the following hot topics:

  • Data Management
  • Vision Inspection
  • Cloud Integration
  • Hardware / Edge-Processing

During a 90min virtual meeting we connected people, exchanged experience, and brainstormed about new ideas. With the new open-innovation initiative www.databooster.ch and the support from Innosuisse there are many possibilities to support companies on their ML-journey.

In a familiar round we discussed about real cases from Roche, Sulzer, SBB and others. One common issue is data quality, availability and working with rare scenarios. How to deal with missing, wrong or corrupted data. How to train robust neural networks based on such datasets. There is no easy solution but there are more and more ideas how to deal with these common industrial issues.

Beside the deep technology discussions another highlight was the “non-virtual apéro package” which all participants received before the event. Even though we only communicated through Bytes over a glass fibre everyone had a real chilled beer and some nuts in their hands – what beer would be better to stimulate the real neurons than the AI beer: DEEPER

Overall a successful event and we hope you tune in for the next get together of the ML-Clinic!

Databooster Officially Kicked-off

On February 10, 2021, NTN Innovationbooster databooster was officially kicked-off. The kick-off brought 34 participants to the online platform.

Jürgen Laubersheimer from Innosuisse started by presenting the NTN Innovation Booster program. The NTN Innovation Boosters foster a culture of open innovation, and the activities are open to all interested Swiss actors. They promote the transfer of knowledge between research, business and society and foster cooperation with partners along the entire value chain of a theme. In doing so, they create sustainable competitive advantages for Swiss SMEs in particular.

Then Gundula Heinatz presented the Databooster and the opportunities it brings for data-based value creation in Switzerland. The databooster is a community with an open innovation culture, in which key players around data-based value creation work together to develop innovation ideas and explore new business areas and approaches. In doing so, they give Swiss companies a decisive competitive advantage and create important added value for the Swiss economy and society. She also outlined the innovation process with its five phases: Scouting, Call, Shaping, Reshaping, Deep Dive.

The presentations were followed by informal discussion and networking on the online platform “Wonder”.

Thank you to everyone who took part in the event!

The Kistler Innovation Lab as a powerful Digitization Booster

On February 4, 2021, the expert group Smart Services got together online for another successful event in the series “Service Lunch”. Dr. Nikola Pascher from Kistler Instrumente AG presented the the Kistler Innovation Lab as a powerful Digitization Booster. The talk was accompanied by lively discussions among the more than 50 participants. Please find a summary of the talk here:

Kistler is the global leader for providing modular solutions in dynamic measurement technology for pressure, force, torque and acceleration measurements. The company looks back on a continuously growing business, selling hardware and system solutions in various markets. Headquartered in Winterthur, Switzerland, and with various locations worldwide, Kistler’s next step is a digital transformation to maintain steady growth within the digital age. This involves the creation of the Kistler Innovation Lab as a powerful digitization booster.

The Innovation Lab follows the general vision “Turning data into value”. This means, that we build on the vast amounts of data created with Kistler’s sensor technology and create value by using digital methods, rooted in data science, mathematics and signal processing. Digital initiatives are pursued in a protected framework at a higher speed than possible in the general corporate context. To accomplish this, the Innovation Lab stands on three pillars: With the co-creation platform, we connect different fields of expertise, share knowledge and data and provide digital know-how. The digital technology incubator is a professional framework for quick experiments and ideas with the ultimate goal to pursue proof of concept projects for digital services and solutions based on Kistler sensor data. With the digital training center, we want to empower the Kistler team and our partners to identify digital business opportunities.

In the first part of this talk, we report on the general digital transformation mechanism at Kistler with a focus on the ramp-up of the Innovation Lab within the corporate context. Despite the challenges, which are imposed by the Covid-19 pandemic, the Innovation Lab turned out to be a powerful tool, delivering first proof of Kistler’s data-based capabilities and strengthening the credibility towards our team, customers and partners.

In the second part of the talk, we focus on technical aspects of data-based services and solutions. All initiatives build on a powerful and scalable technology stack, which allows the quick set-up and deployment of cloud-based APIs. We report on first projects within the co-creation platform and the digital technology incubator. These projects aim at the fast creation of data-based services and solutions. In a co-creation project with our in-house sensor production, we aimed at optimizing a metal machining process inside a turning lathe. Together with the Kistler-internal machine shop, we made an important step towards a predictive maintenance and quality forecasting service. In a second project, we analyzed data from our weigh in motion (WIM) systems and realized, that roughly 30% of all trucks are driving empty. With the help of a machine learning model, we can forecast the flows of empty and full trucks with high accuracy.

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