Expert Groups are aimed at the professionals of our Industrial and Academic Members, and can only be visited by them. The goal is to help the participants develop themselves and to foster collaborations between participants.  In addition, Expert Groups should create a value for the network as a whole, by feeding back results of general interest, participating in events, and letting the other network members participate at the learnings.

They are focused on a specific area of interest, and bring key players in close and highly engaged contact with each other with respect to this topic. Expert Groups are designed to discuss and handle real use cases.

Academic leader: Michael Lustenberger (ZHAW), luse@zhaw.ch

Industrial leader: Fabian Russmann (D-ONE), fabian.russmann@d1-solutions.com

Abstract:

Every business is closely observing new technologies like Big Data Analytics, AI and Internet of Things
as we all can see and understand their rich potentials. However, a new technology that just appeared
on the horizon seems to be not yet understood by many people and companies. Thereby it is clear
for experts: what the Internet was for communication will be the Blockchain technology for
transactions. With the Blockchain technology a decentralized transaction register (distributed ledger)
is managed by a network of computers and can be only changed by a predefined consensus rule.
From our previous research we have identified so far four main implications of the Blockchain
technology for the Supply Chain Management:

1) Transparency: Public data and information which are accessible at all times and displayed in real
time on a Blockchain, increases transparency along the supply chain.

2) Trust: Blockchain technology can replace intermediaries, which implies the technology creates the
confidence that data is correct and synchronized at all times. Blockchain technology thus creates a
single point of truth within an entire supply chain network.

3) Security: The data and smart contracts stored on a Blockchain cannot be changed or manipulated
without consensus among the participants. A single point of failure can be avoided.

4) Automation: Supply chain processes such as payments, customs clearance, data or document
transfers can be automated via smart contracts.

Our vision is to deepen the knowledge about the potential of the Blockchain technology in Supply
Chain Management together with interested companies and research institutes from the Swiss
Alliance for Data-Intensive Services. Every company and industry should understand how this
technology works and how it will transform the operations and management of supply chains. Within
the group of experts, concrete questions shall be raised and explored in the area of the four
identified supply chain topics with the aim to better understand the full potential of the Blockchain
technology in Supply Chain Management.

Academic leader: Lorenz Stähle (ITEM-HSG)

Industrial leader: Hans Peter Gränicher (D-ONE), hanspeter.graenicher@d1-solutions.com

Abstract:

With enhanced connectivity of products and accelerated digitalization of services, the availability of data within firms has grown substantially. Academia and leading domain experts have commented that “Data is the new oil”. However, many firms struggle to create new offerings based on available data or generate revenues from existing data-intensive services. Besides, missing knowledge in data processing and analytics, firms face the challenge to define clear value propositions or even entire business models.   

The expert group will foster the discussion and knowledge exchange on successful commercialization of data-intensive services. The topic will be approached from a management perspective with a focus on business model design and innovation. We will discuss tools and methodologies to define value propositions, suitable revenue and pricing models. Furthermore, we would like to identify critical success factors for data-driven business models. Besides discussions on business models, we would also like to include topics like “make or buy ” of relevant ICT-components and platform approaches into the expert group meetings. However, the detailed topics will be aligned to the needs and interests of the participants.

Prospective members:

The expert group is open to all data+service members. However, we would like to encourage especially manufacturing or service firms that are currently evaluating or introducing data-intensive services to join the Expert Group.

Intended schedule: Quarterly

Academic leader: Olga Fink, ZHAW,  fink@zhaw.ch

Industrial leader: Thomas Palmé, General Electric, thomas.palme@ge.com

Abstract:

The field of predictive maintenance has been progressing in the last years with the decreasing cost of sensors and the increasing availability of condition monitoring data from components and systems. Despite the substantial progress made in this field in the last years, there are still a lot of challenges to be solved, particularly in transferring new scientific research on features, algorithms and models to real-world industrial applications properly interfacing the different disciplines involved.

There is a gap between the scientific advances on algorithm development and their applicability in the real-world due to the limited clarification of assumptions and prerequisites in each method and a lack of a clear guidance of how and when to use the specific algorithms.

In a modern industrial asset, data-driven predictive maintenance approaches comprise merging of different data sources and balancing system availability, safety and economical risk against maintenance costs. Due to the novelty and diversity of the data-driven predictive maintenance field, the applied methods are very broad and diverse, and clear guidelines and recommendations on the suitability of the different approaches are missing.

The mission of the group "Data-driven Predictive Maintenance of Industrial Assets” is to bring together experts in the field of condition-based and predictive maintenance in order to -Define the most promising gap where predictive maintenance has not yet been leveraged due to missing data analytics processes

  • Define specific use cases where industry still struggles to successfully leverage data analytics

  • Define industrial requirements with industrial partners, develop guidelines and recommendations on how and when different methods can be used to solve industrial use cases relying on different assumptions

  • Share experience in applying data-driven approaches to predictive maintenance problems, case studies, success stories etc.

  • Facilitate the transfer of research results to industrial applications by organizing workshops with international experts

  • Ensure knowledge transfer between different application fields through regular meetings, workshops and publications 

Academic leader: Markus Christen, Uni. of Zurich, christen@ethik.uzh.ch  and Christoph Heitz, ZHAW Winterthur, heit@zhaw.ch

​Industrial leader: Karin Lange, Die Mobiliar, karin.lange@mobi.ch 

​Abstract:

With big data comes big responsibility. Issues such as data protection, workplace surveillance, or the potential consequences of future technologies require constant ethical reflection and stakeholder dialogue. This Expert Group will offer a forum to discuss ethical issues that matter to its members in their respective contexts, enabling them to develop concrete solutions for ethical challenges. Such issues may range from professional ethics (legitimate conduct as a professional in various work-related scenarios) to organizational aspects (data collection, usage, and processing within companies) to legal and political issues (data protection, industry agreements, codes of conducts, standards and labels, and public decision-making processes). The main goal of the group is to develop and foster competence regarding ethical issues in data usage, and to make sure that state-of-the-art approaches are shared so they can be applied by group members in their respective fields.

Potential activities of the Expert Group may include:

  • discussing specific industry cases and options for ethically sound implementation of data usage;

  • organizing events with nationally and internationally renowned experts on ethics of data usage in order to exchange ideas and learn about existing standards in Switzerland and abroad;

  • developing ethical standards for companies and organizations;

  • discussing, preparing, and presenting input to political decision makers

Academic leader: David Wannier, HES-SO Valais, David.Wannier@hevs.ch 

Industrial leader: Christian Spindler, PwC, christian.spindler@ch.pwc.com

Abstract:

Imagine there is a secure, anonymous and trustable way to integrate data from various systems, exchange the data and extract synergetic value from data among different, even competing organization. We could call such data “Trusted Data”, and we would need new approaches like smart anonymization, data versioning and certification, a common data language and advanced cyber security, to realize this vision.

This concept of an independent and secure data platform aligns on the “Industrial Data Space”, currently under co-development by the Fraunhofer Society and the large corporations in Germany. The Expert Group focusses on finding and sharing solutions around Trusted Data, tackling typical issues such as the following:

  • Data integration: Lack of a joint data model that data experts understand, and that data experts can manage and develop in an intuitive language with only little involvement of IT.

  • Data governance: Lack of control over data versions, including all dependencies with different entities and source systems. As an example the group may investigate where companies could find a balance between the value generated by exchanging data with others (e.g., value from developing more reliable predictive models due to more accessible data), and the associated impact of leaking trade secrets (or in other terms, how to avoid leaking trade secrets from exchanging data). Both, academic research and corporate innovation projects will contribute to answer this question.

  • Data sharing: Lack of a common data language amongst teams, systems, and organizations. Substantial efforts are required to make data sources compatible and non-trivial conversions are necessary to aggregate items of different granularity. Smart access rules and anonymization algorithms are key enablers for trustful data exchange.

Most users of data today, both inside and within third party organizations, assume the data items they use for processing, decision-making, internal and external reporting, or potentially sharing to be of adequate quality. However, in most enterprises, system integration lacks behind the mentioned requirements thus hampers value creation. This Expert Group sets out to tackle these issues from a conceptual basis to very concrete implementations.

Academic leader: Thilo Stadelmann, ZHAW Datalab, thilo.stadelmann@zhaw.ch

Industrial leader: Michel Benard, Data+Service Alliance, michel.benard@data-service-alliance.ch

Abstract:
Machine learning is the key enabling technology behind data-intensive services. The state of the art is advancing at high pace and is being thoroughly evaluated on certain established benchmark environments like “MNIST”. However, the application of novel techniques in e.g. deep learning or reinforcement learning on different industrial use cases is often delayed due to a lack of points of contact between research capacity and real-world applications.

The Expert Group "Machine Learning Clinic” is geared towards machine learning practitioners from industry and academia. Its goal is to re-link machine learning with industrial practice to collect best practices and facilitate collaborations among members. Real world use cases are being presented, and applicable methods and best practices are identified, discussed, and tried. Industrial Data+Service members benefit from diverse state of the art expertise from other members; and Academic Members benefit from new application scenarios, offering the possibility to try methods in real-world conditions and realize cross-industry transfers.

The Expert Group will revolve around questions like “how to gain theoretical & intuitive insight into how and why a certain algorithm works?” and “how to make machine learning easy to use?”. We will collect our findings and best practices and contribute them back to the Data+Service Alliance as well as the international research community.
 

Academic leader: Martin Jaggi, EPFL, martin.jaggi@epfl.ch

Industrial leader: Silvia Quarteroni, ELCA, silvia.quarteroni@elca.ch

Abstract:

Natural language processing (NLP), born over half a century ago at the crossroads of computer science and linguistics, is now an empowering force behind many AI applications. In recent years, the availability of large datasets from which to learn statistical models has contributed to a considerable progress in several natural language understanding tasks, including spoken language understanding,
machine translation and many others. Nevertheless, in practice there exist hugely varying approaches to tackle such problems depending on the industry. In many cases, the latest results of academic research are well ahead of the industrial practice, for example when it comes to general- purpose natural language understanding “enablers” on the cloud.

The objective of the expert group “NLP in action” is to foster a productive dialogue between the academic and industrial actors of NLP in Switzerland. Our aim is to discuss how the latest models in the academia can quickly turn into successful industrial applications, and how lessons learned in the industry can benefit and motivate academic research. We are aware that NLP research and development is still at an early stage in Switzerland, which is why we believe that the organizations who are active in the domain should reach out to one another and share common experience and –
ideally – data. The expert group features two kinds of activities: on the one hand, we welcome presentations from both the academia and the industry regarding promising topics and success stories, from contributors in and outside the Data+Service Alliance. On the other hand, we also look forward to discussing open challenges in the domain. Examples of such challenges include the complex linguistic landscape of Switzerland or the collection of training data and evaluation for end-to-end NLP applications.

Academic leader: Jürg Meierhofer, Zürich University of Applied Sciences,  juerg.meierhofer@zhaw.ch

Industrial leader: TBD

​Abstract:

Our mission is to discover and apply best practice methodologies for designing data-
intensive services that create personal and business value of data for users in their specific
context. We differentiate between two application scenarios with strong methodological
synergies between them:

1. Services for human individuals, typically consumers

  • Gaining insights into jobs, hidden pains and gains related to a service
  • Modelling the customer journey and developing appropriate value propositions
  • Finding the appropriate equilibrium between the digital and the human side of data-driven services

2. Industrial smart services for business users, typically in production environments

  • Applying the concept of servitization of manufacturing in specific production environments
  • Leveraging the potentials of product-service transformation for increasing customer value and gaining competitive advantages
  • Creating business service ecosystems

What do our Industrial Members get from this Expert Group?

  • Getting to know best practice approaches for smart services from other companies
  • Becoming familiar with innovative concepts for smart services from R&D
  • Discovering the potential of data science to design advanced smart services.
  • Sharing experiences with other companies
  • Benefitting from networking opportunities with experts and like-minded companies
  • Getting access to innovative R&D projects from the smart services science community
  • Having the opportunity to participate in student projects

We bring these benefits to the members of the Expert Group using different formats such as co-creation in workshops, lunch-seminars, extended conferences and direct bilateral exchanges.

Academic leader: Kurt Stockinger, ZHAW Zurich University of Applied Sciences,  Kurt.Stockinger@zhaw.ch and Philippe Cudre-Mauroux, Uni Fribourg, pcm@unifr.ch

​Industrial leader: Christian Gügi, Scigility,  christian.guegi@gmail.com

​Abstract:

Data Science has recently gained significant attention both by academia and industry. Especially in the areas of big data and machine learning the software and tools landscape is changing rapidly. This makes it very difficult for companies to keep up to date and choose the right time to embark on new technologies. Unlike universities that typically experiment with the latest features, companies are often more conservative due to legacy software.

The goal of this Expert Group is to provide a technology radar for software and tools and to discuss new trends as well as best practices. Similar to Gartner’s Magic Quadrant, the idea is to become the major reference to Data and Service Science related technology insights.

Academic leader: Stefan Keller, HSR, sfkeller@hsr.ch

Industrial leader: Nicolas Lenz, geo7, nicolas.lenz@geo7.ch

Abstract:

Location is everywhere, literally. Therefore almost every dataset contains geographic information like coordinates, addresses or toponyms. This information enables the data to be embedded in the space we live in. The embedding of multiple datasets facilitates their combination and comparison and allows for more complex spatial analyses. As in other disciplines the main challenge in spatial analytics is the practical application. That's where the focus of the Expert Group lies, i.e. in :

  • Spatial information integration (i.e. georeferencing): How can data be prepared for spatial analytics, i.e. what are suitable geodata models, formats (and ev. tools)?
  • Open geodata catalog: Is it possible to build and maintain a living list of geo-datasets that can be used for spatial analytics?
  • Applying location-based algorithms: What algorithms exist and what are the dos and don’ts?
  • Visualization: What tools exist for communicating the spatial analytics output?

First meeting: Topics: Introduction, Mission Statement, Intended Schedule, Geo-spotlights (2-3 lightning talks about spatial analytics with short discussion) 

Intended schedule: Quarterly