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Artificial Intelligence in Service Systems

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While there are impactful insights on how AI can solve isolated business problems, there is a gap of insights for AI applications within systems and business networks. To our current understanding, this is mostly due to IP preservation and technical issues (data volume, robustness, distributed sources). In this talk, I will highlight a few possibilities on how to tackle these barriers.

While there are impactful insights on how AI can solve isolated business problems, there is a gap of insights for AI applications within systems and business networks. To our current understanding, this is mostly due to IP preservation and technical issues (data volume, robustness, distributed sources). In this talk, I will highlight a few possibilities on how to tackle these barriers.

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Artificial Intelligence in Service Systems

  1. 1. KIT – The Research University in the Helmholtz Association KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI) www.kit.edu Artificial Intelligence in Service Systems Invited Seminar at the University of Auckland, 19.12.2018 Dr.-Ing. Niklas Kühl
  2. 2. Karlsruhe Service Research Institute www.ksri.kit.edu Agenda  Introduction KIT / KSRI  Research focus on “Artificial Intelligence in Service Systems“  Individual PhD projects  Discussion 27 May 20202
  3. 3. Karlsruhe Service Research Institute www.ksri.kit.edu Where is Karlsruhe? 3
  4. 4. Karlsruhe Service Research Institute www.ksri.kit.edu Karlsruhe Institute of Technology (KIT) is one of the largest research institutions in Europe  In 2009, KIT was created as a merger of Karlsruhe University and a National Research Center: • 25,500 students • 9,300 employees • budget of €902M p.a., o/w 40% third-party  Three equally-weighted pillars: • Research • Education • Innovation  Top ranked education programs e.g. • Computer Science (2,500 students) • Industrial Engineering and Management (3,200 students) 4
  5. 5. Karlsruhe Service Research Institute www.ksri.kit.edu Industry orientation pays off – KIT well in the game... Wirtschaftswoche, Jan 12, 2018 Wirtschaftswoche, June 20, 2014 5
  6. 6. Karlsruhe Service Research Institute www.ksri.kit.edu Discrete Optimization & Logistics Prof. Dr. Stefan Nickel Knowledge Management Prof. Dr. Rudi Studer Prof. Dr. York Sure-Vetter Information & Market Engineering Prof. Dr. Christof Weinhardt Digital Service Innovation Prof. Dr. Gerhard Satzger Energy Economics Prof. Dr. Wolf Fichtner Information Systems & Service Design Prof. Dr. Alexander Mädche Value Stream Services Dr. Markus Bauer, Prof. Dr. Kai Furmans €  €  € € €€ €€ € € € €€€ € €€€€€  €€ KSRI is a successful industry-on-campus model with interdisciplinary research 6
  7. 7. Karlsruhe Service Research Institute www.ksri.kit.edu Technology-Driven Transformation • Data-driven Business Models • Blockchain for Service Systems • Industrial Services • Services in E-Mobility Innovation Management • Service Design Thinking • Open / Collaborative Innovation • Enterprise Crowdfunding • Needmining • Innovation Adoption Advanced Analytics • Applied Artificial Intelligence • Machine Learning in Systems • Cyber Physical Systems • Service Analytics • Social Media Analytics Digital Service Innovation (DSI) Prof. Dr. Gerhard Satzger 7 Our group is focused on Digital Service Innovation with three teams: Innovation Management, Technology-Driven Transformation and Advanced Analytics
  8. 8. KIT – The Research University in the Helmholtz Association www.kit.edu Artificial Intelligence in Systems Dr.-Ing. Niklas Kühl Head of Applied AI Lab, KIT
  9. 9. The application of machine learning methods to real-world business problems yields manifold opportunities Computer Science: Methods 9 Business Studies: Problems Applied Artificial Intelligence
  10. 10. For isolated business problems, there are many application examples—in business and practice… 10 Automatically identifying customer needs from social media Comfort-as-a-Service: Increasing comfort of building occupants Smart Technician Scheduling: Automated dispatching of technicians for industrial maintenance Examples from KSRI Research: Kühl & Satzger (2019): Needmining – Extracting Customer Needs from Social Media Laing & Kühl (2018): Comfort-as-a-Service: Designing a User-Oriented Thermal Comfort Artifact for Office Buildings Vössing, Wolff, Reinerth (2018): Digitalization of Field Service Planning: The Role of Organizational Knowledge and Decision Support Systems
  11. 11. There are many options for new application areas—in business and practice. However, there is a lack of research in system-wide AI 11 Company B Company A Company C Today: Isolated AI Tomorrow: AI within systems? Company A Company B
  12. 12. Nowadays, analytical knowledge is not shared across company boarders… Company A Sales forecast A Knowledge transfer? Knowledge transfer? Hirt, Kühl, Peker, Satzger (2018): How to learn from others? Transfer Machine Learning to Improve Sales Forecasting Company B Sales forecast B Company C Sales forecast C 12
  13. 13. Often, critical components of a system are tiny, non-observable components Martin & Kühl (2019): Holistic System - Analytics as an Alternative to Isolated Sensor Technology: A Condition Monitoring Use Case 13
  14. 14. Karlsruhe Service Research Institute www.ksri.kit.edu Applied AI @ KSRI 14 We are interested in… the utilization of machine learning and artificial intelligence for the development of innovative services. Who we are... We currently research...  Artificial Intelligence in Service Systems - System-wide analytics to gain holistic insights - Exchanging analytical knowledge across entities - Ensuring validity of predictive services  Needmining - Automatically identify customer needs from social media sources, e.g. Twitter - Ongoing analyses on tweets about Blockchain, Internet of Things, Artificial Intelligence and E-Mobility  Smart Technician Dispatching Dr.-Ing. Niklas Kühl Robin Hirt Lucas Baier Dominik Martin Jannis WalkMichael Vössing Clemens Wolff Patrick Kummler
  15. 15. Karlsruhe Service Research Institute www.ksri.kit.edu Analytics for Cyber-Physical Service Systems Motivation  Cyber-Physical Systems address connection of physical and digital word through influence and control  Due to declining hardware costs (i.e., sensor technology) and advanced connectivity capabilities, more and more products become Cyber-Physical Systems  Massive increase of data volume generated by Cyber-Physical Systems along complete value chain of a product  RQ: How can Analytics enhance Cyber-Physical Systems considering the complete value chain? Approach  Identification of problems regarding the general design and architecture of Cyber-Physical Service Systems  Development of analytics method(s) to overcome these problems  Verification of organizational impacts of developed analytics method(s) First Results  Feasibility of applying advanced analytics methods for condition monitoring of non-observable components in a sealing use case 15 Cyber-Physical Service System services CPS CPSS suppliers collaboration customers service providers Cyber-Physical System communication humans cyberspace Embedded System actuators sensors software hardware
  16. 16. Karlsruhe Service Research Institute www.ksri.kit.edu Privacy- and confidentiality-preserving analytics across company borders: Meta and Transfer Learning in smart service systems Challenge  Data in smart service systems is distributed throughout entities and an exchange of data is not possible due to confidentiality issues A: comprehensive analyses can not be performed  No holistic insights  Entities often face similar analytics challenges or possess similar data B: an exchange of analytical knowledge or models is prevented due to confidentiality issues and its complexity  Re-invention of the wheel Method / Approach  A: Perform comprehensive analyses through meta machine learning to derive holistic insights in settings, where data about one common context that needs to be analyzed is distributed across entities  B: Reuse models by applying transfer machine learning in settings, where multiple entities possess similar data and face similar analytical problems Benefit  A: Through meta machine learning and a “directed stacking” approach, we enable privacy- and confidentiality-preserving analytics across company borders  B: Through transfer machine learning, we enable a privacy- and confidentiality-preserving transfer of analytical knowledge in form of models 16 A B
  17. 17. Karlsruhe Service Research Institute www.ksri.kit.edu Ensuring Validity of Predictive Services over Time Motivation  Various industries rely on predictive services based on data streams for (better) service offerings  Over time, context or data may change and false decisions might be taken based on predictive service  RQ: How can we design efficient and effective tools which ensure the long-term validity of predictive services? Approach  Collection of research papers that are dealing with changing data streams on real use cases  Identification of algorithmic options and challenges in operation  Semi-structured expert interviews with machine learning practitioners to identify solution strategies in practice First Results  Taxonomy with design options for creating valid predictive services over time and classification of existing research approaches into this taxonomy 27/05/2017 ML I Productive environment Deployed Machine Learning Model Training / test environment Changing context over time Different data distribution ML model is based on training environment Continuous data stream
  18. 18. Karlsruhe Service Research Institute www.ksri.kit.edu Designing Systems for Automated and Data-Driven Field Service Planning: Leveraging Machine Learning and Operations Research for Service Operations 18 Motivation  Service operations in the context of industrial maintenance (i.e. the scheduling of maintenance, repair, and overhaul) is surprisingly difficult. In practice, planning performance largely depends on the ability of service providers to manage complexity and uncertainty during three planning phases (see below).  Companies need to detect, specify, and prioritize service demand before technicians can be assigned—all tasks currently conducted manually by domain experts based on their tacit knowledge.  Emerging technologies (i.e. cyber-physical systems) and changing customer demand (i.e. asset outsourcing) are driving the adoption of new maintenance policies (i.e. prediction-based) and contracts (i.e. performance-based). Understanding how these phenomena can reduce the dependence on tacit knowledge and change how services are delivered is the focus of this research. Approach 1. Data-Driven Demand Estimation: Data from cyber-physical systems can be used to remotely approximate the service demand of managed machinery by modelling common deterioration patterns (e.g. wear out or fatigue) and detecting abnormal behaviour of individual machinery. 2. Data-Driven Planning Preparation: Detected service demand can be translated into specified work orders (e.g. required service duration, skills, or expected costs) by analysing historic work orders, technician reports, and information available on asset usage & context. 3. Automated Work Order Prioritization and Planning: Anticipatory planning can help solution providers (i.e. performance-based contracts) objectively prioritize and plan work orders. Rolls-Royce Turbines „Power-by-the Hour“ Xerox Printers „Print on Demand“ +
  19. 19. Karlsruhe Service Research Institute www.ksri.kit.edu System-Oriented Service Delivery in Service Systems Motivation  provider-oriented service delivery in service systems (= service delivery such that provider costs are minimal)  customers have delivery-dependent costs (e.g. due to delayed service delivery) that are not taken into account during provider’s delivery resource allocation  provider-centric delivery decision making leads to an inefficient allocation of delivery resources from a system’s viewpoint Approach  system-oriented service delivery that minimizes total system costs (= sum of provider‘s delivery and customers‘ consequential costs)  mechanism to enforce benefit and loss sharing among participants Results  cost-minimal service delivery from a system perspective  prevent individual losses through loss/benefit sharing  Pareto-improvement over provider-oriented service delivery  additional value allows for business model innovation 19
  20. 20. Karlsruhe Service Research Institute www.ksri.kit.edu Vertical Integration Analytics – A Milling Use Case Motivation  Nowadays, advanced analyses (like Machine Learning) are usually not performed across company borders. However, this promises new insights, business models and potential for cooperation.  Use case:  Tool life in Milling operations varies for theoretically identical processes. Can this be explained by combining data from tool producer and customer?  How can benefit be realized when respecting the intellectual property of tool producer and customer? Method / Approach 20 Tool Life Information Customer Information about workpiece and process Information about tool Tool producer Model
  21. 21. Karlsruhe Service Research Institute www.ksri.kit.edu REQUAL - Requirements Quality Analytics Model and Evaluate the Quality of Software Requirements Motivation  Value creation in the automotive industry shifts from hardware to software and services  Software functions are the main distinguishing feature for vehicles  Automotive manufacturers are increasingly faced with challenges of writing appropriate software requirements in natural language Approach  Quality measurement through identification and definition of relevant quality attributes  Expert assessments of software requirements according to defined quality attributes  Analyze software requirements and implement classification algorithm First Results  Assessments of software requirements through experts  Identification of quality attributes & derivation of relevant indicators  Analysis of relationships between measurable indicators and quality attributes  Implementation of prediction model for software requirements quality 21 Indicators for Measurement Overall Quality Requirement Quality Attributes Communication Cycle Requirements Specifications
  22. 22. Karlsruhe Service Research Institute www.ksri.kit.edu References  Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media, Kühl, N.; Mühlthaler, M.; Goutier, M., 2019. Electronic markets, 1–17. doi:10.1007/s12525-019-00351-0  Cognitive computing for customer profiling: meta classification for gender prediction, Hirt, R.; Kühl, N.; Satzger, G., 2019. Electronic markets, 29 (1), 93–106. doi:10.1007/s12525-019-00336-z  Holistic System-Analytics as an Alternative to Isolated Sensor Technology: A Condition Monitoring Use Case, Martin, D.; Kühl, N., 2019. Proceedings of the 52nd Annual Hawaii International Conference on System Sciences (HICSS-52), Grand Wailea, Maui, Hawaii, January 8-11, 2019, 1005–1012  Machine Learning in Artificial Intelligence: Towards a Common Understanding, Kühl, N.; Goutier, M.; Hirt, R.; Satzger, G., 2019. Hawaii International Conference on System Sciences (HICSS-52), Grand Wailea, Maui, Hawaii, Januar 8-11, 2019  Comfort-as-a-Service: Designing a User-Oriented Thermal Comfort Artifact for Office Buildings, Laing, S.; Kühl, N., 2018. Thirty Ninth International Conference on Information Systems (ICIS), San Francisco, CA, 13th-16th December 2018, Association for Information Systems  Cognition in the Era of Smart Service Systems: Inter-organizational Analytics through Meta and Transfer Learning, Hirt, R.; Kühl, N., 2018. 39th International Conference on Information Systems, ICIS 2018; San Francisco Marriott Marquis San Francisco; United States; 13 December 2018 through 16 December 2018, AIS, New York (NY)  System-Oriented Service Delivery: The Application of Service System Engineering to Service Delivery, Wolff, C.; Kühl, N.; Satzger, G., 2018. 26th European Conference on Information Systems: Beyond Digitization - Facets of Socio- Technical Change, ECIS 2018, Portsmouth, UK, June 23-28, 2018. Ed.: U. Frank, Code 143975 27/05/2022
  23. 23. Karlsruhe Service Research Institute www.ksri.kit.edu Prof. Dr. Gerhard Satzger Research Group “Digital Service Innovation” Karlsruhe Service Research Institute (KSRI) Karlsruhe Institute of Technology (KIT) Kaiserstr 89, D-76133 Karlsruhe, Germany Phone: +49 (0) 721 6084-3227 (KIT) Email: gerhard.satzger@kit.edu Dr.-Ing. Niklas Kühl Research Group “Digital Service Innovation” Karlsruhe Service Research Institute (KSRI) Karlsruhe Institute of Technology (KIT) Kaiserstr 89, D-76133 Karlsruhe, Germany Mail: kuehl@kit.edu Web: niklas.xyz Don‘t hesitate to contact us!

Notizen

  • – “T-shaped education”
  • – “T-shaped education”
  • Joint Management ▪ Joint Infrastructure
    Joint Interdisciplinary Research Projects ▪ Joint Graduate Program
  • Service Innovation: Improve the innovation capabilities for enterprises and networks, (Design Thinking, Open Innovation, Crowdfunding)
    Service Analytics: Leverage data across partners to engineer services and service systems (Sales Force Analytics, Customer Intimacy, Service Level Engineering, Customer Contribution Measurement, Outcome-Based Contracts)
    Service Transformation: Develop business models and systems based on services (Services in E-Mobility, Industrial Services, Data-driven Business Models)
  • booking.com ==> Competitive Set
  • ×