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.

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. 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. Karlsruhe Service Research Institute
www.ksri.kit.edu
Where is Karlsruhe?
3

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. 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. 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
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KSRI is a successful industry-on-campus model with interdisciplinary research
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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. 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. 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. 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. There are many options for new application areas—in business and practice.
However, there is a lack of research in system-wide AI
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Company B
Company A
Company C
Today: Isolated AI Tomorrow: AI within systems?
Company A
Company B

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. 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. 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. 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. 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. 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. 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. 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. 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. 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
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Indicators for
Measurement
Overall
Quality
Requirement
Quality Attributes
Communication
Cycle
Requirements
Specifications

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. 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!