fedcis keynote: from digital transformation to digital

FROM DIGITAL TRANSFORMATION TO DIGITAL UBIQUITY:

THE ROLE OF ENTERPRISE MODELING

Prof. Dr. Hans-Georg Fill

Research Group Digitalization and Information SystemsUniversity of Fribourg, Switzerland

UNIVERSITY OF FRIBOURG | DEPARTMENT OF INFORMATICSHans-Georg FILL | Research Group Digitalization and Information Systems – http://www.unifr.ch/inf/digits

Ø Digital Transformation

Ø Enterprise ModelingØ Digital Ubiquity

Ø Digital Ubiquity & Enterprise Modeling

Ø Conclusion and Outlook

AGENDA


DIGITAL TRANSFORMATION


INTEREST IN DIGITAL TRANSFORMATION

Jan 2010 July 2020


DIGITAL TRANSFORMATION EXAMPLE:FREE-FLOATING CAR SHARING

Traditional Car Manufacturer Digitally-Transformed Business

Advertise Cars

SellCars

ServiceCars

Produce Cars

Product:

Processes:

IT-Services: ERP CRM …

Infrastructure:

Mobility as a Service:

Advertise Service

RentCars

ServiceCars

Produce CarsProcesses:

IT-Services: ERP CRM WebApp

Infrastructure:


DIGITAL TRANSFORMATION EXAMPLE:FREE-FLOATING CAR SHARING

Strategy, Business Model, Products/Services

Processes & Organization

Applications / IT Services

IT Infrastructure / Software Services


How to conduct the transformation in/across each layer?

DIGITAL TRANSFORMATION LAYERS

Strategy, Business Model, Products/Services

Processes & Organization

Applications / IT Services

IT Infrastructure / Software Services


ENTERPRISE MODELING

§


ENTERPRISE MODELING

CONCEPTUAL MODELING

MODELING


MODELING FOUNDATIONS (BRIEF)

Universe of Discourse

Model

Abstraction

Mapping

Picture Sources: Pixabay, Lego.com, https://upload.wikimedia.org

Subject

Construction

Interpretation

Perception

Goals


CONCEPTUAL MODELING

Universe of Discourse

Model

Abstraction

Instantiation

Mapping

Algorithms

Picture sources: Pixabay, https://i0.wp.com/architecturaldesigns.blog, Amazon.com

Visual Modeling Language

https://i0.wp.com/architecturaldesigns.blog


ENTERPRISE MODELING EXAMPLE:BUSINESS PROCESS SIMULATION

Business Processes

BPMN Modeling Language

BPMN Model

Abstraction

Instantiation

Path Simulation Algorithm

Simulation ResultsExecution Time: 00:01:02:00:03Cycle Time: 00:01:05:10:04Number of Paths: 23...

Image Source: https://icarus-consult.de/

Mapping


Typical goals of enterprise modeling comprise:

ü Representing the structure and behavior of enterprises

ü Common understanding between stakeholders

ü Eliciting requirements for developing information systemsü Analyzing and optimizing an organization’s capabilities, e.g. products,

services, business processes, IT services and infrastructureü Formalizing knowledge about enterprises for making it machine-processable

ü Supporting decision makers for tasks such as strategy development, performance analysis, business process improvement, risk and compliance management or business continuity management

GOALS OF ENTERPRISE MODELING


BusinessAspects

InfrastructureAspects

ProcessAspects

TechnologyAspects

Scene Models for Business Model

Innovation

BPMN for Business Processes,

Petri Nets for Formal Process Descriptions

Architecture Models for Cyber Physical

Systems

Archimate for Enterprise

Architecture,UML Diagrams

Modeling Methods & Tools from: www.omilab.org

SOME EXAMPLES FOR ENTERPRISE MODELING


DIGITAL UBIQUITY


CHARACTERISTICS OF DIGITAL UBIQUITY

Some Expectations:

Ø Digital Products and Services are Standard

Ø Practically Unlimited IT Resources

Ø Organizations have Digital Twins

Ø Data Analytics are OmnipresentØ Constant Adaptation to New

Technologies


DIGITAL PRODUCTS AND SERVICES ARE STANDARD


PRACTICALLY UNLIMITED IT RESOURCES

AWS S3 Storage 2020-08-12: https://aws.amazon.com

Microsoft Azure Storage, 2020-08-12https://azure.microsoft.com

Amazon Web Services: “Unlimited scale with pay-as-you-go pricing”

Royal Dutch Shell3rd Largest Company Worldwide

SAP HANA: 48TBMS SharePoint: 150TB

Source: cio.de

Microsoft Azure:

Companies using clouds for their IT offerings:

https://aws.amazon.com/solutions/case-studies/


ORGANIZATIONS HAVE DIGITAL TWINS

Picture Sources: Pixabay, M 93, CC BY-SA 3.0 de, https://commons.wikimedia.org/w/index.php?curid=65044519

(adapted from Fuller et al., 2020, p. 108954; Dietz & Pernul, 2020)

EnterpriseAsset

Digital Model

EnterpriseAsset

Digital Model

EnterpriseAsset

Digital Model

Digital Model Digital Shadow Digital Twin

Manual Data Flow Automatic Data Flow


DATA ANALYTICS ARE OMNIPRESENT

tToday

Descriptive Analytics Predictive Analytics

Prescriptive Analytics

Action

Quantum ML

Self-Supervised Learning

Differential Privacy

Adaptive ML

Data Labeling and Annotation Services Explainable AI

Deep Neural Networks(Deep Learning)

Text Analytics

Federated Machine Learning

Some Current and Future Topics in Machine Learning and Data Analytics in 2020:

Cf. Gartner Hype Cycle for Data Science and Machine Learning, 2020


CONSTANT ADAPTATION TO NEW TECHNOLOGIES


Characteristics of Digital Ubiquity:§ Digital technologies already integrated in services and business processes,

e.g. ordering workflow system, smartphone app§ IT infrastructure with practically unlimited connectivity, storage space,

processing power if required – here realized via AWS§ Data analytics available throughout all processes, e.g. detailed information

about order quantity, price, etc.

§ Business partners with similar digital capabilities, here: delivery companies

DIGITAL UBIQUITY EXAMPLE:HOME DELIVERY WORLDWIDE IN 4 MONTHS

Sources: aws.amazon.com, mcdonalds.com


DIGITAL UBIQUITY

ENTERPRISE MODELING


ENTERPRISE MODELING FOR DIGITAL UBIQUITY

§ Abstraction from Complex Technologies

§ Interfaces to Digital Technologies

§ Reference Models and Best Practices


Example: Understanding Business Transactions of Decentralized Autonomous Organizations (DAO): Aragon DAO

ABSTRACTION FROM COMPLEX TECHNOLOGIES

LegendCustomer Relationship: Automated Service

Customer Segment

Revenue Stream:On-going PaymentChannel

Value Proposition

Key Activity

Resource

Resource:Financial

Cost Structure

Key Partnership Method from (Wieland and Fill, 2020)


Example: Understanding Decentralized Autonomous Organizations (DAO):Aragon Enterprise Architecture

ABSTRACTION FROM COMPLEX TECHNOLOGIES


FORMAL LINKS BETWEEN ASPECTS


INTERFACES TO DIGITAL TECHNOLOGIES - I

Semantic Technologies essential for Digital Twins

§ SeMFIS: Flexible engineering platform for semantic annotations of conceptual models

§ Visual semantic annotation models & visual models for OWL, Frames ontologies, SWRL models

§ Foundation for interfaces to cyber-physicalsystems, here collaboration of robots for picking and moving parts

XOMiAC1 - OMiLAB Robotic Arm & Car Experiment 1http://austria.omilab.org/psm/content/xomiac1/info

Visual Modeling Language

ModelModelModelModelModel

Algorithms

OntologyAnnotation

Model

Ontology-based Algorithms, Reasoning

Rules

www.semfis-platform.org (Pittl&Fill, 2020; Fill, 2017; Fill, 2011)

Project Initiated at:

BPMN Business Process Model

Semantic Annotation Model

OWLModel

SWRL Model


Customers Customer-facing Processes

Post

Post

Post

Post

Social MediaPosts

INTERFACES TO DIGITAL TECHNOLOGIES - II

3. Key Performance

Indicators

2. CTQ/CTB Matrix

1. SIPOC Diagram

4. Measurement Matrix

5. Data Collection Plan

6. Histogram

7. Scatterplot

8. Ishikawa Diagram

9. Affinity Diagram 10. Reaction Plan

11. Control Charts

Define Measure Analyse Improve Control

Legend:Sequential order to use the techniques (i.e., control flow)

BPI techniqueFlow indicating a new project and run of the BPI roadmap

Measurement Matrix Model

SIPOC Model

CTQ/CTB Model

ModelingToolbased onADOxx

NLP Configuration DSML & Java-based Execution

Enterprise Data from ERP, Production Systems, etc.

(BIG) Data Analytics Platform

(Fill & Johannsen, 2016); (Bergner, Fill, Johannsen, 2016); (Johannsen & Fill, 2017); (Böhm, 2018)

DMAIC-basedBPI Roadmap

Integration of Business Process Improvement and Natural Language Processing Methods Using Enterprise Modeling


REFERENCE MODELS AND BEST PRACTICES

Smart City Conceptual Reference Models for Best Practices:

§ Smart Cities as an activeresearch subject

§ Integration ofdigitaltechnologiesin cityinfrastructure

§ Exchange withother smartcities and accordingbest practices forgaining efficiency

(Bork et al., 2016)


REFERENCE MODELS AND BEST PRACTICES

Smart City Conceptual Reference Models for Best Practices:

§ Formalrepresentatione.g. of smarttrash canmodel

§ Permitsa-priorianalyses

§ Basis for localimplementation

§ Integrationwith otherenterprisemodels, e.g.IT architecture

(Bork et al., 2016)


v Some companies still occupied with digital transformation.

v Digital Ubiquity already within reach.v Challenges of digital technologies will persist, e.g. such as complexity,

constant change, alignment with existing environments.

v Enterprise modeling as a well-established tool not only in digital transformation.

v Future of enterprise modeling through its abstraction capabilities, interfaces to new technologies, sharing of knowledge and best practices.

v Ample fields of research in enterprise modeling, e.g. for understanding and deploying upcoming technologies such as permissioned blockchains and smart contracts, augmented reality, explainable artificial intelligence, electronic identities and many more.

v High-interdisciplinarity of enterprise modeling requires cooperation between fields.

CONCLUSION AND OUTLOOK


[email protected]

THANK YOU FOR YOURATTENTION!

QUESTIONS?


v Bergner, Martin, Fill, Hans-Georg, Johannsen, Florian (2016): Supporting Business Process Improvement with Natural Language Processing: A model-based approach, in: Mayr, H.C. and Pinzger, M. (eds.)(2016): Informatik 2016, Lecture Notes in Informatics, GI, 717-730.

v Böhm, Clemens (2018): A visual modeling method for analyzing Big Data repositories with ADOxx, Master Thesis, University of Vienna.

v Bork, Dominik, Fill, Hans-Georg, Karagiannis, Dimitris, Miron, Elena-Teodora, Tantouris, Nikolaos, Walch Michael (2016): Conceptual Modelling for Smart Cities: A Teaching Case, Interaction Design and Architecture(s) Journal - IxD&A, No.27, 2015, pp. 10-27.

v Dietz, M. and Pernul, G. (2020): Digital Twin: Empowering Enterprises Towards a System-of-Systems Approach, Business and Information Systems Engineering, 62(2), 179-184.

v Fill, Hans-Georg (2017): SeMFIS: A Flexible Engineering Platform for Semantic Annotations of Conceptual Models, Semantic Web (SWJ), Vol. 8, Issue 5, pp. 747-763.

v Fill, Hans-Georg (2011): On the Conceptualization of a Modeling Language for Semantic Model Annotations, in: Salinesi, C., Pastor, O. (2011): Advanced Information Systems Engineering Workshops, CAiSE 2011, LNBIP Vol. 83, Part 2, 134-148, Springer.

v Fill, Hans-Georg, Johannsen, Florian (2016): A Knowledge Perspective on Big Data by Joining Enterprise Modeling and Data Analyses, in: Bui, T.X., Sprague, R.H.: Proceedings of the Forty-Ninth Hawaii International Conference on System Sciences (HICSS-49), IEEE.

v Fuller, A. et al. (2020): Digital Twin: Enabling Technologies, Challenges and Open Research, IEEE Access, Volume 8, 108952-108971.

v Gartner Hype Cycle for Data Science and Machine Learning, 2020, URL: https://www.gartner.com/en/documents/3988118/hype-cycle-for-data-science-and-machine-learning-2020

v Gartner Hype Cycle for Emerging Technologies, 2020, URL: https://www.gartner.com/smarterwithgartner/5-trends-drive-the-gartner-hype-cycle-for-emerging-technologies-2020/

v Johannsen, Florian, Fill, Hans-Georg (2017): Meta Modeling for Business Process Improvement, Business and Information Systems Engineering, Vol. 59, Issue 4, pp. 251-275.

v Pittl, Benedikt, Fill, Hans-Georg (2020): A Visual Modeling Approach for the Semantic Web Rule Language, Semantic Web Journal (SWJ), Vol. 11, Issue 2, 361-389 URL: http://semantic-web-journal.net/system/files/swj2023.pdf.

v Wieland, Michael, Fill, Hans-Georg (2020): A Domain-Specific Modeling Method for Supporting the Generation of Business Plans, In: Bork, D., Karagiannis, D. & Mayr, H. C. (Hrsg.), Modellierung 2020. Bonn: Gesellschaft für Informatik e.V., pp. 45-60

REFERENCES

https://www.gartner.com/en/documents/3988118/hype-cycle-for-data-science-and-machine-learning-2020

https://www.gartner.com/smarterwithgartner/5-trends-drive-the-gartner-hype-cycle-for-emerging-technologies-2020/

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