Knowledge Management Models for the Smart Factory:

A Comparative Analysis of Current Approaches

Patrick Tinz

, Janik Tinz

and Stefan Zander

Accso Accelerated Solutions GmbH, Berliner Allee 58, 64295 Darmstadt, Germany

Hochschule Darmstadt, University of Applied Sciences, Sch

offerstrasse 8B, 64295 Darmstadt, Germany

Keywords:

Knowledge Management, Industry 4.0, Smart Factory, Industrial Internet.

Abstract:

This paper analyses current and well-known knowledge management models regarding their applicability to

smart factories and the Industry 4.0. In form of a literature study, we surveyed the speciﬁc challenges and

requirements that smart factories and the ongoing digital transition in the industrial sector introduce to knowl-

edge management systems and models. In the second part, we then expound the extent to which those require-

ments are supported by well-established knowledge management models in form of a comparative analysis.

A central result of this work is that an Industry 4.0 compliant knowledge management needs to incorporate

aspects that emphasize human-machine and machine-machine interaction together with data protection and

privacy concerns, besides other well-researched and established aspects.

1 MOTIVATION

Knowledge is now considered an important produc-

tion factor (Karagiannis, 2003). The ongoing digital

transition in the industrial sector, subsumed under the

terms Industry 4.0 (Ustundag and Cevikcan, 2018),

smart manufacturing (Kim and Yoon, 2018), Fac-

tories of the Future (European Commission, 2013)

or Industrial Internet

, introduces new challenges

and requirements to the knowledge management in

smart manufacturing facilities (Feng et al., 2017).

In this context, knowledge management models play

a crucial role as facilitators for implementing digi-

tized knowledge-centered value creation chains and

networks (North and Maier, 2018). A knowledge

management model can be deﬁned as an attempt of

formally or semi-formally systematizing knowledge-

centered artifacts and activities, the logical basis

of which is derived from different epistemological

interest and observational perspectives (Probst and

Romhardt, 1997). The usefulness of such a model

should always be evaluated in relation to a knowl-

edge management model’s and its supportive com-

munity’s aim of interest. Knowledge management

systems provide smart factories with the possibility

to implement and deploy newly established value-

creation networks more efﬁciently and effectively and

The Industrial Internet Consortium: https://www.

iiconsortium.org/index.htm

support their amalgamation with internal manufactur-

ing processes and resources.

Given the importance and relevance of knowledge

management systems and models for handling the

digital transition in smart factories and Industry 4.0,

the present paper analyzes two main research ques-

tions:

RQ1. Which new challenges imposed by the ongo-

ing digital transition need to be considered by

knowledge management systems to meet the de-

mands of smart factories?

RQ2. To what extent are those challenges already re-

ﬂected in current and well-known knowledge

management models?

For answering RQ1, we list a set of knowledge

management challenges in smart factories gathered

through a literature study. Those challenges serve as

criteria for the comparative analysis framework we

deployed for answering RQ2, in which the following

knowledge management models have been analyzed:

(a) The Holistic Knowledge Management Model in-

troduced by Jessica Seiderst

uker (Seidenst

ucker,

2017) that extends the well-known DIKW

model (Rowley, 2007) with additional value-

creation phases and organizational dimensions.

(b) The Knowledge Stairway Model for Indus-

try 4.0 developed by Klaus North and Ronald

Maier (North and Maier, 2018) is an adaptation

398

Tinz, P., Tinz, J. and Zander, S.

Knowledge Management Models for the Smart Factory: A Comparative Analysis of Current Approaches.

DOI: 10.5220/0008348803980404

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 398-404

ISBN: 978-989-758-382-7

of the traditional knowledge stairway towards the

demands of smart factories.

Model introduced by David Snow-

den (Snowden, 2000) that serves as representative

of an organic knowledge management approach

and favors the transition in organizational think-

ing from key-person dependency to knowledge

dependency.

(d) The Munich Knowledge Management

Model (Reinmann-Rothmeier, 2001) which

integrates technological-oriented information

management and human resource oriented com-

petency management, the objective of which

is to solve arising problems and situations in

purposeful ways.

(e) The smart manufacturing centered knowledge

management approach developed in the Assist 4.0

research project

(cf. (Brandl et al., 2015)), that

favors context dependent multimodal knowledge

accessibility and delivery based on mobile and

agent technology.

This work’s objective is to make a contribution

to the overall question about whether current knowl-

edge management models meet the requirements im-

posed by Industry 4.0 and the ongoing digital transi-

tion. Readers get informed about the extent to which a

certain Industry 4.0 speciﬁc requirement is facilitated

by a knowledge management model and what speciﬁc

purposes it pursues in general.

The remainder of this work is structured as fol-

lows: In Section 2, we outline the challenges and re-

quirements that smart factories and the Industry 4.0

impose to knowledge management. The speciﬁc

knowledge management models and systems that

have been considered and analyzed in this work are

introduced in Section 3. Section 4 then presents the

comparative analysis framework and discusses the ex-

tent which a certain requirement is supported. This

work concludes with a summary of all ﬁndings and a

proposition of future actions in Section 5.

2 KNOWLEDGE MANAGEMENT

CHALLENGES OF SMART

FACTORIES

The following sections provides an overview of the

challenges that have been discovered through a litera-

ASHEN is an acronym for the model’s components

Artefacts, Skills, Heuristics, Experience, and Natural Tal-

ent.

Assist 4.0 project page: https://hci.sbg.ac.at/assist-4-0/

ture review. They serve as criteria of the comparative

analysis framework that is presented and discussed in

Section 4.

Knowledge Identiﬁcation. The identiﬁcation of

possible knowledge gaps through human or algo-

rithmic intervention requires transparency about ex-

isting and required knowledge (Frey-Luxemburger,

2014). In consideration of increasing decentralization

and new technological developments such as Linked

Open Data

, identiﬁcation means the acquisition of

new knowledge from sources outside the company

(cf. (Frey-Luxemburger, 2014)).

Knowledge Development. Knowledge develop-

ment refers to the effort of an institution to de-

velop not yet existing internal and external capabil-

ities (cf. (Probst and Romhardt, 1997)) on the ba-

sis of existing knowledge. Knowledge development

must take place with the involvement of all entities

involved in the value creation process. An organiza-

tion must always be open for new ideas and promote

the creativity of its employees (Frey-Luxemburger,

2014).

Knowledge Exchange. Knowledge must be made

available ubiquitously and through multi-modal ways

in relevant departments so that existing information

and experiences can be exchanged among individ-

uals and systems. Incentive systems must be cre-

ated to implement the exchange of knowledge (Sei-

denst

ucker, 2017). The exchange of knowledge is of

central importance for the generation of new knowl-

edge (Lehner, 2003).

Knowledge Use. A key aim of knowledge manage-

ment is the productive use of the knowledge base,

where the application of knowledge at the individ-

ual and community level must be promoted (Frey-

Luxemburger, 2014). In addition, the use of highly

networked machines and processes plays a role

(cf. (North and Maier, 2018)). Corporate knowledge

is created with great effort and is classiﬁed as strate-

gically important, so it must be ensured that it is also

used in daily life (Probst and Romhardt, 1997).

Knowledge Preservation. The acquired capabili-

ties, experiences and documents must be stored ef-

ﬁciently and platform-independently, so that this

W3C’s Web of Data initiative: https://www.w3.org/

standards/semanticweb/data

Knowledge Management Models for the Smart Factory: A Comparative Analysis of Current Approaches

399

corporate knowledge is available for future pur-

poses (Frey-Luxemburger, 2014). Furthermore, reg-

ular updating is necessary to ensure the quality and

validity of the knowledge base.

Knowledge Evaluation. In context of Industry 4.0,

a knowledge evaluation is essential, since machines

and humans jointly extract knowledge from dif-

ferent sources (Seeber et al., 2018) and the rele-

vance of knowledge increases through an evalua-

tion (Seidenst

ucker, 2017). Knowledge is evaluated

by humans in combination with algorithmic deci-

sions (Samulowitz et al., 2014) and by hybrid or com-

bined approaches.

Human-machine Interaction. Human-machine

interaction plays a central role for an Industry 4.0

enabled knowledge management (Kelly, 2015). New

technologies such as augmented reality not only

simplify the retrieval of knowledge, but also the

generation of knowledge by creating multimedia

content through the support of smartphones and

smart glasses (cf. (Brandl et al., 2015)).

Machine-machine Interaction. The consideration

of networked machines is essential for an Indus-

try 4.0 capable knowledge management, because e.g.

through predictive analytics approaches optimiza-

tion possibilities of future maintenance and manufac-

turing processes can be derived (North and Maier,

2018)). The use of cloud-based services accelerates

data analysis and communication between machines

(cf. (Lechler and Schlechtendahl, 2016)).

Data Protection. Due to the increasing data inten-

sity (generation, monitoring, linking, integration, et

cetera) and networking of production facilities, the as-

pect of data security and privacy concerns in an Indus-

try 4.0 enabled knowledge management is of particu-

lar importance. The contents must be secured with

regard to data integrity, authentication, authorization

and conﬁdentiality (cf. (Brandl et al., 2015)).

3 KNOWLEDGE MANAGEMENT

MODELS

This section provides a brief overview of the knowl-

edge management models that are included in the

comparative analysis, which is presented and dis-

cussed in Section 4.

The Holistic Knowledge Management

Model (Seidenst

ucker, 2017) is based on the

DIKW Model

(cf. (Rowley, 2007)). The DIKW

Model according to Aamodt and Nyg

ard illustrates

the value chain of how information and ultimately

knowledge are created from data (Aamodt and

Nyg

ard, 1995; Rowley, 2007). The resulting knowl-

edge leads through a complex decision-making

process to insights or wisdom. The model according

to Seidenst

ucker supplements the value chain of

collecting, using, enriching, sharing, evaluating and

expanding (cf. (Seidenst

ucker, 2017)). Finally, the

whole Model is labeled with the triads of technology,

human and process (cf. (Seidenst

ucker, 2017)). The

term technology deals with the collection of data.

People should use data, give it a meaning and enrich

it with experiences, so that knowledge is created.

In the process, the knowledge is ﬁnally shared and

evaluated.

Knowledge Stairway 4.0 (North and Maier, 2018)

is an extension of the original Knowledge Stair-

way (North, 2016) and corroborates the value chain

from the creation of knowledge to action competence

and competitiveness. Knowledge is seen as the re-

sult of conscious or unconscious processing of in-

formation and forms the basis of action (North and

Maier, 2018). Furthermore, the ability or disposition

to act in a complex situation in an organized man-

ner can be called as competence (North et al., 2018).

Moreover, value creation is the result of the interplay

of several competencies of individuals, networks, in-

telligent systems or institutions on the basis of their

unique information and knowledge resources (North

and Maier, 2018). The top level is called as com-

petitiveness, which can be seen as a combination of

several competencies. In addition, North represents

the dimensions of people and organization (strate-

gic knowledge management) versus the dimension

of technology (operational knowledge management).

The strategic knowledge management dimension de-

scribes the scrutiny of knowledge and competencies

and as such serves as a “dynamising element” (North

and Maier, 2018). The operational knowledge man-

agement dimension serves as “stabilizer” to always

have the necessary knowledge available at the right

place and at the right time (North and Maier, 2018).

The Munich Knowledge Management

Model (Reinmann-Rothmeier, 2001) comprises

not only the knowledge management process but

also the objective and evaluation. The objective

is to deﬁne normative, strategic and operational

goals (Frey-Luxemburger, 2014). In addition, criteria

will be established for the evaluation on which the

DIKW is an acronym for the data-information-

knowledge-wisdom continuum and is sometimes referred to

as the “knowledge pyramid” or “knowledge hierarchy”.

KMIS 2019 - 11th International Conference on Knowledge Management and Information Systems

400

implementation of knowledge management measures

will be measured. The knowledge management

process is divided into knowledge representation,

knowledge communication, knowledge generation

and knowledge use (Reinmann-Rothmeier, 2001).

The identiﬁcation, preparation and documenta-

tion of knowledge plays an important role in the

representation of knowledge. With regard to knowl-

edge communication, everything revolves around

the exchange of existing knowledge. Knowledge

generation includes aspects such as cooperation or

mergers with other companies, but also the expli-

cation of implicit knowledge. The last process area

is the knowledge use, which is about transforming

knowledge into decisions, but also into products and

services (Frey-Luxemburger, 2014).

The ASHEN Model (Snowden, 2000) combines

the ﬁve areas artifacts, skills, heuristics, experiences

and natural gifts (Sch

utt, 2003). The term heuristics

describes a situation-related recommendation for ac-

tion. Artifacts are storage media such as notebooks, in

which explicit knowledge is stored. Natural gifts are

abilities (implicit knowledge) that are bound to a hu-

man being. Skills are implicit knowledge that can be

learned through training. Experience has developed

over a period of time and represents an important

knowledge base in the company. This model focuses

on implicit knowledge, with that you can trace hidden

and silent knowledge (Frey-Luxemburger, 2014).

Assist 4.0 explored new approaches to knowledge

management in manufacturing in a practical con-

text (Brandl et al., 2015). Assist 4.0 is a blueprint

for established knowledge management models, as it

examines the aspect of data protection and the use

of new technologies such as augmented reality. For

these reasons, this project distinguishes itself from

other comparable projects. When creating knowledge

content, not only text-based forms were examined,

but also multimedia forms using mobile devices (e.g.

data glasses). The basic idea of the concept can be de-

scribed as “YouTube for the industry” (Brandl et al.,

2015). This approach offers the possibility to record,

evaluate and share experiences with other employees

using mobile devices. In addition to editorial work,

collaborative approaches were also researched. In se-

curing the content, data integrity was examined on the

one hand to ensure that the content was not manip-

ulated during transport between the service engineer

and the service provider, and on the other hand to en-

sure that only authorized content reached the service

employee.

4 ANALYSIS & DISCUSSION

This section analyses the extent to which a certain re-

quirement is supported by a knowledge management

model. The results of the analysis are presented in

Figure 1.

In the models of Seidenst

ucker and North, knowl-

edge identiﬁcation represents the beginning of the

value chain. At Seidenst

ucker, new knowledge can be

exploit through internal and external market research.

On the other hand, sensors and neural networks pro-

vide syntax in the characters of the Knowledge Stair-

way 4.0. The other knowledge management models

refer primarily to implicit knowledge.

The knowledge development is implemented in

Seidenst

ucker’s model trough the enrichment of inter-

nal and external market research results by humans.

The ideas and creativity of the employees should be

promoted through praise and recognition. Strate-

gic knowledge management at North is responsible

for critically reﬂecting organizational knowledge and

partner knowledge. In the other knowledge manage-

ment models, knowledge created from the processing

of information, but also from experiences.

With regard to the exchange of knowledge, Sei-

denst

ucker exposes that there must be a cross-

departmental and cross-location cooperation, in

which there must be an environment of learning, shar-

ing, open communication and fault tolerance (Sei-

denst

ucker, 2017). In the Knowledge Stairway 4.0

model, it becomes clear that communities and collab-

oration tools can be used to exchange knowledge. The

three other knowledge management models relate to

the exchange of knowledge and experience in knowl-

edge communication.

In the model according to Seidenst

ucker and the

ASHEN Model there should be an individual and

common knowledge use. Knowledge Stairway 4.0

makes clear that the knowledge use must be charac-

terized by fast, simple, comprehensive and ubiquitous

access to the knowledge base. The Munich Knowl-

edge Management Model focuses on the transforma-

tion of knowledge into decisions and actions, but also

into products and services. In Assist 4.0 the knowl-

edge is used during the activity.

The preservation of knowledge is implemented

in the model according to Seidenst

ucker, Knowledge

Stairway 4.0 and Assist 4.0 by a curator-role, who is

responsible for the logical linking and maintenance

of content for the development of a semantic net-

work. The Munich Knowledge Management Model

focuses on documentation, while the ASHEN Model

uses heuristics and artifacts to implement knowledge

preservation.

Knowledge Management Models for the Smart Factory: A Comparative Analysis of Current Approaches

401

Criteria / Models

The Holistic

Knowledge

Management

Model

Knowledge

Stairway 4.0

The Munich

Knowledge

Management

Model

ASHEN Model

Assist 4.0

Knowledge

Identiﬁcation

• Internal and

external

market

research

• Sensors and

neural

networks

• Employee’s

knowledge and

experience

• Focus on

implicit

knowledge

• Focus on

implicit

knowledge

Knowledge

Development

• Enrich

information "

• Promotion of

the creativity of

the employees

• Reﬂection on

organizational

knowledge and

partner

knowledge

• Processing of

information and

experiences

• Processing of

information

and

experiences

• Processing of

information

and

experiences

Knowledge

Exchange

• Cooperation

across

departments

and locations

• Collaboration

Tools "

• Communities

• Focus on

knowledge

communication

• Exchange of

experiences

• Exchange of

experiences

e.g. through

augmented

reality

Knowledge Use

• Individual and

shared use of

knowledge

• Access to the

knowledge

base

• Transformation

of knowledge

into decisions

or actions

• Individual and

shared use of

knowledge

• Use during the

activity

Knowledge

Preservation

• Check for

completeness

and

consistency by

a curator

• Knowledge

maintenance

and updating

by a curator

• Focus on

documentation

• Documentation

of heuristics

and artefacts

• Knowledge

maintenance

and updating

by a curator

Knowledge

Evaluation

• Evaluation and

commenting of

contributions

• Principles of

social media "

• Kuration

• Measurement

of success

during

evaluation

• Employee

communication "

• Evaluation in

the process

• Principles of

social media "

• Kuration

Human-Machine

Interaction

• Preparation of

content by

technology

• Technologies

like e.g

augmented

reality

• Not considered

• Technologies

like e.g

augmented

reality

Machine-

Machine

Interaction

• Algorithms of

machine

learning

• Systems with

automated

learning and

decision

behaviour

• Not considered

• Networking of

machines

Data Protection

• Restriction of

read and write

rights

• Not considered

• Possibilities for

comprehensive

data protection

1

Figure 1: A summarization of the comparative analysis results.

In Seidenst

ucker’s model, Knowledge Stairway

4.0 and Assist 4.0, knowledge evaluation serves to in-

crease the quality of the knowledge stock by incor-

porating principles of social media and curation. In

the Munich Knowledge Management Model, this is

guaranteed by the evaluation phase. In the ASHEN

Model, knowledge evaluation is implemented mainly

through communication between employees.

With regard to human-machine interaction, tech-

nologies such as augmented reality are referred to in

the Knowledge Stairway 4.0 model and the Assist 4.0

project. Seidenst

ucker’s model addresses this aspect

superﬁcially, the other models do not speciﬁcally con-

sidered this aspect.

The machine-machine interaction is addressed in

Seidenst

ucker’s model by utilizing machine learning

algorithms. North presents the concepts “Augmented

Intelligence” or “Cognitive Computing”, which can

KMIS 2019 - 11th International Conference on Knowledge Management and Information Systems

402

learn scaled and communicate with humans in natu-

ral language (Kelly, 2015) in the Knowledge Stairway

4.0. “Augmented Intelligence” refers to the extension

of the competence of experts by a system (Davenport

and Kirby, 2016), “Cognitive Computing” to the in-

tegration of knowledge from different sources (North

and Maier, 2018). Assist 4.0 is the leader in network-

ing machines; the other models do not refer to this

criterion.

Seidenst

ucker states with regard to data pro-

tection that a maximum of data security must be

achieved (Seidenst

ucker, 2017). The Assist 4.0 con-

cept is the only model that presents possibilities for

full data protection. The other models do not consider

data protection.

5 CONCLUSION

The present work contributes to the overall question

of how well current and well-established knowledge

management models are able to handle the new chal-

lenges and requirements introduced by Industry 4.0

and the ongoing digital transition in the industrial sec-

tor. It does that by analyzing two main research ques-

tions (see Section 1).

The question regarding the newly induced chal-

lenges (cf. RQ1) has been answered through a liter-

ature study that revealed that in addition to the al-

ready well-established knowledge processing tasks,

smart factories introduce new aspects such as human-

machine-, machine-machine-communication as well

as data protection and privacy.

The extent to which those requirements are sup-

ported by current knowledge management models

has been analyzed in the second part of this work

(cf. RQ2). The analysis revealed that the aspects of

human-machine-, machine-machine-communication

as well as data protection and privacy are only selec-

tively supported by current knowledge management

models and initiatives.

These results also corroborate the fact that in In-

dustry 4.0, humans and machines (both hardware and

software) need to be considered as equitable partners

that both participate in the knowledge creation and

processing life-cycle. This perspective needs to be

taken into consideration by future knowledge man-

agement models and it must be complemented by ini-

tiatives to establish an organization-wide continuous

learning culture. Future works might built upon the

validation of those ﬁndings and propose models that

integrate the previously mentioned aspects in more

Industry 4.0 compliant ways.

REFERENCES

Aamodt, A. and Nyg

ard, M. (1995). Different Roles

and Mutual Dependencies of Data, Information, and

Knowledge-An AI Perspective on their Integration.

Data Knowl. Eng., 16(3):191–222.

Brandl, P., Aschbacher, H., and H

osch, S. (2015). Mobiles

Wissensmanagement in der Industrie 4.0. In Mensch

und Computer 2015 - Workshopband, Stuttgart, Ger-

many, September 6-9, 2015, pages 225–232.

C Feng, S., Bernstein, W., Hedberg, T., and Barnard Feeney,

A. (2017). Toward knowledge management for smart

manufacturing. Journal of Computing and Informa-

tion Science in Engineering, 17.

Davenport, T. H. and Kirby, J. (2016). Only humans need

apply: winners and losers in the age of smart ma-

chines. Harper Business New York, NY.

European Commission, editor (2013). Factories of the Fu-

ture: Multi-annual Roadmap for the contractual PPP

under Horizon 2020. Publications Ofﬁce of the Euro-

pean Union, Luxembourg.

Frey-Luxemburger, M. (2014). Ans

atze und Modelle

des Wissensmanagements, pages 39–70. Springer

Fachmedien Wiesbaden, Wiesbaden.

Karagiannis, D. (2003). Wissensmanagement: Einige

konzepte & technologien. In Informationswirtschaft:

Ein Sektor mit Zukunft, pages 191–206.

Kelly, J. E. (2015). Computing, cognition and the future of

knowing. Whitepaper, IBM Reseach, page 2.

Kim, D.-W. and Yoon, S. W. (2018). Special issue on smart

automation and manufacturing. International Journal

Computer Integrated Manufacturing, 31(8):675–676.

Lechler, A. and Schlechtendahl, J. (2016). Steuerung aus

der Cloud. Handbuch Industrie 4.0: Produktion, Au-

tomatisierung und Logistik, pages 1–14.

Lehner, F. (2003). Information Sharing und Wissensaus-

tausch in Unternehmen. In Geyer-Schulz, A. and

Taudes, A., editors, Informationswirtschaft: Ein Sek-

tor mit Zukunft, volume 33 of LNI, pages 301–319.

GI.

North, K. (2016). Die Wissenstreppe, pages 33–65.

Springer Fachmedien Wiesbaden, Wiesbaden.

North, K. and Maier, R. (2018). Wissen 4.0 – Wissens-

management im digitalen Wandel. HMD Praxis der

Wirtschaftsinformatik, 55(4):665–681.

North, K., Reinhardt, K., and Sieber-Suter, B. (2018). Was

ist Kompetenz?, pages 35–110. Springer Fachmedien

Wiesbaden, Wiesbaden.

Probst, G. J. B. and Romhardt, K. (1997). Bausteine

des Wissensmanagements — ein praxisorientierter

Ansatz, pages 129–143. Gabler Verlag, Wiesbaden.

Reinmann-Rothmeier, G. (2001). Wissen managen: Das

unchener Modell. Forschungsbericht Nr. 131 –

LMU M

unchen: Lehrstuhl f

ur Empirische P

adagogik

und P

adagogische Psychologie.

Rowley, J. (2007). The wisdom hierarchy: representations

of the DIKW hierarchy. Journal of information sci-

ence, 33(2):163–180.

Knowledge Management Models for the Smart Factory: A Comparative Analysis of Current Approaches

403

Samulowitz, H., Sabharwal, A., and Reddy, C. (2014). Cog-

nitive automation of data science. In ICML AutoML

workshop.

Sch

utt, P. (2003). The post-nonaka knowledge manage-

ment. J. UCS, 9(6):451–462.

Seeber, I., Bittner, E., Briggs, R. O., De Vreede, G.-J.,

De Vreede, T., Druckenmiller, D., Maier, R., Merz,

A. B., Oeste-Reiß, S., Randrup, N., et al. (2018). Ma-

chines as teammates: a collaboration research agenda.

Hawaii International Conference on System Sciences

(HICSS) Waikoloa.

Seidenst

ucker, J. (2017). Wissensmanagement 4.0 – Neue

Technologien ebnen den Weg zu nachhaltiger Markt-

forschung, pages 17–35. Springer Fachmedien Wies-

baden, Wiesbaden.

Snowden, D. (2000). The ashen model an enabler of action

in knowledge management. Knowledge Management,

3(7):14–17.

Ustundag, A. and Cevikcan, E., editors (2018). Industry

4.0: Managing The Digital Transformation. Springer

Series in Advanced Manufacturing. Springer, Cham.

KMIS 2019 - 11th International Conference on Knowledge Management and Information Systems

404