Prerequisites for Applying Artificial Intelligence for Scheduling in

Small- and Medium-sized Enterprises

Tatjana Schkarin and Alexander Dobhan

Business and Engineering, FHWS, Ignaz-Schön Str.11, Schweinfurt, Germany

Keywords: Scheduling, SME, Prerequisites of Artificial Intelligence.

Abstract: With the increasing spread of Artificial Intelligence (AI), the prerequisites for a successful implementation in

practice are becoming more relevant for large enterprises as well as for small and medium-sized enterprises

(SMEs). The latter are usually characterized by flat hierarchies, high flexibility, but also by a lack of AI

experts and data organisation. One field of AI application for SMEs is scheduling as part of production

planning. Scheduling belongs to the most relevant digital solution areas for SMEs. In this article, we examine

the prerequisites for the application of AI methods in scheduling in SMEs. For identifying relevant

prerequisites, we conduct a literature review and combine it with the results of three AI adoption and readiness

models. Afterwards, we describe the results of an interview study on our research question. The main findings

include a list of prerequisites. We connect our list with already existing approaches for AI adoption and AI

readiness with a strong focus on SMEs and scheduling. Furthermore, we conclude that the prerequisites are

dependent on the application context. However, the effect of the size of a company on the prerequisites

remains unclear.

1 INTRODUCTION

The potential and increasing spread of Artificial

Intelligence (AI) in companies is undisputed.

According to a study by PWC, the effect of AI on the

gross domestic product of Germany is to grow by 430

billion euros (PWC, 2021). AI '[…]is a branch of

Computer Science, which is mainly concerned with

automation of Intelligent behavior.’ (Chowdhary,

2020) Intelligent behavior includes perceiving,

analysing, and reacting (Chowdhary, 2020).

One application field of AI is scheduling.

Scheduling means determining the order sequence in

production as part of production planning (Nguyen et

al., 2019, Takeda-Berger et al., 2020). At the same

time, scheduling belongs to the most relevant digital

solution areas for small and medium-sized enterprises

(SMEs) (Schönfuß et al., 2021). Combined with the

importance of SMEs, as evidenced by the fact that

nearly 40% of employees in Germany work in small

and medium-sized enterprises, the application of AI

for scheduling in SMEs is a highly relevant research

topic (IfM, 2021). In addition, various solutions for

AI-based scheduling have been developed (Kumar

and Dimitrakopoulos, 2021, Ramirez-Asis et al.,

2021, Zhang et al., 2021).

SMEs are often associated with attributes in addition

to their size. Starting with flat hierarchies with

frequently person-centered decision-making

structures, through the necessary flexibility to the

qualification of their employees (Kukharuk and

Gavrysh, 2019, Cus, 1997). It raises the question of

what prerequisites are necessary for the effective use

of AI in the scheduling of SMEs. To this end, we first

examine existing research results with a systematic

literature analysis, primarily on the prerequisites for

the use of AI in general and on SME-specific and

scheduling-specific prerequisites. Subsequently, we

describe the design and the outcomes of interview

study, which aims to confirm the findings from the

literature and reveal aspects not previously

considered, particularly about SMEs and scheduling.

2 LITERATURE REVIEW

Our research question clearly refers to AI adoption

and AI readiness research. Here, we build on the

interview study of Jöhnk et al al., (2021). They

developed a list of 18 factors that represents AI

readiness. The factors belong to the five categories

Strategic alignment, Resources, Knowledge Culture,

Schkarin, T. and Dobhan, A.

Prerequisites for Applying Artiﬁcial Intelligence for Scheduling in Small- and Medium-sized Enterprises.

DOI: 10.5220/0011064000003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 529-536

ISBN: 978-989-758-569-2; ISSN: 2184-4992

529

and Data. The model considered the research work of

Alsheibani et al., (2018, 2019), which proposed a

maturity model with four dimensions (AI functions,

data structure, people, organisations). Bettoni et al.

(2021) developed a more specific maturity model of

companies analysing the five dimensions Digital &

Smart Factory, Data strategy, Organisation structure,

Human Resources, Organisation culture. They

detected a difference in AI maturity between 9 large

companies and 30 SMEs. Baabdullah et al., (2021)

investigated the relationship between the AI

acceptance and several factors. According to the

results of their empirical study for SMEs the

acceptance of AI practices is dependent on

technology roadmapping, attitude, infrastructure

awareness, but not on expertise or technicality.

In addition to the findings regarding adoption

theory, we built our research on the results of a

literature review that focuses on research findings

from 2015 - 2021. The review considers also search

terms regarding success factors, integration,

opportunities, etc., which helps us to derive

prerequisites for a successful AI implementation in

companies. We conducted the literature review

according to the recommendations in Webster and

Watson (2002). The literature to be analysed includes

only scientific peer-reviewed articles and conference

papers (Rowley and Slack., 2004). We used the

following online platforms for our search:

ScienceDirect, IEEE Xplore, Springer Link,

EBSCOhost, Google Scholar, and the FHWS

database. The literature search aims to obtain an

overview of the prerequisites for use of AI in

scheduling based on current research and to review

the articles to determine the extent to which the

company size affects the prerequisites. This leads to

the following search terms ("integration of" AND "AI

methods" OR "AI techniques"), ("successful

integration" OR "integration challenges" AND "AI"),

("opportunities and challenges of AI"),

("opportunities and challenges of use of AI"),

("opportunities and challenges of ai" AND

"scheduling" OR "production"), ("requirements for

use" AND "artificial intelligence" OR "AI"), ("needs

to introduce AI in scheduling"), ("implementation of

AI"), ("implementation of AI" AND "in scheduling"

OR "in production"), ("requirements of using AI"),

("successful key factors of using AI"), ("success

factors of using AI"), ("conditions to use artificial

intelligence"). We decided against a more SME and

scheduling specific search to ensure a considerable

number of results, but we categorized the articles

according to their considerations of these two aspects.

Furthermore, the publications found were checked for

their relevance. Afterwards, we identified further

articles from the bibliographies of the received

sources. In this way, we ultimately identified 19

relevant sources. None of the sources focuses

exclusively on prerequisites. They include, for

example, challenges of using AI methods in certain

areas, from which we derived prerequisites. The

results mainly illustrate that although five sources

refer to manufacturing, none focuses on the field of

scheduling. They also show that literature sources on

the topic have increased in recent years. 15 out of 19

articles were published from 2019 to 2021.

Table 1: Prerequisites.

Prerequisite Authors

Digital data storage

and availability

Iliashenko et al. (2019), Patel et al.

(2019), Mansour et al. (2021), Sayyad et

al. (2021), Susar and Aquaro (2019),

Hildesheim (2020).

Server capacity

Iliashenko et al. (2019).

IT security

Susar and Aquaro (2019), Baviskar et al.

(2021), Rathore et al. (2021), Aktolun

(2019), Iliashenko et al. (2019).

Network quality

Mansour et al. (2021), Susar and Aquaro

(2019), Pizon and Lipski (2015), Aktolun

(2019), Iliashenko et al. (2019).

Data management

Susar and Aquaro (2019), Randaliev and

De Roure (2021), Patel et al. (2019),

Baviskar et al. (2021), Rathore et al.

(2021), Mao et al. (2019).

Test environments

Dolgih, 2019, Nguyen et al., 2021.

Cross-disciplinary

expertise

Patel et al. (2019), Aktolun (2019),

Randaliev and De Roure (2021).

AI expertise

Susar and Aquaro, (2019), Qui and Zhao,

(2019), Hildesheim, (2020), Dolgih,

(2019), Aktolun, (2019), Luo et al.,

(2018), Pizoń & Lipski, (2015), Ak'yulov

and Skovpen' (2019).

User training/user

confidence

Patel et al. (2019), Dolgih (2019),

Hildesheim (2020).

Financial resources

Luo et al. (2018), Susar and Aquaro

(2019),

Qui and Zhao (2019), Iliashenko et al.

(2019).

An overview of the detected prerequisites is given

in Table 1. In terms of content, we identified several

prerequisites. First, the equipment and the

management of the equipment are addressed by

numerous authors. Equipment-related prerequisites

are data storage, data availability, data management

server capacity, IT security, network quality, and the

availability of test environments. Furthermore,

expertise plays a certain role. Some authors define AI

expertise and cross-disciplinary expertise as crucial

for success. From the user's perspective, soft facts

such as trust, and confidence are important for AI

success. Finally, a few authors stated that the

availability of financial resources is crucial for AI

implementation. In summary, general prerequisites

are available from the literature. However, the

considered publications lack a discussion on

prerequisites concerning scheduling and company

size. Instead, the results of adoption research and the

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

530

differences to other application contexts indicate the

relevance of the application context.

An indication for it is, for example, a different

relevance for the users, which is significantly higher

in the medical field than in scheduling. Unlike in

medicine, the factor "interdisciplinary experts" could

also be less relevant for scheduling, since production

planning, similar to artificial intelligence, originates

from the field of mathematics or operations research

(based on mathematical calculations or algorithms)

(Gelders and Van Wassenhove, 1981, Ilieva et al.,

2019). Furthermore, the assumption is apparently that

relevant data in the field of scheduling are better

available in a structured way than in other fields

(Cadavid et al., 2019). Other technical factors, such

as external servers, computing power, networks,

testing, and cyber protection, are also prerequisites

for use in scheduling. At the same time, these factors

may be very significant in SMEs. Furthermore,

financial resources, especially investments in time,

personnel, or hardware, are prerequisites that are

related to all application contexts.

3 INTERVIEW STUDY

3.1 Research Design

The results of the literature review allow the

definition of analysis dimensions as a basis for expert

interviews: The general analysis dimensions are

"Expertise", "Equipment", and "Soft facts". The

former aims to measure the influence of AI experts on

the use of AI. Here, we explicitly asked about the

effect of experts and their skills on AI deployment

within a company. The analysis dimension

"Equipment" is intended to identify the technical

requirements. The latter dimension relates to the soft

facts, i.e., what role the employees' trust in AI plays

and why. Moreover, the analysis reveals how trust

can be reached in this context. As the financial

resources follow the dimension expertise and

equipment as well as user training, we do not consider

them explicitly. In addition to the dimensions derived

from the literature review, we add two dimensions

which are explicitly relevant for our research

question: Company size and Relevance of the

application field, in particular scheduling. Each

dimension includes a few questions to get information

on the relevance of these dimensions.

We interviewed a total of nine experts. The

selection of the experts follows purposive sampling in

our professional network. Experts had to fulfil two

main criteria, which we derived from the research

question. Firstly, the interviewee must be active in the

field of artificial intelligence. Secondly, the experts

must have experience in AI projects for SMEs. In

addition, the context of production planning was

relevant for the expert selection process.

The interviews were semi-standardized based on

an interview guideline. The mode of communication

was face-to-face and online via Zoom (Archibald et

al., 2019, Gowda and Ayush, 2020, Hopf, C., 2004,

Xu et al., 2019). An expert interview in the context of

this work lasts approximately 30 minutes. The

evaluation of the expert interviews was computer-

assisted using the standard software MAXQDA

(VERBI Software, 2021) for qualitative content

analyses (Kuckartz, 2019).

3.2 Results

The survey results show that more than three-quarters

of the experts surveyed see the involvement of AI

experts as a prerequisite for deployment. Only two of

the respondents rate this factor as of medium to little

importance. They justify this by stating that all users

should have fundamental AI expertise. Therefore, no

specific AI experts are necessary. The statements of

the interviewees indicate that an AI expert typically

has AI software knowledge, various programming

skills (especially Python), process understanding, and

analytical thinking skills. In addition,

interdisciplinary openness is another crucial attribute.

Six of the nine experts emphasize cross-industry

thinking and data understanding in this context.

Social skills should also supplement AI expertise,

such as communication skills. All experts emphasized

the last aspect.

Furthermore, the study examines whether

employee trust - as the main soft fact - in AI plays a

crucial role in the use of AI. All nine respondents

confirm this and justify it by ensuring acceptance of

AI and avoiding fears of incomprehensible decisions

and job loss. Eight out of nine respondents cite

communication with those affected as a confidence-

building measure. In addition, up to four experts

attributed a confidence-building effect to actively

picking up employees, user participation, expectation

management, and the creation of control options for

employees.

Another prerequisite for the use of artificial

intelligence is the equipment. It includes ERP

systems, AI software, programming and visualization

programs, and libraries, such as Keras, or PyTorch. In

addition, two-thirds of respondents emphasize the

importance of hardware for access to powerful

computers or cloud solutions. Sensors or data

generation are also mentioned. However, the highest

importance in terms of equipment seems to have "data

Prerequisites for Applying Artiﬁcial Intelligence for Scheduling in Small- and Medium-sized Enterprises

531

management," which is mentioned by all interview

participants. It includes data collection, storage,

management, etc.

Three experts mention financial resources,

general or change management as further

prerequisites. Two experts also list the domain

knowledge of the employees, experience with an

agile approach to AI introduction, and the openness

and willingness of the users (see trust). In addition,

one respondent considers the availability of human

resources in general as a prerequisite for the use of

AI. The findings described so far refer to the

application in all contexts and company sizes.

However, our research intends to identify

specific requirements for the use of AI in scheduling

and SMEs. Here, six of the nine AI experts state that

the use of AI is not specific to the company size.

These statements are backed by the fact that even in

large companies, AI projects of varying scope in

different organisational units are taking place.

Furthermore, the experts refer to the scalability of

today's AI solutions, which make them suitable for

both, SMEs and large companies. The remaining 33%

of experts are convinced that the prerequisites for

using AI also depend on the company size. They

suppose that this is primarily caused by the less

pronounced digital data management characteristics

of SMEs. On the other hand, they recognized greater

flexibility concerning the software landscape and IT

systems in general within these companies.

Concerning scheduling, there is an ambiguous

result. Thus, four respondents state that the use of AI

is scheduling-specific, with four also of the opposite

opinion. One expert did not answer about this topic.

Four experts explain the dependence on the

application context with different data formats. For

example, data for scheduling is often available in

structured form in relational databases. Real-time

data from sensors enhance the production data. In

contrast, the statement that the AI use is not context-

specific is backed by the fact that it is purely

dependent on the use case itself. Use cases in different

application contexts can be similar but differ within

an application context. Even though only four experts

confirm an effect of the application context on AI

prerequisites, seven experts stated that introducing AI

in scheduling requires more communication skills

than for other applications. The reason for it is, that

scheduling often bothers more departments and

stakeholders than, for example, an AI-optimized

production machine.

Nevertheless, according to the experts, artificial

neural networks are currently gaining ground as a

method in scheduling, for example, and are, therefore,

a current and future research topic.

4 DISCUSSION

The discussion of our results will consider mainly

three perspectives. Firstly, we compare the results of

our interview study with those of our literature review

(Table 1). Secondly, we connect our results with the

AI readiness and adoption theory. Thirdly, we

emphasize again the results regarding the company

size and the application field.

Figure 1 shows a comparison between the

prerequisites from literature and those from the

interview study for each category. The dimensions

with an asterisk were addressed by all experts.

Starting with the equipment (hardware resources in

the literature include the network infrastructure and

software) it appears that the results of the empirical

study back findings in the literature. AI software and

ERP systems affect data management as another

crucial prerequisite of the dimension equipment.

(Randaliev and De Roure, 2021, Susar and Aquaro,

2019, Aktolun, 2019, Mao et al., 2019, Iliashenko et

al., 2019, Patel et al., 2019, Hildesheim, 2020,

Poniszewska-Maranda and Kaczmarek, 2015, Luo et

al., 2018, Qui and Zhao, 2019, Pizon and Lipski,

2015, Dolgih, 2019).

The results regarding the expertise show that AI

experts exert a high effect on the use of AI, as

indicated by 80% of the experts, which social

competence belongs to. It is mentioned in addition to

the prerequisites in literature. The empirical analysis

confirms the specification of expertise in the form of

technical competencies, specialized knowledge, and

interdisciplinary knowledge. It is supplemented by

cross-industry knowledge, data understanding, and

software and process knowledge. (Patel et al., 2019,

Aktolun, 2019, Susar and Aquaro, 2019, Qui and

Zhao, 2019, Hildesheim, 2020, Dolgih, 2019, Luo et

al., 2018, Ak'yulov and Skovpen', 2019, Pizon and

Lipski, 2015).

The results of the empirical study regarding “Soft

facts” show that the item "trust" of employees in AI

is crucial. Communication and education foster trust.

Moreover, the results of our expert interviews

confirm the results in the literature. (Patel et al., 2019,

Dolgih, 2019, Hildesheim, 2020).

Furthermore, financial and human resources were

also empirically confirmed as prerequisites, whereas

time resources were not addressed at all. However,

change management, internal domain knowledge,

and agile approaches to AI implementation were

complementary (Luo et al., 2018, Susar and Aquaro,

2019, Qui and Zhao, 2019, Iliashenko et al., 2019).

As of the literature review, no effect of company size

on AI prerequisites is identified. However, the

specific attributes of SMEs suggested that specific

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

532

prerequisites exist here. Here, rather negative

tendency occurs in the expert interviews.

Figure 1: Matching interview study results with literature

review outcome.

The literature analysis on AI prerequisites

covered various fields, such as medicine or the public

sector. For this reason, one objective of the empirical

expert survey was to investigate the influence of the

application context. Concerning this aspect, we

achieved ambiguous results. But it should be noted

that the prerequisites depend on the specific

application either in the application context or the use

case itself.

Our interview study complements and confirms

existing research on AI adoption. Thus, we connect

our prerequisites with the factors of Alsheibani et al.

(2018, 2019), Jöhnk et al. (2021), and Bettoni et al.

(2021). It becomes clear, that our findings regarding

Expertise, Equipment, Soft facts, and Others confirm

the importance of the categories in literature.

Figure 2: Research findings and adoption models.

Additionally, the organisational factor of

Alsheibani et al. (2018, 2019) includes the company

size. Bettoni et al. (2021) explicitly show that SMEs

have a different maturity level than large companies.

According to their results, in particular, the

Digitalisation (Digital & Smart Factory) and the

Organisational structure are on a more mature level at

large enterprises. Our results seem to contradict that.

One reason might be that our interviews were

strongly project-related. The experts answered

considering their project experience. Within large

companies, there might be departments on a high

digitalisation level and those with a low one. The

reason for the statement could be also that the

prerequisites are use-case specific, but not

application-field specific. That was one argument of

the four experts that denied scheduling-specific

prerequisites. Figure 2 shows that none of the three

approaches considers the application context.

However, four of nine experts consider the

application field (scheduling) as relevant for the

prerequisite. And one reason for the denial of the

others was that they believe that the prerequisites are

use-case specific, which means that even the

application field is too aggregated. Furthermore, the

literature review shows, that we have several fields of

application. Five of our sources in table 1 analyse AI

in the context of medicine, five in the context of

manufacturing, and the others in other fields. It seems

very clear that patients as users may act differently

and are different than experienced production

planners. The personal involvement of patients is

usually higher than the one of the production

planners. This may affect the prerequisites in the

categories soft facts and Expertise. This is just one

example for the effect of the application use-case on

the perquisites.

Prerequisites for Applying Artiﬁcial Intelligence for Scheduling in Small- and Medium-sized Enterprises

533

5 CONCLUSION

In this article, we described the results of both a

literature review and an interview study on AI

prerequisites for scheduling in SMEs. With our

results, we gave an overview of current research work

of AI prerequisites from the perspectives of SMEs

and scheduling. Our overview shows that there is no

research work in the field of AI adoption so far, which

covers both concepts (SME and scheduling), or at

least scheduling. Furthermore, we figured out

indications for the relevance of the application field

for defining AI prerequisites. Regarding the scientific

discussion on the company size, we contributed some

arguments for both sides (against in our interview

study and for in our literature review). Finally, we

confirmed most of the factors of Alsheibani et al.

(2018, 2019), Jöhnk et al. (2021), and Bettoni et al.

(2021).

Future research should clarify the results on SMEs

and shed light on the question of how the application

field and the use case itself affect the prerequisites.

Thereupon further research is required on how to

make an SME AI ready for specific AI application

fields and use cases. At the same time, measures to

become AI ready must be examined for their

effectiveness and, in particular, their effect on the

application goals (e.g., the acceptance of the models,

the accuracy of the models, the number of model

users, etc.).

ACKNOWLEDGEMENTS

This research was conducted as part of the project

"OberA" funded by the research program

"Informations- und Kommunikationstechnik" of the

Bavarian State Ministry of Economic Affairs,

Regional Development and Energy (IUK-1709-

0011//IUK530/010), submitted by the University of

Applied Sciences Würzburg-Schweinfurt (FHWS).

REFERENCES

AI marketplace, https://ki-marktplatz.com/en/ (Accessed:

26.08.2021)

Ak’yulov, R., Skovpen, A., (2019). The Role of Artificial

Intelligence in Transforming Today’s Labor Market. In

Diskussiya (Discussion) 94, 30-40.

Aktolun, C., (2019). Artificial intelligence and radiomics in

nuclear medicine: potentials and challenges. In

European Journal of Nuclear Medicine and Molecular

Imaging 46, 2731–2736.

Alaskari, O., Pinedo-Cuenca, R., Ahmad, M., (2019).

Framework for Selection of ERP System: Case Study.

In 29th International Conference on Flexible

Automation and Intelligent Manufacturing, pp. 69-75,

Procedia Manufacturing 38, Limerick.

Allweyer, T., (2016). BPMN 2.0: introduction to the

standard for business process modeling. Books on

Demand, Nordenstedt.

Alsheibani, S., Cheung, Y., & Messom, C. (2018).

Artificial Intelligence Adoption: AI-readiness at Firm-

Level. In PACIS, p. 37.

Alsheibani, S., Cheung, Y., & Messom, C. (2019,).

Towards An Artificial Intelligence Maturity Model:

From Science Fiction To Business Facts. In PACIS, p.

46.

Archibald, M., Ambagtsheer, R., Casey, M., Lawless, M.,

(2019). Using zoom videoconferencing for qualitative

data collection: perceptions and experiences of

researchers and participants. In International Journal of

Qualitative Methods 18, 1-8.

Baabdullah, A. M., Alalwan, A. A., Slade, E. L., Raman,

R., & Khatatneh, K. F. (2021). SMEs and artificial

intelligence (AI): Antecedents and consequences of AI-

based B2B practices. In Industrial Marketing

Management, 98, 255-270.

Baviskar, D., Ahirrao, S., Potdar, V., Kotecha, K., (2021).

Efficient Automated Processing of the Unstructured

Documents using Artificial Intelligence: A Systematic

Literature Review and Future Directions. In IEEE

Access 9, 72894-72936.

Becker, H., (2008). Investition und Finanzierung:

Grundlagen der betrieblichen Finanzwirtschaft.

Gabler, Wiesbaden.

Bettoni, A., Matteri, D., Montini, E., Gładysz, B., &

Carpanzano, E. (2021). An AI adoption model for

SMEs: a conceptual framework. In IFAC-

PapersOnLine, 54(1), 702-708.

Cadavid, J., Lamouri, S., Grabot, B., Fortin, A., (2019).

Machine Learning in Production Planning and Control:

A Review of Empirical Literature. In IFAC-

PapersOnLine 52, 385-390.

Chies, S., (2016). Change Management bei der Einführung

neuer IT-Technologien: Mitarbeiter ins Boot holen–mit

angewandter Psychologie. Springer, Wiesbaden.

Chowdhary, K. R. (2020). Fundamentals of artificial

intelligence. Springer Nature.

Cus, F., (1997). Produktionsmanagement in kleinen und

mittleren Unternehmen. In e&i (Elektrotechnik und

Informationstechnik) 114, 176-181.

Dahm, M., Constantine, B., (2020). Machine Learning für

den Mittelstand: Eine empirische Annäherung. In:

Dahm, M., Thode, S.Digitale Transformation in der

Unternehmenspraxis, pp. 327-344. Spinger,

Wiesbaden.

Davenport, T., (1998). Putting the enterprise into the

enterprise system. In Harvard business review 76, 121-

131.

De Lellis, A., Di Leva, A., Sulis, E., (2018). Simulation for

change management: an industrial application. In

Procedia Computer Science 138, 533-540.

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

534

Dolgih, S., (2019). A Robust Model for Integration of

Artificial Intelligence Methods in Primary Care. In II

International Scientific and Technical Workshop on

Information and Data-driven Medicine, pp. 319-329.

CEUR Workshop Proceedings, Lviv.

Federal Ministry of Economic Affairs and Energy,

https://www.bmwi.de/Navigation/EN/Home/home.htm

l (Accessed: 26.08.2021)

Federal Ministry of Education and Research,

https://www.bmbf.de/bmbf/en/home/home_node.html;

jsessionid=1120713E6036E83DA70871AB75D88206.

live382 (Accessed: 26.08.2021)

Gallina, V., Lingitz, L., Karner, M., (2019). A New

Perspective of the Cyber-Physical Production Planning

System. In 16th IMEKO TC10, pp. 60-65. Conference

on Testing, Diagnostics & Inspection as a

comprehensive value chain for Quality & Safety,

Berlin.

Geisler, F., (2014). Datenbanken: Grundlagen und Design.

MITP, Heidelberg.

Gelders, L. F., Van Wassenhove, L. N., (1981). Production

planning: a review. In European Journal of

Operational Research 7(2), 101-110.

Gonzalez Zelaya, C., (2019). Towards Explaining the

Effects of Data Preprocessing on Machine Learning. In

35th International Conference on Data Engineering,

pp.2086-2090. IEEE Press, Macao.

Gowda, R., Ayush, G., (2020). A study on advantages and

disadvantages of online teaching during Covid-19 with

special reference to mangalore university students. Int.

e-Conf. on Impact of COVID-19 on Various Areas of

Global Economy, Sci. & Humanities, Vidyabharati

International Interdisciplinary Research Journal, 73-

78.

Hietala, H., Päivärinta, T., (2021). Benefits Realisation in

Post-Implementation Development of ERP Systems: A

Case Study. In CENTERIS International Conference on

ENTERprise Information Systems, pp. 419-426. In

Procedia Computer Science, Quarteira.

Hildesheim, W., (2020). Künstliche Intelligenz im Jahr

2020: Von der Experimentierphase in die Praxis. In

Wirtschaftsinformatik & Management, 1-3.

Hopf, C., (2014). Qualitative interviews: An overview. In:

Flick, U., von Kardorff, E., Steinke, I.. A companion to

qualitative research. pp. 203-208. Rohohlt

Taschenbuch Verlag, Reinbek in Hamburg.

IfM Bonn, https://www.ifm-bonn.org/en/ (Accessed:

26.08.2021).

Iliashenko, O., Bikkulova, Z., Dubgorn, A., (2019).

Opportunities and challenges of artificial intelligence in

healthcare. In E3S Web of Conferences vol. 110,

p.02028. EDP Sciences, St. Petersburg.

Ilieva, R., Anguelov, K., Nikolov, Y., (2019). Mathematical

algorithms for artificial intelligence. In AIP Conference

Proceedings, 2172, 110015.

Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or Not,

AI Comes—An Interview Study of Organizational AI

Readiness Factors. In Business & Information Systems

Engineering, 63(1), 5-20. 18.

KI-Bundesverband, https://ki-verband.de/ (Accessed:

26.08.2021)

Kreutzer, R., Sirrenberg, M., (2019). KI-Challenge – wie

Künstliche Intelligenz im Unternehmen zu verankern

ist. In: R. Kreutzer, M. Sirrenberg. Künstliche

Intelligenz verstehen. pp. 271-315. Springer Gabler,

Wiesbaden.

Kuckartz, U., (2019). Qualitative content analysis: From

Kracauer's beginnings to today's challenges. Technical

report, Forum: Qualitative Social Research (Forum

Qualitative Sozialforschung).

Kukharuk, A., Gavrysh, J., (2019). Competitiveness of

SMEs in Terms of Industry 4.0. In 2019 International

Conference on Creative Business for Smart and

Sustainable Growth, pp. 1-4. IEEE Press, Sandanski.

Kumar, A., Dimitrakopoulos, R., (2021). Production

scheduling in industrial mining complexes with

incoming new information using tree search and deep

reinforcement learning. In Applied Soft Computing 110,

1568-4946.

Lernende Systeme Germanys platform for artificial

intelligence, https://www.plattform-lernende-

systeme.de/home-en.html (Accessed: 26.08.2021)

Luo, J., Meng, Q., Cai, Y., (2018). Analysis of the Impact

of Artificial Intelligence application on the

Development of Accounting Industry. In Open Journal

of Business and Management 6, 850-856.

Mansour, R. F., El Amraoui, A., Nouaouri, I., Díaz, V. G.,

Gupta, D., Kumar, S., (2021). Artificial Intelligence

and Internet of Things Enabled Disease Diagnosis

Model for Smart Healthcare Systems. In IEEE Access

9, 45137-45146.

Mao, S., Wang, B., Tang, Y., Qian, F., (2019) Opportunities

and challenges of artificial intelligence for green

manufacturing in the process industry. In Engineering

5, 995-1002.

Montavon, G., Samek, W., & Müller, K., (2018). Methods

for interpreting and understanding deep neural

networks. In Digital Signal Processing 73, 1-15.

Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A.,

Li, J., Poor, H. V., (2021). Federated Learning for

Internet of Things: A Comprehensive Survey. In arXiv

preprint arXiv, 2104.07914.

Nguyen, S., Zhang, M., Johnston, M., Tan, K. C., (2019).

Genetic Programming for Job Shop Scheduling. In:

Bansal J., Singh P., Pal N. (eds) Evolutionary and

Swarm Intelligence Algorithms. Studies in

Computational Intelligence, vol 779, 143-167 Springer,

Cham.

Oberc, H., Fahle, S., Prinz, C., Kuhlenkötter, B., (2020). A

Practical Training Approach in Learning Factories to

Make Artificial Intelligence Tangible. In 53rd CIRP

Conference on Manufacturing Systems, pp. 467-472.

Elsevier, Chicago.

Papadopoulos, G., (2014). Moving from Traditional to

Agile Software Development Methodologies Also on

Large, Distributed Projects. In International

Conference on Strategic Innovative Marketing, pp.

455–463. Elsevier, Madrid.

Prerequisites for Applying Artiﬁcial Intelligence for Scheduling in Small- and Medium-sized Enterprises

535

Patel, D., Shah, Y., Thakkar, N., Shah, K., Shah, M., (2019)

Implementation of artificial intelligence techniques for

cancer detection. In Augmented Human Research 5, 1-

10.

Pizoń, J., Lipski, J., (2015). Manufacturing Process Support

Using Artificial Intelligence. In Applied Mechanics and

Materials 791, 89-95.

Poniszewska-Maranda, A., Kaczmarek, D., (2015).

Selected methods of artificial intelligence for Internet

of Things conception. In Federated Conference on

Computer Science and Information Systems, pp. 1343-

1348. IEEE Press, Lodz.

PWC, https://www.pwc.de/de/business-analytics/sizing-

the-price-final-juni-2018.pdf (Accessed: 14.01.2021).

Qiu, L., Zhao, L., (2019). Opportunities and Challenges of

Artificial Intelligence to Human Resource

Management. In Academic Journal of Humanities &

Social Sciences 2, 144-153.

Radaliev, P., De Roure, D., (2021). Review of algorithms

for artificial intelligence on low memory devices. In

IEEE Access 9, 109986-109993.

Ramirez-Asis, E., Vilchez-Carcamo, J., Thakar, C.M.,

Phasinam, K., Kassanuk, T., Naved, M., (2021). A

review on role of artificial intelligence in food

processing and manufacturing industry, Materials

Today. In Proceedings, ISSN 2214-7853,

https://doi.org/10.1016/j.matpr.2021.11.616.

Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E.,

Bakiras, S., (2021). The Role of AI, Machine Learning,

and Big Data in Digital Twinning: A Systematic

Literature Review, Challenges, and Opportunities. In

IEEE Access 9, 32030-32052.

Rowley, J., Slack, F., (2004). Conducting a literature

review. In Management Research News 27, 31-39.

Rump, J., Stracke, S., Wilms, G., Zapp, D., (2020).

Praktische Umsetzung der strategischen

Personalplanung in kleinen und mittelständischen

Unternehmen. In: Rump, J., Eilers, S. Strategische

Personalplanung. pp. 95-125. Springer, Heidelberg.

Saßmannshausen, T., Heupel, T., (2020). Vertrauen in KI –

Eine empirische Analyse innerhalb des

Produktionsmanagements. In: R. Buchkremer, T.

Heupel, & O. Koch, Künstliche Intelligenz in Wirtschaft

und Gesellschaft: Auswirkungen, Herausforderungen

und Handelsempfehlungen. pp. 170-187. Springer,

Wiesbaden.

Sayyad, S., Kumar, S., Bongale, A., Kamat, P., Patil, S.,

Kotecha, K., (2021). Data-Driven Remaining Useful

Life Estimation for Milling Process: Sensors,

Algorithms, Da-tasets, and Future Directions. In IEEE

Access 9, 110255-110286.

Schmelzer, H., & Sesselmann, W., (2013).

Geschäftsprozessmanagement in der Praxis: Kunden

zufrieden stellen-Produktivität steigern-Wert erhöhen.

Carl Hanser, Munich.

Schön, E., Escalona, M., Thomaschewski, J., (2015). Agile

Values and Their Implementation in Practice. In

International Journal of Interactive Multimedia and

Artificial Intelligence, 61-66.

Schönfuß, B., McFarlane, D., Hawkridge, G., Salter, L.,

Athanassopoulou, N., & De Silva, L. (2021). A

catalogue of digital solution areas for prioritising the

needs of manufacturing SMEs. In Computers in

Industry, 133, 103532.

Schwarz, D., (2010). Srategische Personalplanung und

Humankapitalbewertung: Simulation anhand der

Cottbuser Formel. Gabler, Wiesbaden.

Shakirov, E., Brandl, F., Bauer, H., Kattner, N., Becerril,

L., Fortin, C., Reinhart, G., (2019). Integration of

Engineering and Manufacturing Change Management:

Infrastructure and Scenarios for Teaching and

Demonstration. In CIRP Manufacturing Systems

Conference, pp. 535-540. Elsevier, Ljublijana.

Susar, D., Aquaro, V., (2019). Artificial intelligence:

Opportunities and challenges for the public sector. In

Proceedings of the 12th International Conference on

Theory and Practice of Electronic Governance, pp.

418-426. Springer, Melbourne.

Takeda-Berger, S., Frazzon, E., Broda, E., Freitag, M.,

(2020). Machine Learning in Production Scheduling:

An Overview of the Academic Literature. In

International Conference on Dynamics in Logistics, pp.

409-419. Springer, Cham.

Toreini, E., Aitken, M., Coopamootoo, K., Elliott, K.,

Zelaya, C., van Moorsel, A., (2020). The relationship

between trust in AI and trustworthy machine learning

technologies. In Proceedings of the 2020 Conference

on Fairness, Accountability and Transparency, pp.

272-283. Association for Computing Machinery,

Barcelona.

Venkatesh, V., (2021). Adoption and use of AI tools: a

research agenda grounded in UTAUT. In Annals of

Operations Research, 1-12.

VERBI Software. “Online Manual.” https://www.maxqda.

com/download/manuals/MAX2020-Online-Manual-

Complete-EN.pdf. (Accessed: 26.08.2021)

Webster, J., & Watson, R. T. (2002). Analyzing the past to

prepare for the future: Writing a literature review. In

MIS quarterly, xiii-xxiii.

Xu, Y., Yu, C., Li, J., Liu, Y., (2019). Video telephony for

end-consumers: Measurement study of Google+, iChat,

and Skype. In Proceedings of the 2012 Internet

Measurement Conference, pp. 371-384. ACM, Boston.

Zhang, H., Li, J., Hong, M., Man, Y., (2021). Chapter 16 -

Artificial intelligence algorithm-based multi-objective

optimization model of flexible flow shop smart

scheduling, Editor(s): Jingzheng Ren, Weifeng Shen, Yi

Man, Lichun Dong, Applications of Artificial

Intelligence in Process Systems Engineering, Elsevier.

Zhang, J., Ding, G., Zou, Y., Qin, S., Fu, J., (2017). Review

of job shop scheduling research and its new

perspectives under Industry 4.0. In Journal of

Intelligent Manufacturing, 30(4), 1809-1830.

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

536