Determing the Decentrality of Production Processes Due to Analysis of

Their Communication Structure

Hanna Theuer

Chair of Business Information Systems, Processes and Systems, University of Potsdam,

August-Bebel-Str. 89, 14482 Potsdam, Germany

Keywords:

Decentralized Production Control, Social Network Analysis, Autonomy, Communication Structure, Decision

Structure, Industry 4.0, Production Planning and Control.

Abstract:

This paper motivates the beneﬁts of the analysis of the communication structure for process improvement.

Therefore ﬁrst, the paper presents a three-stage model for determining the decentralization of production pro-

cesses. This is based on the analysis of the communication and decision structure of the process’ actors. In

addition, it presents a way of visualizing communication relationships. To conclude, this paper presents a

practical example and the results of a simulation study. It depicts the advantages of analyzing the communi-

cation structure.

1 INTRODUCTION

Decentralized production helps companies to meet

today’s market requirements (Petschow et al., 2014;

Kluth and Storr, 1997; Sundermeier et al., 2020). Pos-

itive beneﬁts are an increased speed of response and

adaptability (Hichert et al., 1996; Ten Hompel and

Henke, 2014) as well as cost advantages by a task

integration over centralized production control (Mil-

berg, 1991; Westk

amper et al., 1998). They allow a

short-term reaction to unforeseen events and thus con-

tribute to an increase in the reaction speed (Mussbach-

Winter, 1997). Further advantages include the in-

crease of transparency, a reduction of the forecast

shares, the improvement of the planning quality, and

a reduction and control of the complexity (K

ohler

et al., 1997). Characteristics of decentralized produc-

tion control are few hierarchical levels with homo-

geneously distributed decision-making authority [10],

which independently decide on optimal manufactur-

ing methods, product quality, and the timing of orders

(Mussbach-Winter, 1997).

Scientists have been discussing the advantages of

decentralized control for over 20 years (see for ex-

ample (K

ohler et al., 1997; Ramsauer, 1997)). How-

ever, developments in recent years have created ex-

tensive opportunities for technical implementation

(Faber, 2019; Huber, 2018; Wang et al., 2017). The

transfer of decision-making, execution, and commu-

https://orcid.org/0000-0002-2238-5699

nication capabilities to actors involved enables decen-

tralized production control. These technologies are

summed up under the term autonomous technologies.

Autonomous technologies are a basis for decentral-

ized production planning (Zeidler et al., 2019).

Although the beneﬁts of decentralized control

structures are well known (Windt, 2006; Ramsauer,

1997), it is not possible to make general statements

about the best decentralization of a production pro-

cess. Instead, it is necessary to examine on a process-

speciﬁc basis which implementation is best suited

to achieving the desired goals (Gronau and Theuer,

2016). Therefore, it is important to operationalize the

decentrality of a production process.

Though the importance of the communication

structure increases, there is no method that uses the

communication structure as the basis for operational-

izing decentralization. Therefore, the author of this

paper suggests a three-stage model. It allows deter-

mining decentralization based on the communication

and decision structure of the process actors.

Therefore ﬁrst, the paper presents a three-stage

model for determining the decentralization of produc-

tion processes. This is based on the analysis of the

communication and decision structure of the process’

actors. In addition, it presents a way of visualizing

communication relationships. To conclude, this paper

presents a practical example and the results of a sim-

ulation study. It depicts the advantages of analyzing

the communication structure.

Theuer, H.

Determing the Decentrality of Production Processes Due to Analysis of Their Communication Structure.

DOI: 10.5220/0011526700003329

In Proceedings of the 3rd Inter national Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2022), pages 66-74

ISBN: 978-989-758-612-5; ISSN: 2184-9285

2 DETERMING DECENTRALITY

IN PRODUCTION PROCESSES

The communication and control structure of the ac-

tors involved in the process takes an important role

in decentralized production control. For determing

actor-based decentrality in production processes, it is

necessary to deﬁne when an actor reaches the maxi-

mum or minimum structural centrality and what the

mathematical relationship is between the factors and

the target value (decentrality) in terms of increase and

decrease of distances and number of reachable actors.

The focus is on one element at a time. Relevant fac-

tors are, according to the social network analysis, the

number of elements the considered element reaches,

and the steps required for this.

Based on the four forms of control structures (see

ﬁgure 1), four deﬁnitions are necessary (for detailed

argumentation and mathematical examination of the

deﬁnitions, please see (Theuer, 2022)): Dilts et al.

distinguish between controlling and executing units.

Controlling actors are actively involved in decision-

making and can pass on the decisions made to their

subordinate units. However, they cannot execute de-

cisions. Executing actors implement the instructions

they have received from their upstream entities. They

are not involved in decision-making (Boccella et al.,

2020). The model presented in this paper removes this

separation, as modern technologies enable the uniﬁ-

cation of controlling and executing activities by one

actor.

1. The structural autonomy of an actor is maximal

if and only if it reaches all actors in the network

directly.

2. The structural autonomy of an actor is minimal if

and only if it cannot reach any other actor in the

network.

3. The structural autonomy of an actor decreases

with an increasing distance to the other actors

(given a constant number of reachable actors).

4. The structural autonomy of an actor increases

with an increasing number of reached actors

(given a constant sum of distances).

3 THREE-STAGE MODEL

This paper presents a three-stage model for the analy-

sis of actor-based decentrality in production processes

(see ﬁgure 1). Stage 1 analyzes the communication

and decision structure of autonomous actors. Social

network diagrams visualize the communication struc-

ture (nodes and directed edges). Afterward, stage 2

level 1

level 2

Central

DCB

level 1

level 2

level 3

Proper Hierarchy

CBA

level 1

level 2

Heterachy

DCB

level 1

level 2

level 3

Modied Hierarchy

Central

Decentral

Figure 1: Four Forms of Control Structures (following

(Dilts et al., 1991)).

aggregates the results into a key ﬁgure that describes

the decentralization of the process step. Finally, stage

3 evaluates the decentralization of the process

3.1 First Stage: Actors’ Autonomy

The starting point of the model is the three ac-

tor classes: human, factory software (software in-

tegrated into machines), and operational application

system. Their instances fulﬁll the requirements of au-

tonomous actors (Theuer, 2018): independent infor-

mation processing, decision making, and decision ex-

ecution (Windt, 2008).

It is necessary to create an understanding of de-

centralization of production processes ﬁrstly. Four

requirements quantify the communication structure.

A systematic analysis conﬁrms the suitability of the

social network analysis (SNA) key ﬁgure, harmonic

Determing the Decentrality of Production Processes Due to Analysis of Their Communication Structure

actors’ autonomy

decentrality of

process step

actor

production step

production process

aggregation

Autonomy Index AI

Figure 2: Three-stage model for determination of decentralization of production processes.

closeness, for operationalizing the structural auton-

omy of individual autonomous actors. A correction

factor considers self-executing actors in the calcula-

tion. Those combine decision-making and decision-

execution and have no directed connection to another

actor.

However, because of the possibility of integrating

control and execution capabilities in one actor, it is in-

sufﬁcient to only focus on structural relations to deter-

mine decentralization. Therefore, the model consid-

ers a second value describing the decision structure. It

is determined by the ratio of the decision alternatives

of the actor to the sum of the decision alternatives in

the process step.

Actors’ autonomy R

a,ps

is calculated by multiply-

ing the two factors.

a,ps

= C

′

a,ps

· d

a,ps

= (C

a,ps

− 1

) ·

a,ps

∑

a,ps

(1)

with

a,ps

: Actors’ autonomy of actor a

in process step ps

′

a,ps

: Structural Autonomy of self executing

actor a in process step ps

a,ps

: Harmonic Closeness of actor a

in process step ps

a,ps

: Decision autonomy of actor a

in process step ps

a,ps

: Quantity of decisions of actor a

in process step ps

: Number of actors in process step ps

3.2 Second Stage: Decentrality of

Process Steps

The calculation of the decentrality of the process step

is performed using the graph centrality of social net-

work analysis. The SNA distinguished two differ-

ent approaches to centrality determination. While

other explanations for the analysis of social structures

focused on the centrality of individual actors (“lo-

cal point centrality”) (Bavelas, 1950; Flament, 1963;

Beauchamp, 1965; Sabidussi, 1966), Freeman (Free-

man, 1978) succeeded in his work to consider the

whole network (“graph centrality”). He deﬁnes such

graphs as central, where one point dominates the other

points.

For this purpose, the sum of the differences be-

tween maximum and all other nodal centralities is ﬁrst

calculated. Subsequently, the ratio of this value to the

theoretical maximum value is determined.

Freeman’s approach classiﬁes the control struc-

ture (homogeneous/heterogeneous) of a graph. Thus,

it is suitable for determining the decentrality of pro-

duction processes. A square root increases the disper-

sion and allows better discrimination of the results.

= 1 − C

= 1 −



′

− R

a,ps



− 1

(2)

with

: Decentrality of process step ps

: Centrality of process step ps

′

: max Actors’ autonomy in process step ps

a,ps

: Actors’ autonomy of actor a in process step

: Number of actors in process step

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

3.3 Third Stage: Decentrality of Process

The third step (second aggregation) combines the pre-

viously determined characteristic values for the de-

centralization of process steps to a characteristic value

describing the decentralization of the process: the Au-

tonomy Index AI.

It was developed in accordance with the Lean

Index-a key metric of lean production. It compares

the value-added time of a process to the total cy-

cle time of a product through the process (Drees and

Sack, 2011; Erlach, 2020). During initial research ac-

tivities, the calculation was based on a binary value

(0 or 1) (Theuer, 2011; Gronau, 2016). However, re-

search work has shown that a continuous value (be-

tween 0 and 1) is more suitable. The Autonomy Index

increases as the process steps become more decentral-

ized. An AI of 1 indicates a total decentralized, an AI

of 0 a total central production.

AI =

∑

(3)

with

AI: Autonomie Index

: Decentrality of process step ps

: Number of actors in process step

4 INDUSTRIAL EXAMPLE

The analysis uses a process from the variant-rich

small batch production of an industrial company. The

process comprises 13 process steps, with 24 actors of

the three actor classes involved; material transported

is via trolleys, with a one-to-one relation. Some parts

of the process use FIFO (ﬁrst in ﬁrst out) lines. The

process includes branching. Sometimes there is a

need for jumps back and external manufacturer. In-

formation sharing is via paper-based information ob-

jects.

The following paragraphs present the analysis of

one process step and interpret the results. After, pro-

cess improvement, based on the analysis of its com-

munication structure, follows.

4.1 Analysis of Process

The analysis of the practice process is based on doc-

umentation of the process as extended event-driven

process chains (eEPC). Besides the temporal and log-

ical sequence, the diagrams contain the human actors

responsible for the work steps, the used information

objects, and other technical, non-autonomous actors

(e. g. machines).

employee warehouse

SAP

disponent

employee sales

employee

warehouse office

worker

manufacturing

controller

Figure 3: Visualization of the communication relationship

of the considered process step 6.

First, the communication relationships are deter-

mined and next visualized in ﬁgure 2. Rules for the

consideration of non-autonomous actors and informa-

tion objects are applied.

The ﬁgure depicts that SAP occupies a very cen-

tral structural position. Every other actor is a direct

neighbor. From and to the worker, there are commu-

nication relations to the actor employee warehouse of-

ﬁce, employee warehouse, and production controller.

Table 1 presents the results of the ﬁrst stage of the

presented evaluation model.

Table 1: Characteristics of process step 6 (central scenario).

actor d

a,6

disponent 0,091 0,583 0,053

manufacturing controller 0,045 0,667 0,030

employee warehouse 0,045 0,667 0,030

employee warehouse ofﬁce 0,045 0,667 0,030

employee distribution 0,045 0,583 0,027

SAP 0,682 1,000 0,682

worker 0,045 0,833 0,038

0,196

Analogously, the further twelve process steps of

the practice process are examined. Table 2 shows the

number of autonomous actors involved #a, the num-

ber of actors #c

ca,ps

> 0 and decision-making auton-

omy d

a,ps

> 0, and the decentralization of the process

step D

. It also includes the Autonomy Index AI as a

characteristic value, describing the decentrality of the

process.

It shows varying decentralization of the process

steps. While process step 4 (warehouse preparation)

has the lowest decentralization of 0.065, process step

2 (creation of production order) has the highest value

of 0.470. The resulting Autonomy Index is 0.224.

Overall, decentralization is rather low, e.g. a central

authority largely controls the process.

Determing the Decentrality of Production Processes Due to Analysis of Their Communication Structure

Table 2: Characteristics of the industrial process (central scenario).

process step #a #c

a,ps

1 creation planned order 3 3 3 0,084

2 creation of production order 5 5 5 0,470

3 purchasing 3 3 3 0,138

4 storage preparation 2 2 2 0,065

5 storage preparation 4 4 3 0,353

6 manufacturing component A 7 7 7 0,196

7 manufacturing component B 7 7 4 0,161

8 manufacturing component C 7 7 6 0,196

9 manufacturing component D 7 7 7 0,173

10 assembly 3 3 3 0,214

11 testing 5 5 5 0,213

12 varnishing 5 5 4 0,277

13 shipping 4 4 4 0,373

4.2 Process Improvement

The visual representation of the communication rela-

tionships can provide clues for improving the process

in terms of decentralization. It allows the identiﬁca-

tion of very central and very distant actors that can

only be reached via a long communication path. The

possibilities of cyber-physical elements offer a ba-

sis for the development of alternative scenarios. Be-

sides the ﬂow of information and the decision-making

structure, it is also important to consider the imple-

mentation of the material ﬂow. Therefore, the mo-

bility of processing actors, workpieces, and tools as

well as the design of the material ﬂow (for exam-

ple, rigid transport routes, ﬂexible transport routes

(human-controlled/unmanned)) have to be analyzed.

The previously described process was analyzed

based on the visualizations of the communication re-

lationships and process alternatives were developed.

Figure 3 shows the resulting communication diagram.

disponent

manufacturing

controller

employee sales

SAP

employee

warehouse

office

employee

warehouse

drilling machine

worker

saw

lathe

AGV

Figure 4: Visualization of the communication relationship

of the decentralized process step 6.

The following general considerations underlie the

process improvement:

• transport carriage, drilling machine, lathe and saw

are designed as autonomous actors (actor class

factory software) by integrating smart technolo-

gies. This empowers them to take part actively in

decision- making and promotes the beneﬁts of de-

centralized production control. That reduces the

strong centrality of the SAP system.

• transport carriage is a driverless transport sys-

tem that independently determines the route to the

next processing step. Manual operation becomes

obsolete so that the human actors involved in the

process can pursue value-adding activities.

• an operable display at transport carriage enables

its operation by human operators. The human op-

erator can directly assign processing results or an-

notations to the workpiece on-site (1:1 relation-

ship of transport carriage and workpiece). This

avoids the need to enter data via production data

acquisition (PDA) terminals connected to the cen-

tral SAP.

Besides these general considerations, there are

two changes to the process considered:

• the SAP operational application system notiﬁes

the transport cart of the order data at the begin-

ning of the process step. After competition of all

process steps, the transport cart reports back to the

system. This parallelizes the material and infor-

mation. Also, it signiﬁcantly reduces the num-

ber of communication relationships from or to the

SAP system.

• the transport carriage independently schedules the

processing sequence and times by communicat-

ing with the autonomous actors drilling machine,

lathe, and saw. Thus, the decentralized decision-

making process can always consider the current

situation, which increases ﬂexibility in the face of

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

deviations compared to planning by a central con-

troller.

Table 3 lists the characteristic values of the decen-

tralized scenario. It highlights an increased centrality

of the transport trolley and a decreased centrality of

the SAP system. The ERP solution now only has bidi-

rectional communication relationships with employee

sales, production controller, and scheduler. These

three human actors perform tasks upstream and down-

stream of the actual value-added manufacturing pro-

cess. SAP and shop ﬂoor communicate exclusively

via a communication relationship between SAP and

AGV.

Table 3: Characteristics of process step 6 (decentral sce-

nario).

actor d

a,6i

AGV 0,296 0,800 0,237

disponent 0,074 0,442 0,033

drilling machine 0,074 0,220 0,041

employee distribution 0,037 0,442 0,016

employee warehouse 0,037 ,0533 0,020

employee warehouse ofﬁce 0,037 0,533 0,020

lathe 0,037 0,550 0,020

manufacturing controller 0,037 0,442 0,016

saw 0,074 0,550 0,041

SAP 0,259 0,683 0,177

worker 0,037 0,525 0,019

0,556

The actor characteristics are more homogeneous

in the improved process than in the original process.

It shows that the decentralization of the process step

has increased in comparison. Additionally, the struc-

tural and decision-making autonomy and the resulting

autonomy of the actors are more balanced.

In conclusion, more actors are involved in

decision-making in the improved process than in the

original process. This is due in particular to the en-

dowment of previously non-autonomous actors with

the criteria decision making, information processing,

and decision execution.

Table 4 presents the results of process adaptation

for all 13 process steps. The number of actors in-

volved has increased in most process steps. The de-

centralization of the process steps has risen signiﬁ-

cantly because of shifting away from a few central ac-

tors and a more even distribution of decision-making

authority. In summary, the Autonomy Index increases

to 0,511.

The comparison of alternative scenarios and

theirs’ effects is very time-consuming and cost-

intensive. Therefore, presented industrial example

could not highlight the effects of decentralization on

the production process (model’s stage 3). To perform

the complete model with all three stages and to so

show the impacts of the process changes, the author

carried out a simulation study with two scenario (cen-

tral and decentral control) at the Center Industry 4.0

Potsdam. The next section presents the results.

5 SIMULATION STUDY

The simulation process is the production of optical

lenses. It was conducted at Center Industry 4.0 Pots-

dam (LSWI, 2022). It comprises the four process

steps of grinding, marking, dyeing, and quality con-

trol. There are two parallel machines for grinding.

Figure 5 visualizes the value stream diagramm of

the process. The communication relationships are

recorded, stored in a database, processed, classiﬁed,

ﬁltered, and aggregated with SQL. Subsequently, an

R script evaluates them. Additionally, an assignment

of the decision alternatives to the actors takes place.

This Evaluation also includes the decision structure.

The process under consideration starts with the

regular operation of lens production. A workpiece

carrier transports a batch of lenses. After a short time,

a rush job is set up. The goal is to produce the rush

order in the required time of two minutes and ﬁfteen

seconds. At the same time, the regular operation is to

be produced with the shortest possible lead time.

Two scenarios are simulated: a central and a de-

central one. In the central scenario, the Control Cen-

ter performs production planning, the control com-

mands are passed on to the other actors. Ten actors

are involved. The decentralized scenario distributes

decision-making among 14 autonomous actors. For

this purpose, the decentralized actors have local intel-

ligence. The example process is suitable for the val-

idation of the developed evaluation model, since the

differentiation of centralized and decentralized con-

trol is possible. The communication relationships and

the distribution of the share in the decision-making

process per actor can be varied. Thus, the parameters

relevant for the model can be changed, so that the ef-

fects can be analyzed. Each simulation run considers

a normal and a rush order. Twenty simulation runs

were executed from both scenarios. The communica-

tion structure and the decision structure are recorded.

Additionally, lead time is collected as a metric to eval-

uate the beneﬁts. Table 5 presents the results.

The analysis highlights that the decentralization of

the process step is much more homogeneous in the

decentralized scenario. The autonomy index AI in-

creases signiﬁcantly from 0.175 to 0.680. Decentral-

ization lowers lead time both in normal order and in

rush order. The robustness (standard deviation of the

Determing the Decentrality of Production Processes Due to Analysis of Their Communication Structure

Table 4: Characteristics of the industrial process (decentral scenario).

process # a #c

a,ps

> 0 #d

a,ps

> 0 D

1 creation planned order 3 3 3 0,084

2 creation of production order 5 5 5 0,437

3 purchasing 4 4 4 0,353

4 storage preparation 3 3 3 0,684

5 picking 5 5 5 0,476

6 manufacturing component A 11 11 11 0,556

7 manufacturing component B 11 11 11 0,556

8 manufacturing component C 10 10 10 0,559

9 manufacturing component D 10 10 10 0,548

10 assembly 4 4 4 0,608

11 testing 6 6 6 0,652

12 varnishing 6 5 5 0,552

13 shipping 5 5 5 0,571

Dyeing

Duration sec25

Decision pc.5

Duration sec30

Decisions pc.7

Grinding 1

Grinding 2

Duration sec35

Decisions pc.7

Marking

Duration sec25

Decision pc.5

Quality Control

Duration Sec30

Decision pc.6

Figure 5: Value Stream Diagram of Process Used in Simulation Study

collected lead times) rises. Thus, decentralization im-

proves process planning and the adherence to delivery

of this process.

6 CONCLUSIONS AND

LIMITATIONS

This papers presents a model that, for the ﬁrst time,

focuses on the autonomy of the actors for technically

dominated systems. Therefor it considers both the

structural and the decision-making autonomy of each

actor. The model enables an evaluation of the overall

system through 2-level aggregation.

An industrial process was used to show how vi-

sualization of the communication structure can serve

as a basis for process improvement. The presented

model was run under laboratory conditions in a sim-

ulation environment. Transfer and application of the

model in a real production environment bear the risk

that the data acquisition is incomplete. Experience

shows that the interaction of human actors with each

other and with the technical components of the pro-

duction process is characterized by actions re-

acting to the individual situation. These can be short

agreements and shouts or even smaller manual inter-

ventions in the process ﬂow, such as the alignment of

a workpiece. Complete monitoring requires a great

deal of effort, especially with spatially distributed

processes - either by human observers or by technical

instruments such as cameras and microphones,

although legal restrictions must be expected here

regarding the monitoring of individual persons and

their work performance.

Another challenge of practical application con-

cerns the operationalization of decision autonomy.

Both the complete identiﬁcation of decision alterna-

tives and their allocation to the actors involved is dif-

ﬁcult to realize in reality. Reasons are especially deci-

sions with continuous values, such as processing du-

rations, which theoretically contain an inﬁnite num-

ber of decision alternatives. It is necessary to make

a reasonable discretization - a wrong choice distorts

the model’s results. Further challenges may be the as-

signment of actors to decision alternatives, especially

because it is not always possible to assign decisions

unambiguously.

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

Table 5: Results of the simulation study.

process step

central decentral

a,ps

> 0 #d

a,ps

> 0 #D

> 0 #c

a,ps

> 0 #d

a,ps

> 0 #D

> 0

1 7 1 0,437 12 5 0,768

2 5 1 0,051 9 4 0,529

3 7 1 0,127 9 6 0,721

4 4 1 0,087 5 5 0,703

AI 0,175 0,680

lead time 3’07” (normal order ) — 3’05” (rush order) 3’02” (normal order) — 2’14” (rush order)

σ 15” (normal order) — 12” (rush order) 5” (normal order) — 2” (rush order)

Table 6: Results of the simulation study.

process step

central decentral

a,ps

> 0 #d

a,ps

> 0 #D

> 0 #c

a,ps

> 0 #d

a,ps

> 0 #D

> 0

1 7 1 0,437 12 5 0,768

2 5 1 0,051 9 4 0,529

3 7 1 0,127 9 6 0,721

4 4 1 0,087 5 5 0,703

AI 0,175 0,680

lead time 3’07” (normal order ) — 3’05” (rush order) 3’02” (normal order) — 2’14” (rush order)

σ 15” (normal order) — 12” (rush order) 5” (normal order) — 2” (rush order)

Another restriction of the practical feasibility is

the realization of different scenarios of a real pro-

cess in the factory because high time and ﬁnancial ef-

fort are expected for the restructuring in each case.

The scenarios allow the effects of different control

strategies to be compared. In addition, production

downtimes occur, resulting in further ﬁnancial losses.

Particularly in the case of existing processes (brown-

ﬁeld), the beneﬁt of the evaluation model, therefore,

lies in the retrospective evaluation of implemented re-

structuring measures. In the case of newly planned

processes (greenﬁeld), the model can help in design-

ing process control.

The limits of the developed model are therefore

the determination of the real input data and the high

effort for implementing different scenarios of a pro-

duction process. Examining the processes in suitable

simulation environments such as Center Industry 4.0

Potsdam can reduce the risks. However, a prerequisite

here is also a suitable abstraction of the real process

and the modeling based on it.

Based on the results of the work on which this pa-

per is based, there is a need for further research, which

relates in particular to the practical application of the

model in a real production process with the challenges

described in the previous section. Another research

approach is to apply the model to different types of

production processes - for example, classiﬁed by dif-

ferent types of manufacturing and organization or in-

dustries.

Comparing the results makes it possible to answer

the questions of whether it is possible to classify the

beneﬁts of decentralized control structures according

to certain criteria and if there are best practices.

In this context, a comparative study with other

evaluation models is possible as well.

REFERENCES

Bavelas, A. (1950). Communication patterns in task-

oriented groups. In The Journal of the Acoustical So-

ciety of America, volume 6, pages 725–730.

Beauchamp, M. A. (1965). An improved index of central-

ity. In Behavioral Science – Journal of the Society

for General Systems Research, volume 10, pages 161–

163.

Boccella, Centobelli, P., Cerchione, R., Murino, and Riedel

(2020). Evaluating centralized and heterarchical con-

trol of smart manufacturing systems in the era of in-

dustry 4.0. Applied Sciences, 10:755.

Dilts, D., Boyd, N. P., and Whorms, H. H. (1991). The eval-

uation of control architectures for automated manu-

facturing systems. Journal of Manufacturing Systems,

10:79–93.

Drees, J. and Sack, C. (2011). Produktionsoptimierung

durch kennzahlen der wertstrom-modellierung.

Zeitschrift f

ur wirtschaftlichen Fabrikbetrieb,

106(6):466–470.

Erlach, K. (2020). Wertstromdesign - Der Weg zur

schlanken Fabrik, volume 3. Springer Vieweg.

Faber, O. (2019). Digitalisierung–ein megatrend: Treiber

& technologische grundlagen. In Management 4.0–

Unternehmensf

uhrung im digitalen Zeitalter, pages 3–

42. Springer.

Determing the Decentrality of Production Processes Due to Analysis of Their Communication Structure

Flament, C. (1963). Applications of Graph Theory to Group

Structure. Prentice-Hall Series in Mathematical Anal-

ysis of Social Behavior.

Freeman, L. C. (1978). Centrality in social networks con-

ceptual clariﬁcation. Social Networks, 1(3):215 – 239.

Gronau, N. (2016). Handbuch der ERP-Auswahl, volume 2.

GITO mbH.

Gronau, N. and Theuer, H. (2016). Determination of the op-

timal degree of autonomy in a cyber-physical produc-

tion system. In Proceedings of 49th CIRP Conference

on Manufacturing Systems (CIRP-CMS 2016).

Hichert, R., Roos, A., Ziegler, J., Reichwald, R., M

oslein,

K., Meitner, H., Schweiggert, F., and Ebersp

acher, J.

(1996). Information und kommunikation in dezen-

tralen strukturen. In Bullinger, H.-J. and Warnecke,

H.-J., editors, Neue Organisationsformen im Un-

ternehmen, pages 657–752. Springer-Verlag.

Huber, W. (2018). Industrie 4.0 kompakt–Wie Tech-

nologien unsere Wirtschaft und unsere Unternehmen

ver

andern. Springer.

Kluth, R. and Storr, A. (1997). Hohe produktivit

at durch

werkergerechtes, situationsorientiertes informations-

management. In f

ur Fertigungstechnik, G., editor,

FTK’97: Fertigungstechnisches Kolloquium, pages

325–349.

ohler, A., L

ammle, C., and Wiendahl, H.-H. (1997).

Dezentralisierung und vernetzung der produktionspla-

nung und -steuerung. In f

ur Fertigungstechnik, G.,

editor, FTK’97: Fertigungstechnisches Kolloquium,

pages 199–217.

LSWI (2022). Center industry 4.0 potdam – a worldwide

unique opportunity for an encounter with the future.

Webseite.

Milberg, J. (1991). Wettbewerbsfaktor zeit in produktion-

sunternehmen. In Wettbewerbsfaktor Zeit in Produk-

tionsunternehmen, pages 11–31. Springer.

Mussbach-Winter, U. (1997). Dezentrale produktionspla-

nung und -steuerung. In f

ur Fertigungstechnik, G.,

editor, FTK’97: Fertigungstechnisches Kolloquium,

pages 420–431.

Petschow, U., Ferdinand, J.-P., Diekel, S., and Fl

amig, H.

(2014). Dezentrale produktion, 3d-druck und nach-

haltigkeit. Schriftenreihe des I

OW, 206:14.

Ramsauer, C. (1997). Dezentrale PPS-Systeme Neue Struk-

turen bei hoher Innovationsdynamik. Gabler Verlag.

Sabidussi, G. (1966). The centrality index of a graph. Psy-

chometrika, 31(4):581–603.

Sundermeier, J., Gehlhoff, F., and Fay, A. (2020). Devel-

opment of a simulation model to analyze the perfor-

mance of decentral rescheduling algorithms in pro-

duction systems. Simul. Notes Eur., 30(1):15–22.

Ten Hompel, M. and Henke, M. (2014). Logistik 4.0. In

Industrie 4.0 in Produktion, Automatisierung und Lo-

gistik, pages 615–624. Springer.

Theuer, H. (2011). Extension of value stream design for

the simulation of autonomous production systems. In

ElMaraghy, H., editor, Proceedings of the 4th Inter-

national Conference on Changeable, Agile, Reconﬁg-

urable and Virtual production (CARV 2011), pages

586–591. Springer Verlag.

Theuer, H. (2018). Autonome akteure in der dezentralen

produktionssteuerung. Industrie 4.0 Management,

34(6):41–44.

Theuer, H. (2022). Communication in decentralized pro-

duction system (accepted). In Proceedings of 31rd

International Conference on Flexible Automation and

Intelligent Manufacturing.

Wang, Y., Anokhin, O., and Anderl, R. (2017). Concept

and use case driven approach for mapping it security

requirements on system assets and processes in indus-

trie 4.0. Procedia CIRP, 63:207–212.

Westk

amper, E., Wiendahl, H.-H., and Balve, P. (1998).

Dezentralisierung und autonomie in der produktion.

ZWF – Zeitschrift f

ur wirtschaftlichen Fabrikbetrieb,

9(93):407–410.

Windt, K. (2006). Selbststeuerung intelligenter objekte in

der logistik. In Vec, M., H

utt, M., and Freund, A., ed-

itors, Selbstorganisation – Ein Denksystem f

ur Natur

und Gesellschaft. B

ohlau Verlag, K

oln.

Windt, K. (2008). Ermittlung des angemessenen selbsts-

teuerungsgrades in der logistik – grenzen der selbst-

steuerung. In Nyhuis, P., editor, Beitr

age zur Theo-

rie einer Logistik, pages 349 – 372. Springer-Verlag

Berlin Heidelberg.

Zeidler, F., M

uller, D., Ten Hompel, M., and Henke, M.

(2019). Wip-balancing by autonomous control using

ﬂexibility oriented decision-making. In Proceedings

of 48th International Conference on Computers and

Industrial Engineering, CIE.

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics