MULTI-SCALE PRODUCTION SYSTEM MODELLING

Motivation – Concepts – Method

M. Neumann

1,2

and C. Constantinescu

Graduate School of Excellence advanced Manufacturing Engineering – GSaME, Nobelstraße 12, Stuttgart, Germany

Fraunhofer Institute for Manufacturing Engineering and Automation – IPA, Nobelstraße 12, Stuttgart, Germany

Keywords: Multi-scale modelling, Factory and process planning, Production system.

Abstract: This paper presents the first research steps and required foundations for the development of a method for a

structured and multi-scale modelling of production systems. Thereby the importance to support the

modelling process is stated and the motivation for the development of such a method is pointed out. State-

of-the-art concepts in system theory as well as suitable modelling methods and their applicability on the

production system modelling are analysed. The approach and the process of modelling a multi-scale

production system are firstly introduced. The paper concludes with the selection of suitable enabling

technologies and a roadmap of future activities.

1 INTRODUCTION

Manufacturers have to face shorter product life

cycles, increasing number of variants and efficient

integration of new technologies. These challenges

induce an increasing number of adaptations of

existing production systems and associated

processes. Therefore production systems are

characterized by evolved structures with high

complexity and diversity, caused by permanent

adaptation and integration of new technologies.

However, reliable models of production systems are

essential to understand the complex structure and are

the basis for efficient processing of further planning

and continuous adaptation as well as optimization

(Jovane et al., 2009).

Production systems are complex socio-technical

and multi-scale systems consisting of performance

units. A multi-scale model of a production system

can be developed to support a permanent “look

ahead” with e.g. simulation to optimize and adapt

existing production systems. Additionally it enables

the development of suitable workflows or best

practices. The increasing frequency of adaptation

projects, the growing variety and complexity of

production system structures and the suitable

characterization of interdependences lead to an

exponential increasing effort when developing,

adapting and maintaining corresponding models

(Brinkkemper, 1999).

This paper introduces our first research steps for

developing a method for multi-scale production

system modeling. As a first step in this research

topic the technical aspects of a production system,

consisting of machines, resources, equipment and

production processes are considered. The

organizational and social aspects are at the moment

leaned in secondary plan.

Some terms used in this paper have to be

clarified in advance. Today, a production system is

approached as a complex socio-technical system,

consisting of subsystems called performance units

(Westkämper and Zahn, 2009). A performance unit

is understood as e.g. a production cell, or a single

workstation.

Thereby a performance unit can consist of

several sub-performance units. Every sub-

performance unit has different manufacturing

objects, e.g., information, resources, material, tools

or products. These objects and their related sub

performance unit as well as performance units are

related by such called interdependencies. These are

represented by e.g. material, informational, energetic

or functional properties.

After emphasizing the potential and the

requirements for such a method, an overview over

the state of the art in the fields of system theory as

well as suitable modeling methods regarding their

applicability on the production system modeling is

given. Afterwards the approach is introduced. The

211

Neumann M. and Constantinescu C..

MULTI-SCALE PRODUCTION SYSTEM MODELLING - Motivation – Concepts – Method.

DOI: 10.5220/0003649202110216

In Proceedings of 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2011), pages

211-216

ISBN: 978-989-8425-78-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

paper concludes with a roadmap for future research

activities to support a multi-scale modeling of the

current state of a production system.

2 PRODUCTION SYSTEM

MODELLING – MOTIVATION

A model is an important instrument to manage the

complex structure of a production system and to

enable its active configuration and optimisation,

regarding its continuous planning and adaptation

(Mertins et al., 1994). It comprises the needed

performance units, objects and interdependencies in

a transparent and application-oriented way and

supports a consistent understanding of the current

state of the production system (Vernadat, 2002). A

model enables an efficient communication of all

involved planning actors about relevant information

and supports the explanation of the functionality of

processes. It allows a fast reaction to a turbulent

market through a permanent foresight and enables a

continuous adaptation and balancing of the

production system (Westkämper and Zahn, 2009).

Furthermore a model is the basis for the description

and analysis of different planning solutions (Vanja et

al., 2009).

However, before and while modelling a

production system certain issues emerge. Firstly the

purpose of the model has to be clarified. Is the

model used as a model to understand the complexity

of a system or as a basis for further planning and

adaptation processes. Regarding the last point a

possible employment for simulations has to be

considered in the modelling process. Secondly, an

important step is to define system boundaries. These

boundaries vary for every application of the model,

depending on the performance units, objects and

production processes taken into account (Mertins et

al., 1994). Thirdly, a suitable application-oriented

characterisation of the interdependencies between

the objects and the implementation of necessary data

and information, as well as knowledge is needed

(Vanja et al., 2009). Fourthly, the modelling of

complex structures has to be supported by suitable

modelling methods (Scheer, 1994). Today a huge

number of modelling methods exist and each has its

own advantages and disadvantages as well as

limitations. The selection of a suitable method to

generate a specific application-oriented model is a

difficult task, and is mostly done using common

sense and intuition (Schen et al., 2004). Fifthly, the

comprehensibility of a model and its level of detail

have to be suitable for its specific application. An

extensive model with all performance units, objects

and interdependencies is not expedient, because of

the huge modelling effort (Mertins et al., 1994). A

model with a reduced level of detail may not be able

to provide a suitable accuracy (Feig et al., 2004).

Also the development of a single and overall

production system reference model is not suitable to

represent the conditions and to consider all the needs

of different manufacturers, due to its generic

structure (Vernadat, 2002). However owing to the

high variety and complexity of production systems a

range of smaller generic reference models of

production systems are not efficient. These smaller

reference models have to be adapted for every

individual application, which generates huge effort

(Mirbel and Jolita, 2005).

Modelling of productions systems, as presented,

is a complex task which requires a huge amount of

experience, effort and implicit as well as explicit

knowledge. Based on this fact, the development of a

model is usually performed interdisciplinary

between a factory and process planner and a

modelling expert. The planner has the necessary

knowledge of the production system and its

corresponding technical processes and the modelling

expert possesses the required knowledge of the

modelling methods and languages. To develop a

model, the planner has to describe the functionalities

of production system objects and the

interdependencies in an understandable language for

the modelling expert. Different ways of thinking and

different terminologies often make this a complex,

difficult and time consuming task (Scheer, 1994).

This paper proposes a method that enables an

efficient development of a structured and

application-oriented model of the current state of a

production system. With such a method the selection

of a suitable modelling method is based on

theoretical and empirical foundations, which allows

an efficient application-oriented modelling of the

considered part of the production system (Shen et

al., 2004). Additionally, this method is able to

decrease the effort and complexity of developing a

specific production system model by structuring the

modelling process in an application-oriented way.

Through embedding knowledge of the modelling

process the planner is able to develop an application-

oriented model of a complex structure like a

production system. Furthermore the modelling of

interdependencies between performance units or

objects and their appropriate characterisation is

supported, so that they require less experience and

effort.

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3 PRODUCTION SYSTEM

MODELLING – TODAY

Today’s production system modelling relies on

different concepts, approaches and methods. These

provide the foundations to represent the performance

units, objects and interdependencies of a production

system in a suitable and multi-perspective way. The

first section addresses the state-of-the-art concepts in

system theory and their applicability on the

production system. The second section addresses

and examines modern modelling methods in order to

evaluate their employment for MePro. The third

section explains the foundations for developing a

modelling method as a basis for MePro.

3.1 System Theory as a Basis for

Production System Modelling

Since the beginning of the sixties the system theory

is an interdisciplinary science and is employed and

further developed in different research fields like

biology, physics or economics (Bertalanffy, 1964).

The overall goal is the representation of general

systems on an abstract level through the use of

consistent terms and tools, to be able to predict the

future behaviour and performance of a system

(Westkämper and Zahn, 2009). Thereby a system or

a production system is described as an open,

dynamic, productive and socio-technical

organisation (Hermann, 2010). A model developed

from the system theory perspective comprises an

arrangement of elements, which are defined through

specific attributes, connections and different

activities within a determined system boundary.

Today production systems are modelled through

the employment of generic system theory, due to the

fact that they are defined as complex socio-technical

and multi-scale systems (Westkämper and Zahn,

2009). Thereby a production system can be

approached using different concepts. (Ropohl, 2009)

(Figure 2).

• Functional concepts are also known as black box

systems. In these concepts a system is described

through the attributes input, output and condition of

an object (Schenk et al., 2010).

• Structural concepts represent objects and their

interdependencies within a system. Thereby the

behaviour and characteristic of the system is more

than the sum of its parts (Ropohl, 2009).

• Hierarchical concepts represent a system with their

related sub- and supersystems. Several subsystems

represent a system. A supersystem in turn can

consist of several individual systems (Westkämper

and Zahn, 2009).

There are several approaches to represent a

production system through the employment of one,

two or all mentioned concepts (Schenk et al., 2004).

To visualise process chains the functional

concept is employed. Thereby the sequence of

processes is shown and potential for improvement,

e.g. parallelisation of processes, can be revealed.

This concept is used in the product development

process (PEP). The structural concept is employed to

model the arrangement of the production system

layout. Thereby machines, resources and

information systems and their interdependencies are

taken into account. Hernandez developed a generic

system theory model, considering all three concepts,

of an organisation to assess the flexibility and

adaptability of a factory (Hernández, 2003). Schenk,

Wirth and Müller developed a model, based on

system theory, to represent and structure existing

flows (e.g. material, information, energy, cash flow)

in a factory (Schenk et al., 2010). One of the most

advanced approaches to represent a production

system through the use of system theory is the

“Structure Model of Factory Scales” (Jovane et al.,

2009).

Figure 2: System theory concepts of production system

structure (adapted from Hermann, 2010).

Thereby the mentioned concepts are employed

and unified to one generic structure model of a

production system. These approaches provide the

foundation for the representation of a complex

system like a production system, regarding its

performance units, objects and interdependencies,

which will be analysed to be used and/or adapted for

MePro.

Production System

Workplaces

Factory

Functional view

Hierarchical view

object

interdepen-

dencies

supersystem

system

subsystem

MULTI-SCALE PRODUCTION SYSTEM MODELLING - Motivation - Concepts - Method

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3.2 Modelling Methods

To model complex systems, like production systems,

with their objects and interdependencies state-of-the-

art modelling methods have to be employed. They

consist of modelling notations, -languages and

instructions to describe a system and to support the

structuring, representation and visualisation of a

specific part of a system. But even for a specific part

of a system, a great number of models and different

points of view have to be considered for an

application-oriented representation.

However, each modelling method has its specific

advantages, disadvantages and limitations regarding

the representation of a system (Shen et al., 2004). To

cluster the modelling methods, they are classified

according to their modelling paradigms. The main

modelling methods are described in the following

section.

• Object-oriented modelling methods (OOMM)

enable an integrated modelling of information and

functions of objects. These objects are defined

through attributes and linked with relations. The

object oriented modelling is employed in the

research field of production modelling in several

research works (Schady, 2007). The modelling of

non-physical objects however is insufficient

(Schady, 2007).

• Process-oriented modelling methods (POMM)

enable the representation of defined sequences of

transformation processes regarding an object like

material or information. Within the production

system modelling production- and logistics

processes, like material flows or value streams are

modelled. The description of objects behind the

processes with POMM is insufficient (Scheer,

1994).

• Ontology-based modelling methods (OBMM)

enable a detailed description of interdependencies

between objects of a production system model.

These modelling methods create a higher effort

while modelling, but offer great possibilities

regarding maintenance of knowledge bases and

expressiveness of knowledge representation (Hitzler

et al., 2008).

• Graphic-oriented modelling methods (GOMM)

enable the representation of objects in a 2D or 3D

model. These models are used to visualize e.g. the

layout of a production system and the material flow

in a production system (Schady, 2007).

These modelling methods can provide a valuable

contribution to the approach introduced here. In this

context it has to be clarified which modelling

method or combination of modelling methods

enables an application-oriented and efficient

modelling of a production system.

4 DEVELOPMENT OF

MODELLING METHODS

To support an efficient, structured and application-

oriented production system modelling, a suitable

method has to be developed. In this case a method is

an approach to perform a model development, based

on a particular way of thinking and consists of

constructs and rules and also knowledge that allow a

structured and systematic modelling of a specific

part or perception of a production system

(Brinkkemper, 1999). On the one hand a modelling

method limits the flexibility of modelling through

the use of constructs and rules, on the other hand it

allows making models more comparable and

reusable. Within the development of a method for

production system modelling, different aspects have

to be considered (Lankhorst, 2009; Wenzel et al.,

2005):

• Modelling (syntactic aspect), identifies the

concepts and modelling methods for modelling a

particular system. It deals with data integration and

data maintenance as well as exchange formats.

• Working (semantic aspect), defines tasks and

subtasks and provides guidelines and workflows for

the modelling process. It includes the meaning of

used signs, elements and symbols and their

structuring concepts. This aspect ensures the correct

meaning of the modelled information.

• Communicating (pragmatic aspect), defines the

representation (e.g. spatial, format, application) of a

model and puts the meaning of the used elements in

the correct context.

• Using (pragmatic aspect), defines for what

situation, perception or application a model is

suitable.

As a basis for such a method and to consider these

aspects a metamodel has to be employed (Mirbel,

2005). A metamodel can be seen as a conceptual

model of a development method. Considering this

metamodelling takes place at one level of abstraction

higher than standard modelling (Brinkkemper,

1999). A metamodel comprises suitable concepts for

modelling and related modelling methods for a

particular system e.g. a particular part or perception

of a production system. It defines the used exchange

formats, communication protocols and architectural

concepts. Through this, the mentioned aspect of

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modelling (syntactic aspect) is represented in the

method to develop. Additionally, a metamodel

considers the process of modelling and provides

paradigms and guidelines in form of method

knowledge and rules to generate a model, with

respect to the objects and interdependencies in a

system (Lankhorst, 2009). It comprises conventions,

tasks and concepts to include semantic aspects.

Owing to this, the aspect of working (semantic

aspect) is considered in MePro. Every modelling

method provides a range of elements and fulfils

different purposes regarding modelling a system

(Vernadat, 2002). A metamodel can suggest suitable

modelling methods for a specific modelling

application, through the employment of comparison

and systematisation methods that characterise

modelling methods according different attributes

(Söderström et al., 2002). So, the mentioned aspects

of communication and using (pragmatic aspect) are

considered.

The analysis of existing metamodels and

workflow schemes for modelling a production

system provides a basis for the development of the

Method for Multi-Scale Production System

Modelling (MePro).

5 PRODUCTION SYSTEM

MODELLING – MEPRO

The pursued approach aims at the employment of a

method to enable a structured and multi-scale

modelling of production systems in an application-

oriented way. Here, structured means to support the

modelling process by workflows or guidelines for an

efficient development of a production system model.

A workflow, in this context, is a semi-automated

realization of a modelling process on the basis of

schemes and management systems. It defines which

element, information or document is used in which

step of the production system modelling. It also

supports the description of interdependencies

between objects and/or performance units (Wenzel

et al., 2005). Through this, necessary inter-

dependencies between performance units and its

objects to model a specific aspect of a production

system can be represented.

To structure the MePro modelling process

suitable system theory models of production systems

will be analysed, employed and refined. This enables

an efficient application-oriented development of a

model. Parts of models which can be reused or

attributes of objects and interdependencies which

can be predefined have to be identified and provided

by MePro, to enable an efficient modelling process.

Therefore a supporting resource library for objects

and interdependencies will be employed.

Additionally suitable detailed reference models for

application-oriented production system modelling

have to be identified and analysed regarding their

applicability and integration in MePro.

Multi-scale considers the possibility of different

views on a production system for different end users.

This means, that a specific model of a production

system is represented in the way of e.g. business

processes, information flow, material flow (Mertins

et al., 1994). Multi-scale also means that the model

of a production system is represented with the

appropriate level of detail along the different scales

and regarding specific aspects of a production

system. Therefore multiple modelling methods along

the production system structure, which reaches from

processes, machines and workplaces to production

systems, have to be employed. The selection of a

suitable modelling method for a specific application

and representation is supported by a selection

method. This selection method uses criteria and

suggests a suitable modelling method for a

modelling application regarding the developer, the

end user, the level of detail and the application field.

Fields of modelling of a production system, which

have to be considered, are e.g. information and

material flow modelling (Mertins et al, 1994). To

enable a structured and multi-scale modelling

process a metamodel, which conduces as a

foundation for MePro, is employed. This metamodel

supports the development of a modelling process by

providing knowledge and rules concerning the

aspects mentioned in Section 3.3.

Figure 3: Method for Multi-Scale Production System

Modelling (MePro).

MePro - Method for Multi-Scale

Production System Modelling

Supporting Resource Library

Application-oriented Models

of a Production System

Machines

…

Ressources

Process

Predefined and/or configurable

Metamodel

Existing Production System

Machines

Workplaces

Perfomance Unit

Media and Information

Layout

Model

Material flow

Model

…

Model

Process

Model

MULTI-SCALE PRODUCTION SYSTEM MODELLING - Motivation - Concepts - Method

215

With respect to the mentioned facts an efficient

and application-oriented modelling of the current

state of the production system with a method called

Method for Multi-Scale Production System

Modelling (MePro) is supported (Figure 3).

6 ROADMAP TO MEPRO

The development of a method for structured and

multi-scale modelling of an existing production

system is a new and complex research topic where

future steps are of huge interest. Thus, the next

research steps for building the foundations of MePro

are the analysis and evaluation of:

• System theory for production system modelling;

• Application fields for production system modelling

and of existing modelling methods;

• Existing production system resource libraries;

• Metamodels for modelling processes;

The research results will be merged in a MePro.

Different challenges have to be considered on this

way:

• Development of criteria for application-oriented

modelling method selection;

• Development of workflows for an application-

oriented modelling of a production system;

• Development of a supporting resource library with

predefined and/or configurable objects and

interdependencies.

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