=Paper=
{{Paper
|id=Vol-2060/mekes6
|storemode=property
|title=New Opportunities using Variability Management in the Manufacturing Domain during Runtime
|pdfUrl=https://ceur-ws.org/Vol-2060/mekes6.pdf
|volume=Vol-2060
|authors=Birte Caesar,Wolfram Klein,Constantin Hildebrandt,Sebastian Törsleff,Alexander Fay,Jan Christoph Wehrstedt
|dblpUrl=https://dblp.org/rec/conf/modellierung/CaesarKHTFW18
}}
==New Opportunities using Variability Management in the Manufacturing Domain during Runtime==
https://ceur-ws.org/Vol-2060/mekes6.pdf
Ina Schaefer, Loek Cleophas, Michael(ed.): 2018,
< book title>,
Modellierung
Lecture Notes in Informatics (LNI), Gesellschaft für Informatik, Bonn 91
in der Entwicklung von kollaborativen eingebetteten Systemen (MEKES) 15
New Opportunities using Variability Management in the
Manufacturing Domain during Runtime
Birte Caesar 1, Wolfram Klein 2, Constantin Hildebrandt1, Sebastian Törsleff1,
Alexander Fay1, Jan Christoph Wehrstedt2
Abstract: Fast changing markets require new manufacturing strategies to meet customers’ desires.
Therefore, concepts of flexible and adaptable manufacturing systems are considered. This paper
provides an overview of different approaches dealing with flexibility. A new concept based of the
software product line engineering presents opportunities for easier reconfiguration at runtime. This
concept extends the software product line engineering approach due to flexibilities, which have to
be considered for runtime analysis. Furthermore, this concept is applied to industrial problems as
well as to a demonstrator. Based on the application to industrial fields, requirements and
challenges are identified, which have to be considered in future work. The aim of this paper is to
briefly sketch the new concept and identify the requirements for the development of the required
methods.
Keywords: Variability Management, Adaptable and Flexible Manufacturing System, Modular
Manufacturing System, Product Line Engineering, Reconfiguration, Variability Model
1 Introduction
The manufacturing domain has to face different changes. According to [Wi05, Ny08]
these changes can be (i) changes to existing products, (ii) introduction of new products,
due to new customers, changed requirements, or a changing market structure, (iii)
changes in order parameters, e.g. lot size or lead times, (iv) changed delivery
requirements with respect to longer delivery distances, (v) changing national or foreign
rules and standards, as well as (vi) increasing resource scarcity. In order to cope with
such changes in the production systems’ environment, [SS90] introduce the concept of
flexible manufacturing systems. Flexibility is therein described as “the ability to respond
effectively to changing circumstances” [SS90, P.292]. Furthermore, [SS90] distinguish
different types of flexibility. Considering the manufacturing domain with a focus on the
manufacturing system itself, three flexibility types are of particular relevance. First,
routing flexibility, which permits manufacturing processes to be performed in alternate
orders. Second, process flexibility, which is the range of different parts that can be
1
Helmut-Schmidt University, Institute of Automation, Holstenhofweg 85, 22043 Hamburg, birte.caesar@hsu-
hh.de, constantin.hildebrandt@hsu-hh.de, sebastian.toersleff@hsu-hh.de, alexander.fay@hsu-hh.de
2
Siemens AG, Corporate Technology, Otto-Hahn-Ring 6, 81739 München, wolfram.klein@siemens.com,
janchristoph.wehrstedt@siemens.com
92 Birte Caesar, Wolfram Klein, Constantin Hildebrandt, Sebastian Törsleff, Alexander
Fay, Jan Christoph
16 Birte Wehrstedt
Caesar, Wolfram Klein
produced without major setups referring to parameterization. Third, machine flexibility,
which is the flexibility that is provided by a machine to adapt it to changing
requirements without major changes to the software or hardware.
[SFJ15] defines variability based on [PBL05] as the flexibility that is integrated into a
system and that is used to adapt a system during development as well as during runtime.
This paper shows that during runtime, different variability categories, based on the
flexibilities presented in [SS90], must be considered. From now on, the term variability
will be used instead of flexibility. The paper is structured as follows. Section 2 provides
an introduction into variability management as well as an overview of related work.
Section 3 shows that variability should be classified into different categories.
Furthermore, the opportunities of using variability management during runtime will be
discussed. Section 4 outlines which industrial needs can be addressed by applying the
proposed concept. Finally, Section 5 presents the challenges that will be addressed in our
future research endeavours.
2 Related Work
Variability management is a method, which belongs to the software product line (SPL)
engineering concept, which is concerned with developing applications in a way that
allows for systematic reuse of individual components. The SPL engineering concept is
an opportunity to deal with mass customisation, while reducing the complexity of
variant-rich product portfolios [PBL05]. Variability management is not limited to the
software engineering domain, as has been shown with concepts focusing on variability
management in the development of production plants, e.g. product series, modular
approaches and platform design [SFJ15]. Most of these concepts are component-oriented
and aim to increase the number of equal parts to reduce development and operating costs
based on economies of scale. SPL, on the other hand, are customer-oriented in that they
focus on functional and product-related aspects, i.e. meeting market changes are the
main goals [CN09]. A core component of SPL engineering is the explicit modelling of
variability by using dedicated models, which represent the common and the variable
features of the product [Be13]. From these variability models (VMs) individual variants
can be derived. Additionally, VMs can be enriched with constraints and dependencies. A
differentiation between problem space VM and solution space VM is recommended
[PBL05]. A problem space VM is domain-specific and contains stakeholder needs as
well as desired features. A solution space VM, on the other hand, contains diverse
reusable artefacts, which may be arbitrary engineering artefacts, e.g. requirements,
specific components, or tests created during the engineering workflow. A mapping
between problem space and solution space VMs is necessary since they provide different
perspectives on the SPL and contain distinct knowledge for deriving variants [Sc12].
Each domain has different requirements, engineering processes and tools. Due to this
reason, various approaches exist that apply the SPL concept to specific domains and
their specific requirements. [SFJ15] and [Ma13] both adapted the SPL concept, including
New Opportunities using Variability Management in the Manufacturing Domain during
New Opportunities using Variability Management during Runtime
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17
the variability management, to the automation domain, though none of them focused on
the specifics of the discrete manufacturing domain. [FLV15] and [Ko17] extended the
SPL concept specifically to the manufacturing domain. Their concepts consider the
typical plant engineering workflow by separating the solution space VM into three
different models, the mechanical, the electrical and the software VM. This reflects the
standard separation of trades in engineering of manufacturing systems and offers several
advantages. First, a reduction of complexity is achieved by presenting only relevant and
trade-specific information to engineers. Second, the different variability of the individual
trades can be modelled separately, without affecting the model in its entirety. A mapping
between the problem space and solution space VM is necessary to take into account the
customer’s requirements. The mapping matrix presented in [FLV15] describes a direct
connection between a customer’s choice and a technical realisation for each trade-
specific VM. The mapping matrix is only a rough description of the real dependencies
between the engineering artefacts. Both, [FLV15] and [Ko17] include the three views,
however, a method for modelling all three views on a manufacturing system is not
provided. An approach for addressing this shortcoming is described in [Hi17]. Here a
system meta model is proposed including dependencies between functional, structural
and behavioural models. The approach has not yet been applied to variability
management and needs to be extended for that. Fortunately, the approach described in
[Hi17] is not domain-specific, which is advantageous as the basis for a common use.
The previously discussed approaches are mainly focused on machine flexibility.
Extending the variability management concept for the use at runtime, allows the
utilisation of the variability information captured during the engineering process to
support context-aware reconfiguration at runtime. A reconfiguration at runtime can be a
functional, structural, or behavioural change, either supported by a worker or
automatically. [FLV15] describes the evolution of manufacturing systems as one
possibility to react to changing requirements. Delta Modelling is a concept to identify
needed runtime changes of an existing system to meet changing requirements. The
concept also allows to manage variability and evolution similarly. Its goal is to identify
the gap between the actual system and the required system. Based on this analysis a
solution for closing the gap should be derived [Ko16]. This approach is a solution for
adapting to the changes mentioned above. However, to find a systematic method to react
to the changes, both the process and routing flexibility have to be considered, too.
In the following section, a novel approach to integrate process flexibility and routing
flexibility is described. Furthermore, the need for consistent and integrated VMs in all
variability categories will be shown. The development of a domain-independent method
for consistent variability modelling is one of the goals of the project CrESt
(Collaborative Embedded Systems 3), which started in February 2017.
3 Identification of variability categories in the manufacturing
3
https://crest.in.tum.de
94 Birte Caesar, Wolfram Klein, Constantin Hildebrandt, Sebastian Törsleff, Alexander
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domain
In this section only the solution space variability is considered. The mapping to the
problem space variability will be the subject of future contributions. The three identified
variability categories (macro process, micro process and trade & component variability)
are subsequently described and explained using the MPS500 demonstrator.
First, the demonstrator is introduced. The MPS500 is a modular manufacturing system,
which consists of six individual modules and a conveyor belt (see figure 1). Each
module consists of components, which together provide one or more manufacturing
processes. The considered product is a pneumatic cylinder (see figure 2), which
comprises of five components: a body (1), which is produced with the MPS500, a piston
(2), a spring (3), a sealing ring (4), and a cap (5). The last four components are vendor
parts, which is why only the body will be considered in this example.
Figure 1: Demonstrator MPS500
Macro process variability model (MaPVM): Each product consists of features, some
of which can be manufactured by single manufacturing process step, while others need
to be manufactured by multiple manufacturing process steps. For some features, the
order of the manufacturing process steps can vary as well. Taken together, these aspects
present the first category of variability based on the routing flexibility by [SS90]. The
MaPVM includes information about the available manufacturing process steps of a
manufacturing system consisting of different modules. The MaPVM is an overall VM
relevant within the field of manufacturing systems. This provokes the need for
developing a meta model in order to integrate VMs from different manufacturing
modules from different vendors into one overall VM.
New Opportunities using Variability Management in the Manufacturing Domain during
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Figure 2: Pneumatic cylinder
Considering the MPS500, the body contains two holes: one for the piston and one for the
vent. These are two features that have to be manufactured. Figure 1 shows that the
MPS500 has a cutting drilling module as well as a laser drilling module, i.e. the MPS500
provides two different process steps to manufacture holes. The MaPVM should cover all
process steps provided by MPS500, such that it can be automatically analysed if the
needed process steps to manufacture the product feature ‘hole’ are provided. In order to
determine the manufacturability, the required manufacturing process have to be analysed
in detail, which can be accomplished based on the second variability category. Figure 3
shows an example how the VM of the Demonstrator MPS500 could be modelled. In the
VM, only the modules of the MPS500 are represented. The mapping between the
product features and the needed manufacturing process as well as the structure and the
detailed information, which have to be modelled in the VM, will be the subject of future
work.
Figure 3: MPS500 MaPVM
Micro process variability model (MiPVM): A manufacturing module can be described
by its possible manufacturing process, which has certain process parameters that must be
set for manufacturing a particular product feature. The range of each parameter is fixed
and specified as per the components of the manufacturing module. A detailed description
of how the parameter range can be identified is given in [Ho17]. The micro process
variability category takes the set of valid parameter settings into account and relates to
the process flexibility defined in [SS90].
Figure 4: Drilling MiPVM
According to the MPS500 demonstrator, each module has process parameters that can be
set. The drilling module can create holes with a diameter of five to ten millimetres and a
depth of ten to 40 millimetres. On the other hand, the laser drilling module can
96 Birte Caesar, Wolfram Klein, Constantin Hildebrandt, Sebastian Törsleff, Alexander
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manufacture holes with a diameter of 40 to 2000 micrometres. Likewise manufacturing
tolerances can be distinguished and be represented in the MiPVM. An analysis based on
the MiPVM could provide a clarification which module can manufacture which product
feature. The product feature ‘piston hole’ has a diameter of ten millimetres, which
excludes the laser drilling module to manufacture this product feature. Contrariwise, the
product feature ‘vent hole’ has a diameter of two millimetres, which can only be
manufactured by the laser drilling module. Figure 4 shows an example of the MiPVM of
the cutting drilling module. The final structure of the VM and the relation to the product
features as well as the connection between the MaPVM and the MiPVM will be the
subject of future work.
Trade & component variability model (TCVM): The TCVM can be used to derive a
variant of a manufacturing system during its development. Its target application is within
the engineering of manufacturing systems. Due to its use during the engineering process,
it is necessary to take all relevant trades into account. Therefore, three viewpoints are
used: the behaviour, structure and function viewpoint according to [Ko16] and [Hi17].
The TCVM contains all information about a product line and the variants that can be
derived from it. This information, provided in a TCVM, already contains information
which components must be changed to change the properties of the manufacturing
process provided by a module. In terms of reconfiguration at runtime, this information is
fundamental, because a reconfiguration’s goal is to change the manufacturing process so
that the changed requirements can be met. Therefore, the TCVM should be considered at
runtime. To use the VM of a particular variant (which has been created during
engineering) later during runtime, it has to be enriched with additional information. Inter
alia, the information about the actual variant has to be saved within the VM.
Furthermore, each feature that limits a parameter range has to be marked. The TCVM is
an extension of the conventional VM used during the engineering process.
Figure 5: Drilling TCVM
Regarding the MPS500 demonstrator, the TCVM of the drilling module contains
information about the incorporated components and, due to the extension for the runtime
use, also the information which component influences which parameter range. For
example, the drill bit is 60 millimetres long, but the drilling depth that can be provided is
New Opportunities using Variability Management in the Manufacturing Domain during
New Opportunities using Variability Management during Runtime
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only ten to 40 millimetres. In this case the drilling depth is limited by the distance
between drill bit and table. Accordingly, the TCVM should contain the information that
the linear axis is the component that limits the parameter range of the process parameter
drilling depth. Based on this information, a reconfiguration plan is easier to develop.
Figure 5 shows an example of the TCVM of the drilling module. Future work will deal
with modelling the three views and the connection between the MiPVM and the TCVM.
4 Application of the variability categories to industrial fields
Figures
To identify requirements concerning industrial needs, the introduced variability
categories need to be merged into an overall concept. This concept supports the order
acceptance process due to possible analysis based on MaPVM and MiPVM.
Furthermore, analyses based on TCVM support creating reconfiguration plans. It enables
managing the variability of a manufacturing system consisting of different
manufacturing modules, which results in the name of manufacturing system variability
management (MSVM).
This section will provide insights of industrial needs applied to the MSVM proposed in
the previous section. The main goal when developing a VM should be its applicability to
real world problems. Therefore, the applicability of the MSVM as mentioned before to
manufacturing systems will be analysed. The changes explained in Section 1 show an
urgent need for building new, innovative plants. Also they show the need for
investments to redesign and retrofit the machinery and production lines of existing
manufacturing systems. A very poor preparedness of factories especially regarding
structural changes to its design according to business needs is observed in daily business.
Therefore, the focus of this analysis of the applicability of the VMs is not to the design
phase but its model extension to the operation phase and runtime of the manufacturing
system as proposed here. The MSVM can be considered as a step towards a flexible and
adaptable factory. However, several implications need to be taken into account, which
are mapped to the three proposed variability categories. To gain even more insight, the
VM will also be discussed with respect to a concrete use case at Siemens. A daily task
for plant managers is the planning of the product mix. It is an optimization problem with
respect to multiple variables, i.e. resources like machines, worker, material, energy costs
and time as part of the context of the system. Here, an automated analysis based on the
MSVM is desirable to check whether the required products can be manufactured using
the given resources. This analysis could be validated by means of simulations for
different scenarios.
The MaPVM combines variability information from different manufacturing systems.
Enterprise cooperation and strategies for a cross-company cooperation improve the
production diversity and help to interconnect different manufacturing systems. Variable
strategies for a cross-company product mix using external resources e.g. production
sites, workers etc. for larger quantities, reduced costs, or specialized expertise will result
98 Birte Caesar, Wolfram Klein, Constantin Hildebrandt, Sebastian Törsleff, Alexander
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in a higher flexibility. Additionally, cooperating companies are needed as extended
workbenches, which leads to the need of a common and standardized purchasing of
services, especially in the construction but also from an operation and maintenance point
of view. Based on a MaPVM, the combination, the flexible exchange, and possibly the
restructuring of different manufacturing modules with respect to the actual situation can
be analysed more easily. Even the decision of buying a new manufacturing module itself
can be supported on a high level based on the information about the available
manufacturing processes summarised in the MaPVM. From an industry point of view,
the following requirements regarding a MaPVM can be stated. First, the MaPVM should
be applicable for cross-company application (R1). Second, the MaPVM should comprise
all information required for simulating different strategies within this category (R2). An
MaPVM can support multiple business processes: production planning, work planning,
and order planning. The expected benefits for these business processes are quick
feasibility checks. This can accelerate production and work planning, as well as order
planning. Table 1 shows an overview of the variability categories, their goals, and their
related business processes as well as the affected IT-systems based on the automation
pyramid.
Variability
Goal Business process IT-system
category
- Production planning
Macro process
Feasibility check - Work planning ERP
variability
- Order planning
- Product planning
Micro process
Parameterization - Planning for new machines MES
variability
- Order control
Trade & - Resource planning
Reconfiguration
component
planning - Work planning SCADA
variability - Production schedules
Table 1: Classification of the variability categories
In the next step, variations in process parameters for set-up times of systems and the
reusability of installations have to be analysed to come to an optimised production. The
concrete outcome of an analysis of the MiPVM as well as the acceptance criteria and
operation instructions of a valid system configuration (“buy a new machine” vs. “accept
certain production delay”) are clearly dependent on the individual optimisation problem.
Potential changes of machine functionalities based on parameter settings should be
possible in a quick and well structured way. From an industry point of view, two
requirements regarding an MiPVM can be stated. First, the extension of the parameter
range has to be accomplishable within a reasonable time window (R3). Second, a
MiPVM has to consider set up times so that they can be minimised (R4). A MiPVM can
support three business processes: product planning, planning for new machines, and
order control. Expected benefits with respect to these business processes are detailed
information about the manufacturability of a certain product based on the actual
parameter range, respectively the available parameter range. Furthermore, better order
control is expected based on faster and easier match between product and manufacturing
module.
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The reconfiguration of a plant based on functional, structural, or behavioural changes
was proposed as the third variability category. For this purpose, a production design of a
modular manufacturing system consisting of differentiated (type-bound) manufacturing
modules and even decoupled modules is required. Furthermore, the production must
consist of flexible cells, modular systems, and test engineering work places. From an
industry point of view, a TCVM has to allow for modular manufacturing systems (R5).
Furthermore, a TCVM should support extendable systems (R6). The following business
processes are covered by this variability category: resource planning, work planning, and
production scheduling. Expected benefits for these business processes are improved
production planning, an adapted production schedule due to actual situation, better order
control, as well as shorter lead times.
5 Conclusion
Due to the changes described in Section 1, manufacturing systems are required which are
able to adapt to these changes. An approach from the software engineering domain is
considered, because the variability modelling is explicitly demanded. The SPL
engineering focuses on the development of systems and does not consider all aspects
relevant for an approach of flexible manufacturing systems during runtime. The
presented variability categories summarised in MSVM integrate the additional aspects
during runtime. The relevancy of the approach has been shown for both, concrete
industrial requirements and a demonstrator.
In this paper, we outlined that different kinds of variability are relevant in manufacturing
systems, especially during runtime. The various industrial requirements taken into
account demand new variability modelling methods. Furthermore, different open
challenges have to be analysed and handled. First, each variability category needs a
coherent modelling concept, based on commonly used semantics and industrial
standards. These concepts have to take into account the dependencies between each
variability category. Additionally, economical aspects have to be integrated. The
problem space variability has to be considered as well as the requirements analysis. This
results in the second challenge: How can dependencies be modelled such that changes in
each category are reflected in the other categories and can be traced back? This
challenge is already being worked on in the software engineering domain and some
aspects will be considered in the ongoing CrESt project. The third challenge is to
integrate the modelling method into the engineering workflow, such that the developed
models can be used and maintained at runtime and also be re-used in other engineering
projects. The integration into the engineering workflow has been investigated in various
research projects. However, the defined methods do not consider using these models at
runtime.
Acknowledgements. This work is a result of the project CrESt funded by the German
Federal Ministry of Education and Research under the grant no. 01IS16043U and
01IS16043Q. The whole responsibility for the content rests with the authors.
100 Birte Caesar, Wolfram Klein, Constantin Hildebrandt, Sebastian Törsleff, Alexander
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