Using autonomous modular material handling equipment for manufacturing flexibility
ABSTRACT This paper describes a modular autonomous material handling equipment solution for flexible automation. Discrete event simulation is in this case used as a tool for shortening time spent in many different phases of a manufacturing systems lifecycle. The paper presents the concept of autonomous modular material handling equipment, and how simulation is used as a support tool and lead time reducer in each lifecycle phase. Furthermore, we describe the knowledge levels needed for using the simulation support and conclude with examples of how this methodology are reducing lead times within a company.
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Conference Paper: REALISING THE OPEN VIRTUAL COMMISSIONING OF MODULAR AUTOMATION SYSTEMS[Show abstract] [Hide abstract]
ABSTRACT: To address the challenges in the automotive industry posed by the need to rapidly manufacture more product variants, and the resultant need for more adaptable production systems, radical changes are now required in the way in which such systems are developed and implemented. In this context, two enabling approaches for achieving more agile manufacturing, namely modular automation systems and virtual commissioning, are briefly reviewed in this contribution. Ongoing research conducted at Loughborough University which aims to provide a modular approach to automation systems design coupled with a virtual engineering toolset for the (re)configuration of such manufacturing automation systems is reported. The problems faced in the virtual commissioning of modular automation systems are outlined. AutomationML -an emerging neutral data format which has potential to address integration problems is discussed. The paper proposes and illustrates a collaborative framework in which AutomationML is adopted for the data exchange and data representation of related models to enable efficient open virtual prototype construction and virtual commissioning of modular automation systems. A case study is provided to show how to create the data model based on AutomationML for describing a modular automation system.7th International Conference on Digital Enterprise Technology, 28th-30th September 2011,, Athens; 09/2011
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ABSTRACT: A modular discrete event simulation methodology is presented. The methodology aims at separating the simulation expert from the actual model building through the use of parametric modules. Recent developments on automated manufacturing systems and discrete event simulation coincide to the methodology, especially for OEM suppliers of automation equipment. Copyright © 2006 IFAC01/2006;
Conference Paper: Mobile Manufacturing System Characteristics[Show abstract] [Hide abstract]
ABSTRACT: Abstract There is an increasing demand,of geographically flexible production capacity to cope with global competition. The Mobile Manufacturing System is an approach to meet these demands,where the core competence,is kept within the company. This article is based on the results of two case studies. It investigates why there is a need for Mobile Manufacturing Systems and emphasizes the challenging factors when,creating a Mobile Manufacturing System to be able to operate in a geographically changing environment. The mobility set increased demands on technology, strategy, organization, information, and knowledge reuse in order to maintain high performance.OM in the New World Uncertainties, in Proceedings of the 17th Annual Conference of POMS; 04/2006
Proceedings of the 2004 Winter Simulation Conference
R .G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters, eds.
USING AUTONOMOUS MODULAR MATERIAL HANDLING EQUIPMENT
FOR MANUFACTURING FLEXIBILITY
Department of Product and Production Development
Chalmers University of Technology
Gothenburg, 412 96, SWEDEN
This paper describes a modular autonomous material han-
dling equipment solution for flexible automation. Discrete
Event Simulation is in this case used as a tool for shorten-
ing time spent in many different phases of a manufacturing
systems lifecycle. The paper presents the concept of
autonomous modular material handling equipment, and
how simulation is used as a support tool and lead time re-
ducer in each lifecycle phase. Furthermore, we describe the
knowledge levels needed for using the simulation support
and conclude with examples of how this methodology are
reducing lead times within a company.
The demands on manufacturing industry are ever changing,
and become more and more challenging (Nahmias 2001).
Fulfilling the customer requirements is not good enough.
Products have to be produced before customer needs in or-
der to be first on the market. Needs can be created through
commercials and other enticements. Meanwhile the lifecy-
cles of products and production systems become shorter
and shorter and the low-cost countries, with labor flexibil-
ity as the main tool for production planning and control,
are gaining more and more industrial competitiveness. This
is a threat in itself to European SME’s, since today’s in-
flexible automated manufacturing equipment has a hard
time competing with the labor flexibility used in low cost
countries. The above statements put very high pressure on
the future production facilities in the western world. The
need for truly flexible manufacturing systems which can
support many products and product variants, is vital to the
maintenance of competitiveness. There are many automa-
tion solutions available on the market today; however, their
potential is not fully utilized. A chain is no stronger than its
weakest link and the chain in automated production facili-
ties is long. The chain consists of not only the equipment;
but also the usage of the equipment in terms of working
procedures, organizational structure, knowledge and learn-
ing environments for continuous improvements and the
striving to keep the competencies within the company. Ad-
ditionally, automation equipment sets demands on process
efficiency, including communication, PDM, ERP, Produc-
tion IT in combination with the empowerment of operators
which is one of the key enablers for successful manufactur-
ing in the factory of the future.
In order to empower operators in a reconfigurable
modular manufacturing system, the necessity of well de-
veloped Human Machine Interfaces (HMI) is of the great-
est importance (Wickens et al. 2004). One successful pro-
totype plant has been made in the concept of PLM (Product
and Process Lifecycle Management), which is proof of the
concept, and a taste of future automation solutions for
Large companies who sell automation software solu-
tions, like ERP and PDM, often take the upper hand over
SME who invest in their solutions. There is a need for a
complete reconfigurable modular manufacturing system
which can be easily adapted to product changes and de-
mands. This system will also need to have a built-in soft-
ware interface, including extensive HMI’s for operators,
which give the operators more control and responsibility.
This increased work content will keep the production fa-
cilities competitive and profitable in the western world,
caused by the demand of competence levels on the em-
Edward J. Williams
Production Modeling Corporation
Three Parklane Boulevard, Suite 1006 West
Dearborn, MI 48126, U.S.A.
Department of Signals and Systems
Chalmers University of Technology
Gothenburg, 412 96, SWEDEN
Johansson, Williams, and Alenljung
ployees. The approach of an intelligent modular recon-
figurable manufacturing system will last through many
products and product variants. It will also support SME in
the competitive environment on the future market, since
the long term investment cost will be much lower with
modular autonomous material handling equipment. This
paper will examine the possibilities concerning autono-
mous modular material handling equipment for manufac-
2 AUTONOMOUS MODULAR MATERIAL
There are numerous institutes and research departments
looking into the area of intelligent manufacturing systems,
in the context of core machine and/or process intelligence
(Koren et al. 1999, Koren et al. 2001, Mehrabi and Ulsoy
1997). Most of these approaches focus on the core proc-
esses in each company, for example grinding machines,
milling machines, multi-task machines etc., plus how to
reconfigure and control them in order to support high pro-
ductivity. Many of these approaches ignore the material
handling aspects of the manufacturing system entirely.
However, the approach presented in this paper represents
another point of view. We try to control all activities by
looking at the manufacturing system with the material han-
dling equipment as the driving force. Supervision, flow
control, and dynamic buffering capabilities are some ex-
amples of effects generated by using intelligent material
handling equipment. On the contrary, we do not consider
the core process in the manufacturing system, which we
believe is very different from product to product. The core
process is also in most cases thoroughly analyzed in each
company, whereas the material handling equipment is not.
By autonomous we mean that each material handling mod-
ule has its own functionality, which is independent of all
other modules: i.e., the smallest system possible consists of
only one module.
The module itself has a specified functionality, which can
be altered through the PLC, which has preprogrammed al-
ternatives to choose from. The module boundaries are
standardized and specified for safety, signals, electricity,
and the carried loads are specified in three dimensions. The
module itself has some degrees of freedom when it is cre-
ated. Take for example one straight conveyor:
straight conveyor module. Other modules such as curves,
incline/decline, buffers, transfers, turntables, manual work-
stations etc. do have other characteristics relative to their
degrees of freedom.
Figure 1 below shows one example when using a
Figure 1: Straight Conveyor Module with Three Degrees
of Freedom Visible
2.3 Controlling the Modules
Many of the controlling activities concerning a module are
by nature local, i.e. they concern only the module itself and
cooperating equipment such as neighboring modules. An
example is the handshaking between two connected convey-
ors. Control code for such activities is therefore easy to reuse
and often beneficial to distribute. Activities such as routing
of products might have global impact on balancing and
blocking situations. A central controller is therefore still rec-
ommended, in addition to the distributed module controllers.
2.4 Module Behavior
Along with data concerning properties such as width, height,
and capabilities, the module might also exhibit data that de-
scribes its behavior. For control purposes, the behavior de-
scribed by a discrete event model is of particular interest. By
modeling the possible and desired behavior of the module in
a formal manner, this model can be used for generating the
control code for the module itself, and also function as input
to simulations and formal verifications of the manufacturing
system. Modular process models result in high degree of
flexibility in reconfiguration of the system and in introduc-
tion of new products (Adlemo et al. 1995, Fabian et al.
1997). Code generation would guarantee that the model co-
incides with the actual behavior.
2.5 Global Behavior
If each module knows the identity of its connected
neighbors, and this information can be accessed, then it
would be easy to get a global view of how the modules are
arranged. The identification is preferably done automati-
cally upon connection. If the discrete event model of each
module can be extracted, and the arrangement of the mod-
ules is known, then we can construct a global model of the
system. This model describes the possible behavior of the
Johansson, Williams, and Alenljung
system. The risk of inducing unwanted situations such as
blocking increases as the manufacturing system becomes
more complex. A global perspective is generally needed
for discovering and avoiding such situations, which often
can be considered as resource booking problems (Lennart-
son et al. 1998, Lennartson et al. 2002) By specifying what
we allow or not allow the system to do, we can automati-
cally generate a supervisor, using Supervisory Control
Theory (Ramadge and Wonham 1987), that gives as much
flexibility to the system as possible while avoiding blocked
states and forbidden configurations.
In a flexible manufacturing system there can be several al-
ternative routes for a product. The routing questions for a
material handling module can be:
From where to order? The question arises, if the
system is using the pull approach, in modules
where product flows merge.
Where to send? Arises, if the system is driven by
push, in modules that can split a product flow.
Answering those questions can be undertaken in different
Fixed routing. Each product has only one possible
route. Probably the most common situation today.
Static routing. The first currently feasible route in
a priority list is selected.
Local dynamic routing. The decision is based on
some information of the current situation that the
module has. This information can be based on
queries to connected modules of the type: When
can you deliver product A? How fast can you ship
product B to the end station?
Global routing. The central controller has most in-
formation on the current global situation of the
manufacturing system, and should therefore be
able to make optimal routing decisions.
2.7 Virtual Tracking of Goods
Keeping track of goods during manufacturing can be done
in several ways, for example by using escort memory or
bar codes. An alternative is to have virtual tracking of
goods, where the control system tracks the order of the
products without being able to identify the product by its
escort memory or bar code. The concept with autonomous
modules simplifies the implementation of virtual tracking,
where each module is responsible for monitoring the prod-
ucts that it is carrying at the moment. The necessary prod-
uct information is handed over to the next module simulta-
neously with the product itself.
2.8 Virtual Development
By drawing a clear distinction between the process model,
i.e. the possible behavior of the system, and the controller,
the control code developed for the simulation can also be
used for controlling the real system. All information
needed for programming the controller does already exist
within the simulation. There should be no need for doing
the same thing twice.
2.9 Reconfigure the System
The system can easily be reconfigured to handle other prod-
ucts and/or rearrange the routing of the products and the
geometrical translations/rotations of the products both in the
simulation model and in reality. A smaller rearrangement of
a module or two including ramp-up of production takes less
than five minutes for two persons to execute.
3 SIMULATION SUPPORT
The Discrete Event Simulation (DES) support tool used in
this paper is the software package 3DCreate, and product
derivatives 3DRealize and 3DVideo from Visual Compo-
nents. This simulation package has proven to be the most
suitable for line builders and machine providers in terms of
modularization capabilities, learning curve, and the graphi-
cal representation of the software and its content (Johans-
son et al. 2004).
This kind of DES support has large benefits when it
comes to lead time reduction in many aspects during the
lifecycles of both products and production systems. A
study regarding input data for DES shows that only 6% of
industrial companies do have all required data for a DES
model available (Johansson et al. 2003), which a modular
DES approach would increase through reusability of input
data as well as simplifications of the verification and vali-
dation steps of a simulation study (Banks et al. 2001).
As described in Johansson et al. (2004) simulation
support for autonomous modular material handling equip-
ment benefits form the following characteristics of the
Modular library of “masters,” i.e. offline represen-
tations of each standard module including its
specifications and behaviour
Pre-made logic built into each of these modules
Degrees of freedom in each module, according to
what is available in the real word, i.e. length of
conveyors, width of conveyors, buffer size etc.
Real scale 3-D graphical representation of each
Easily manageable connection points for each
module, including connection type, i.e. logics,
material, operator etc.
Johansson, Williams, and Alenljung
However, the software is not simplified in all aspects,
as will be clarified in the next section.
4 USER KNOWLEDGE LEVEL DEMANDS
To be able to handle all aspects of this simulation approach a
person needs to be as skilled with this software as with any
other software such as, for example, QUEST, Automod or
WITNESS. However, with a modular approach only one or
two people in larger companies need this competence level
and will develop the modules, while all the other employees
and sales personnel need only have the basic simulation
skills to build a model out of the predefined modules. Even
less skill is required to run a simulation model.
According to the models used for vocational knowl-
edge and learning described in Nordell et al. (2003), this
modular simulation concept will package some of the
simulation expert’s explicit and tacit knowledge into the
software module representation of the real module, which
will be used only by the simulation user without any de-
mands on understanding the internal structure of the mod-
ule. In other words, the simulation expert’s knowledge will
be embedded into the module, which simplifies and lessens
the demands on knowledge for the simulation user. Tradi-
tionally a continuous striving towards converting tacit
knowledge to explicit knowledge is desired. However, this
is not an easy task, since tacit knowledge is “Unutterable
and Unarticulated.” But the modular approach of simulat-
ing autonomous modular material handling equipment
simplifies the conversion to a large extent. Figure 2 below
shows the relations of tacit and explicit knowledge, modi-
fied from Nonaka (1994) and Gustafsson (1999).
Tacit knowledge Explicit knowledge
Figure 2: Matrix Model of the Different Aspects of
This figure can be used to explain how the knowledge is
transformed from tacit to explicit. When the simulation ex-
pert is formulating the modules from his mind into the com-
puter (from A to B), the knowledge will change form from
tacit to explicit on the acquaintance level. Then the simula-
tion software package architecture does the rest of the work
while using the modular approach by transforming the ex-
plicit knowledge from acquaintance to practical skill (from
B to C), which can then be used by the simulation users with
less experience. Traditional simulation software packages
does only make the transformation from A to B, which then
sets the simulation user in the acquaintance level of proposi-
tional knowledge. This level requires many months of ex-
perience to master when it comes to traditional simulation
software packages. However, the traditional packages can be
used with tailor-made user interfaces to enable the same kind
of modularity as in the Visualcomponents software package.
In the coming three subsections a description of skills
and their knowledge level needed for each type of user in
the modular approach is described.
4.1 Simulation Specialist
In order to build modules out of nothing, the specialist
skills are at about the same level as before, such as when
using traditional simulation software, Extend, WITNESS,
QUEST, Automod, ED, etc…
Needed skills for the simulation specialist are:
Advanced mathematical skills in terms of statis-
tics and probability.
Lead time for skill development for a non simulation
specialist is long, approximately half a year. Typical users
are module designer and simulation specialists who belong
to “A” in the Vocational knowledge model.
4.2 Simulation User
In order to build simulation models out of the predefined
modules made by a specialist, the simulation user needs
awareness of the system impacts form the different mod-
ules, as well as general system knowledge, in this case
manufacturing systems knowledge.
Needed skills for the simulation user are:
General production system
Lead time for skill development for a non simulation
user is short, approximately a few days. Typical users are
sales personnel, system builders, plant designers, system
integrators, continuous improvement personnel who belong
to “C” in the vocational knowledge model.
4.3 Simulation Observer
In order to watch and run the simulation models made by
the simulation user out of the simulation expert made mod-
Johansson, Williams, and Alenljung
ules, the simulation observer does not need to have any
skills beforehand at all, except for being able to handle a
computer for normal work activities.
Needed skills for the simulation observer:
Lead time for skill development for a non simulation
observer is very short, approximately five minutes. Typical
users are everybody with interest of the system, but espe-
cially managers and operators, who belong to “C” in the
vocational knowledge model.
5 LEAD TIME REDUCTION
Discrete Event Simulation as a lead time reducer is the main
contributor for productivity improvements for both the line
builder and the user of the autonomous modular material
handling equipment. Lead time reduction can be identified in
several stages during the lifecycle of both the products and
the autonomous modular material handling equipment.
5.1 Sales Process
Early offering stages when no real system exists, the cus-
tomer and the line builder salesman can share, build, and
discuss different layouts and concepts for the manufactur-
ing system in order to prevent expensive mistakes.
5.2 Manufacturing Line Design Process
Predefined modules give the possibility for automatic gen-
eration of BOM, parts list, drawings etc. This integration
will dramatically decrease the effort for CAD drawings and
rework of additional BOM’s and part lists for each and
5.3 Implementation Process
The real-world implementation process will be shorter and
more accurate since more testing and validation can be
made offline and offsite (It can be done at the line builder
and not necessarily at the customer.)
5.4 Operational Process
During the operational phase of the autonomous modular
material handling equipment, the simulation model of the
system can be used for production planning and testing of
future production, as well as be connected to the daily
work in the system, such as surveillance, maintenance, and
continuous improvements activities.
5.5 Reconfiguration Process
Large lead time reductions can also be attained when NPI
(New Product Introduction) is going to take place in the
manufacturing system. The DES model can then be used
for testing various possible scenarios for reconfiguration of
the system, product mix and batching, additional capacity
requirements etc. Since the simulation model already ex-
ists, only minor changes in layout and products will be
needed in order to find a new solution for future manufac-
turing and reusability of the modules for the next genera-
tion of products.
5.6 Other Processes
Internally at the line building company, other processes can
also be reduced in their effort and lead time. New modules
can be created and tested offline by internal technicians, in
the virtual environment before the construction of real
modules in sales situations, thereby shortening and enhanc-
ing the introduction of the new module into the presently
As always when it comes to implementing new technology
into an existing organisation, there will be barriers and
hindrances to overcome in order to be successful. Accord-
ing to Östman (1998), the technological challenge is only a
minor part when it comes to implementing new technolo-
gies, compared to development of working procedures and
integration of them into the existing organisation.
Another requirement on the organisational aspects is
competence development and learning activities for all em-
ployees in order to face the implementation and use of the
new technology. The learning activities needs to be accom-
panied with information and plans on how the development
evolves, and what is to be expected in the near future. Such
activities will enable all employees to become part of the
implementation and the success rate will increase.
Even though the presented approach is striving towards
standardisation, the real world today always requires some
special solution in the implementation phases. However, if
80% of the solution can be made with a standardized ap-
proach, there is much to be gained. Compare that with to-
day’s activities where most solutions are made as a one-off-
solution with no possibilities for reuse and reconfiguration.
6.1 Module Leasing Activities
By using this approach the reusability of the equipment,
data and processes is then very high. A real proof that the
equipment is fully modularized appears when it can be of-
fered as short term leasing or renting modules by suppliers.
The investment need with this approach is far lower than
traditional “one off kind solutions”, and also spread over
time. The user invests only when a capacity need arises and
de-invests when it’s the opposite, tightly connected to mar-
keting demands or order intake. SME’s, which are strug-
gling with losing jobs to low-cost countries, have now a
Johansson, Williams, and Alenljung
way of automating with a high flexibility at a low invest-
ment level. At the ISR2002 in Stockholm the PLM Factory
concept proved the above is reality today (Bagiu and Jo-
6.2 Future Module Compatibility
An issue which needs further attention is version handling,
which needs to be addressed in every PDM system. This
issue is not yet solved for autonomous modular material
handling equipment. However, since almost all PDM sys-
tems have that feature, it should be a minor issue to solve it
for this technology. But to enable old and new modules to
be compatible with each other in an integrated system --
that is more of a challenge.
In this paper we have presented how support from a modular
DES software can generate benefits and lead-time reductions
in all lifecycle stages of products and manufacturing proc-
esses for autonomous modular material handling equipment.
The conclusion indicates that the benefits from using DES
are numerous and the potential is obvious. The effort of im-
plementing these technologies is another step towards mak-
ing simulation a corporate norm (Williams 1996).
8 FUTURE RESEARCH
The approach described in this paper coincides with devel-
opment and implementation at FlexLink in Sweden and
will be further developed during the years to come, how-
ever additional research is required in many fields in order
to complete the full concept. Areas where efforts are
needed are for example: Standards, version handling of
new and old equipment, safety and regulation adaptations,
and holistic systems control functions. However, small
steps, one at a time, will bear fruit each year in quest for
the optimal solution for autonomous modular material han-
dling equipment for manufacturing flexibility.
The funding for this research is granted by FlexLink AB,
IMIT (Institute for Management of Innovation and Tech-
nology), VINNOVA (Swedish Agency for Innovation Sys-
tems, integrates research and development in technology,
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BJÖRN JOHANSSON was born in Gothenburg, Sweden,
1975. He attended Chalmers University of Technology at
Mechanical Engineering, where he obtained his M.S. de-
gree in Production Engineering in 2000, and his Licentiate
Degree in 2002. He is now working as a PhD student in the
field of Discrete Event Simulation and Productivity Im-
provements in Manufacturing Systems at the Department
of Product and Production Development, Chalmers Uni-
versity of Technology, Sweden. His email address is
EDWARD J. WILLIAMS holds bachelor's and master's
degrees in mathematics (Michigan State University, 1967;
University of Wisconsin, 1968). From 1969 to 1971, he
did statistical programming and analysis of biomedical data
at Walter Reed Army Hospital, Washington, D.C. He
joined Ford Motor Company in 1972, where he worked un-
til retirement in December 2001 as a computer software
analyst supporting statistical and simulation software. Af-
ter retirement from Ford, he joined Production Modeling
Corporation, Dearborn, Michigan, as a senior simulation
analyst. Also, since 1980, he has taught evening classes at
the University of Michigan, including both undergraduate
and graduate simulation classes using GPSS/H, SLAM II,
SIMAN, ProModel, SIMUL8, or Arena®. He is a member
of the Institute of Industrial Engineers [IIE], the Society for
Computer Simulation International [SCS], and the Michi-
gan Simulation Users' Group [MSUG]. He serves on the
editorial board of the International Journal of Industrial
Engineering - Applications and Practice. During the last
several years, he has given invited plenary addresses on
simulation and statistics at conferences in Monterrey,
México; Istanbul, Turkey; Genova, Italy; and Riga, Latvia.
His e-mail address is: <firstname.lastname@example.org.
umich.edu> and his university Web-page can be found
coeditor> for the WSC 2004 conference.
TORD ALENLJUNG was born in Mariestad, Sweden,
1977. He obtained a master’s degree in Automation and
Mechatronics at Chalmers University of Technology in
2001. During 2001-2003 he worked as a software devel-
oper at Carmenta, Gothenburg, developing, among other
things, an air traffic surveillance system. Since 2003 he is
working as a PhD student at the Department of Signals and
Systems, Chalmers University of Technology, Sweden,
where his field of research is in specification and modeling
of discrete event systems. His email address is <torda@