Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol69.Issue2.Jan2001:

Int. J. Production Economics 69 (2001) 239}252

Dynamic analysis of changes in decisional structures of
production systems
Gert ZuK lch, Andreas Rinn*, Oliver Strate
Ifab-Institute of Human and Industrial Engineering, Institut fu( r Arbeitswissenschaft und Betriebsorganisation, University of Karlsruhe,
Kaiserstra}e 12, D-76128 Karlsruhe, Germany
Received 2 April 1998; 28 October 1999

Abstract
In the realm of enterprise reorganization, terms such as process orientation, group work, segmentation, and
de-layering of hierarchical structures are frequently discussed. Growing needs of support, before and during enterprise
reorganization, calls for methods to assist the change process to the highest possible degree. During recent years, several
methodologies and tools for modelling and designing enterprises were developed. This paper describes an approach using
the GRAI-methodology for modelling the functional, physical, and decisional structure of production systems, respectively. Upon completion, the created enterprise models are transferred into the simulation system FEMOS for dynamical
analysis. After describing the theoretical background, the paper demonstrates the e!ects of decisional adaptations using
two case studies completed during the ESPRIT project REALMS (re-engineering application integrating modeling and
simulation). The "rst case looks into the impact of decreasing the number of hierarchical layers with respect to
a one-of-a-kind production system. The second case shows the e!ects of re-engineering a function-oriented production
system with several department interfaces to a process-oriented organization. ( 2001 Elsevier Science B.V. All rights
reserved.

Keywords: Enterprise modeling; Simulation; GRAI integrated methodology; FEMOS

1. Organizational changes in production systems
Implementation of high technology production
systems during the 1980s and the 1990s has helped
} to some extend } in sharpening the competitive
edge of companies by reducing manufacturing lead
times and increasing #exibility. However, more potential for improvement lies hidden in the departmental structure of an industrial organization. For

* Corresponding author. Tel.: #49-721-608-4839.
E-mail address: andreas.rinn@mach.uni-karlsruhe.de (A. Rinn).

example, the number of decision-making layers
determines to a great extent how long it takes
to complete a task, as well as the #exibility of
decision making. If one thinks about public administration systems, the number of hierarchical layers
also determines the transparency of the organization. This is not to say that one single layer is
enough and any more hierarchical layers are redundant. Some means for control are necessary,
and this can be achieved using a superior layer
in decision making. To countersign a work plan

or design drawing by a superior is only one
example.

0925-5273/01/$ - see front matter ( 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 9 9 ) 0 0 1 3 2 - 2

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G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

Here, the focus is on the employee as the core cell
of decision making in an enterprise. Recent developments concentrate on job enrichment of the
single employees on the shop-#oor level by adding
control functions. Several authors deal with this
topic. The main problem is the adequate design of
centralized and decentralized decision making (cf.
[1,2]).
Re-engineering projects have rather often failed
to deliver the aspired results in the past due to
various shortcomings. One prominent problem is

that a good theoretical approach is chosen after
careful scienti"c work, but all theoretical bene"ts
are overshadowed by a less ambitious implementation of this approach. To overcome this problem, it
is necessary to "nd a systematic approach which
supports best the re-design of existing organizational structures. For this task, several methods
and tools have been developed during the recent
years trying to support the planning and implementing task on static as well as on dynamic basis with
simulation functionality. Concerning the static and
dynamic analysis of decisional structures it turned
out that no method or tool is able to support this
task su$ciently.
First attempts to transfer the static decisional
structure modeled by GIM into the simulation
environment have been made by Carrie and
Suparno (cf. [3], p. 82). Further attempts to study
organizations with respect to the decisional structure statically and dynamically are unknown.
Therefore, the paper will present an approach on
how this problem can be solved successfully in
linking the already broadly applied modeling
method GIM with the simulation tool FEMOS.

2. Methodological support for re-engineering tasks
Re-engineering in industry, be it manufacturing
or service, can be supported by a number of tools.
Mostly, one tool only supports speci"c aspects of
the re-engineering task. No support for enterprisewide modeling is given. Existing support tools are
usually oriented on: supporting the installation of
IT systems, order-oriented modeling of production
systems, material #ow analysis, simulation, or
decision making. The typical organizational re-

engineering task should consider material #ow and
order-oriented tools as well as decision-making
tools.
Decision-making tools shall therefore describe
the decision-making nodes within an organization,
as well as the layout and structure of the industrial
system in which decisions are to be made. The
nodes usually represent human beings making the
decisions, and the layout or structure stands for the

departmental structure of the organization including its sub-departments. Apart from the system
elements' descriptions, the decision-making criteria
along with their limitations are also listed. The
decision structure is applied to administration and
planning tasks. Therefore, the information is
needed from the immediate shop #oor level to the
highest strategic or operational level in the organization (cf. [4], p. 461).
Decision-making modeling does not describe
any real production process, nor can it be easily
simulated. Therefore, a symbiosis with an order or
material-oriented simulation tool is necessary.
Examples for modeling or simulation tools are
ARIS, BONAPARTE, FEMOS, Simple## or
FLOWCHARTER (cf. e.g. [3,5,6]). Material-#oworiented tools are well capable of simulating production processes in "xed plant layouts. Changing
production programs can be simulated, as can
many stochastic e!ects, for example related to order arrival or uptime of the resources (cf. [7]). But
all approaches do not consider the whole decisional
structure of order processing adequately.

3. Advanced enterprise modeling and simulation

The aim of advanced enterprise modeling is to
analyze and document all aspects necessary in order to ful"ll given industrial tasks and to achieve
the objectives of the enterprise (cf. [8], p. 4). Analyzing production systems in a static as well as
a dynamic way should support the analysis and
design process. This is usually done by applying
simulation tools. For a successful re-engineering
process, the production system has to be studied
from di!erent points of view. For the correct selection of modeling aspects, the global objectives of
the company must be studied in detail. During this

G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

study it is veri"ed whether the focus should be on
information technology or the business processes.
Reorganizing the company usually leads "rst to
a business process-oriented approach ([5], p. 5).
Several concepts for enterprise modeling tools
have been developed in recent years (cf. e.g. [9]).
One initial point seems to be the planning and
implementation of IT-systems in companies. Several methodologies exist for documenting the

actual system and adapting the planned IT structure. The open system architecture (CIM-OSA; cf.
[10]) as well as the Architecture of Information
Systems (ARIS; cf. [5,11]) which is strongly linked
with the CIM-OSA approach are well-known
examples. The advantage of ARIS is its operational
implementation through an advanced modeling
tool called the ARIS Toolset. Besides the modeling
functionality, the ARIS Toolset provides an interface to the simulation package Simple##. But
this interface does not consider all modeling elements of ARIS. Furthermore, the approach does
not provides any possibility either for modeling
decisional structures nor for studing these structure
in a dynamic environment by simulation. Other
approaches are still being researched and developed,
but these are not available as PC-based tools.
In most cases, precedence diagrams of processes
are the starting point for enterprise modeling. On
this basis, several methods were developed and
applied. Terms such as SADT (structured analysis
and design technique; cf. [12], p. 6) are common in
this "eld. Also, the theory of graphs and nets has

been studied in depth in the Operations Research
"eld (cf. [13]).
In the realm of organizational structures and
decision modeling the GRAI integrated methodology (GIM) of the groupe de recherche en
automatisation inteH greH e of the University of Bordeaux I combines a few methodologies to support
real re-engineering tasks (cf. [4]). It has already
shown its practicability in a number of cases [14].
From the author's point of view, this approach
seems to be the most feasible solution for analyzing
decisional structures. But one encounters several
problems in the study of the dynamical behavior of
the GIM models.
When simulating decisional structures, the focus
is on employees. For this reason, no material #ow-

241

oriented simulation system should be used, save an
order-oriented one. In a material-oriented simulation, it is di$cult to model human abilities and
availability of personnel independently from the

functionality and availability of machines and
workplaces. Even though this can be achieved with
sophisticated modeling, a better way is to use a tool
that is already equipped with the capability of
modeling human functions and their properties (cf.
[15], p. 176).
Carrie and Suparno ([3], p. 85) amired the result
that standard simulation packages like e.g.
PROSIM and WITNESS do not provide the "tting
modeling elements to represent the GIM models.
Due to this fact, they have demonstrated "rst steps
in modeling decisional structures and simulating
them with the support of FLOWCHARTER. But
the approach using FLOWCHARTER only
focuses on the time horizon of the single decision
and its e!ects on the overall order processing in
a company. The detailed elements which are represented in the form of the GRAI nets in the GRAI
approach are not considered.
In order to simulate the decisional structure with
GIM, the authors decided in the frame of the

REALMS project to use the simulation system
FEMOS because it is close to the GRAI approach
with regard to its internal structure (cf. [14,16,17]).
Especially, the ways of modeling the order processing in an industrial environment are very similar in
both methods.

4. Analysis of production systems
4.1. Static analysis
The GRAI integrated methodology (GIM) stresses the organizational structure and the linked decisional system of production systems. The general
objective of this approach is the analysis of the
existing production systems in order to detect weak
points, to design alternative system conceptions
and to support the realization (cf. [4], p. 461;
[18], p. 86). The approach allows to investigate the
impact of new forms of organization on the decisional structure with minimum e!ort.

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G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

Within the framework of ECOGRAI, which is
part of the GIM approach, the planner can evaluate existing system and develop alternative production systems (cf. [19]). This evaluation is performed
on a static basis without any simulation. The GRAI
approach itself is divided into four parts or models
(cf. Fig. 1):
f
f
f
f

the
the
the
the

physical model,
operational model,
decisional model, and
information model.

The physical model is the basis of the GRAI
model. It consists of personnel types, workplaces
and products. The physical model contains the
resources which are needed to ful"ll the operations
represented in the operational model, which is on
the next level of the GRAI approach. Over the
physical and the operational model, the decisional
model is layered, and this is split into two levels (cf.
Fig. 2):
f The higher level represents the general decisional
structure of the production system by using a decisional matrix, the so-called GRAI-grid, and

f the lower or operational level describes in detail
the single-decision centers using the so-called
GRAI-net.
Fig. 2 shows the graphical notation of the
GRAI-grid and the GRAI-net as well as their interdependency. The fourth model, the information
model, is modeled in parallel to the other models.
By using di!erent models in an integrated approach, the methodology allows a representation of
several aspects of reality.
Modeling of higher levels of the decisional level is
done by using the GRAI-grid. The horizontal or
production axis is separated into the di!erent processes e.g. development, planning, etc. and the vertical axis represents the di!erent time horizons of the
processes and their decisions.
After modeling di!erent views of the production
system the objectives for the processes are identi"ed. This is achieved by a top-down approach. It
starts from the global enterprise objectives and
ends on the speci"c objectives of each department,
group or even employee. Thus, it can be checked
whether every objective is supported by a decision
activity.

Fig. 1. Graphical representation of the GRAI integrated methodology (cf. [11]).

G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

243

Fig. 2. GRAI-grid and GRAI-net.

In parallel, a coherence analysis is done to ensure
that all objectives are linked which means "nally
that the entire enterprise follows a distinct path.
A further question is are there any adequate performance indicators, to "nd what degree an objective
is really achieved.
The general idea of GIM is to support the process of analyzing and documenting the existing
structure of a production system. Doing this, the
planner learns a lot about the system. Using the
GIM approach several weak points are usually detected in the existing system which would not have
been found without a structured analysis method.
The following examples demonstrate possible
results of a static analysis by using the GRAI integrated methodology:
f Analyzing the physical and the functional model,
it has to be checked if the resources are well

assigned to the di!erent operations. Firstly, this
means whether every operation inside the function has an assigned human and/or technical
resource. Furthermore, it is questioned if the
correct resource is processing the operation. Factors such as technical feasibility and skill of
workers have to be investigated by using the
physical and the operational model.
f By studying the physical and the operational
model the planner is able to check the existing
organization. One possible result may be the
strong separation of operations and their resources into departments which is usually called
functional organization. By analyzing the operation itself it may seem that it is working very well,
but by investigating the interference to all other
linked operations badly de"ned linkages may be
found. Due to the interfaces between the single
departments, this kind of organization usually

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G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

leads to long lead times and growing bu!ers
between the departments, etc. Thus, the physical
and the operational model represent a good
basis for analyzing and classifying the existing
organization.
f The decisions linked to a speci"c process are
sometimes not well allocated. This means that
e.g. a decision is performed on an annual level as
well as on a weekly level. The result may be that
decisions taken on the annual basis in#uence the
operational work on the weekly basis. The decisions are neither on a quarterly basis nor on
a monthly basis checked for validity or for necessary modi"cations due to changes of the initial
planning situation. This periodic checking is
strongly linked with the de"nition of the horizons and their periodical check and modi"cation. A well-de"ned horizon includes around
three to six periods. This means at t"0 the
monthly planning is done for e.g. six months in
advance. In a perfect manner the planning
should be checked monthly and again prepared
for the next six months. This rolling horizon
assures a perfect adaptation to the changing environment of the business. If e.g. the horizon and
the period are identical this can be a hint for
improving the existing process. This holds also if
the horizon includes too many periods. This
would lead to a detailed long-term planning
which loses its reliability with a growing number
of periods.
These examples give an impression of how possible weak points in existing production systems
can be detected by using the static analysis of GIM.
Due to the complexity and the general di!erences
among production systems there are no general
rules for determining weak points. The planner
needs some know-how about where bottlenecks
can be hidden in the resulting GIM model.
4.2. Dynamic analysis
For the dynamic analysis of production systems
several simulation tools have been designed in recent years (cf. for an overview [20], p. 166). By
studying all developed and published simulation
approaches one major factor is missing: A simula-

tion approach which o!ers the possibility to model
and simulate a production system as close as possible to reality by taking into account the decisional
structure of a production system. Most approaches
o!er a wide scope of simulation functionality. But
most approaches do not consider the decisional
structure adequately. First steps in this direction
are described by Carrie and Suparno [3].
To solve this problem was the major aspect of
the REALMS approach. Therefore, the simulation
tool FEMOS has been chosen besides the GRAI
approach to develop an overall modeling and simulation method which succeeds in solving this task.
The simulation tool FEMOS has been developed
at the Ifab-Institute since 1988 (cf. [6], p. 254). This
simulation tool can be used for analyzing organizational structures, besides others. It represents a personnel-oriented approach which means that it is
based on the separation of personnel and operating
facilities. The modeling process is supported by an
integrated tool for modeling activity networks (cf.
[21]).
With the help of GIM, the decisional structure is
evaluated on a static basis without any simulation.
This limitation of the GRAI integrated methodology can be recognized during the dynamic analysis
of production systems (cf. [18], p. 93; [16], p. 19).
However, the modeling capabilities of the simulation tool FEMOS are not comparable to an
advanced enterprise modeling technique as GIM
provides.
In this context, a major aspect of the REALMS
project was to combine the two methodologies in
order to provide an advanced enterprise modeling
tool with the support of simulation. When combining the two methods it is important to de"ne their
links "rst. Therefore, GIM has to be studied in
detail, and the di!erent models namely the physical
model, the operational model, and the decisional
model, have to be considered.
The "rst step is to analyze the physical model,
which represents all resources of the production
system considered. These physical resources are
transferred into the simulation model distinguishing the resources into human and technical.
Human resources are represented in the simulation
tool FEMOS by personnel units and technical resources by machines or work places.

G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

The second step is to transfer the operational
model into the simulation model. In order to simulate reality adequately, the simulation tool needs
a detailed structure of all operations. Therefore, the
part of the production system which should be
studied dynamically using simulation should be
modeled with more details in the GRAI method
than usually. By doing so, the operations can be
easily transferred from GIM to FEMOS.
The third step concerns the transfer of the decisional model into the simulation model. Due to its
complexity, this is a step still being researched.
During the REALMS project some "rst results of
realizing this interface have been developed and
applied successfully, shown in Section 5.
One problem in modeling and simulating real
decisions is the relevant time horizon (cf. [18],
p. 93). In analyzing the GRAI-grid, it is almost
impossible to model and simulate decisions with
a time horizon equal to or greater than one year. Of
course, these strategic decisions strongly in#uence
the whole business process sometimes to a full
change of the products, the production location or
similar extreme changes. Modeling these decisions
would mean to consider all theoretical possibilities
such as new production locations, new enterprises,
etc. This is not within the scope of a simulation
tool.
During the REALMS project the focus of simulating the decisional structure was concentrated on
the operational and midterm level such as changing
the organization of an existing production system.
Possible decisions within this time horizon are limited, and so the frame of speci"c decisions can be
forecasted and modeled with acceptable e!ort.
The analysis of decisional structure with respect
to operational and midterm decisions has been
done by studying the di!erent GRAI-nets which
represent the detailed structure of the GRAI-grid.
This study focuses on involved resources, needed
information, restrictions and decisional frames. In
the following the transfer of the data from the
GRAI approach to the simulation tool FEMOS is
described in relation to these groups.

245

tion elements work places (e.g. machines, o$ces,
tools) and employees. Due to the high modeling
#exibility of FEMOS, very complex organizational
structures can be modeled. This means that the
abilities of every individual employee as well as his
assignment to speci"c production resources can be
modeled.
4.2.2. Information needed
Information is a resource which is needed before
initiating a speci"c activity. Therefore, information
has a nature similar to material. The only di!erence
is that it cannot be consumed. FEMOS provides
a speci"c modeling element which represents this
behavior of an information element during simulation. The only problem is that information is either
available or not. Information which leads to further
activities or prolongs speci"c activities could be
modeled so far.
4.2.3. Restrictions
The main restrictions considered are related to
the availability of resources, the ability of the sta!
and the functionality of machines. This is related to
their dynamic behavior during a simulation run.
4.2.4. Decisional frames
Every decision has a decisional frame which
means that the nature of the output and the expected type of value is already prede"ned. In
FEMOS, every activity has a pre-de"ned input,
assigned resources which are processing the activity
with a pre-de"ned duration and a pre-de"ned output which is related to the input and assigned
resources.
During the REALMS project it turned out that
most important types of decisions can be transferred adequately from the GRAI approach to
FEMOS without losing major elements. The
following section demonstrates in detail the
realization of these transfer of data using two di!erent case studies.

5. Application 5elds
4.2.1. Resources involved
The resources involved can be easily modeled by
FEMOS. Therefore, FEMOS provides the simula-

The principle scope of application of the combined methodology is rather broad. One major

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G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

aspect, which is in the focus of the described methodology, is the modi"cation of the decisional system by reducing the number of decisional levels.
This is called the de-layering process (cf. e.g. [18],
p. 92). Reducing the number of decisional levels
means to modify the whole decisional structure of
a company. Assignments of di!erent decisions to
activities as well as the links between the new decisional levels have to be analyzed and determined.
The following two case studies are results of
the REALMS project and shall demonstrate the
application of the combined methodology.
5.1. Case 1: De-layering of hierarchical structures
5.1.1. The company and its existing organization
The "rst study deals with a metal wholesaler and
sheet manufacturer. The production is of a one-ofa-kind type and the related production orders contain between two and "ve operations. The existing
situation production system consists of 14 workers
and is functionally organized. This means every
worker is assigned to one speci"c machine on the
shop #oor. Furthermore, some machines run in two
shifts, which means they operate from 6 a.m. to 10
p.m. Furthermore, every shift is managed by one
foreman. For the overview of the production system see Table 1.
Approximately 1800 orders are processed per
month which equals 90 orders per day. The main
problem is the large number of customer orders
and their low probability of repetition. The existing
production facilities are able to produce the same
customer order with di!erent machines but with
di!erent qualities and production times. On this
basis it is an important planning task to assign the

Table 1
Characteristics of the existing situation at the metal wholesaler
Production type

One-of-a-kind production

No. of orders per month
No. of production steps per
customer order
No. of sta! members
No. of machines
Working time

1800
2}5
14 workers and two foremen
12
two shifts

customer orders including the demanded quality to
the appropriate production facilities.
In the initial situation, the whole production
planning task is performed by the production manager who is usually working during normal o$ce
hours. Breakdowns caused by machines or quality
problems early in the morning or late in the evening
have to wait until the production manager is available again. The production manager is also involved in general management of the company,
which means he is not all the time available for
production decisions. The quality assurance is operated by the foremen of the shifts.
Fig. 3 shows the order processing in the initial
situation by using the physical model of the GRAI
approach. Furthermore, Fig. 4 represents the
GRAI grid of this situation. The physical model of
the GIM-methodology is designed here in an
SADT-shape (cf. [12]). Due to the strong expansion
of the company in recent years, it was necessary to
transfer more operational decisions to the foremen
and the sta!, thus saving the production manager
time for management tasks. But, due to the low
quali"cation level of the workers in the initial situation, the motivation to overtake new responsibilities was rather low. This resulted in a high failure
rate as well as low productivity. Due to the low
motivation and the possibility of in#uencing the
machining speed, workers tended to reduce the
speed during the end of their shift so that they were
not forced to start a new order.
5.1.2. Design alternatives
In order to eliminate the bottleneck of decisions
by the production manager and the foremen and
also to motivate the workers, alternatives of new
organizational forms were designed (Table 2).
Variant A. In order to support decisions originally taken only by the production manager, the
foremen were required in the model to process the
production planning by themselves. Only complex
planning tasks are still done with the support of the
production manager. Furthermore, the workers
were assumed to be able to perform the quality
assurance tasks for standard customer orders by
themselves. These changes ensured that most of the
customer orders can be processed without the support of the production manager.

G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

247

Fig. 3. Physical model of the existing situation and the design variant C.

Fig. 4. GRAI grid of the initial situation.

Table 2
Characteristics of existing and alternative design
Feature

Existing design

Production type
No. of orders per month
No. of tasks per customer order and employee
No. of employees
No. of workplace types
Working time

Continuous production
2400}2500
1}3
5
4
1 shift

Alternative
design

1}4
4
3

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G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

Variant B. One bottleneck in the starting situation as well as in variant A was the functional
assignment of the workers only to speci"c machines. Therefore in Variant B, the workers were
grouped into two teams which then were assigned
to two newly de"ned machine groups. Within
a group, each worker was modeled to be able to
handle each machine of one group. This would
radically improve the #exibility of the whole production system.
Variant C. As an alternative, in variant C the
workers were assumed to be able to process the
production planning inside one group. On this
basis, the decisional level of the foremen is no more
needed. Individual workers process standard customer orders fully by themselves. Only complex
orders are planned and checked by the production
manager. Combined with an adequate incentive
wage system, this would result in motivating
workers to quickly "nish customer orders. Fig. 3
also represents the related physical model and
Fig. 5 shows the new GRAI grid of this variant. The
most remarkable parts of the new GRAI grid are
the newly de"ned quality management as well as
the production management, which is operated on
the short-term level by the groups. Furthermore,
the di!erent planning levels have been adjusted to
each other.
5.1.3. Dynamic analysis of design alternatives
In order to analyze the dynamic behavior of the
design alternatives, all models were transferred into

FEMOS, including a representative order spectrum
of one month. For evaluating the di!erent design
alternatives logistically as well as "nancially, some
relevant performance indicators were applied.
Fig. 6 shows the relative changes of the alternatives in comparison with the starting situation.
Some of the key "gures are evaluated as percentage
degrees of an ideal situation (see for details [22],
p. 311).
It is obvious that higher abilities and responsibilities of the foremen and the workers lead to an
improved lead time (variant A). But the functional
organization still provokes delays because of the
interfaces between individual functions. By introducing group work signi"cant improvements can
be achieved (variant B), although at higher workforce costs due to the higher quali"cation level.
Transferring most of the production planning to
the groups, the lead time degree, which means the
average of the minimum lead time (work content on
the critical path) divided by the simulated lead time,
grows by 46% which means a strong improvement.
Furthermore, the percentage of operative work
load on the production manager is reduced by
61%. The time saved can be used for managerial
tasks e.g. customer contacts or tasks related to
work organization.
5.1.4. Results of the de-layering process
This example has demonstrated the application
of the GRAI methodology in combination with the
simulation tool FEMOS. First by modeling the

Fig. 5. GRAI grid of variant C.

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249

Fig. 6. Simulation results of the di!erent design alternatives.

existing situation and then designing new alternative organizational structures with the GRAI methodology the dynamic analysis and evaluation of the
modi"ed decisional structure can be done by
FEMOS in a third step. Studying the simulation
results, variant C seems to be the most interesting
solution for re-organizing the production system.
5.2. Case 2: Process-oriented re-organization
The second case study gives an example for the
application of the combined GRAI-FEMOS tool
and the REALMS methodology of processoriented re-organization of a production enterprise.
The objective of the study is to evaluate the
e!ects of information and decision availability on
the order lead time and related performance indicators. It can be shown that the number of decision-making layers in the hierarchy has an impact
on the logistic performance of the system.
5.2.1. The company and its existing situation
The company regarded here is a producer of wire
rod and construction steel (rebars) used for building construction. A typical process-oriented enterprise, it has a lean administration and planning
section. The path of a customer order through

administration and production planning departments, namely manufacturing planning, distribution planning, and shipping, are looked into while
the actual manufacturing process is not considered
here. The focus is on the #ow of orders between
departments and sub-departments, where a subdepartment performs a single task, and the decision
making related to this work#ow.
One department, such as distribution scheduling,
may have several sub-departments using the same
human resources and the same o$ce space. The
processes performed by such sub-departments are
distinguished by their respective input and output
data.
The departmental organization of the company
is rather lean. But, the GRAI analysis shows that
the number of hierarchical layers is comparatively
large, featuring eight di!erent scopes of time in
which decisions are made. Production planning
and distribution scheduling are performed by different human resources. Identical human resources
are involved in more than one task, leading to one
person controlling and reviewing his own work.
The main point is that annual, quarterly and
monthly plans are created at distinct time intervals,
but must be updated in shorter, also regular, intervals. Due to the lean structure, this is done by one

G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

250

and the same person. The complete GRAI grid and
a more detailed description of the relationship can
be found in [14], p. 98.
5.2.2. Design alternative
Possible improvements focussed on the reduction of hierarchical layers and a more streamlined
order processing. The re-organization aimed at
shorter lead times for orders, at savings in workforce count, and quicker information or decision
availability. These goals were achieved by reducing
the number of hierarchical layers and by pooling
several tasks on one resource.
The improved design alternative of the system
was then modeled. First, a GRAI model was established, and from this a simulation model was
derived according to the REALMS-methodology
(cf. [14], p. 87). The GRAI grid features four decision-making time scopes compared to the original
eight. Furthermore, one human resource was freed
by pooling manufacturing planning and distribution scheduling task and assigning them to only
one resource instead of two.
5.2.3. Dynamic analysis
For the simulation comparison with FEMOS,
a time horizon of four months was used for both
simulation models. This horizon encompasses one
business quarter plus an additional month for system warm-up. This makes sure that no transition
e!ects of the system a!ect the simulation results.
Evaluation of actual order arrival data had not
shown any particular order inter-arrival pattern.
Thus, no parameter-based order generation mechanism was employed, instead historic customer
orders were used for simulation. The existing situ-

ation and the design alternative were made subject
to the same representative order spectrum.
5.2.4. Simulation results
The main outcome concerned the improvement
of the order processing. The manufacturing planning resource proved to be extremely less utilized in
the existing situation, which prompted the design
alternative to free this particular resource. The
manufacturing planning tasks were additionally assigned to the distribution scheduling department.
The utilization for all simulation runs are depicted in Table 3. The design alternative has lower
utilization of resources, especially in the then
pooled manufacturing planning and distribution
scheduling department. The reasons for the lower
utilization are twofold. One e!ect is the better coordination between manufacturing planning and
distribution scheduling. The distribution scheduler
has to re-schedule each time a new manufacturing
plan is made. Additionally, each time a disturbance
on the shop #oor level occurs, some orders have to
be expedited while others are delayed. In the design
alternative, this re-scheduling is done concurrently,
while previously it was done in sequence. The second reason is a reduction of total job processing
time. There is a reduction of set-up times due
to a more streamlined process, and, additionally,
non-productive coordination tasks have been
eliminated.
The shipping department remains on the same
utilization level. This is due to an unchanged number of orders to be processed and is an expected
outcome.
It is worth mentioning that order-related, or productive, work contents for each department in

Table 3
Utilization of human resources
Resource

Existing design
Alternative design
!CU"currency units.

Utilization
Manufact.
planning

Distr.
sched.

Shipping
(domestic)

Shipping
(abroad)

38%

76%
31%

53%
53%

70%
70%

Lead
time

Order
cost

1222 h
1015 h

45,70 CU!
32,10 CU!

G. Zu( lch et al. / Int. J. Production Economics 69 (2001) 239}252

the design alternative were at least equal to work
contents in the existing situation. This means that
due to a streamlined structure, the proposed model
is truly more e$cient, and the lower utilization is
not due to shorter order-related processing times
nor due to a reduced production program. The
main reason for shorter lead time lies in the fewer
number of hierarchical layers, i.e. fewer required
decision-making and unproductive coordination
tasks. This could be interpreted as the existence of
capacity bottlenecks in information acquisition or
decision making. To a certain extent, this may
certainly be true. Another explanation is the misalignment of tasks. This prevents high-level tasks to
be performed although low-level information or
decisions are still missing due to poor interface
design. This simulation study cannot, due to its
limited scope and funding, determine what the
exact reason for insu$cient information #ow in the
current situation may be. However, the existence of
poor information and decision #ow is demonstrated and a possible improvement is shown.
Currently, there is new information technology
being installed at the company. It is expected that
order-related manufacturing planning and distribution scheduling processing times can be reduced
to some extent. One human resource may be freed
using the proposed design alternative, and this
change will not a!ect the company's performance
in a negative way.

251

indeed possible using a GRAI model as a primary
source of information. Some information is not
delivered by the GRAI approach, such as work
contents and number of orders or order arrival
patterns (cf. [14], p. 90). But, these patterns are
important input information for a simulation tool
such as FEMOS and have to be collected additionally. On the other hand, in this particular case the
basic GRAI models deliver information that is not
required by the simulation tool FEMOS. The information o!ered by the GRAI model is particularly interesting for further studies dealing with
various di!erent decision structures where this reserve may come in handy. This is a valuable insight,
and more combined studies are encouraged.
Most important, however, is that the adaptation
of a GRAI decision model into an order-oriented
manufacturing simulation software like FEMOS
gives additional, valuable information and insight
for the analyst. In the GRAI-approach, dynamic
aspects cannot be illustrated or proved, and human-resource related properties of a production
system cannot be described. In real life, these aspects are of vital importance when re-engineering
a system. Using GRAI together with FEMOS combines a structured, high-level planning approach
with an activity level simulation system. This is an
important result of the REALMS project and leads
to a new analysis methodology where two methods
are used to complement each other, leading to the
dynamic evaluation of decision structures on the
planning and on the operational level.

6. Conclusions and further development
It should be noted that sta$ng is not a factor
in the GRAI-grid; therefore the GRAI model remains basically unchanged even if resources are
freed. This is, of course, not true for the FEMOS
simulation model, where sta$ng is an important
issue.
Conclusions for the simulated production systems are primarily to redesign the customer order
process according to the above recommendations.
Improvements are possible primarily due to
a smaller number of hierarchical decision making
levels and centers.
In terms of the methodology used it is demonstrated that simulation of decision structures is

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