*Corresponding author. Tel.:#49-721-608-4839.
E-mail address:[email protected] (A. Rinn).
Dynamic analysis of changes in decisional structures of
production systems
Gert Zu
K
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, respec-tively. 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-engineeringapplication integratingmodeling 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 po-tential for improvement lies hidden in the depart-mental structure of an industrial organization. For
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 admin-istration systems, the number of hierarchical layers also determines the transparency of the organiza-tion. This is not to say that one single layer is enough and any more hierarchical layers are re-dundant. 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.
Here, the focus is on the employee as the core cell of decision making in an enterprise. Recent devel-opments 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 implementa-tion of this approach. To overcome this problem, it is necessary to "nd a systematic approach which supports best the re-design of existing organiza-tional structures. For this task, several methods and tools have been developed during the recent years trying to support the planning and implemen-ting 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 struc-ture 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 enterprise-wide 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 includ-ing 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 organ-ization (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-# ow-oriented tools are well capable of simulating pro-duction processes in"xed plant layouts. Changing production programs can be simulated, as can many stochastic e!ects, for example related to or-der 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
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. Sev-eral methodologies exist for documenting the actual system and adapting the planned IT struc-ture. 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 inter-face to the simulation package Simple##. But
this interface does not consider all modeling ele-ments 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 metho-dology (GIM) of the groupe de recherche en automatisation inteHgreHe of the University of Bor-deaux 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-oriented simulation system should be used, save an order-oriented one. In a material-oriented simula-tion, 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 repre-sented 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 process-ing in an industrial environment are very similar in both methods.
4. Analysis of production systems
4.1. Static analysis
Fig. 1. Graphical representation of the GRAI integrated methodology (cf. [11]).
Within the framework of ECOGRAI, which is part of the GIM approach, the planner can evalu-ate existing system and develop alternative produc-tion systems (cf. [19]). This evaluaproduc-tion is performed on a static basis without any simulation. The GRAI approach itself is divided into four parts or models (cf. Fig. 1):
f the physical model, f the operational model, f the decisional model, and f the 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 de-cisional 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 inter-dependency. The fourth model, the information model, is modeled in parallel to the other models. By using di!erent models in an integrated ap-proach, 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 pro-cesses e.g. development, planning, etc. and the verti-cal 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
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 perfor-mance indicators, to"nd what degree an objective is really achieved.
The general idea of GIM is to support the pro-cess 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 de-tected 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 integ-rated 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 func-tion has an assigned human and/or technical resource. Furthermore, it is questioned if the correct resource is processing the operation. Fac-tors 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
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 deci-sions are neither on a quarterly basis nor on a monthly basis checked for validity or for neces-sary modi"cations due to changes of the initial planning situation. This periodic checking is strongly linked with the de"nition of the hor-izons and their periodical check and modi" ca-tion. 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 en-vironment 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 pos-sible 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 re-cent 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 pos-sible 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 simu-lation 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 organiza-tional structures, besides others. It represents a per-sonnel-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 methodo-logy can be recognized during the dynamic analysis of production systems (cf. [18], p. 93; [16], p. 19). However, the modeling capabilities of the simula-tion 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 combin-ing 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 second step is to transfer the operational model into the simulation model. In order to simu-late 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 deci-sional 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 simu-lating 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 lim-ited, 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.
4.2.1. Resources involved
The resources involved can be easily modeled by FEMOS. Therefore, FEMOS provides the
simula-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 simula-tion. 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 ex-pected 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 out-put which is related to the inout-put and assigned resources.
During the REALMS project it turned out that most important types of decisions can be transfer-red 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! e-rent case studies.
5. Application5elds
Table 1
Characteristics of the existing situation at the metal wholesaler Production type One-of-a-kind production No. of orders per month 1800
No. of production steps per customer order
2}5
No. of sta!members 14 workers and two foremen
No. of machines 12
Working time two shifts
aspect, which is in the focus of the described meth-odology, is the modi"cation of the decisional sys-tem 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 deci-sional 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-of-a-kind type and the related production orders con-tain 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 sys-tem 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
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 man-ager 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 avail-able again. The production manager is also in-volved in general management of the company, which means he is not all the time available for production decisions. The quality assurance is op-erated 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 situ-ation, the motivation to overtake new responsibili-ties 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).
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 Alternative
design
Production type Continuous production
No. of orders per month 2400}2500
No. of tasks per customer order and employee 1}3 1}4
No. of employees 5 4
No. of workplace types 4 3
Fig. 5. GRAI grid of variant C. Variant B. One bottleneck in the starting
situ-ation as well as in variant A was the functional assignment of the workers only to speci"c machin-es. 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 pro-duction 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 cus-tomer 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 alterna-tives 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 responsibil-ities 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 intro-ducing group work signi"cant improvements can be achieved (variant B), although at higher work-force 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
Fig. 6. Simulation results of the di!erent design alternatives.
existing situation and then designing new alterna-tive organizational structures with the GRAI meth-odology 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 process-oriented 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 indi-cators. It can be shown that the number of deci-sion-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 build-ing construction. A typical process-oriented enter-prise, it has a lean administration and planning section. The path of a customer order through
administration and production planning depart-ments, namely manufacturing planning, distribu-tion 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 departments, where a sub-department 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.
Table 3
Utilization of human resources
Resource Utilization Lead Order
Manufact. planning
Distr. sched.
Shipping (domestic)
Shipping (abroad)
time cost
Existing design 38% 76% 53% 70% 1222 h 45,70 CU!
Alternative design 31% 53% 70% 1015 h 32,10 CU!
!CU"currency units.
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 reduc-tion of hierarchical layers and a more streamlined order processing. The re-organization aimed at shorter lead times for orders, at savings in work-force 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 estab-lished, and from this a simulation model was derived according to the REALMS-methodology (cf. [14], p. 87). The GRAI grid features four deci-sion-making time scopes compared to the original eight. Furthermore, one human resource was freed by pooling manufacturing planning and distribu-tion 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 sys-tem 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 mecha-nism 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 plann-ing 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 as-signed to the distribution scheduling department.
The utilization for all simulation runs are depic-ted 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 co-ordination 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 sec-ond 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 num-ber of orders to be processed and is an expected outcome.
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 mis-alignment 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$cient information#ow in the current situation may be. However, the existence of poor information and decision #ow is demon-strated and a possible improvement is shown.
Currently, there is new information technology being installed at the company. It is expected that order-related manufacturing planning and distri-bution scheduling processing times can be reduced to some extent. One human resource may be freed using the proposed design alternative, and this change will not a!ect the company's performance in a negative way.
6. Conclusions and further development
It should be noted that sta$ng is not a factor in the GRAI-grid; therefore the GRAI model re-mains 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 sys-tems 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 demon-strated that simulation of decision structures is
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 addition-ally. On the other hand, in this particular case the basic GRAI models deliver information that is not required by the simulation tool FEMOS. The in-formation o!ered by the GRAI model is parti-cularly interesting for further studies dealing with various di!erent decision structures where this re-serve 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 hu-man-resource related properties of a production system cannot be described. In real life, these as-pects are of vital importance when re-engineering a system. Using GRAI together with FEMOS com-bines 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.
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![Fig. 1. Graphical representation of the GRAI integrated methodology (cf. [11]).](https://thumb-ap.123doks.com/thumbv2/123dok/3115148.1378404/4.544.91.427.367.598/fig-graphical-representation-grai-integrated-methodology-cf.webp)
