21st International Conference on Production Research ..... (2). The production of the supplier's manufacturing system is synchronized with the production of the customer's manu- ..... [1] Abele, E.; Wiegel, F.; Kuske, P., 2010, Kleine Schritte.
21st International Conference on Production Research
Simulation of an Automotive Supplier Plant towards Designing optimally flexible Manufacturing Systems 1
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M. Weyrich , S. Schnell , P. Stratil 1
Chair of Automated Manufacturing and Assembly, University of Siegen, Paul-Bonatz-Straße 9-11, Siegen, Germany 2 SAS Automotive Group, Siemensallee 84, Karlsruhe, Germany Abstract This paper discusses a simulation-based approach to the design of optimally flexible manufacturing systems based on a volume production use case of an automotive supplier. In order to develop our approach, a simulation model of the most representative plant of the supplier was developed. The flexibility of certain manufacturing subsystems of this plant was evaluated within the simulation model using key performance indicators. Different constraints, in the form of alternating demand fluctuations, were subsequently simulated while their influence on the subsystems was measured using the performance indicators. The analysis of the indicator values revealed a mismatch between the flexibility potential of major manufacturing subsystems and their specific requirements for flexibility. On the basis of these findings, measures were suggested to better match the flexibility of certain assembly and transportation systems with their requirements. Keywords: Simulation, Manufacturing Systems, Flexibility Measurement, Design for Flexibility, Customer Demand Fluctuations
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INTRODUCTION
Global competition and market dynamics with strong deviations in demand, increasing product variance and evershorter product life cycles force industrial companies to master new challenges [1,2]. In order to operate efficiently in this dynamic environment, manufacturing systems are required to quickly adapt to new requirements with a reasonable level of effort [3]. This ability of manufacturing systems is known as flexibility [4,5]. Companies, however, face the challenge of determining the right degree of flexibility for their manufacturing systems with regard to the volatility of their environments [6]. If the manufacturing flexibility is too low, quick adjustments to market changes are suppressed. In contrast, the costs incurred from flexibility levels that are too high can negatively affect a company’s competitive strength. Despite the importance of flexibility for efficient operations in today’s dynamic markets, the majority of companies neglect its explicit planning [7]. “Flexibility” is, in many cases, nothing more than additional and usually unutilized capacities [8]. This is, inter alia, due to the lack of simple methods for evaluating the flexibility status quo of manufacturing systems as well as their subsystems [3] in comparison to market requirements for flexibility. The use of simple methods for identifying mismatches between flexibility and its requirements would help companies to deliberately invest in flexibility and thus improve their ability to adapt to customer needs, respond to competitive pressure and be closer to the market [9]. In this paper, a simulation approach for measuring the flexibility of manufacturing subsystems and their market requirements for flexibility is presented. The focus is to describe a project during which this approach was deployed in a globally operating automotive supplier company that faces increasing short-term demand fluctuations at several locations. In order to provide a detailed analysis of the manufacturing system as well as suggest measures for further actions, the following objectives are defined for the project:
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Measure the current flexibility of the manufacturing subsystems of the most representative plant considering assembly-, pre-assembly- and transportation systems with regard to their market requirements for flexibility. Evaluate the influence of different short-term demand fluctuations on these subsystems. Develop, if necessary, measures for matching the flexibility of subsystems with their specific requirements.
2 APPROACH Manufacturing systems consist of several subsystems, such as production-, assembly-, transportation- and storage systems, in which a variety of different, and often, stochastic processes occur that are connected between the subsystems through varying material-, information- and medium flows. The complexity of manufacturing systems makes it complicated to evaluate the effect that changes in market requirements have on their subsystems. The analysis of these effects requires a holistic approach that includes all subsystems, their interconnections and stochastic behavior. In such a case, heuristic methods that approach the optimal solution through systematic attempts and modeloriented experimenting are applied [10]. Simulation belongs to this group of methods. In order to analyze the complex manufacturing system of the automotive supplier, a simulation model of its most representative plant, including all relevant operative subsystems, is developed. The simulation model also includes two modules. The supplier module fulfills the function of representing the stochastic material-flows from the suppliers to the analyzed plant. The customer module defines the qualitative and quantitative customer demand for the final products. Furthermore, indicators for measuring the flexibility and its requirements for the subsystems are developed and integrated into the simulation model. During the simulation experiments, the short-term demand fluctuations are systematically altered and represent current as well as expected fluctuations for the future, while
3 STATE OF THE ART 3.1 Simulation and its application in the automobile industry According to Harrel et al. [11], simulation is the imitation of a real system, computer-modeled, for evaluation and improvement of its performance. In other words, simulation is the depiction of reality in a controlled environment to study its behavior and improve its performance under various conditions without physical risks and large costs involved [12]. While the major phases of simulation studies are widely agreed-upon and standardized, the approach to each phase is generally determined on a case-by-case basis. In recent years, however, researchers have strived to standardize approaches to the major phases of simulation. For instance, Kuhnt and Wenzel [13] described methods for data acquisition, and Miller-Sommer as well as Straßburger [14] presented an approach for checking the plausibility of the data. To support the efficient modeling of processes, a reference model was developed by the Frauenhofer Society [15] and several authors have suggested methods for automatically generating models [16]. Wang and Lehmann [17] developed a framework to attempt to standardize verification and validation processes and Kleijnen et al. [18] presented a toolkit for the selection of experiment designs and methods. In most of the simulation studies, the performance of the investigated system is indirectly improved through manual adjustment and verification of the model in an iterative manner until the objected results are achieved. In recent years, simulation models were in some cases combined with optimization algorithms in order to systematize and automate this iterative procedure and to find an optimal or at least near-optimal solution. The combination of simulation and algorithms requires the objectives for simulation to be representable in a function that is to be minimized or maximized [19]. Despite the potential of this approach, the practical application is up to the present rather rare [20]. This is in particular because standardized procedures for combining simulation with algorithms and its parameterization are missing [20] and because the definition of the objective function can be challenging [19]. However, some applications can be found in the automobile industry [20,21]. Surveys have revealed that the automobile industry takes a leading role in the use and development of simulation techniques [22]. In this industry, simulation is used during each phase of the life-cycle of its manufacturing systems and applied to each element of the system’s value chain, starting with the body shop, the paint shop, the trim shop and the final assembly [23]. Extensive studies of these elements are often hierarchically subdivided into several simulation types in order to reduce the complexity and specifically address the requirements of different abstraction levels (see Figure 1). The application of simulation provides automotive suppliers in particular opportunities for differentiation since the application of this technique and the ability to exchange simulation data is growing in importance as a prerequisite for cooperation with automobile manufacturers [22].
discrete-event simulation abstraction levels
their effect on the subsystems is measured with the flexibility indicators. The indicator values are subsequently analyzed and measures are, in necessary cases, developed for matching the flexibility of subsystems with their individual requirements.
production plant level kinematic-, discrete-event simulation production area level multi-body and FEM simulation component level
Figure 1: Typical simulation types for different abstraction levels 3.2 Volume flexibility and its measurement Flexibility in the context of manufacturing systems has been studied since the 1930s [4] in different scientific and managerial disciplines and has thus been manifested in numerous definitions and dimensions [24]. Several attempts were made to develop a flexibility taxonomy to universally capture the multi-dimensional characteristics of this term; Sethi and Sethi [4], De Toni and Tonchia [5] as well as De’Souza and Williams [25] all made attempts. Despite the importance of flexibility, its measurement is still an under-developed subject, both for its multidimensionality and the lack of indicators for its adequate measurement [5]. In order to examine the issue faced by the automotive supplier, this paper focuses on the ability of manufacturing subsystems to economically absorb short-term demand fluctuations and thus be operated at different overall output levels is studied. This ability is known as short-term volume flexibility [26]. The volume flexibility is one dimension of the manufacturing flexibility [5]; its importance is contextspecific and depends on the requirements for flexibility. These requirements are initiated by changes in the outputlevel. In order to meet the requirements, and therefore enable a high reactivity, manufacturing subsystems have to possess specific flexibility potentials [6]. The potentials, however, should match the requirements as accurately as possible to enable stable and efficiently operating manufacturing systems [27]. The measurement of volume flexibility is, like the term flexibility itself, characterized by several approaches. According to De Toni and Tonchia [5], the different measures can be classified as follows: ● direct measures (objective or subjective); ● indirect measures (performances related to flexibility or characteristics of the manufacturing system). The direct measures are further subdivided into objective and subjective measures. The direct objective measures generally consist of the evaluation of possible options in a certain moment. Mandelbaum and Brill, [28] for instance, determine flexibility of machines by measuring their effectiveness in fulfilling a certain variety of tasks. Kumar [29] proposes the use of entropy, a measure from thermodynamic and information theory, to evaluate the flexibility of manufacturing systems. In some cases, direct objective measures consist of an analysis of certain output features. Gerwin, [24] for instance, measures volume flexibility by the ratio of average volume fluctuation over a given period of time to the production capacity limit. Direct subjective measures are based on nonobjective methods, like questionnaires regarding opinions about certain aspects concerning flexibility. The flexibility is then determined for instance by Likert’s scales [5]. The measurement of flexibility in a direct manner, however, can be challenging. Therefore, several authors [9, 24, 26] suggest using indirect measures.
21st International Conference on Production Research
The indirect measures are taken considering the performance of manufacturing systems related to volume flexibility. Lanza [30] measures this flexibility type according to the degree of variation of output unit costs with regard to different output levels. Indirect measures can also be taken considering the characteristics of manufacturing systems – also known as sources – that enable volume flexibility [5]. Jack and Raturi [26] concluded from their analysis that companies rely primarily on three sources for short-term volume flexibility: slack capacities, inventory buffers and labor flexibility. Manufacturing systems that operate at less than full capacity can use their slack capacity buffer to absorb changes in demand. Sethi and Sethi [4] present an approach in which the slack capacity and thus the volume flexibility is measured as the relative proportion by which the current operating time can be increased until the manufacturing system is fully utilized. The volume flexibility enabled by slack capacities can also be measured through the capacity utilization range in which the manufacturing system can run profitably [31]. The inventory of finished goods can be used in the shortterm to meet demand peaks that would otherwise exceed the capacity of the manufacturing system and thus increase the volume flexibility. The degree to which the inventory of finished goods can contribute to volume flexibility depends on the system’s ability to economically hold inventory [31] and could therefore be evaluated by the inventory cost. The lower these costs, the more efficiently a company can use inventory to cover demand peaks and the higher the potential short-term volume flexibility. The labor flexibility concerns two aspects: the reactiveness with which the number of workers can be adjusted to meet fluctuations in the level of demand and the reactiveness with which the tasks performed by workers can be changed in response to varying business demand [4]. The capability of adjusting the personnel capacity and the ability of workers to perform different tasks enables a manufacturing system to economically process a wider output range, both in terms of quantity and quality, and therefore increases its volume flexibility. The first aspect of labor flexibility can be measured by the relative number of workers that can be varied in the short-term with regard to the total number of workers. The second aspect of labor flexibility is employee-specific and can be evaluated by the relative number of tasks a worker can perform to the total number of tasks occurring. The above-mentioned measures differ from each other in terms of underlying methods, complexity and usefulness for practical applications. 4 SIMULATION OF THE REPRESENTATIVE PLANT 4.1 Development of flexibility indicators The measurement of flexibility in terms of its sources allows the use of simple and case-specific indicators and fosters the detailed understanding of the underlying mechanisms driving flexibility. Therefore, this approach is used to evaluate the volume flexibility of the manufacturing subsystems of the supplier’s plant. The indicators developed for this project have to enable the evaluation of assembly-, pre-assembly- and transportation systems and must consider the three main sources for short-term volume flexibility: slack capacities, the inventory buffer of finished goods and labor flexibility. Furthermore, these indicators must evaluate the manufacturing subsystems in terms of their flexibility potential and the flexibility requirements determined by different short-term demand fluctuations.
The assembly systems of the analyzed plant are manually operated. The system’s capacity is thus defined as the sum of the capacities of all its operators. Each transportation system consists of one operator using a single transportation technique and performing a specific transportation task. The capacity of each transportation system is limited by the capacity of its technique. The short-term volume flexibility of these subsystems in the form of slack capacities is measured using their capacity utilization (see Figure 2). Capacity utilization 100% pvf xmean
rvf
time Figure 2: Visualization of a system’s pvf and rvf The indicator for the volume flexibility potential (pvf), measures the capacity of a subsystem that is on average unutilized and thus available to absorb fluctuations of demand. The flexibility potential of a system is determined by n values of its capacity utilization (xt) measured over time and calculated using equation 1. (1) The volume flexibility requirement (rvf) measures the degree to which the slack capacity is utilized due to quantitative and qualitative fluctuations of demand. The flexibility requirement of a system is determined by n values of its capacity utilization (xt) as well as its mean (xmean) utilization and is calculated using equation 2. (2) The production of the supplier’s manufacturing system is synchronized with the production of the customer’s manufacturing system. Consequently, the supplier’s inventory of finished goods is so small that it can, as a source for the volume flexibility, be neglected. The evaluation of the labor flexibility is subsystem-specific and needs to consider both aspects: the reactiveness with which the personnel capacity as well as the work content can be adjusted. The reactiveness with which the personnel capacity of the assembly system can be adjusted is indirectly considered using the approach for measuring volume flexibility in terms of slack capacities. This is because the higher the reactiveness, the faster the capacity can be adjusted to the demand and thus the lower the mean slack capacity and the requirements. The speed with which the capacity of the assembly system can be adjusted is, within the practical relevant range of variation, solely limited by the reactiveness of the operators to perform new tasks. The higher this reactiveness, the faster the capacity can be adjusted and the lower the mean slack capacity is. Therefore, this reactiveness – as a main enabler for quick adjustments of the personnel capacity – is also indirectly considered in the approach introduced above. The personnel capacity and the variety of tasks performed by the operators of the transportation and pre-assembly systems are constant in the short-term. Therefore, their
labor flexibility remains unutilized and is thus neglected in the simulation model. 4.2 Development of the simulation model
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FL5 GI: Goods income Assembly System
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The assembly plant of the automotive supplier is modeled using the event-discrete and object-oriented simulator Plant Simulation from Siemens. The objective of the simulation study is defined as measuring the flexibility potential as well as the requirements for flexibility of the manufacturing subsystems regarding different short-term demand fluctuations, all with the use of the developed indicators. The objective defines the scope and detail of the simulation model. In order to achieve the objective, the following subsystems have to be modeled: one assembly system with 18 stations, five pre-assembly systems, one warehouse and seven transportation systems (see Figure 3). Furthermore, the internal processing logic of the manufacturing subsystems as well as the functionality of the material (MRP I) and manufacturing (MRP II) requirement planning systems must be programmed. The digital representation of the subsystems as well as the planning and controlling functionalities have to be detailed enough to enable an accurate measurement of the capacity utilization of each operator and transportation technique. The modeling of the representative plant requires a great quantity of system load, organizational and technical data. The majority of data was accessible in a quality and format that allowed further processing. Nevertheless, some data had to be manually collected and checked for plausibility. In order to reduce the complexity and thus the effort for modeling the transportation systems and the warehouse, the entire spectrum of parts that are conveyed in the assembly plant is consolidated into representative part clusters. All parts that are similar in volume and size, that are stored in the same storage section of the warehouse and that are held by the same load carrier in the same quantity, define a part cluster. The outcome of the consolidation is the definition of 90 part clusters representing all 965 parts assembled in the plant. Consequently, the modeling effort is reduced without affecting the representativeness of the simulation model. The modeling of the representative plant is conducted based on a bottom-up approach in which single elements are stepwise synthesized to subsystems and finally to the model of the entire manufacturing system. This approach has the advantage that the size and complexity of the model increase over time together with the degree of understanding of the real manufacturing system.
PA x: Pre-assembly no. x FL y: Fork lift no. y Tt z: Tow tractor no. z
Figure 3: Schematic overview of the assembly plant The validation and verification of the model is conducted throughout the entire modeling process for each submodel individually by using the debugging functionality of Plant Simulation and techniques such as animation, historical data validation, event validity tests, monitoring, trace anal-
ysis and turing tests. These tests ensure that the general functionality of the submodels is correctly implemented and that their dynamics represent the dynamic behavior of the corresponding subsystems in reality as accurately as necessary. These tests are necessary but insufficient to declare the model as validated and verified since interactions between submodels are neglected. Therefore, another test is conducted that includes the entire model. This test is based on a historical data validation in which the input and output of the simulation model is represented by historical data of the real plant. During a simulation run, several system parameters are measured and compared to the values of the same parameters measured in reality. The historical data validation showed that the dynamics of the entire model match the behavior of the real plant to a very high degree. 4.3 Definition and execution of experiments Simulation experiments are goal-oriented investigations of the simulation model in which parameters are systematically varied. In the case of this simulation study, the varied parameter is the fluctuating customer demand for final products. The goal is to evaluate the effect on the flexibility requirements of the manufacturing subsystems. In order to define values for this parameter and subsequently the experiments, the daily demand of the three most recent years is collected and analyzed. The first graphical illustration of the data was, due to several outliers, of insufficient quality for immediate analysis. Therefore, several mathematical filters were used to prepare the data before experts from the automotive supplier checked its plausibility. The analysis of the filtered data revealed that the daily demand for final products is normally distributed with a significance level of 10%. Moreover, it is shown that the demand fluctuated between 5 and 15% in the short run. The automotive supplier, however, expects the fluctuation to further increase up to 20% in the future. The potential spectrum of 5 to 20% demand fluctuation is equidistantly subdivided into four fluctuation corridors that each define one simulation experiment. Each experiment is conducted over a relatively long period and repeated multiple times to increase the reliability of the simulation results. 5
ANALYSIS AND DERIVATION OF MEASURES
The flexibility values recorded during the four simulation experiments are for the purpose of analysis exported in a spreadsheet format. 5.1 Assembly systems The flexibility values of the assembly and pre-assembly systems are consolidated and represented in Figure 4. The flexibility potential (pvf) of each subsystem is graphed just once since its value is unaffected by different demand fluctuations. The values of the subsystems’ flexibility requirements (rvf) are sorted based on the experiments by increasing demand fluctuations from the left to the right. The volume flexibility potential of the assembly system in the form of slack capacities is the lowest in comparison to the pre-assemblies and matches its requirements the best. Moreover, the assembly system absorbs increasing demand fluctuation better than the pre-assembly systems, which is indicated by the small increases in flexibility requirements relative to the flexibility potential.
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pvf rvf of experiment 1 rvf of experiment 2 rvf of experiment 3 rvf of experiment 4
Figure 4: Volume flexibility of the assembly systems The demand fluctuations are absorbed by the assembly system through adjustments of the personnel capacity – hence utilizing the labor flexibility of this system. In order to keep the slack capacities of the assembly system as low as indicated in Figure 3, the personnel capacity of this system must be adjusted almost on a daily basis. This, in turn, leads in reality to learning effect losses of the assembler and substantial efforts – assembly contents have to be reallocated according to the changing amount of workers, assembly instructions have to be updated, equipment has to be relocated and new operators might have to be trained. Consequently, the personnel capacity is in reality adjusted less frequently, thus the flexibility potential as well as the requirements are slightly higher. The volume flexibility potential of the pre-assemblies in form of slack capacities is much higher than that of the assembly system and clearly exceeds their requirements even during periods of the highest demand fluctuations. This is to a certain extent due to the design of these preassemblies as single-operator workstations that technically prevent the adjustment of the personnel capacity to the demand. It is shown that the assembly system absorbs demand fluctuations better than the pre-assemblies and that its potential matches the requirements quite accurately. Furthermore, it is indicated that this is enabled by the system’s labor flexibility. The use of this flexibility type is in practice, however, restricted. In order to better utilize the labor flexibility, different measures are suggested to reduce the related efforts and losses. The effectiveness of on-the-job the training for new operators can for instance be supported by using animated instructions that show the operator how to properly perform the assembly tasks. These instructions could effortlessly be created from already existing Digital Mock-Up models. It is also shown that the flexibility potential of the preassemblies is clearly higher than the flexibility requirements and that this is, inter alia, due to the design of these assemblies. In order to be able to utilize the labor flexibility and thus reduce the slack capacities, the work content of the preassemblies should preferably be integrated into the main assembly system. However, this is in some cases unfeasible because of technical restrictions, the unavailability of space for additional material at the assembly system and the probability that a strictly serial assembly process would threaten Just-In-Time delivery. For these cases, several pre-assembly systems are to be merged into an assembly island and designed so that it can be operated by varying numbers of operators, enabling the adjustment of the personnel capacity to the demand.
5.2 Transportation systems The volume flexibility of the transportation systems in the form of slack capacities are, as mentioned before, determined by the transportation techniques which are referred to in this section. The volume flexibility values of the transportation techniques are consolidated and graphed in Figure 5. In contrast to the flexibility situation of the assembly systems, the flexibility potential (pvf) and requirements for flexibility (rvf) differ greatly among the transportation techniques. The tow tractors as well as fork lifts no. 4 and 5 (see Figure 3) have in common that an increase in the demand fluctuations raises their flexibility requirements. In contrast, the requirements of fork lifts no. 1, 2 and 3 seem to be unaffected by changes in short-term demand fluctuations. It is to be noted that the first group of transportation techniques are used for delivering material directly to the assembly systems while the other fork lifts are separated from these systems by an inventory buffer that could cover the demand for a few hours. The flexibility potential of both tow tractors is higher than necessary with regard to the flexibility requirements. These techniques are nonetheless used to deliver parts to the assembly system with the relatively low inventory buffer. With regard to the risk of causing an out-of-stock situation it seems reasonable to maintain a small flexibility buffer. The volume flexibility potential of fork lifts no. 1 and 2 are higher than that of the tow tractors, yet they reasonably match their flexibility requirements. Fork lifts no. 3, 4 and 5, in contrast, are characterized by flexibility potentials that markedly exceed their requirements. In order to better match the flexibility potentials with the requirements it is further analyzed whether alternative transportation techniques can be used. Different alternative techniques are preselected considering the characteristics of each transportation task and the requirements for flexibility following an approach of Heinecker [32]. The technical feasibility of the alternative techniques is analyzed by experts from the automotive supplier and their profitability is evaluated based on the technique’s amortization period. The affect that these techniques would have on the flexibility potential is analyzed using the simulation model. Based on the analysis, the use of two automated guided vehicles instead of fork lift no. 3 and 4 is suggested, as well as using a powered roller conveyor as an alternative to fork lift no. 5. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
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volume flexibility
21st International Conference on Production Research
pvf rvf of experient 1 rvf of experiment 2 rvf of experiment 3 rvf of experiment 4
Figure 5: Volume flexibility of the transportation techniques
The use of these techniques would result in a better match between flexibility potential and requirements for flexibility and improve the economical situation of the entire manufacturing system. 6
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CONCLUSION AND DEMAND FOR FUTURE WORK
The measurement of flexibility potentials and requirements with indirect indicators that consider the sources of flexibility fosters the understanding of the underlying mechanisms driving flexibility. The application of simulation enables the systematic analysis of the effect that potential changes in the environment of a manufacturing system would have even on its subsystems. The approach discussed in this paper combines indirect indicators with simulation and thus enables detailed evaluations of manufacturing systems’ flexibility and helps to easily identify mismatches between flexibility potential and requirements for flexibility. Furthermore, this approach supports the development of measures for matching the potential with the requirement for flexibility. In conclusion, the introduced approach enables a detailed analysis of the flexibility of manufacturing systems and simplifies adjustments of their flexibility to the demand. Therefore, it enables companies to deliberately invest into the flexibility of their manufacturing systems and thus supports the design of optimally flexible manufacturing systems. In order to broaden the scope of potential applications for this approach, indicators for the evaluation of long-term volume flexibility and other dimensions of the manufacturing flexibility should be developed in the future.
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REFERENCES
Abele, E.; Wiegel, F.; Kuske, P., 2010, Kleine Schritte für große Flexibilität, wt Werkstattstechnik Online, 100-4, p.291-297. [2] Wiendahl, H.-P., 2004, Variantenmanagement in der Montage, Springer. [3] Abele, E.; Kuhn, S.; Hueske, B., 2008, Overall Equipment Flexibility, wt Werkstattstechnik Online, vol. 103, no. 5, p. 322-327. [4] Sethi, A. K.; Sethi, S. P., 1990, Flexibility in Manufacturing: a survey, The International Journal of Flexible Manufacturing Systems, vol. 2, p. 289-328. [5] De Toni, A.; Tonchia, S., 1998, Manufacturing flexibility – a literature review, International Journal of Production Research, vol. 36, no. 6, p. 1587-1617. [6] Roscher, J., 2007, Bewertung von Flexibilitätsstrategien für die Endmontage in der Automobilindustrie, dissertation University of Stuttgart. [7] Schuh, G.; Wemhöner, N., 2004, Lebenszyklusbewertung flexibler Produktionssysteme, wt Werkstattstechnik Online, vol. 94, no. 4, p. 116-121. [8] Westkämper, E., 2002, Die Digitale Fabrik - Kontinuierliche Partizipative Planung, in: Erfolg in Netzwerken, Milbert, J, Schuh, G. (ed.), Springer-Verlag. [9] Slack, N., 1987, The Flexibility of Manufacturing Systems, International Journal of Operations and Production Management, vol. 7, no. 4, p. 35-45. [10] Biethan, J. et al, 1999, Simulation als betriebliche Entscheidungshilfe: State of the art, Physica-Verlag. [11] Harrel, C. R.; Ghosh, B. K.; Bowden, R., 2000, Simulation using Promodel, MCGraw Hills. [12] Montevechi, J. A. B.; et al., 2007, Application of design of experiments on the simulation of a process in
[15]
[16]
[17]
[18]
[19] [20] [21]
[23]
[24]
[25]
[26]
[27] [28]
[29]
[30]
[31]
[32]
an automotive industry, Proceedings of the 39 conference on Winter simulation, IEEE Press Piscataway. Kuhnt, S.; Wenzel, S., Information Acquisition for Modelling and Simulation of Logistics Networks, Journal of Simulation, vol. 4, no. 2, p. 109-115. Müller-Sommer, H.; Straßburger, S., 2010, Methods for the Plausibility Check of Input Data for IntraLogistic-Simulation, in: Integrationsaspekte der Simulation: Technik, Organisation und Personal, Zülich, G.; Stock, P. (ed.), KIT Scientific Publishing. Rabe, M., 2000, Future of simulation in production and logistics: Facts and Visions, in: The new simulation in production and logistics: prospects, views and th attitudes, 9 ASIM-Symposium, IPK. Selke, C., 2004, Entwicklung von Methoden zur automatischen Simulationsmodellgenerierung, Dissertation University of Munich. Wang, Z.; Lehmann, A., 2007, A Framework for Verification and Validation of Simulation Models and Applications, Communication in Computer and Information Science, vol. 5, no. 10, p. 237-246. Kleijnen, J. P. C. et al., 2005, State-Of-The-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments, INFORMS Journal on Computing, vol. 17, no. 3, p. 263-289. Kühn, W., 2006, Digitale Fabrik, Hanser-Verlag. März, L. et al., 2011, Simulation und Optimierung in Produktion und Logistik, Springer-Verlag. Spieckermann, S. et al., 2000, SIMULATION, vol. 75, no. 5, p. 276-286. Bayer, J. et al., 2003, Simulation in der Automobilindustrie, Springer-Verlag. Ulgen, U.; Gunal, A., 1998, Simulation in the Automobile Industry, in: Handbook of Simulation, Banks, J. (ed.), John Wiles & Sons. Gerwin, D., 1987, An agenda for research on the flexibility of manufacturing processes, International Journal of Operations & Prodution Management, vol. 4, no. 1, p. 38-49. De’Souza, D. E.; Williams, F. P., 2000, Toward a taxonomy of manufacturing flexibility dimensions, Journal of Operations Management, vol. 18, p. 577-593. Jack, E. P..; Raturi, A., 2001 Sources of volume flexibility and their sources on performance, Journal of Operations Management, vol. 20, p. 519-548. Wolf, J., 1989, Investitionsplanung zur Flexibilisierung der Produktion, Deutscher Universitäts Verlag. Mandelbaum, M.; Brill, P. H., 1989, Examples of Measurement of Flexibility and Adaptivity in Manufacturing Systems, Journal of Operations Research Society, vol.40, no. 6, p. 603-609. Kumar, V., 1987, Entropic Measures of Manufacturing Flexibility, International Journal of Production Research, vol. 25, no. 7, p. 957-966. Lanza, G.; Rühl, J.; Peters, S., 2010, Monetäre Flexibilitätsbewertung von Produktionssystemen, wt Werkstattstechnik Online, vol. 100, no. 6, p. 530-534. Das, S.,K., 1996, The Measurement of flexibility in Manufacturing systems, The International Journal of Flexible Manufacturing Systems, vol. 8, p. 67-93. Heinecker, M., 2006, Methodik zur Gestaltung und Bewertung wandelbarer Materialflußsysteme, Dissertation University of Munich.
