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ScienceDirect Procedia CIRP 12 (2013) 354 – 359
8th CIRP Conference on Intelligent Computation in Manufacturing Engineering
Scenario-based simulation approach for layout planning U. Dombrowskia, S. Ernsta* a Institute of Production Systems and Enterprise Research Technische Universität Braunschweig, Langer Kamp 19, Braunschweig 38106, Germany * Corresponding author. Tel.: +49 (0) 531 391 2703; fax: +49 (0) 531 391 8237; E-mail address:
[email protected] .
Abstract The requirements of manufacturing companies are growing continuously. The customers demand more and more product variants, shorter delivery times and declining prices. To meet these demands, the production needs to be more flexible, highly automated and changeable. Future success is determin The future is less predictable than it used to be. One way to consider this, is to look at different future scenarios. This paper shows a scenariobased simulation approach that uses scenario technique, morphological analysis and discrete event simulation to find out factory layout variants that are adequate for future requirements. The approach is verified by a case study. © 2013 The Authors. Published by Elsevier B.V. Open access under CC BY-NC-ND license. © 2012 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Professor Roberto Teti. Selection and peer review under responsibility of Professor Roberto Teti Keywords: Factory planning; digital factory; discrete event simulation.
1. Introduction Manufacturing companies are faced with changing conditions like higher complexity in planning, higher competitive pressure and unsteady demand. This leads to new requirements in factory planning like a high flexibility of the production facilities, a high changeability and a shorter time to market. Generally, factories are built for decades rather than for years. Accordingly, there is a high uncertainty about the future requirements that has to be taken into account. [1] E.g. the market success of innovative products is hardly predictable. In addition to that, companies are diversifying their product portfolio by developing new products and more new variants of their existing products. [2] Hence, layout variants which are developed during the factory planning process are seldom considering different future scenarios. The consideration of different scenarios during factory planning allows preparing strategies for these scenarios beforehand. According to the different strategies, companies are able to flexibly adapt their production to the market conditions in a fast way. The digital factory provides tools that support companies by testing different variants
in different scenarios, e.g. the discrete event simulation of the material flow. [3] This paper shows a company in a case study that gets along with the described situation. Based on the starting situation and the challenges of manufacturing companies, the aim of this paper is the development of different factory layout variants in a very short planning period. The scenario-based simulation approach is described in chapter 2. Afterwards the verification of the approach is shown in a case study in chapter 3, which deals with a redesign of a midsized company . In chapter 4 the results of the case study are evaluated. 2. Scenario-based simulation approach The design of the new or the redesign of existing factory layouts has to consider different future situations to ensure profitability in all cases. In addition, factory layouts have to have high changeability. The approach considers different future scenarios and it integrates advantages of the digital factory by using the discrete event simulation of the production. [4] (see figure 1) The network of digital models, methods and tools including simulation and 3D visualization integrated by a
2212-8271 © 2013 The Authors. Published by Elsevier B.V. Open access under CC BY-NC-ND license. Selection and peer review under responsibility of Professor Roberto Teti doi:10.1016/j.procir.2013.09.061
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continuous data management system. Its aim is the holistic planning, evaluation and ongoing improvement of all the main structures, processes and resources of the real factory . [5] The adoption of digital factory has many advantages for companies. According to an inquiry, the advantages are an up to 30% reduction of time to market and reduced change-related costs of 15 % in average. Other advantages are a higher level of maturity of machinery and equipment as well as 5 % savings of investments.[6]
product variety result in an increase of inaccurate prognoses. The concept of a multiple future is often ignored within planning. This causes in extrapolations of actual trends, which is not realistic in the long run. Also the future situation is described depending on interests of the describing person. The scenario technique is a method which takes into account the multiple futures. A way to illustrate multiple futures is a funnel as shown in figure 2. It starts in present and ends in many possibilities for the future. [9], [10] Future projection
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Fig. 1. Steps of the scenario-based simulation approach
The core of the digital factory is the simulation, because users of simulation are able to analyze physically not existing or existing systems without any disturbance of operations. Other advantages are the possibility to compare different alternatives and to analyze the long-term behavior of a system.[7] These benefits are used in the scenario-based simulation approach by integrating the manufacturing simulation which considers material flow and utilization of machines. 2.1. Step 1: Development of different scenarios A method to illustrate different, further development possibilities is the scenario technique. It is an effective and efficient method for factory and production planning for determining the necessary level of changeability and flexibility. The findings of the method are used to determine future-robust and cost-optimal plant concepts for certain future situations. [8] Simple, conventional techniques for prognosis are not appropriate for contemporary requirements, because business environment is very dynamic and networked. So, the future cannot be described with one-dimensional systems. The shortening of product cycles and the higher
In the context of the scenario technique, a scenario is described as a possible picture of the future which does not come true with certainty. The scenario is mainly based on projections and forecasts. The complex image of the future comes from a combination of possible developments of several influencing factors. [9] The influencing factors are identified in analogy to phase -field. Therefore, the conditions of a factory are described by parameters which are essential for future success. These factors are projected for a period of five years. This corresponds to the second phase scenario projection o , which analyzes different f possible outcomes of influencing factors. These different future developments are clustered formula on of factors and formulation of future scenarios. It is appropriate to limit the number of scenarios. The scenario-based simulation approach ffocuses on the best case, best-estimate and worst-case scenario [7]. The account by using the scenario in the simulation study. [9] 2.2. Step 2: Design of layout variants A creativity technique for the development of different factory layout variants is the morphological analysis. Its systematical approach has different advantages like high informative content and its good visualization of solutions. The task of the method is a complete decomposition of a problem in sub-problems by defined criteria. [11] Hence, the morphological box considers a broad range of possible solutions of a
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problem. Its creator Zwicky defines five steps that should be used to design a morphological box[12]: 1. Brief problem description 2. Definition of problem effecting parameters 3. Construction of a multidimensional matrix that represents the morphological box 4. Evaluation of solutions within the morphological box 5. Selection of the best and most suitable solutions The initial problem of the scenario-based simulation approach is designing factory layouts (1). This problem can be divided into two types of parameters: organization and technology. Each type can be described with detailed parameters concerning factory layout variants. Examples of type organization are work organization, concept of quality assurance, manufacturing concept or material flow. Parameters of technology are information technology, production technology or production resources. The final parameter set depends on the application and industry sector. (2) [13] When all parameters are defined, the morphological box is constructed while various solutions for parameters are identified and outlined in a multidimensional matrix. (3) Afterwards all solutions are evaluated (4) and suitable factory layout variants are selected and combined out of the morphological box (5). The next step is dealing with these variants. 2.3. Step 3: Modeling of the variants In the third step, the factory layout variants of the previous step are transferred to a simulation model. Simulation is defined as the reproduction of a real system with its dynamic processes in a model. The aim is to reach transferable findings for reality. In a wider sense, simulation means preparing, implementing, and evaluating specific experiments with a simulation model 14] In the case of the scenario-based simulation approach, simulation starts with the modeling of a factory or of a segment of the production which is the real system. The simulation model is used for different experiments x to gain new knowledge about the real factory. Activities are derived from this knowledge which is aiming at an improvement. [7] The approach adopts discrete event simulation, because it is qualified for dynamic systems as complex production systems. Its characteristic is the timedependent variable of state that describes the system status on appointed events. [15] The discrete event simulation is extra beneficial for systems with diversifying intervals between changes of state. [7] There are many software tools for discrete event simulation, e.g. Plant Simulation from Siemens. By using this software, it is very important to cut the effort for modeling. This can be achieved by realizing a high degree of reuse. The degree of reuse is influenced by subdividing the model into modules. Modules are built
according to parameters of the morphologic box and to the structure of the factory. Every module is a part of a kit which is easily used to develop a model with less effort. Another important part of the discrete event simulation is the gathering of data for the model. After the system has been modeled, the data is implemented into the model. Therefore raw data is gathered and prepared for the simulation model. The data quality is critical for success of the approach as poor data quality may lead to wrong conclusions. [16] 2.4. Step 4: Experiments and optimiz i ation A simulation experiment is defined as a targeted empiric examination that analyzes the behavior of a model by repeating simulation runs with a systematical variation of parameters and structure. Simulation itself is not an optimization of the model. Nevertheless, mathematic optimization approaches can support the systematic variation of parameters to find an optimal outcome in terms of simulation goals. [14] This means for the scenario-based simulation approach that parameters of the simulation are varied with regards to scenario parameters and variants. There are two kinds of simulation experiments: terminating and non-terminating systems. Terminating systems starts with a defined initial conditions and a defined ending. However, many of the production problems are non-terminating systems. They need time to reach the steady state, called setting time. The steady state is stable without an end. During the setting time, system behavior is not representative. The setting time can be determined by statistic or by graphic analysis of a quality criterion. The results during setting time are discarded [17]. 2.5. Step 5: Evaluation of the factory layout variants The fifth step is the evaluation of the optimized factory layout variants. The utility analysis is used for the evaluation. First of all, three main categories are defined with total 13 criteria (see figure 3). These categories are technical, logistic and monetary criteria. Evaluation criteria Technical criteria
Logistic criteria
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- Capacity utilization
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Fig. 3. Step 5: Evaluation criteria[18]
Each of these criteria is weighted according to specific importance for the company. Then the optimized layout variants are graded from 0 for worst to
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improvement potentials, which is realized by interpretation of previous steps of the approach and the derivation of measures includes the testing and improving of the measures. diligently. If it is successful, then the measure is described as standard. Finally, the standard is rolled out to the production and audited constantly . [20] 3. Case Study The scenario-based simulation approach has been evaluated in a case study. Firstly, the company and the case will be described. Afterwards, the application of the six steps of the simulation approach is explained. 3.1. Description of the case study The case study deals with the redesign of the production of a medium-sized company (fig. 4), which is a manufacturer of stainless-steel products. As described in the introduction, the company also introduces new customized products. The market success of those products is not surely predictable. Nevertheless, the company expects that sales are increasing and fluctuating very much. Furthermore the demand for customized products is rising compared to the demand for standardized products. Another challenge is the relocation of production volume back to Germany. This allows new investments for higher productivity and economy of scales. The analysis showed that the company loses competitiveness due to its outdated production structure, e.g. old machinery. This results in raising order lead times and high work in process. Another handicap of the company is the originally used trial-and-error planning approach. This causes many planning mistakes like suboptimal dimensioning of machines. In this situation, the company wants to redesign and to improve the factory layout. For these reasons, the earlier shown scenario-based simulation approach is used to develop and to compare different
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The final step of the scenario-based simulation approach is the definition and implementation of improvement measures. A suitable method for this is the PDCA cycle (also known as Deming or Shewhart cycle). The cycle consists of the steps plan, do, check and act.
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factory layout variants. Especially the consideration of different scenarios is suitable to take the uncertainty of the demand and the future situation into account.
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Fig. 4. Requirements of the case study
3.2. Step 1: Development of different scenarios First of all, the different scenarios are developed. Therefore the different influencing factors are identified within the as-is analysis of the original factory layout and by interviewing the plant manager and master craftsmen. The following influencing f factors have an impact on future development of the factory: output development of the considered products ability of the products for automated manufacturing portion of customized products number of variants fluctuation of customers demand demand for other products Based on the possible future development of each of these influencing factors, the best-case, best-estimate and worst-case scenario are deduced. The best-case scenario contains an output development from 32,000 in 2010 to 300,000 units of the considered products in 2015. The ability of the products for automated manufacturing is predicted to grow from 60% in 2010 to 85% in 2015. The weekly demand fluctuation will declines fr f om 75% in 2010 down to 30% in 2015. A value is similarly defined for all influencing factor in each year from 2010 to 2015 and for the scenarios, as shown in figure 5. 300.000 250.000 Quantity
10 for best. So, a value for each criterion is calculated by multiplying the weight and grade. Finally the total utility of factory layout variant is determined by the sum of the values of all criteria. Hence, every factory layout variant gets assigned a utility that is needed to rank and to indentify the best variant.[18],[19]
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Fig. 5. Step 1: Forecast of three scenarios
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3.3. Step 2: Design of layout variants The factory layout variants are designed with help of the morphological box. The problem, which is described in the morphological box, is the design of the stainlesssteel production over all production steps. The problem will be subdivided according to the parameters. As described in the scenario-based simulation approach, there are two types of parameters in the morphological box. The organizational parameters like working time, number of shifts, transportation system, manufacturing principle (production line, island production, job shop production). The second type of parameters is the manufacturing technology. The company of the case study produces stain-less steel products. The main production steps are cutting parts out of a metal sheet, bending the parts, welding and electrolytic polishing. For each production step different technologies and different machines with respect to the size and capacity are defined. Furthermore, there are both technological alternatives (e.g. punching, laser cutting or a combination of both) and different methods of a technology (like MIG and WIG welding). The following four factory layout variants are combined out of the morphological box: 1. The first variant is the manual production. Solutions of the morphological box are selected that maximizes manual activities and minimizes automation. 2. The second layout variant is the specialized production. The principle of this variant is to get for every product group its own machinery. 3. The third layout variant is the automated production. So the degree of automation is maximized and the number of deployed worker is minimized. 4. The fourth layout variant is advancement of the actual production combined with already ordered machines.
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Fig. 6. Step 3: Simulation model with the layout of the production
3.5. Step 4: Experiments and optimization Four layout variants and three scenarios for example induce to 12 experiments. Each scenario includes data sets for 6 years. Hence, every layout variant model has to be analyzed and improved according to the experiment results. Therefore bottlenecks are identified and eliminated by different measures, e.g. new machines or additional shifts. The optimization ends, when the improved factory is able to meet the customer demand in all years and the under-utilization is moderate. 3.6. Step 5: Evaluation of the factory layout variants The results of the four optimized factory layout variants are evaluated according to the criteria in figure 3. Subtotals of each group of criteria and the total for all variants are illustrated in table 1. The best evaluated variant is the optimized advancement variant. The reasons for that are the high flexibility and high changeability of this variant.
3.4. Step 3: Modeling of the variants
Table 1. Evaluation of layout variants
Due to the differing structure of the four variants, it is impossible to establish one model for all variants which distinguishing features are different parameters. For that reason, various modules are built which are put together according to the variant (see figure 6). The following components are examples for parts of simulation model: workplaces and machinery worker with various skills (welder, operator of welding robots, operator of cutting and welding machines) control of incoming orders controls for data analysis e.g. worker utilization Another important part is gathering of data for the model. For example 316 work schedules of different products types and products variants were considered and clustered into 10 types of customized products.
Value of Manual Specialized Automated Advancement Technical criteria 270 299 336 354 Logistic criteria 295 324 327 375 Monetary criteria 199 322 320 341 Sum 764 945 983 1070
3.7. Step 6: Suggestions for the factory redesign The results of the approach are described as measures and suggestions for the factory redesign. The first suggestion is a step-by-step plan for the expansion of the capacity (see figure 7). The capacity should not be extended in one step, because it is not sure which scenario will get real. If the worst-case scenario eventuates, the capacity of the production is large enough to meet the demand (step 1 in figure 1). When the demand goes beyond 125.000 parts p.a., the capacity
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can be extend to 150.000 by using extra shifts on Saturdays and Sundays (step 2). In addition to that, there are new machines needed, when the demand exceeds the quantity of 150.000 (step 3 and 4). Besides the step-bystep plan, there are other suggestions identified. Most of the measures have been implemented to the production by using the PDCA-cycle. The results are reduced order lead time and cut in production costs. The case study affords the company ways to reduce the lead time by 70 %. This can be achieved by introducing an island production combined with a welding robot. 4 300.000
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Fig. 7. Step 6: Step-by-step plan for capacity expansions
4. Evaluation The case study shows, that the scenario-based simulation approach is feasible for developing, analyzing and evaluation various variants of the production. The step-by-step plan indicates where and when bottlenecks of machines and production steps appear. It also points out ways to remove these limitations. In addition the simulation determinates the effects of changes in the production layout according to the morphological box. The morphological box in combination with the kit of simulation modules enables the company to analyze alternative factory layouts with little extra effort when general conditions are changing. 5. Conclusion This paper has shown a scenario-based simulation approach for the analysis of factory layout variants. Firstly the approach was described in general and each step was explained in detail. Afterwards the approach was verified in a case study of a company that produces stainless steel products. By using this approach, potentials for improvements are identified for this company. An outlook for the approach is that it put small- and medium-sized enterprises in a position to benefit from the advantages of the digital factory with
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reduced effort for simulation. However, there is still a large initial effort for the scenario-based simulation approach, because kit for modules needs to be modeled. References [1] Dombrowski, U., Quack, S., Schulze, S., 2008. Development of a Continuous Factory Improvement Process for a Sustainable Proceedings of International Conference on Sustainable Product Development and Life Cycle Engineering 2008, Busan, Korea. [2] Wagner, W., 2006. Fabrikplanung für die standortübergreifende Kostensenkung bei marktnaher Produktion. München: Utz.. [3] Blumenau, J., 2006. Lean Planning unter besonderer Berücksichtigung der Skalierung wandlungsfähiger Produktionssysteme. Saarbrücken: Universität des Saarlandes. [4] Bangsow, S., 2010. Manufacturing Simulation with Plant Simulation and Simtalk. Berlin: Springer. [5] Verein Deutscher Ingenieure, 2008. Digital factory Fundamentals. VDI 4499. Blatt 1. Düsseldorf: VDI. [6] Bracht, U., Spillner, A., 2009. Die Digitale Fabrik ist Realität. ZWF 2009, 7-8, p. 648. [7] Kühn, W., 2006. Digitale Fabrik: Fabriksimulation für Produktionsplaner . München: Carl Hanser. [8] Wiendahl, HP., Hernández, R., Grienitz, V., 2002. Planung wandlungsfähiger Fabriken. ZWF 2002, 1-2, p. 12. [9] Gausemeier, J., 1996. Szenario-Management: Planen und Führen mit Szenarien . München: Carl Hanser. [10] Dombrowski, U., Engel, C., Schulze, S., 2011. Scenario Management for Sustainable Strategy Development in the Automotive Aftermarket . In: Hesselbach, J., Herrmann, C., editors. Functional Thinking for value creation: Proceedings of 3rd CIRP IPS². Berlin: Springer. [11] Disselkamp, M., 2005. Innovationsmanagement. Instrumente und Methoden zur Umsetzung im Unternehmen. Wiesbaden: Gabler. [12] Zwicky, F., 1969. Discovery, invention, research through the morphological approach. New York: MacMillan. [13] Verein Deutscher Ingenieure, 2011. Factory planning Morphological model of the factory for the target definition in the factory planning. VDI 5200. Blatt 2. Düsseldorf: VDI. [14] Verein Deutscher Ingenieure, 2000. Simulation of systems in materials handling, logistics and production - Fundamentals. VDI Richtlinie 3633. Blatt 1. Düsseldorf: VDI. [15] Domschke, W., Drexl, A., 2005. Einführung in die Operations 6th ed. Berlin: Springer. [16] Rabe, M., Spieckermann, S., Wenzel, S., 2008. Verifikation und Validierung für die Simulation in Produktion und Logistik, Berlin: Springer. [17] Wenzel, S., Weiß, M., Collisi-Böhmer, S., Pitsch, H., Rose, O., 2008. Qualitätskriterien für die Simulation in Produktion und Logistik. Berlin: Springer. [18] Kumpf, A., 2001. Anforderungsgerechte Modellierung von Materialflusssystemen zur planungsbegleitenden Simulation. München: Herbert Utz. [19] Weirich, P., 2011. Decision space. Multidimensional utility analysis. Cambridge: Cambridge University Press. [20] Walton, M., 1986. The Deming Management Method New York: The Berkly Publishing Group.
