Flexible Configuration Logic for a complexity oriented design of production systems

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1 Abstract no Flexible Configuration Logic for a complexity oriented design of production systems Volker Stich, Henrik Wienholdt Research Institute for Operations Management (FIR) at RWTH Aachen University FIR at RWTH Aachen University, Pontdriesch 14/16, D Aachen, Germany Henrik.Wienholdt@fir.rwth-aachen.de, Phone: POMS 20th Annual Conference Orlando, Florida U.S.A. May 1 to May 4, 2009 Abstract To keep work and production sites in high-wage countries like Germany, companies have to focus on the production of complex and customer individualised products. This results in the necessity of flexible and efficient production systems. In a subproject of the cluster of excellence Integrative Production Technology for high-wage countries at RWTH Aachen University a configuration logic is developed that enables companies to configure their production systems in a way that customer specific products can be produced at the costs of mass production. In the project a holistic description model for production systems has been defined. With numerous attributes in the sub models (e.g. Supply Chain) a detailed characterization of the production system is possible. Case studies helped to identify the interdependencies among all attributes. Finally external and internal complexity drivers have been identified to allow a configuration of a production system that is able to deal with dynamic influences.

2 I. Customer specific products at the costs of mass production During the last years the increasing globalization lead to a higher competitive pressure at producing companies in high-wage countries like Germany [1]. To persist within the competition, companies in high-wage countries produce customer specific and high-quality products. To be able to react quickly on individual customer needs, a high flexibility in the production planning and the production itself is obligatory [2]. Nevertheless, costs have to be held low to keep the difference to mass products that have been produced in low wage countries as small as possible. Therefore a production system is necessary that reduces the existing polylemma of production between economies-of-scale vs. economies-of-scope on the one axis and individual, flexible, value oriented production vs. planning orientated production on the other axis [1]. Vision of Integrative Production Technology Resolution of the polylemma 2020 Reduced dilemmas Scale Minimized, fixed cycle times Specific processes High synchronization value-orientation Reduce waste Standardize Focus on value adding processes 2006 timeline Dilemma planning-orientation Extensive planning Modeling, simulation Knowledge, information and data generation Scope One-piece-flow Alterable, dynamic processes Limited output and synchronization Fig. 1: Resolution of the Polylemma of Production [1] The reduction and finally the resolution of the polylemma of production is the main objective of the major research project Integrative Production Technology for High-Wage Countries at RWTH Aachen University. One of the subobjectives is the development of a configuration

3 logic that helps to optimally design production systems which allow a production of customer specific products at the costs of mass production. Therefore a description model has been developed that based on a comprehensive literature analysis describes production systems in a holistic way. The description model is the basis for the development of the configuration logic. II. Scientific and practitioners definitions of production systems From a scientific perspective the term production as the value-creating-process describes simply a transformation of materials, services, rights and information. All can be either input or output of the transformation process [3]. Furthermore, systems are collectives of objects that are related to each other in a certain way [4]. Combining both definitions a production system is the describing element for the holistic organization of the production [5]. A production system includes all necessary concepts, methods and tools for an efficient and effective transformation process of several input factors into products and services. The scientific definition of production systems implies the question for the methods, concepts and tools that are the key factors for an efficient production and thus efficient production systems. In this context practical examples for successful production systems have to be analysed. There are generally three different approaches for the configuration of the production systems: Taylorism, partly-autonomous group work and the Toyota Production System (TPS) with its derivates [6]. Taylorism in this context is the application of work separation based on F.W. Taylors Scientific Management approach to achieve economies-ofscale by a takt-based mass production [7]. On contrary the approach of partly-autonomous group work was based on the flexibilisation of work in the hope for a higher productivity with highly educated and autonomous workers [6]. The third approach, the Toyota Production System, seems to be the most successful approach within the last decades. Continuously developed by the japanese car manufacturer Toyota since the mid of the last century, the TPS

4 is a success story by its own [8, 9]. Aiming at the elimination of all not-value-adding production steps, the TPS is nowadays a worldwide known and applied collection of methods like KANBAN and KAIZEN. Derivates of the TPS can be found in most of the big companies around the world. III. A holistic model for the detailed description of production systems Within the research project Integrative Production Technology for High-Wage countries, a framework for production systems has been defined. This includes the whole value chain from sourcing at the resource market with its suppliers via different production steps within a company until the distribution logistics to the customer. Thus, it allows a holistic view on the whole production system. Additionally in focus are the labour market as supplier of qualified work force and the technology market which mainly influences the quality of the companies production and assembly facilities as well as abilities. The central element of the model is - in relation to Porter s value chain - the production process [10]. All elements within the production system are linked with the companies quality management system. Labour market Inter-company production system (production network) Procurement market Product architecture Quality Management System Product Supply Chain Product programme Supplier Procurement Production processes Technology Distribution Customer Sales market Technology market Fig. 2: Scope of the configuration logic

5 Therefore the model consists of different partial models: Distribution, Sourcing, Quality Management, Production Process, Production Technology and Product Architecture (see Fig. 2). In a multi-staged procedure all these partial models have been detailed with describing and characteristic elements. In analogy to the morphological analysis a detailed model for the production system was described. Within the next steps, relations and interdependencies among different partial models have been identified. Finally the unified modelling language (UML) has been used to combine the describing elements and its interdependencies in one form of notation (see Fig. 3). Only by the consideration of all directly or indirectly participating elements of the valuecreation-process a holistic description of the production system can be assured [11]. The description model has been validated in several industrial case studies with different companies. This assured the completeness of the description model. Further, more detailed case studies will assure the validity of the identified interdependencies and restrictions between the describing elements. This yields to the regulations that are needed for the development of a configuration logic. market 1. Kundenverhalten Labour 1 2 Inter-company production system (production network) Preissensibilität hohe Preissensibilität m ittlere Preissensibilität geringe Preissensibilität Quality Management System vereinbarte Reaktionszeit vereinbarte Reaktionszeit von Verfügbarkeitsanforderungen von Stunden wenigen Tagen vereinbarter Servicelevel von % Product Product architecture Product programme Nachfragevolatilität stark schwankend m ittel schwankend leicht schwankend Procurement market Supply Chain Supplier Procurement Production processes Distribution Technology Customer Sales market Verbrauchsmodell konstant trendartig saisonal sporadisch Kundenänderungseinflüsse in großem Umfang gelegentlich selten gar nicht 2. Produktionseigenschaften a) nicht-physische Produktionseigenschaften Produktionsneuauflagezyklus < 6 Monate 6 Monate-3 Jahre 3 Jahre-9 Jahre > 9 Jahre Produ kttyp innovativ überwiegend innovativ überwiegend funktional funktional Technology market Partial model: Supply Chain Partial model: Production process Partial model: Partialmodell: Product model: External markets Partial model: Technology 4 Partial model: QM-System 3 Fig. 3: Development of a holistic description model

6 IV. Complexity in Production Systems In intuitively lead discussions, there is surely consensus that production systems are complex systems. Nevertheless, a more detailed definition of complexity is needed for a scientific view on complexity in production systems. Schwaninger [12] defines the complexity of a system as the number of states and behaviors the system can adopt. Therefore the complexity of a system is proportional to the amount of information needed to a) describe the system and b) dissolve the associated insecureness of the systems possible states. In Cybernetics as the related science the complexity of a system is measured with variety which describes the number of possible states a system can adopt. The variety is directly dependent of several further measures, which include the amount and difference of the elements within a system and all possibilities of interaction of these elements. Furthermore there is a dynamic inherent to the system: the predictable and non-predictable change of the systems behavior. Therefore, Schuh et al. [13] define the real complexity within a production system as the unpredictability of the production system s behavior; the system s dynamic. The numbers of elements and their interdependencies can be defined as the complicated character of systems. It is not complex as it can be dealt with structuring approaches like the above described holistic model of a production system (see Fig. 4). In addition to the description model an analysis of the complexity within the production system is necessary for the development of a complexity oriented configuration logic for production systems. In this context complexity drivers have to be identified within production systems. Complexity drivers are all influence factors and elements that lead to a rise of the complexity level within the system [14]. According to the above given definition it has to be distinguished between complexity drivers that increase the complexity of the system (the dynamic) and those that make the system more complicated. Therefore there are two kinds of complexity drivers. Firstly there are complexity drivers that simply lead to an

7 increase of the number of elements within the system like the number of products and product variants, the length of the bill-of-material or the number of production steps necessary to produce the finished goods. Secondly there are complexity drivers that lead directly to an increase of the dynamic in the system. Measurable examples for those complexity drivers are the deviations of the lead time, the cycle time, the production time, the production quality or the deviation of the customer demand. All those complexity drivers can be measured with the standard deviation. Fig. 4: Classification of complexity [15] V. Complexity oriented configuration of the production system A detailed analysis of the complexity drivers, especially of measurable complexity drivers, allows the use of different approaches for a complexity oriented configuration of a production system. In this context a classification of the complexity drivers is helpful. Thus, Suh s definition of different classes of complexity has been used [16]. The approach distinguishes four different kinds of complexity: real complexity time-independent imaginary complexity

8 time-dependent combinatorial complexity time-dependent periodic complexity Imaginary complexity in this context is defined as complexity due to a lack of information about the system and its behaviour. Imaginary complexity can be eliminated by improving the understanding of the system and by gaining more knowledge about the system. This is comparable to the structuring approaches for better understanding the complicated parts of systems. On contrary, periodic complexity increases over time. Nevertheless, it can be eliminated periodically by a restart of the system. Simple examples are the periodic cleaning and elimination of all splints in metal-cutting production technologies or implemented buffers within flow oriented production lines [16]. Therefore a simple guideline for different complexity drivers according to their classification can be developed. It will help to configure the production system with a minimum of complexity as soon as all complexity drivers of production systems are characterized and classified. % of total demand 80% Make-to-Order High priority in production schedule Utilise quick response and continuous replenishment concepts Forecast for capacity, execute to demand Lean Make-to-forecast Low priority in production schedule Manage inventory centrally Seek economies of scale Agile 20% % of products Fig. 5: Segment specific configuration of the Supply Chain In addition to the above described classification of complexity drivers, it is helpful to distinguish the complexity drivers by the impact they have on the complexity of the whole system. A pareto-analysis of the complexity drivers will help to identify, which complexity

9 drivers have over-proportional impact on the system. Distinguishing by the impact of complexity drivers for different subsystems of the production system, segments of the production system can be identified, that can be treated differently. An example for a segment specific configuration of a supply chain according to Christopher and Towill [17] is given in Fig. 3. VI. Conclusion In this paper the development of a holistic description model for production systems has been shown. The model consists of all describing elements for the subsystems of a production system. The subsystems as defined in this paper are the production technologies, the production processes, the products and its architectures, the supply chain, the quality management and the related resource market as well as the market for the finished goods. Furthermore it has been discussed, that complexity is inherent in production systems. Methods have been described, that will help to measure the complexity in the system, structure it and hence yield to a complexity oriented design of the production system. The next steps of the research are the development of detailed guidelines for different complexity drivers. This is based on the definition of measurable complexity drivers and their classification. The result of the research will be a configuration logic that on the one hand describes the production system with all its elements and their interdependencies. On the other hand, methods to deal with dynamical influences are defined by developing guidelines that allow managers to focus on the complexity drivers that mainly lead to the unpredictable behavior or the system. VII. Acknowledgments The illustrated Polylemma of Production and the derived Configuration Logic for Production systems are research results of the major research project Integrative Production Technology

10 for High-Wage Countries, wherein the development of the Configuration Logic is a subproject. The depicted research has been funded by the German Research Foundation DFG as part of the Cluster of Excellence Integrative Production Technology for High-Wage Countries. More information about the Cluster of Excellence can be found on the webpage ( or by contacting the authors of this publication. VIII. Literature [1] Schuh, G.; Kreysa, J.; Orilski, S.: Integrierte Produktionstechnik. In: Schuh, G.; Klocke, F.; Brecher, C.; Schmitt, R. (Hrsg): Excellence in Production. Aachen: Apprimus 2007, p [2] Fleischer, J.; Ender, T. und Wienholdt, H.: Ein simulationsgestütztes Optimierungskonzept für Produktionssysteme. In: Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF), 101 (9) 2006, p [3] Dyckhoff, H.: Grundzüge der Produktionswirtschaft, Springer, Berlin and Heidelberg, [4] DIN Teil 5: Regelungstechnik und Steuerungstechnik - Funktionelle Begriffe, Beuth [5] Schuh, G. (Hrsg.): Produktionsplanung und steuerung Grundlagen, Gestaltung und Konzepte. 3rd edition, Springer, Berlin and Heidelberg [6] Bullinger, H.-J.; Korge, A.; Lentes, H.-P.: Produktion und Arbeitspolitik - Herausforderungen und Perspektiven im Rahmen der Globalisierung. In: Forum Automobilindustrie, 1999, p [7] Taylor, F.: The Principles of Scientific Management. Harper & Brothers Publishers, New York and London [8] Ohno, T.: The Toyota Production System: Beyond Large-Scale Production. Productivity Press, Portland, 1988.

11 [9] Womack, J.; Jones, D. und Roos, D.: The Machine that changed the World: The Story of Lean Production, Harper Collins, New York [10] Lindemann, U.; Reichwald, R.; Zäh, M.: Individualisierte Produkte - Komplexität beherrschen in Entwicklung und Produktion. Springer, Berlin and Heidelberg [11] Hinke, C. u. a.: Individualisierte Produktion. In: Schuh, G.; Klocke, F.; Brecher, C.; Schmitt, R. (Hrsg): Excellence in Production. Aachen: Apprimus 2007, p [12] Schwaninger, Markus: Systemtheorie. University of St. Gallen, Institut für Betriebswirtschaft, [13] Schuh, G.; Gottschalk, S.; Kupke, D.: Individualisierte Produktion Flexible Konfigurationslogik zur Gestaltung von integrativen Produktionssystemen. In: wt online 98 (2008) H. 4, p [14] Meyer, C. M.: Integration des Komplexitätsmanagements in den strategischen Führungsprozess der Logistik. Haupt, Bern [15] G. Schuh, Produktkomplexität managen. Strategien - Methoden Tools 2nd ed., Hanser, Munich [16] Suh, N. P.: Complexity - Theory and Applications, Oxford Press, [17] Christopher, M. und Towill, D.: An Integrated Model for the Design of Agile Supply Chains, International Journal of Physical Distribution and Logistics Management, Vol. 30, No. 4, 2001.