Abstract no. 011-0535
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-52062 Aachen, Germany Email:
[email protected], Phone: +49 241 477 05 421
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.
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 Reduced dilemmas
2020 Scale
planning-orientation
Minimized, fixed cycle times Specific processes High synchronization
Extensive planning Modeling, simulation Knowledge, information and data generation
2006 Dilemma value-orientation
Reduce waste Standardize Focus on value adding processes
Scope
timeline
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
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
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 Procurement market
Inter-company production system (production network)
Quality Management System Product Product programme
Product architecture
Supply Chain Procurement
Production processes
Technology
Distribution
Customer
Sales market
Supplier
Technology market
Fig. 2: Scope of the configuration logic
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. Labour market
Procurement market
1
1. Kundenv erhal te n Preissen sibilitä t Auspräg ung in %
Inter-company production system (production network)
ho he P re iss ensibilität
m it tlere Preiss ensibilitä t
gering e P re is se ns ibilität
ve re inb arte Rea k tion sz eit vo n___ _Stu nden
vereinbarte Reaktionsze it v on wenigen Tage n
v erein barter S ervice level von ____ %
Nac hfrag evo latilität Auspräg ung in %
s tark sc hwa nk end
m itte l sc hwanken d
le icht sc hwank en d
Verbra uch sm od ell Auspräg ung in %
kons tant
trenda rtig
s aiso nal
s porad is ch
in große m Um fa ng
gele gent lich
se lte n
gar nicht
Quality Management System Verfü gbarkeitsanfo rd erunge n
Product Product programme
Product architecture
Supply Chain Procurement
Production processes
Distribution
Customer
Sales market
Supplier
2
Auspräg ung in %
Technology
Kun denä nderun gsein flü sse Auspräg ung in % 2. P roduktionseige nschaften a) nich t-p hysisc he Produ ktion se ige nscha ften Produ ktion sn euau fla gezyk lus Auspräg ung in % Produ kttyp Auspräg ung in %
< 6 Mo nate
6 Mon ate-3 J ah re
3 Ja hre-9 Jahre
> 9 Jahre
innovativ
überwiegen d innovativ
übe rwiege nd f unk tional
f unktio nal
Technology market
Partial model: Supply Chain Partial model: External markets
Partial model: Production process
Partial Partialmodell: model: Product
Partial model: Technology
Partial model: QM-System
4
Fig. 3: Development of a holistic description model
3
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
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
• 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 • Make-to-Order • High priority in production schedule • Utilise quick response and continuous replenishment concepts • Forecast for capacity, execute to demand
80%
Lean
• Make-to-forecast • Low priority in production schedule • Manage inventory centrally • Seek economies of scale
20%
Agile
% 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
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
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 (www.production-research.de) or by contacting the authors of this publication.
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J.;
Ender,
T.
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