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ScienceDirect Procedia CIRP 17 (2014) 645 – 650
Variety Management in Manufacturing. Proceedings of the 47th CIRP Conference on Manufacturing Systems
Complexity patterns in the advanced Complexity Management of value networks Jens Jägera*, Andreas Klutha, Anja Schatza, Thomas Bauernhansla,b a
Fraunhofer Institute for Manufacturing Engineering and Automation – IPA, Nobelstraße 12, 70569 Stuttgart, Germany b Institute of Industrial Manufacturing and Management – IFF, University of Stuttgart
* Corresponding author. Tel.: +49-711-970-1899; fax: +49-711-970-1927. E-mail address:
[email protected]
Abstract The way of dealing with the strongly increasing complexity of the company itself and its environment has become a key competitive factor. Complexity factors in a variety of different business areas require an advanced Complexity Management. Therefore, knowledge regarding the specifics of the respective complexity, the so-called Complexity Footprint, is decisive to meet requirements and to derive measures by using appropriate instruments. The current Fraunhofer IPA empirical study “advanced Complexity Management – the new management discipline” with more than 190 industrial participants shows, that companies expect a future increase in complexity, but not yet have the tools to deal with it. Furthermore, complexity management is mostly focused on the complexity field product and here in product modularization and variety management. The importance of ideal complexity, of product profitability in response to product complexity in connection with complexity in process and organization is mostly ignored. Within this paper the different activities and instruments of advanced Complexity Management are presented. This includes the approach of complexity patterns in value networks including production and supply chain as well as the summary of several complexity patterns to the Fraunhofer IPA Complexity Footprint. First an up-to-date survey on complexity in value networks is given. Then, the Stuttgart complexity comprehension is introduced. To define the external and internal complexity in socio-technical systems like value networks, the differences are presented. The difference between complicacy and complexity is given, within the complexity dimensions variety, heterogeneity, dynamics and opacity. After this, complexity fields such as goods and services, process and organization as well as their several subfields connectivity and interdependency are established. Examples for complexity in each field are given to highlight the different appearance of complexity. Following, the advanced Complexity Management is introduced and finally the Fraunhofer IPA Complexity Footprint is introduced. Within this Complexity Footprint the complexity patterns in value networks are located and a description is given.
© 2014 2014The Elsevier B.V. This is an access © Authors. Published byopen Elsevier B.V.article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the International Scientific Committee of “The 47th CIRP Conference on Manufacturing Selectioninand responsibility of the International Scientific Committee of “The 47th CIRP Conference on Systems” thepeer-review person of theunder Conference Chair Professor Hoda ElMaraghy. Manufacturing Systems” in the person of the Conference Chair Professor Hoda ElMaraghy” Keywords: advanced Complexity Management; complexity pattern; production and supply chain
1. Introduction Economic policy megatrends such as demographic change, climate change or increasing digitalization are key drivers for the current and future success of worldwide value added networks [1-2]. In addition, the rise of many developing countries leads to the emergence of new power centers and a new balance of forces in the global markets; the consolidation
pressure is increasing in Western countries. The trend of increasing digitalization and current developments towards the so-called fourth industrial revolution (Industry 4.0) show that in the near future there will be demand and potential for tremendous flexibility and adaptability of value networks [3]. These developments pose a challenge on global value added networks, including their production and supply chain, with a variety of complex design and decision tasks.
2212-8271 © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the International Scientific Committee of “The 47th CIRP Conference on Manufacturing Systems” in the person of the Conference Chair Professor Hoda ElMaraghy” doi:10.1016/j.procir.2014.01.070
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The significant increase in complexity in recent years is already perceived by industrial companies worldwide [4]. In a study of Camelot Management Consultants AG [4] 83 % of the 150 surveyed senior executives perceive the achieved complexity level in their companies as too high. Despite this growing importance only 11 % of companies have access to adequate tools for complexity management. In a study by Fraunhofer IPA [5] of about 190 participants 82 % believe that complexity in the future will become increasingly relevant. A Study of IBM Corporation [6] with more than 1500 chief executive officers worldwide documents three substantially matching views. First, the respondents expect that the soaring increase of complexity will be the biggest challenge. Second, they state that their enterprises are not able to deal with this global complexity effectively. Third, the participants identify creativity as the most important leadership skill for enterprises that want to find their way through the complexity jungle. This leads to the conclusion that enormous improvement potential can be released by a successful advanced Complexity Management in value networks. 2. Up-to-date survey on complexity in value networks Before introducing the advanced Complexity Management approach, already existing approaches are presented. In the theoretical approach of Kaluza [7] complexity dimensions of value networks are described with simple mathematical formulas. In Wilson’s practical method of triangulation [8] complexity is quantified, but not in an exact scientific method. The approach of the international complexity management in the automotive industry from Schoeller [9] focuses on the product complexity and its impact on process and organization. The complexity of most value distribution of Schuh [10] serves as a model for the representation and design of the system behavior of production networks in the site and site structure planning. In the model of Giessmann [11], the causal relationships between analytical complexity in logistics are described empirically. In the approach used by Lammers [12] for complexity management of distribution, systems complex vectors are elaborated, based on subjective management decisions, which serve as subsequent recommendations for strategic decisions. In the design model of Mayer [13] for the management of complexity in industrial logistics, the logistics is modularized to the economic modules, in order to optimize the logistics management using suitable instruments. In the method of Meyer [14], the requirements of complexity management in the strategic management process of logistics are integrated, based on an approach of the Balanced Scorecard. The complexity evaluation model by Blockus [15], based on the Analytic Network Process (ANP), which is based essentially on the model of the mathematician Saaty [16], is an approach to solving decision problems. The model of Blockus is specifically designed to determine the complexity of service companies. A unified picture of complexity measurement or quantifying is missing. Although complexity management in value networks comprises theoretical methods to manage the existing complexity, a unified understanding has not been
achieved. Successful complexity management in value chains requires a holistic approach as well as the consideration of hidden monetary potentials in the socio-technical system. 3. The Stuttgart complexity comprehension The Stuttgart complexity comprehension is based on two principles: distinction between external and internal complexity as well as division of the internal complexity into the four complexity dimensions. 3.1. Demarcation external - internal complexity Progressively increasing external complexity can only be met with an equivalent internal complexity. Therefore, complexity is adjusted within the value networks to external complexity required and cannot be viewed in isolation [17]. In this context, internal complexity represent complexity inside a value network, external complexity describes complexity of the value network environment [18]. Internal complexity is ideal if it corresponds to the respective external complexity [19]. Therefore it has the right level of internal complexity. If internal complexity is low, external complexity cannot be met sufficiently. The complexity management is therefore not effective. If internal complexity is too high, unnecessary expenses incurred in the value network, so the management of complexity in the value network is not efficient [20]. 3.2. Complexity dimensions The German dictionary Duden describes complexity as the multi-layer nature or the interplay of many features [21]. Latest scientific approaches which deal with complexity directly or indirectly do not have a consistent definition of complexity [22-27]. Due to the variety of definitions of complexity that do not allow unambiguous determination, in everyday speech the term complexity is often equated with the term complicacy [11, 28-29]. With a closer look at the two concepts of complicacy and complexity it gets clear that they are indeed in a narrow context to each other, but complicacy only maps a part of complexity [29]. In the literature complexity is often defined as the variety and heterogeneity of systems, problems algorithms or data [30]. Complex systems, therefore, include only the two dimensions variety and diversity, and represent predictable and accurately previsible systems [29]. However, this definition with two dimensions describes complicacy only. The disadvantage of complicated systems is given through the fact that they do not occur in the context of social systems [29]. To meet this aspect, a total of four dimensions must be taken into account [31], including variability and ambiguity [32-34] respectively variability and uncertainty [11]. Thus, complexity involves the four complexity dimensions variety, heterogeneity, dynamics and opacity [20]. 4. The complexity fields A first step to improve transparency is the systematic subdivision into complexity fields. These are the areas in the
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socio-technical system in which complexity arises [35], such as in processes, organization or the product itself [8, 30]. Therefore, the Fraunhofer IPA complexity fields are divided as following: x Goods and services with sub-fields goods and services portfolio, customer portfolio, markets and materials x Process with sub-fields technologies, order processing and IT-systems x Organization with sub-fields network, production and staff In Fig. 1 external complexity, complexity fields and complexity dimensions are presented in context. The complexity field goods and services as interface between external complexity (orange) and internal complexity fields (dark green) are shown with a corresponding color gradient. To map, how various complexity types in production and supply chain can occur, different appearances of complexity are presented in the following section. 4.1. Goods and services complexity Today's goods and services contain a high degree of complexity. Good examples are goods and services in automotive industry. Automobiles contain a particularly high product complexity, because it is manufactured of more than 10,000 existing individual parts [36] nowadays. Furthermore, automotive corporations offer a wide variety and diversity of product configuration options [37]. The goods and services becomes complex due to the combinatorics of the product with various services during the purchase and operation of the vehicle. One way to deal with high goods and services complexity, in detail in sub-field goods and services portfolio complexity, is the approach of a big technology enterprise from California. It selects a minimalist approach and provides only a minimal variant selection. This is based on the recognition that variety complicates the decision for the customer and, therefore, leads to so-called customer confusion [38]. A negative side effect: variants cause additional costs along the supply chain [39]. 4.2. Process complexity Increased variety and heterogeneity of machining processes result in an increasing number of choices for processing and an even greater number of combinations. This leads to the question, which processing technology is best to use for which processing step.
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For instance, in the automotive sector during the process step door welding, the process technologies resistance spot welding or laser point welding can be operated. Which process is the best choice for which product and organization? Maybe the best solution is the most cost-effective, but which one is more cost-effective depending on the duration of use? Perhaps a sequential combination of both methods could be the best solution. Another major issue in the complexity field process is the unification and standardization of processes in particular manufacturing and assembly processes. An example is the standardization of assembly processes at a manufacturer of sports cars [40]. The aim is, that components differ in appearance, but can be mounted in the same way. The product variants are to be created as late as possible in the production process. Therefore, the assembly concepts for all series are unified, despite a completely different appearance. For example, the rear lights of two different series have a completely different appearance. For the mechanic, however, they are in the same assembly process [40]. IT should serve the company coping with complexity. However, often IT causes additional complexity. IT complexity results from the variety and heterogeneity of the individual IT elements in operation. It is influenced by the dependencies, the inconsistencies and the redundancies of IT elements. Another big influence has the IT inherent change dynamics in relation to changes of individual IT systems, but also the change of the interface compatibility with other IT systems. Furthermore, the changing decision makers over the years left their mark in the IT department of an enterprise, each one with own ideas, goals and requirements. Finally, every few years there are new technology waves, so-called pile-up effects [41]. Two approaches to oppose this growing IT complexity are presented here shortly. The first approach is IT governance. IT governance provides the frame for decision making rights and responsibilities to promote desired behavior in the use of IT as well as to ensure the optimal operation of IT to achieve the business goals [42]. The second approach is Enterprise Architecture Framework. Enterprise Architecture Framework is the structuring and development of the alignment of enterprise IT with business goals. Technical standards are set and monitored, simplification and flexibility potentials are determined as well as the future corporate IT landscape is designed purposefully [43-44]. 4.3. Organization complexity
Fig. 1 External complexity, complexity fields and complexity dimensions
When dealing with organization complexity, it is necessary to have the adequate enterprise structures. In recent years, this is in research under the label of organization bionics or biorganics [45]. But Warnecke already accepted this challenge with the concept of the fractal company [46]. The fractal organization takes natural systems as a model [45]. These natural systems move in a turbulent and chaotic environment. The organization of production and supply chain is the opposite: often deterministic. The structures of the fractal organization are the flexible link between these two extremes. In the approach, the organization represents a living
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organism in which the fractals, organizing units themselves, pursue the objectives of the overall system broken down for the fractal. This leads to decentralized managed organizations with small control loops, clear rules and self-similar structures that enable continuous communication and continuous improvement development. This is based on the principles of fractal organization [46]: x Self-similarity: In every part of the organization (fractal), the structure of the entire organization is reflected. x Self-organization: Organization at operational and strategic levels. Responsible individual action, evaluation of their own effectiveness and verification of compliance of own targets. x Dynamic and vitality: Effective response to changing market conditions. x Goal-orientation: Self-defined goals are in line with corporate objectives. They are matched with the upstream and downstream units. The objectives are based on processes and not on structures, they are feasible and manageable. For years, the largest European automobile manufacturer has used the approaches of the fractal company consistently [47]. Now they are on their way to become the world’s leading automobile group [6]. The current developments in Integrated Industry direction supply a further increase of complexity in the organization structure [3]. The variety and heterogeneity number and variety of cyber-physical systems, each equipped with sensors and actuators generate a strong increase in data traffic within the production system. Within the value stream employee, material, machinery, infrastructure and maintenance provide their current state and respond to relevant messages. This takes place all across value streams, fractals, areas and factories within the entire value network. 5. Advanced Complexity Management approach In the following section the advanced Complexity Management approach will be explained from leads to the right level of complexity in order to be successful on the market and react optimally to external complexity. It is important to use the right strategies in order to approach complexity, to approximate the right level of complexity and to deal with complexity. 5.1. Human beings’ response strategies on complexity In situations where human beings are confronted with complexity challenges there are five general human response strategies [48]. The first human strategy on complexity is trial and error. Human beings try to solve complexity and fail. Then, the next try and fail again. This does not pursue a learning strategy; in each step success is more likely. The second human strategy to deal with complexity is to hide or ignore the complex problem. A problem appears and people don’t look at it, but the problem still exits. The third human strategy is to obtain the rational of complexity in detail. This strategy is based on the assumption that once a problem is
detected entirely, it can be mastered. However, complex problems, by definition, cannot be understood in detail. The fourth human strategy is to focus on individual factors, the trivialization by subdivision and abstraction. The approach to see and treat a complex system as a complicated system, destroys the complex system. The fifth human strategy is intuitive evaluation on complexity and the reduction of complexity by pattern formation on the basis of the learned. However, the knowledge base of an individual human being’s intuition is too narrow to be successfully on time. 5.2. Successful strategies for the identification and quantification of complexity Based on the five general human response strategies [48] the Fraunhofer IPA approach on successful strategies for the identification and quantification of complexity combines three promising strategies and thus compensates their respective weaknesses. Therefore, the three strategies are adjusted according to their deficiencies and sensibly combined to obtain the best possible result as follows: The Feeling: the human being intuitive review leads to the reduction of complexity by pattern formation on the basis of the learned. To compensate the lack of experience of human being individuals the Feeling is based on the knowledge of the diversity of heterogeneous groups. The Mind: the rational understanding and penetration leads to an understanding in detail. To compensate the disability of human beings to fully understand the complex challenge, the Mind uses the 80/20 rule: with 20% of detailed understanding, 80% of problems can usually be solved. Therefore, first action is a prioritization of the level of detail and an efficient approximation. The View: Focusing on individual factors leads to a trivialization by subdivision. To compensate the weakness of destroying the complex system and abstract it to a complicated system it is required to consider the associated network and relations as e.g. connectivity or divergence of the system elements. 5.3. Level of complexity The right level of internal complexity is crucial for value networks to be successful on the market, to act and respond on external complexity optimally. This is done first with the analysis of the goods and services and leads to the question: with which products my value network makes money and with which products my enterprise loses money? Profitability (which complexity affects enhances/lowers?), Liquidity (which complexity promotes/disables?) and cash flow (which complexity is progressive/regressive?) are taken into account. Thereafter, it is investigated how complexity can be more through successful advanced Complexity efficient Management. For this, the growth effects (which product classes have growth potential?), synergy effects (which product classes have similarities?) and spill-over effects (where work umbrella effects or cannibalization effects?) are considered.
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5.4. Systematic derivation of procedures in place Crucial for successful advanced Complexity Management is the choice of the right strategy. The following advanced Complexity Management strategies are available: x Dealing with complexity: efficient handling of unavoidable internal complexity, e.g. the adaptation of organizational structures, the increase of transparency in order processing or transformation of process interfaces. x Reducing complexity: targeted degradation of the identified over-complexity, e.g. the elimination of unprofitable and not worthwhile product variants, reduction of non-value added process steps or reduction of interfaces, both on the side of the IT systems as well as from an organizational perspective. x Avoiding complexity: preventive the emergence of complexity, e.g. modularization and standardization of products, processes or organizational structures. x Pricing complexity: reasonable pricing of product complexity (e.g. for which complexity the customers are willing to pay) and their caused complexity in processes and organization. x Generating complexity: external complexity is higher than internal complexity, complexity is not effective, more internal complexity is needed. 6. Fraunhofer IPA Complexity Footprint The Fraunhofer IPA Complexity Footprint as part of the advanced Complexity Management contains following work steps: selection, identification and quantification as well as orchestration. In selection, the goods and services are analyzed. Profitability, liquidity and cash flow as well as growth effects, synergy effects and spill-over effects are considered. This leads to an allocation of goods and services which influences the next work step. In identification and quantification the complexity fields process and organization including their sub-fields are investigated. Therefore, the value network is split into its subdivisions and aligned with the complexity field. To get first approximation of the complexity in the different fields, methodical workshops and systematical interviews with at least five employees in different functions are evaluated and documented. With this wide data base it is possible to focus on challenging fields first. In these fields, the complexity dimensions are methodically broken down into complexity performance indicators. To support this progress, existing key performance indicators and data are used, which are already recorded and stored by the value network. Furthermore, the indicators are placed in this common context and their connectivity is studied. Subject to the goods and services this leads to new connections and links and is mapped in the complexity pattern Fig. 2. Goods and services are divided regarding profitability and growth potential and presented in three colors: from green with high profitability and growth potential to red with low profitability and growth. Several complexity patterns are built for each complexity sub-field. In total, this complexity patterns form a general overview of the whole value network: the Complexity Footprint.
Fig.2 Complexity patterns in Complexity Footprint
The results allow the derivation of the best strategies for successful advanced Complexity Management. A distinction is made between dealing, reducing, avoiding, pricing and generating complexity as well as their respective combinations, the orchestration. It is the key to select the right instruments and tools on a case-by-case basis and to orchestrate them with other instruments or tools, if necessary. Therefore, the most appropriate advanced Complexity Management instruments and tools can be selected and recombined to obtain the best result for each unique value network. The successful implementation of the Complexity Footprint generates following results: x Monetary potential: reduce non-value and value-adding costs, increase revenue and profit. x Pricing potential: suitable price of goods and services regarding complexity. x Priority and roadmap: high potential complexity fields and successful (profitable) complexity patterns. 7. Conclusion Worldwide constant complexity growth leads to increasing complexity in value chains. Therefore complexity management is an important competitive factor. Only futureoriented approaches can be sustainable through profitability and growth in the market. Therefore the holistic approach of advanced Complexity Management offers the matching strategies. It takes into consideration. The Complexity Footprint contains complexity patterns for the complexity fields goods and services, processes and organization. The analysis of the complexity patterns provides actual and future monetary and pricing potentials as well as linked priorities. To apply the Complexity Footprint in industrial value chains, it takes appropriate tools for implementation. For this purpose existing tools will be adapted and new tools developed. Additional research in comparing and benchmarking of complexity profiles of industry-specific profiles, as well as the balance of internal company-with external complexity is required. References [1] Gregosz, D.: Economic policy Megatrends 2020. Wirtschaftspolitische Megatrends bis 2020. What to expect in the coming years? Berlin: Konrad-Adenauer-Stiftung (Analysis & Arguments, 106), 2012. [2] Kojm, C.: Global trends 2030. Alternative worlds: a publication of the National Intelligence Council. Washington DC, 2012.
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