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Procedia CIRP 17 ( 2014 ) 645 – 650

Variety Management in . 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

aFraunhofer Institute for Manufacturing Engineering and Automation – IPA, Nobelstraße 12, 70569 Stuttgart, Germany bInstitute 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 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.

©© 20120144 The Elsevier Authors. B.V. Published This is an by openElsevier access B.V. article under the CC BY-NC-ND license Selection(http://creativecommons.org/licenses/by-nc-nd/3.0/ and peer-review under responsibility of the International). Scientific Committee of “The 47th CIRP Conference on Manufacturing Systems”Selection in and the peer-review person of the under Conference responsibility Chair Professor of the International Hoda ElMaraghy Scientifi. c Committee of “The 47th CIRP Conference on Manufacturing Systems” in the person of the Conference Chair Professor Hoda ElMaraghy” Keywords: advanced Complexity Management; complexity pattern; production and supply chain

1. Introduction pressure is increasing in Western countries. The trend of increasing digitalization and current developments towards the Economic policy megatrends such as demographic change, so-called fourth industrial revolution (Industry 4.0) show that climate change or increasing digitalization are key drivers for in the near future there will be demand and potential for the current and future success of worldwide value added tremendous flexibility and adaptability of value networks [3]. networks [1-2]. In addition, the rise of many developing These developments pose a challenge on global value added countries leads to the emergence of new power centers and a networks, including their production and supply chain, with a new balance of forces in the global markets; the consolidation 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.2014.01.070 646 Jens Jäger et al. / Procedia CIRP 17 ( 2014 ) 645 – 650

The significant increase in complexity in recent years is achieved. Successful complexity management in value chains already perceived by industrial companies worldwide [4]. In a requires a holistic approach as well as the consideration of study of Camelot Management Consultants AG [4] 83 % of hidden monetary potentials in the socio-technical system. the 150 surveyed senior executives perceive the achieved complexity level in their companies as too high. Despite this 3. The Stuttgart complexity comprehension growing importance only 11 % of companies have access to adequate tools for complexity management. In a study by The Stuttgart complexity comprehension is based on two Fraunhofer IPA [5] of about 190 participants 82 % believe that principles: distinction between external and internal complexity in the future will become increasingly relevant. complexity as well as division of the internal complexity into A Study of IBM Corporation [6] with more than 1500 chief the four complexity dimensions. executive officers worldwide documents three substantially matching views. First, the respondents expect that the soaring 3.1. Demarcation external - internal complexity increase of complexity will be the biggest challenge. Second, they state that their enterprises are not able to deal with this Progressively increasing external complexity can only be global complexity effectively. Third, the participants identify met with an equivalent internal complexity. Therefore, creativity as the most important leadership skill for enterprises complexity is adjusted within the value networks to external that want to find their way through the complexity jungle. complexity required and cannot be viewed in isolation [17]. In This leads to the conclusion that enormous improvement this context, internal complexity represent complexity inside a potential can be released by a successful advanced Complexity value network, external complexity describes complexity of Management in value networks. the value network environment [18]. Internal complexity is ideal if it corresponds to the respective external complexity 2. Up-to-date survey on complexity in value networks [19]. Therefore it has the right level of internal complexity. If internal complexity is low, external complexity cannot be Before introducing the advanced Complexity Management met sufficiently. The complexity management is therefore not approach, already existing approaches are presented. In the effective. If internal complexity is too high, unnecessary theoretical approach of Kaluza [7] complexity dimensions of expenses incurred in the value network, so the management of value networks are described with simple mathematical complexity in the value network is not efficient [20]. formulas. In Wilson’s practical method of triangulation [8] complexity is quantified, but not in an exact scientific method. 3.2. Complexity dimensions The approach of the international complexity management in the automotive industry from Schoeller [9] focuses on the The German dictionary Duden describes complexity as the product complexity and its impact on process and multi-layer nature or the interplay of many features [21]. organization. The complexity of most value distribution of Latest scientific approaches which deal with complexity Schuh [10] serves as a model for the representation and design directly or indirectly do not have a consistent definition of of the system behavior of production networks in the site and complexity [22-27]. Due to the variety of definitions of site structure planning. In the model of Giessmann [11], the complexity that do not allow unambiguous determination, in causal relationships between analytical complexity in everyday speech the term complexity is often equated with the are described empirically. In the approach used by Lammers term complicacy [11, 28-29]. With a closer look at the two [12] for complexity management of distribution, systems concepts of complicacy and complexity it gets clear that they complex vectors are elaborated, based on subjective are indeed in a narrow context to each other, but complicacy management decisions, which serve as subsequent only maps a part of complexity [29]. In the literature recommendations for strategic decisions. In the design model complexity is often defined as the variety and heterogeneity of of Mayer [13] for the management of complexity in industrial systems, problems algorithms or data [30]. Complex systems, logistics, the logistics is modularized to the economic therefore, include only the two dimensions variety and modules, in order to optimize the logistics management using diversity, and represent predictable and accurately previsible suitable instruments. In the method of Meyer [14], the systems [29]. However, this definition with two dimensions requirements of complexity management in the strategic describes complicacy only. The disadvantage of complicated management process of logistics are integrated, based on an systems is given through the fact that they do not occur in the approach of the Balanced Scorecard. The complexity context of social systems [29]. To meet this aspect, a total of evaluation model by Blockus [15], based on the Analytic four dimensions must be taken into account [31], including Network Process (ANP), which is based essentially on the variability and ambiguity [32-34] respectively variability and model of the mathematician Saaty [16], is an approach to uncertainty [11]. Thus, complexity involves the four solving decision problems. The model of Blockus is complexity dimensions variety, heterogeneity, dynamics and specifically designed to determine the complexity of service opacity [20]. companies. A unified picture of complexity measurement or 4. The complexity fields quantifying is missing. Although complexity management in value networks comprises theoretical methods to manage the A first step to improve transparency is the systematic existing complexity, a unified understanding has not been subdivision into complexity fields. These are the areas in the Jens Jäger et al. / Procedia CIRP 17 ( 2014 ) 645 – 650 647 socio-technical system in which complexity arises [35], such For instance, in the automotive sector during the process as in processes, organization or the product itself [8, 30]. step door welding, the process resistance spot Therefore, the Fraunhofer IPA complexity fields are divided welding or laser point welding can be operated. Which as following: process is the best choice for which product and organization? x Goods and services with sub-fields goods and services Maybe the best solution is the most cost-effective, but which portfolio, portfolio, markets and materials one is more cost-effective depending on the duration of use? x Process with sub-fields technologies, order processing and Perhaps a sequential combination of both methods could be IT-systems the best solution. x Organization with sub-fields network, production and staff Another major issue in the complexity field process is the In Fig. 1 external complexity, complexity fields and unification and standardization of processes in particular complexity dimensions are presented in context. The manufacturing and assembly processes. An example is the complexity field goods and services as interface between standardization of assembly processes at a manufacturer of external complexity (orange) and internal complexity fields sports cars [40]. The aim is, that components differ in (dark green) are shown with a corresponding color gradient. appearance, but can be mounted in the same way. The To map, how various complexity types in production and product variants are to be created as late as possible in the supply chain can occur, different appearances of complexity production process. Therefore, the assembly concepts for all are presented in the following section. series are unified, despite a completely different appearance. For example, the rear lights of two different series have a 4.1. Goods and services complexity completely different appearance. For the mechanic, however, they are in the same assembly process [40]. Today's goods and services contain a high degree of IT should serve the company coping with complexity. complexity. Good examples are goods and services in However, often IT causes additional complexity. IT automotive industry. Automobiles contain a particularly high complexity results from the variety and heterogeneity of the product complexity, because it is manufactured of more than individual IT elements in operation. It is influenced by the 10,000 existing individual parts [36] nowadays. Furthermore, dependencies, the inconsistencies and the redundancies of IT automotive corporations offer a wide variety and diversity of elements. Another big influence has the IT inherent change product configuration options [37]. The goods and services dynamics in relation to changes of individual IT systems, but becomes complex due to the combinatorics of the product also the change of the interface compatibility with other IT with various services during the purchase and operation of the systems. Furthermore, the changing decision makers over the vehicle. years left their mark in the IT department of an enterprise, One way to deal with high goods and services complexity, each one with own ideas, goals and requirements. Finally, in detail in sub-field goods and services portfolio complexity, every few years there are new waves, so-called is the approach of a big technology enterprise from California. pile-up effects [41]. It selects a minimalist approach and provides only a minimal Two approaches to oppose this growing IT complexity are variant selection. This is based on the recognition that variety presented here shortly. The first approach is IT governance. complicates the decision for the customer and, therefore, leads IT governance provides the frame for decision making rights to so-called customer confusion [38]. A negative side effect: and responsibilities to promote desired behavior in the use of variants cause additional costs along the supply chain [39]. IT as well as to ensure the optimal operation of IT to achieve the business goals [42]. The second approach is Enterprise 4.2. Process complexity Architecture Framework. Enterprise Architecture Framework is the structuring and development of the alignment of Increased variety and heterogeneity of machining enterprise IT with business goals. Technical standards are set processes result in an increasing number of choices for and monitored, simplification and flexibility potentials are processing and an even greater number of combinations. This determined as well as the future corporate IT landscape is leads to the question, which processing technology is best to designed purposefully [43-44]. use for which processing step. 4.3. Organization complexity

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 Fig. 1 External complexity, complexity fields and complexity dimensions 648 Jens Jäger et al. / Procedia CIRP 17 ( 2014 ) 645 – 650

organism in which the fractals, organizing units themselves, detected entirely, it can be mastered. However, complex pursue the objectives of the overall system broken down for problems, by definition, cannot be understood in detail. The the fractal. This leads to decentralized managed organizations fourth human strategy is to focus on individual factors, the with small control loops, clear rules and self-similar structures trivialization by subdivision and abstraction. The approach to that enable continuous communication and continuous see and treat a complex system as a complicated system, improvement development. This is based on the principles of destroys the complex system. The fifth human strategy is fractal organization [46]: intuitive evaluation on complexity and the reduction of x Self-similarity: In every part of the organization (fractal), complexity by pattern formation on the basis of the learned. the structure of the entire organization is reflected. However, the knowledge base of an individual human being’s x Self-organization: Organization at operational and strategic intuition is too narrow to be successfully on time. levels. Responsible individual action, evaluation of their own effectiveness and verification of compliance of own 5.2. Successful strategies for the identification and targets. quantification of complexity x Dynamic and vitality: Effective response to changing conditions. Based on the five general human response strategies [48] x Goal-orientation: Self-defined goals are in line with the Fraunhofer IPA approach on successful strategies for the corporate objectives. They are matched with the upstream identification and quantification of complexity combines three and downstream units. The objectives are based on promising strategies and thus compensates their respective processes and not on structures, they are feasible and weaknesses. Therefore, the three strategies are adjusted manageable. according to their deficiencies and sensibly combined to For years, the largest European automobile manufacturer has obtain the best possible result as follows: used the approaches of the fractal company consistently [47]. The Feeling: the human being intuitive review leads to the Now they are on their way to become the world’s leading reduction of complexity by pattern formation on the basis of automobile group [6]. the learned. To compensate the lack of experience of human The current developments in Integrated Industry direction being individuals the Feeling is based on the knowledge of the supply a further increase of complexity in the organization diversity of heterogeneous groups. structure [3]. The variety and heterogeneity number and The Mind: the rational understanding and penetration leads variety of cyber-physical systems, each equipped with sensors to an understanding in detail. To compensate the disability of and actuators generate a strong increase in data traffic within human beings to fully understand the complex challenge, the the production system. Within the value stream employee, Mind uses the 80/20 rule: with 20% of detailed understanding, material, machinery, infrastructure and maintenance provide 80% of problems can usually be solved. Therefore, first action their current state and respond to relevant messages. This is a prioritization of the level of detail and an efficient takes place all across value streams, fractals, areas and approximation. factories within the entire value network. The View: Focusing on individual factors leads to a trivialization by subdivision. To compensate the weakness of 5. Advanced Complexity Management approach destroying the complex system and abstract it to a complicated system it is required to consider the associated In the following section the advanced Complexity network and relations as e.g. connectivity or divergence of the Management approach will be explained from leads to the system elements. right level of complexity in order to be successful on the market and react optimally to external complexity. It is 5.3. Level of complexity important to use the right strategies in order to approach complexity, to approximate the right level of complexity and The right level of internal complexity is crucial for value to deal with complexity. networks to be successful on the market, to act and respond on external complexity optimally. This is done first with the 5.1. Human beings’ response strategies on complexity analysis of the goods and services and leads to the question: with which products my value network makes money and In situations where human beings are confronted with with which products my enterprise loses money? Profitability complexity challenges there are five general human response (which complexity affects enhances/lowers?), Liquidity strategies [48]. The first human strategy on complexity is trial (which complexity promotes/disables?) and cash flow (which and error. Human beings try to solve complexity and fail. complexity is progressive/regressive?) are taken into account. Then, the next try and fail again. This does not pursue a Thereafter, it is investigated how complexity can be more learning strategy; in each step success is more likely. The efficient through successful advanced Complexity second human strategy to deal with complexity is to hide or Management. For this, the growth effects (which product ignore the complex problem. A problem appears and people classes have growth potential?), synergy effects (which don’t look at it, but the problem still exits. The third human product classes have similarities?) and spill-over effects strategy is to obtain the rational of complexity in detail. This (where work umbrella effects or cannibalization effects?) are strategy is based on the assumption that once a problem is considered. Jens Jäger et al. / Procedia CIRP 17 ( 2014 ) 645 – 650 649

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 Fig.2 Complexity patterns in Complexity Footprint interfaces, both on the side of the IT systems as well as from an organizational perspective. The results allow the derivation of the best strategies for x Avoiding complexity: preventive the emergence of successful advanced Complexity Management. A distinction complexity, e.g. modularization and standardization of is made between dealing, reducing, avoiding, pricing and products, processes or organizational structures. generating complexity as well as their respective x Pricing complexity: reasonable pricing of product combinations, the orchestration. It is the key to select the right complexity (e.g. for which complexity the are instruments and tools on a case-by-case basis and to willing to pay) and their caused complexity in processes orchestrate them with other instruments or tools, if necessary. and organization. Therefore, the most appropriate advanced Complexity x Generating complexity: external complexity is higher than Management instruments and tools can be selected and internal complexity, complexity is not effective, more recombined to obtain the best result for each unique value internal complexity is needed. network. The successful implementation of the Complexity Footprint generates following results: 6. Fraunhofer IPA Complexity Footprint x Monetary potential: reduce non-value and value-adding costs, increase revenue and profit. The Fraunhofer IPA Complexity Footprint as part of the x Pricing potential: suitable price of goods and services advanced Complexity Management contains following work regarding complexity. steps: selection, identification and quantification as well as x Priority and roadmap: high potential complexity fields and orchestration. In selection, the goods and services are successful (profitable) complexity patterns. analyzed. Profitability, liquidity and cash flow as well as growth effects, synergy effects and spill-over effects are 7. Conclusion considered. This leads to an allocation of goods and services which influences the next work step. Worldwide constant complexity growth leads to increasing In identification and quantification the complexity fields complexity in value chains. Therefore complexity process and organization including their sub-fields are management is an important competitive factor. Only future- investigated. Therefore, the value network is split into its sub- oriented approaches can be sustainable through profitability divisions and aligned with the complexity field. To get first and growth in the market. Therefore the holistic approach of approximation of the complexity in the different fields, advanced Complexity Management offers the matching methodical workshops and systematical interviews with at strategies. It takes into consideration. The Complexity least five employees in different functions are evaluated and Footprint contains complexity patterns for the complexity documented. With this wide data base it is possible to focus fields goods and services, processes and organization. The on challenging fields first. In these fields, the complexity analysis of the complexity patterns provides actual and future dimensions are methodically broken down into complexity monetary and pricing potentials as well as linked priorities. performance indicators. To support this progress, existing key To apply the Complexity Footprint in industrial value performance indicators and data are used, which are already chains, it takes appropriate tools for implementation. For this recorded and stored by the value network. Furthermore, the purpose existing tools will be adapted and new tools indicators are placed in this common context and their developed. Additional research in comparing and connectivity is studied. Subject to the goods and services this benchmarking of complexity profiles of industry-specific leads to new connections and links and is mapped in the profiles, as well as the balance of internal company-with complexity pattern Fig. 2. 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