Data-Based Determination of the Product-Oriented Complexity Degree

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Procedia CIRP 00 (2017) 000–000 Procedia CIRP 70 (2018) 144–149 www.elsevier.com/locate/procedia

28th May 2018, 2018, Nantes, Nantes, France France 28th CIRP CIRP Design Design Conference, Conference, May

Data-Based the Complexity Data-Based Determination Determination ofConference, the Product-Oriented Product-Oriented Complexity Degree Degree 28th CIRP Designof May 2018, Nantes, France a

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Schuha,, Christian Dölle a, Stephan Schmitzb, Jan Koch a*, Marius Hödinga, Alexander Günther Schuh Christianto Dölle , Stephan Schmitz , Jan Koch Marius Höding , Alexander AGünther new methodology analyze theMenges functional and *,physical architecture of a a existing products for an assemblyMenges oriented product family identification Laboratory for Machine Tools and Production Engineering WZL at RWTH Aachen University, Campus Boulevard 30, 52074 Aachen, Germany a a

Laboratory for Machine Tools b and Production Engineering WZL at RWTH Aachen University, Campus Boulevard 30, 52074 Aachen, Germany Ortlinghaus-Werke GmbH, Kenkhauserstraße 125, 42929 Wermelskirchen, Germany b Ortlinghaus-Werke GmbH, Kenkhauserstraße 125, 42929 Wermelskirchen, Germany * Corresponding author. Tel.: +49 (0)241-80-27566; fax: +49 (0)241-80-627566. E-mail address: [email protected] * Corresponding author. Tel.: +49 (0)241-80-27566; fax: +49 (0)241-80-627566. E-mail address: [email protected] École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France

Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

*Abstract Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]

Abstract

Available data within today´s digitalized product life cycles comprise necessary information and is currently used in an unstructured way without Available data within today´s digitalized product life cycles comprise necessary information and is currently used in an unstructured way without sufficient description of interrelations. One major challenge in managing product complexity is the tradeoff between standardization measures sufficient description of interrelations. One major challenge in managing product complexity is the tradeoff between standardization measures Abstract and customized solutions. Therefore, a transparent overview and effective controlling of product-induced complexity is required and could be and customized solutions. Therefore, a transparent overview and effective controlling of product-induced complexity is required and could be supported by product-related data analysis. This paper aims at the development of a generic approach for the data-based determination of the by product-related data analysis. This paper aims at the development of a generic approach for the data-based determination of the Insupported today’s business complexity degree. environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of complexity degree. agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems well as to choose theby optimal product matches, product analysis Indeed, most of the known methods aim to © 2017 2018 as The Authors. Published Elsevier Ltd. This is an open access articlemethods under theare CCneeded. BY-NC-ND license B.V. © 2017aThe Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/3.0/) analyze product one product family the physical level.of Different families, however, 2018. may differ largely in terms of the number and Peer-review underorresponsibility of the on scientific committee the 28thproduct CIRP Design Conference Peer-review under of committee of the the 28th 28th CIRP Design Design Conference 2018.family combinations for the production Peer-review under responsibility responsibility of the the scientific scientific committee of CIRP Conference 2018. nature of components. This fact impedes an efficient comparison and choice of appropriate product system. A new methodology is proposed to analyze existing products in view product of theirdata functional and (PDM); physicaldata architecture. The aim is to cluster Keywords: complexity management; innovation management; portfolio management; management driven design Keywords: complexity management; innovation management; portfolio management; product data management (PDM); data driven design these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both,level. production system and product designers. 1. Introduction So far, the planners use of data generated duringAntheillustrative product 1. Introduction level. So far, the use of data generated during the product example of a nail-clipper is used to explain the proposed methodology. Anlifecycle industrialhas casebeen studyaddressed on two product families of inadequately [7,steering 8, 9]. columns of lifecycle hasproposed been addressed inadequately [7, 8, 9]. thyssenkrupp Presta France isfor thencompanies carried out to give a the first trade-off industrial evaluation the The main challenge facing Anofintegral part approach. of an "Industrie 4.0"-approach is the The main challenge for companies facing the trade-off An integral part of an "Industrie 4.0"-approach is the ©between 2017 Thestandardization Authors. Published by Elsevier B.V. measures and customized solutions is consistent and integrated use of relevant data [10]. In the between standardization measures and customized solutions is consistent and integrated use of relevant data [10]. In the Peer-review under responsibility of the committee thewell 28th CIRP Design to offer marketable products that fitscientific the customer needsofas context ofConference "Industrie2018. 4.0", it is necessary to enable companies

to offer marketable products that fit the customer needs as well context of "Industrie 4.0", it is necessary to enable companies as the company's interests in economic success. Shifting to exploit data-insights by usage and analysis of productShifting to exploit data-insights by usage and analysis of productconditions, such as the transition from a seller’s to a buyer's oriented data [11–14]. These data-insights may comprise conditions, such as the transition from a seller’s to a buyer's oriented data [11–14]. These data-insights may comprise market, shortened product lifecycles and the growing relevant information concerning the complexity degree of market, shortened product lifecycles and the growing relevant information concerning the complexity degree of differentiation of customer needs have led to increased product products [15]. Thus, the paper combines the "Industrie 4.0"differentiation of customer needs have led to increased product products [15]. Thus, the paper combines the "Industrie 4.0"[1]. As a result, companies are confronted with an approach regarding creation and use ofmanufactured a uniform database 1.variety Introduction of the product rangethe and characteristics and/or variety [1]. As a result, companies are confronted with an approach regarding the creation and use of a uniform database increase in product-induced complexity and need a suitable with the in analysis of theIn this complexity degree. This degree assembled this system. context, the main challenge in increase in product-induced complexity and need a suitable with the analysis of the complexity degree. This degree variant and complexity management [1, in 2]. the domain of illustrates the product variety-induced complexity by opposing Due to the fast development modelling and analysis is now not only to cope with single variant and complexity management [1, 2]. illustrates the product variety-induced complexity by opposing The complexity management is responsible for and the the externally offered product to the sold families, product communication and an ongoing trend of digitization products, a limited product range variety or existing product The complexity management is responsible for the the externally offered product variety to the sold product coordination, manufacturing implementation and evaluation ofimportant product variety. Moreover, the paper aims at the development of a digitalization, enterprises are facing but also to be able to analyze and to compare products to coordination, implementation and evaluation of product variety. Moreover, the paper aims at the developmentdefine of a standardization measures with environments: regard to marketable prices generic and holistic approach forobserved a data-based determination of challenges in today’s market a continuing new product families. It can be that classical existing standardization measures with regard to marketable prices generic and holistic approach for a data-based determination of [4, 5]. Hence, companies are challenged to reduce and master the product-oriented complexity degree. Therefore, necessary tendency towards reduction of product development times and product families are regrouped in function of clients or features. [4, 5]. Hence, companies are challenged to reduce and master the product-oriented complexity degree. Therefore, necessary the product variety-induced costs. key figures are determined quantify the complexity degree shortened product lifecycles. complexity In addition, and therecomplexity is an increasing However, assembly oriented to product families are hardly to find. the product variety-induced complexity and complexity costs. key figures are determined to quantify the complexity degree This requires an analysis ofbeing the product and its components in andOn a method for the calculation and visualization of these key demand of customization, at the same time in a global the product family level, products differ mainly in This requires an analysis of the product and its components in and a method for the calculation and visualization of these two key order to create transparency about product complexity [3,trend, 6]. figures in a so-called(i)"complexity cockpit" is introduced. The competition with competitors all over the world. This main characteristics: the number of components and (ii) the order to create transparency about product complexity [3, 6]. figures in a so-called "complexity cockpit" is introduced. The Although there the are development numerous approaches for analyzing methodology is validated within a component manufacturer. which is inducing from macro to micro type of components (e.g. mechanical, electrical, electronical). Although there are numerous approaches for analyzing methodology is validated within a component manufacturer. product complexity, there is a lack a concept a holistic markets, results in diminished lot of sizes due to for augmenting Classical methodologies considering mainly single products product complexity, there is a lack of a concept for a holistic and data-based analysis of the product-oriented complexity product varieties (high-volume to low-volume production) [1]. or solitary, already existing product families analyze the and data-based analysis of the product-oriented complexity To cope with this augmenting variety as well as to be able to product structure on a physical level (components level) which identify in the existing causes difficulties regarding an efficient definition and 2212-8271 possible © 2017 The optimization Authors. Publishedpotentials by Elsevier B.V. 2212-8271 ©system, 2017 The it Authors. Publishedtobyhave Elsevier B.V. knowledge production is important a precise comparison of different product families. Addressing this Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. Keywords: Assembly; Design method;inFamily identification as the company's interests economic success.

Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018.

2212-8271 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) 2212-8271 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of scientific the scientific committee theCIRP 28thDesign CIRP Conference Design Conference Peer-review under responsibility of the committee of the of 28th 2018. 2018. 10.1016/j.procir.2018.03.293

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2. Theoretical background In this chapter relevant terminology and existing approaches of the analysis of product complexity are described. 2.1. Relevant Terminology 2.1.1. Product complexity The variety and multiplicity of elements and their interrelatedness are defined as complexity [16]. Kluth et al. [17] distinguish complexity into internal and external complexity. Internal complexity results from externally offered variety. Dehnen [18] differentiates between complexity of parts and complexity of the product program. The author defines complexity of parts as the number of parts and components. Complexity of the product program is defined as the number of products and product variants. In the context of this paper, the aggregation of both is defined as product complexity. According to Schuh [3], the management of product complexity includes the design, control and development of product variety across the entire range of services within a company. In addition to four basic measures for structuring products, Schuh [19] also uses the term standardization as a decisive enabler for the management of internal product complexity [20]. The aim of standardization is to control product variety across all stages of the value chain in order to achieve a maximum contribution to customer benefit [3]. 2.1.2. Data model The large volume of data generated in companies requires efficient systems for the corresponding management and organization [21]. Databases provide a standard solution for modelling data and their relationships. The description of the required information structure by means of a data model is helpful for creating a physical database. First, an extract of reality is mapped in a conceptual model describing the information structure. Subsequently, the structure is transferred to a logical data model and finally stored in a physical database [22]. Both the conceptual and the logical model are not used to collect the information itself, but to represent the structure of the information to be stored. Therefore, the preliminary work is helpful and recommended in the early phase of database development. 2.1.3. Decision Support System Decision support systems (DSS) are supporting the user with problem-relevant data in the decision-making process [23]. DSS are computer-based systems that link data from a variety of sources. Thus, DSS provide the opportunity to get a holistic insight into decision-relevant data [24]. In the context of this paper, the DSS is defined as "complexity cockpit". The user is offered a limited selection of options within the "complexity cockpit" only. 2.2. Analysis of product complexity The challenges described above are addressed by various scientific approaches concerning the analysis of product-

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induced complexity. In the following, the results of a comprehensive literature analysis are presented. Several approaches pursue the development and analysis of modular product platforms [25-28]. In the following exemplary approaches that integrated key figures for assessing product complexity are emphasized. The key figures determine the potential for a suitable modularization of products. Kohlhase [29] and Marti [30] take into account both technical and economic criteria to evaluate modular product platforms. Kota et al. [31] consider only technical criteria for analyzing and optimizing the product architecture. The approaches of Nußbaum [32], Rennekamp [33] and Schuh et al. [8] consider a cross-perspective approach to assess complexity by usage of key figures. Nußbaum [32] and Schuh et al. [8] focus on an overarching view on the complexity of the product-production system. Schuh et al. [8] also defines a performance measurement system for product platforms and deals with the availability of necessary data. Rennekamp [33] describes a methodology for assessing the complexity degree of companies. The approach involves the analysis by means of a "complexity footprint" as well as the quantification of interrelations and the derivation and evaluation of optimization scenarios. In the following, data-based approaches for the analysis of product complexity are considered. The approach of Ji et al. [34] proposes a three-step data mining approach to analyze product complexity using quality performance data. The approach provides measuring, scoring and clustering of quality performance data to support the interpretation of product complexity. Kreimeyer et al. [35] focus on the variant design process based on an improved requirement management process. The Authors build a data model, which consistently manages product design related data to support the variant design process. Schuh et al. [36] present a data mining approach to analyze interdependencies of products and manufacturing processes. Within this concept data from various IT systems are evaluated in order to identify critical product features that cause instable manufacturing processes. This enables companies to offer customized products with costeffective manufacturing processes. Schmidt et al. [37] uses a graph-based analysis of product complexity by analyzing and visualizing BOM data to support variety reduction efforts. Kissel [38] provides a method to manage complex product portfolios by using graphs for pattern recognition of complex products. The author pursues transparency about product complexity to support the work with complex product portfolios. Numerous approaches show a broad use of key figures and key figure systems for the analysis and evaluation of productinduced complexity. Only a few of the concepts considered focus on the use of product-related data. This also results in a lack of possibilities to implement the concepts through IT systems and actively integrate them into the corporate IT landscape. A data-based analysis of key figures is essential for an effective and real-time controlling. For both the descriptive analysis as well as predictive analysis, a dynamic consideration of the complexity degree is necessary [39]. This enables companies to ascertain deviations from strategic targets and to be able to derive optimization measures. Concluding, existing

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approaches do not adequately address the scope of this paper. A comprehensive consideration of the product-induced complexity by a data-based determination of the complexity degree has not been investigated by research so far. In particular, the practical implementation of a data-based analysis of the complexity degree as well as its integration into the existing IT landscape is not considered adequately. A comprehensive framework is presented in chapter 3. 3. Framework for the data-based determination of a product-oriented complexity degree

A

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A key objective of the developed methodology is the assessment of the product-oriented complexity degree by means of key figures. Therefore, the complexity degree is considered by different perspectives, such as the product program, the product structure and form elements. The named perspectives were chosen with regard to the assessment of external as well as internal complexity. In the following, the four steps of Phase A are illustrated (see Figure 2). B KPI 2

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Figure 2. Definition of relevant key figures (Phase A)

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Existing approaches reveal that there is no holistic and practical framework for analyzing the complexity degree. Hence, this paper introduces a methodology for the data-based determination of a product-oriented complexity degree. The overall research question can be formulated as follows: "How can a methodology for determining the productoriented complexity degree be defined using data generated within a digitally documented product life cycle?"

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Ca… Ca…

Visualization of the complexity degree

Figure 1. Framework for the determination of a product-oriented complexity degree

As shown in Figure 1 the methodology consists of three phases. Phase A focuses on the development and identification of suitable key figures concerning a product-oriented complexity degree. For this purpose, key figures are determined, evaluated developed. Subsequently, Phase B describes the design of a uniform and consistent data model as well as the development of a database. As the complexity degree requires data from different IT systems, the key figures are decomposed to individual data fields and their availability in IT systems is determined. After data requirements are defined, the data model is set up. The description of such a data model, which forms the basis for a data-based determination of product complexity, has not been focused on in literature so far. In Phase C a suitable visualization of identified key figures is discussed and implemented within a „complexity cockpit“. This decision support system provides the user with decisionrelevant data concerning the product oriented complexity degree. 4. Methodology for the data-based determination of a product-oriented complexity degree One major problem in practice is the assessment of product complexity. Existing approaches do not adequately focus on a comprehensive consideration of different product complexityinducing levels. Likewise, there is a lack of suitable and practical key figures.

First, the information needs for an assessment of the complexity degree are derived based on company- and department-specific objectives. In particular, the information needs of complexity management have to be determined by considering a holistic view on different product perspectives. The identified perspectives concern product program, product structure and form elements. The product program-perspective considers the product variety offered on the market. The resulting variety within the product structure is covered by the product structure-perspective. Variety induced by geometric form elements is analyzed in the form elements-perspective, e.g. the reuse of design rules or guidelines. These information needs have to be derived for the three described perspectives. In the second step, existing key figures for the previously defined perspectives are investigated. In order to identify suitable key figures, they are evaluated based on six criteria, complexity, determinability, effort, significance, comprehensibility and overall suitability [7, 33, 40, 41]. As a result, relevant key figures are derived or adapted and if applicable, new key figures are determined. The result of Phase A are key figures for an assessment of the complexity degree based on the described information needs. 4.2. Phase B: Development of a data model Companies often neither use the product-related data in IT systems effectively nor efficiently in order to assess product complexity-relevant questions such as the complexity degree. In addition, the various IT-systems do not use uniform interfaces. Therefore, the availability and access to productrelated data is insufficient and relevant analyses require high manual effort. The presented methodology intends to connect the product-relevant data within a data model. Such a data model forms the basis for an implementation within a database structure or a data warehouse. This enables a real-time assessment of the complexity degree based on the defined key figures and available (see Figure 3).

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Günther Schuh et al. / Procedia CIRP 70 (2018) 144–149 Author name / Procedia CIRP 00 (2018) 000–000 A

B KPI 2

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Decomposition of key figures B.1

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Conceptual model

Visualization of the complexity degree

Logical model

B.2

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B.3

B.4

Figure 3. Development of a data model (Phase B)

In the first step, the defined key figures are decomposed to original data fields and assigned to data sets. Moreover, the data sources required for the implementation of a „complexity cockpit“ are identified. The second step comprises the design of a conceptual data model. The conceptual data model contains data objects and visualizes their relationships in a database-independent depiction. In the third step, the logical data model is derived. On the basis of a relational data model, the implementation of the database is completed. It is of particular importance to record the required data via a uniform interface and to ensure a stable data connection. The analysis and visualization within the „complexity cockpit“ is conducted in Phase C of the methodology. 4.3. Phase C: Visualization of the complexity degree After defining key figures (Phase A) and identifying associated data sources (Phase B), the visualization of key figures within the „complexity cockpit“ is described in the third phase (Phase C) as shown in Figure 4. For this purpose, the company's targets for visualizing complexity are classified by means of a requirement morphology with regard to companyspecific requirements for implementing a „complexity cockpit“. In addition, appropriate visualization types are assigned to the identified key figures and their implementation in the „complexity cockpit“ is presented. A

B KPI 2

Definition of relevant key figures

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Definition of visualization type C.2

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Visualization of the complexity degree

Visualization C.4

Figure 4. Visualization of the complexity degree (Phase C)

First, requirements and characteristics for the „complexity cockpit“ are derived in a requirement morphology. Therefore, criteria such as the range of coverage or the area of application and the corresponding characteristics are defined. Based on the classification, the requirements for a company-specific implementation can be described. In the next step, possible visualization types of identified key figures are discussed. The assignment of a visualization type is based on the scale type of

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the key figures. Hence, all identified key figures are clustered according to their scale types and a visualization type is proposed. The third step comprises the definition of operations within the database in order to display the key figures in the „complexity cockpit“. For this purpose, operations for the preparation of data from the database are derived based on previously defined visualization types. The operations take place in the database level, which is invisible to the end user but already dependent on defined visualization types. Thus, the aggregated data is processed into a front-end and calculation rules for the individual key figures are implemented. The description of the visualization within the „complexity cockpit“ and the associated usage is elaborated in the last step. The result of the last phase (Phase C) is a company-specific „complexity cockpit“ illustrating the defined key figures with systematically derived visualization types. In summary, the three presented phases illustrate a holistic methodology for the data-based determination of the productoriented complexity degree. The validation of the presented methodology is described in the following. 5. Validation results In the following, the application and validation of the presented methodology is described for a medium-sized component manufacturer. As a manufacturer of customized single-parts and small-lot series, the company faces the challenge of meeting sophisticated customer needs while maintaining competitiveness. Due to the long company tradition, a high proportion of customized adaptations and solutions as well as different fields of application cause a high variety of products and corresponding components. The company is also characterized by a heterogeneous ITinfrastructure due to the use of different IT-systems like ERP-, PDM- and CAD-systems. Despite of the data availability, there is no transparency about the product-oriented complexity degree. Moreover, the combination of a low reuse rate for an increasing number of product variants, small batch sizes and a high share of in-house production implicates a high level of product variety-induced complexity along the entire value chain. This use case illustrates a need for the data-based determination of a product-oriented complexity degree with regard to improving transparency and controllability of product complexity. For this purpose, relevant key figures are defined and data availability for the assessment of defined key figures is investigated. Subsequently, the adaptation of the data model to the company-specific IT-infrastructure is conducted. The structure and usage of the "complexity cockpit" is explained at the end of the section. The management of material data and order processing is supported and coordinated within the company's ERP system. Moreover, material data of products and components is managed and parts lists are maintained. Parts lists assign components to products. The company's PDM system contains metadata about the products. The PDM system is used in particular during product development. It manages different versions of drawings, the classification of materials as well as the change process. The CAD system comprises form element data, which is extracted and stored from 3D-models. The

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described IT systems provide the basis for the subsequent derivation of corresponding key figures within the „complexity cockpit“. According to the presented evaluation criteria, eight suitable key figures are identified for a holistic determination of the product-oriented complexity degree. In a next step, the determinability of the identified key figures is evaluated based on available data of the presented IT-systems. Since the company does not define the release of a new product as a new product generation, it was not possible to determine the CarryOver-Index, which quantifies the re-usage of parts from a former product generation for example [27]. As a result of the evaluation, five key figures were identified for processing in the "complexity cockpit" (see Table 1). Table 1 Determined key figures for the determination of the product-oriented complexity degree Name Portfolio-FitnessIndex (PFI) [33]

Sales-N-Index (SNI) [33]

Product-SalesBalance-Index (PSI)

Key figure

Explanation

v PFI  sales voffered

v SNI  n v sales

PSI 

v80% v sales

[33] Commonality Index (CMI) [33]

Norm-Part-Index (NPI)

N N

a

total

X X

n

total

Ratio of number of product variants sold (𝑣𝑣𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ) to number of product variants offered (𝑣𝑣𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 )

Number of product variants sold less than n times (𝑣𝑣80% ) in relation to the total number of product variants sold (𝑣𝑣𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ) Ratio of number of product variants sold which comprise 80% of sales values (𝑣𝑣80% ) to the total number of product variants sold(𝑣𝑣𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 ).

Ratio of the number of single parts (∑ 𝑁𝑁𝑎𝑎 ) (parts that are not included in any other product) to the total number of parts(∑ 𝑁𝑁𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ).

Ratio of standard form elements of a component (∑ 𝑋𝑋𝑛𝑛 ) to the total number of used form elements(∑ 𝑋𝑋𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ).

Based on the determined and decomposed key figures in Phase A, the data model is designed. Therefore, all relevant data fields are identified by the decomposition of key figures, such as material number, revenue or date of revenue. These are summarized by data objects and relationships between defined data objects are described. After the conceptual data model has been transferred to the logical model, it serves as the basis for the physical implementation of the database. For this purpose, the data model is implemented in a physical data warehouse. The data connection to the company-internal ERP-, PDM- and CAD-system takes place via the automated integration of flatfiles (structured tables). The processing and calculation of key figures is carried out in accordance with the calculation rules for the key figures.

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Once the key figures have been identified (Phase A) and the database has been set up (Phase B), visualization requirements will be determined in the last Phase (C). According to the company's objectives for implementing a "complexity cockpit", the characteristics are determined as follows: analysis focus, product management as area of application, companywide information spectrum and daily data update frequency. Moreover, the organizational implementation of the "complexity cockpit" creates acceptance in use, taking into consideration the determined company characteristics. Subsequently, the adaptation of the visualization types to the company's requirements and to the characteristics of identified key figures formed the basis for the definition of operations. The data is processed in queries within the database and then transferred to MS-Excel. The calculated key figures are finally displayed in the "complexity cockpit". The procedure for the validation of the introduced methodology including the implementation of a corresponding database is shown in figure 5. Material

Material

Customer

1 1 n

Sales

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MatNr

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Price

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01.03

50 $

1215

04.05

…

Conceptional model

Logical model

Complexity cockpit (screenshot)

Data base (screenshot)

Figure 5. Set-up of a "complexity cockpit" based on the introduced methodology

The application of this methodology showed that a connection to the IT-infrastructure in the company is possible. Based on this, the methodology has enabled the user to link distributed data from the PDM, ERP and CAD systems and to discover new information. The automated calculation and visualization of key figures in the complexity cockpit was also achieved. The Cockpit provides the user with an overview of product-induced complexity at various levels. The cockpit thus contributes to a target-oriented complexity management in the company and supports the decision-making process in complexity-relevant issues. To fully utilize the potentials of the "complexity cockpit" the application needs to be integrated into the organizational structure. 6. Conclusion and future research In order to establish a suitable variant and complexity management, a methodology for determining the productoriented complexity degree was presented. A practicable and data-based approach through the analysis of available productrelated data does not exist yet. Thus, current challenges and existing approaches in the field of complexity management



Günther Schuh et al. / Procedia CIRP 70 (2018) 144–149 Author name / Procedia CIRP 00 (2018) 000–000

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were examined. A three-phase methodology was introduced, based on a derivation of suitable key figures, the modelling of a uniform database was shown and the development of a socalled „complexity cockpit“ was described. The methodology enables companies to exploit datainsights by the assessment of product-related data with focus on the complexity degree, which may comprise decisionrelevant information. As a result, the user gets a user-friendly „complexity cockpit“. The cockpit enables a real-time capable analysis of the product-, component- and form elementportfolio in the company. The validation of the methodology at a component manufacturer demonstrated the applicability and usability of the presented methodology. The application in practice has also shown that the adaptation of the developed data model is dependent on the IT systems used within the company. Depending on the company, the effort required to set up the data structure may differ from the application shown in this paper. Further research is focused on the validation of derived key figures as well as the developed data structure of the „complexity cockpit“ considering the transferability to different industries. In a next step, the extension of the presented approach to further product complexity-relevant questions should be considered. The presented „complexity cockpit“ focuses mainly on optimizing the fit of the product program based on analyzing the externally offered and sold product variety within three different perspectives. Moreover, additional targets for managing product complexity as well as different roles should be considered. In addition, companies should focus on optimizing data quality and availability in order to ensure simple linkage between different IT-systems with regard to data analyses. References [1] [2] [3] [4] [5] [6] [7] [8] [9]

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