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ScienceDirect Procedia CIRP 26 (2015) 185 – 189

12th Global Conference on Sustainable Manufacturing

Support of Innovation Networks in Manufacturing Industries Through Identification of Sustainable Collaboration Potential and Best-Practice Transfer Holger Kohlb, Ronald Ortha, Oliver Riebartscha*, Mila Galeitzkeb Jan-Patrick Capa b

a Fraunhofer Institute for Production Systems and Design Technology, Berlin, Germany Department Sustainable Corporate Development, Institute for Machine Tools and Factory Management, Technical University of Berlin, Germany

* Oliver Riebartsch. Tel.: +493039006-262; fax: +49303932503. E-mail address: [email protected]

Abstract The increasing global competition within today's manufacturing industries is confronting organizations with interdisciplinary challenges that require intellectual expertise and innovative technological solutions in various knowledge areas. Research organizations and manufacturing companies can improve their overall performance by bundling expertise in collaborative innovation networks. For this purpose a systematic Benchmarking approach has been developed by Fraunhofer IPK to match the competencies and capacities within a pool of organizations in order to facilitate a sustainable cooperation in terms of resources, customers and R&D topics. Furthermore, a KPI-based identification of best performing network-partners allows an initiation of Best-Practice transfers to gain sustainable competitive advantages for the whole network. Based on a methodological approach, the identification of collaboration potentials and Best-Practices is supported by software tools that visualize the results in an understandable and applicable way. © 2015 Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2014 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of Assembly Technology Factory Management/Technische Universität Berlin. Peer-review under responsibility of Assembly Technology and Factoryand Management/Technische Universität Berlin. Keywords: Benchmarking; Collaboration; KPIs; Best Practice Transfer; Innovations Networks; Competence and Capacity Matching

1. Introduction The increasing complexity and competition on global markets as well as the necessity for shorter product lifecycles nowadays are putting manufacturing companies under pressure and forcing them to react. The innovation capability of an organization that allows a quick adaptation to the increasing variety of customer requirements is an essential prerequisite to stay ahead of the global competition. While large companies dispose of the financial means to apply comprehensive R&D projects, small and medium-sized enterprises as the economy’s driver for innovation and employment, have to search for alternative solutions, since they are not able to rely on equally powerful resources. Collaboration can be a key success factor for companies that cannot finance major projects solitarily. Therefore, the significance of collaboration networks in manufacturing in-

dustries gains constantly more importance. In particular, the number of interdisciplinary projects is constantly growing, since the need for collaboration in terms of expertise and technical applications from different research fields becomes indispensable for a flexible and diversified service delivery. Following this development, the Information Centre Benchmarking (IZB) at Fraunhofer IPK and the Technical University of Berlin developed a methodological benchmarking approach to identify the collaboration potentials of innovation networks in manufacturing industries. The approach aims to create multiple-win situations for all involved participants. The elaborated results of participating enterprises, universities and research institutes should take each participant’s performance to the next level in terms of acquisition, problem solving and efficiency. However, this approach is not supposed to deliver quick-wins, but rather enables strong and sustainable commitments among the network on the long-run.

2212-8271 © 2015 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/). Peer-review under responsibility of Assembly Technology and Factory Management/Technische Universität Berlin. doi:10.1016/j.procir.2014.07.055

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2. Framework 2.1. Benchmarking & Best Practice Transfer Benchmarking as a management tool for organizations was developed by Robert C. Camp, who defined Benchmarking as “the search for solutions, which are based on the best methods and procedures of the industry, the Best Practices, and are leading enterprises to top performances” [1]. Since industries are constantly changing, benchmarking must be seen as a dynamic and continuous process of comparing products, services, processes and methods among multiple enterprises to identify improvement potentials [2]. In particular, branch-independent benchmarking facilitates the acquisition of innovative external ideas and best practices that can be adapted and implemented as an individual solution for the own organization. Therefore, benchmarking can be perceived as a possibility to internalize valuable knowledge that exists outside the own organization. Thereby, a company is able to strive for a leading position among the competition by setting new standards in the world of the world’s best. Such companies are considered to be "best in class" [1]. However, the scope of benchmarking goes far beyond the comparison of processes or products, since almost any aspect of an organization can be subject to a benchmarking project. Furthermore, the benefit of a benchmarking project is not only limited to a single organization. The selection of benchmarking partners is supposed to enable a mutual learning from each other, since every organization can be excellent in specific areas and needs support in other ones. Consequently, the participants of an innovation network have many possibilities to compare and learn from each other, given the right benchmarking scope and approach. 2.2. R&D Collaboration in Innovation Networks Innovation networks provide an important contribution to the development of the national innovation system and the economy in general as well as to the manufacturing industries in specific. The successful collaboration of innovative organizations and individuals foster the development of business opportunities and jobs creation. According to Ritter and Gemünden “network competence” is furthermore an essential prerequisite to facilitate product and process innovations [3]. The composition of an innovation network is determined by its players, individuals and organizations. Participants of those innovation networks can be start-ups, universities, research institutes, venture capitalists or business angels. Rosenfeld distinguishes broadly between soft and hard networks. According to his definition, soft networks consists of three or more organisations that cooperate in an informal way on issues such as sharing information, acquiring new skills or solving common problems. Hard networks on the other hand are formed by three or more firms that cooperate on aspects such as co-production or co-marketing [4].

More specific examples for innovation networks are x Community of Practice, which is defined as “groups of people, who share a concern or a passion for something they do and learn how to do it better as they interact” [5], x Networked Organization, which is defined as “companies that are bound to some short- or limited-term contractual agreements aimed at a targeted joint business activity, such as the joint delivery of some service to final customers”[6], x Virtual Community, which is defined as “a group of people who come together through computer-aided communication mechanisms to share information of interest” [7]. The relevant factors that contribute to successful operating innovation networks are shown below [8]: x Trust between the participants x Relations usually designed in a long-term time perspective x Redundancies within the network, i.e. options and absence of hierarchy x Openness, dynamics and flexibility x Competition between the network actors x Independence and voluntary cooperation x Scale economics through cooperation Innovation networks are clearly beneficial for the economy as a whole, and single organizations alike. But what are the individual motivations of organizations that participate in innovation networks to collaborate on R&D topics? Hansen suggests three possible outcomes of collaboration that are suitable to motivate an organization to cooperate: “Better innovation, better sales, and better operations” [9]. Camarinha-Matos aggregates the benefit for collaboration activities only to an increase in efficiency [10]. In other words, an organization can save costs by collaborating with others. This statement is further supported by the research of Levermore and Hsu, who argue that collaboration can lead to the reduction of societal transaction costs and cycle time [11]. How efficient R&D collaboration can be shows the work of Audretsch and Vivarelli, who highlighted that SMEs have an even higher R&D productivity than big companies, which can be addressed to their ability to internalize knowledge that was created outside the firm [12]. As a consequence, a collaborative benchmarking approach needs to consider the stakeholder requirements, the principles of a successful cooperation in the innovation network and a clear focus on the topic of collaboration. The benchmarking approach has to integrate these views aiming for an informal and unlimited long-term collaboration that facilitates the target of developing joint R&D solutions by resource sharing and knowledge transfer to address mutual customer demands in manufacturing industries. Therefore, a workshop concept will build the orientation for a traditional and/or virtual collaboration among the network.

Holger Kohl et al. / Procedia CIRP 26 (2015) 185 – 189

3. Assessment and Evaluation of Collaboration Potential in Sustainable Innovation Networks 3.1. Methodology The workshop-based benchmarking approach developed by Fraunhofer IPK and the Technical University of Berlin aims to help organizations in Germany identifying their collaboration potential in a sustainable way. It is a practical approach for either developing existing collaborations or supporting the establishment of new innovation networks in manufacturing industries. The method focuses on three collaboration objects which are stated below (see Fig. 1): Customer Collaboration: Two or more organizations that target the same customers through joint activities and/or jointly deliver project to the same customer. x Levels: Collaboration in terms of market analysis, marketing and sales activities based on current demand of the industry (Market Pull). x Benefit: Increase revenues, master broader project scopes and deliver complex solutions. Resource Collaboration: Two or more organizations that share the same resource (e.g. machinery, human resources, analytics) without any or very little additional effort. x Levels: Collaboration in terms of purchasing or using equipment and software as well as sharing external services (e.g. training, consulting) and human resources and transferring existing knowledge. x Benefit: Increase efficiency, optimize costs and access broader resource base. Topic Collaboration: Two or more innovation organizations that combine their knowledge and technologies to work together on a research, development and innovation topic a single innovation organization could not master on its own. x Levels: Building up competence-fields for future technologies and current transversal research topics (Technology Push). x Benefit: Drive innovation activities and develop new technologies.

Resource

Network

Customer

Topic

Fig. 1: Resource, Customer and Topic Collaboration

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For each of these areas the collaboration potentials are analyzed in a structured process resulting in recommendations for best practice transfers. The methodology consists of three main steps that guide the participants through the process: 1.

2.

3.

Collaboration Pitch (get to know): Build a common understanding between the organizations and identify best-practices or need for support. Collaboration (work together): Map of existing collaboration (in comparison to the results from the last year), identify success factors and elaborate an action plan for further collaboration. Best-Practice Transfer (learn from each other): Generalize existing best-practices and transfer them to the other organizations.

As a preparation to initiate this process it is necessary to define whether the participants are already organized in an innovation network or plan to build one. In the first case, the existing collaborations will be considered as well, so that the assessment for collaboration potential can be even more accurate. When the network has been defined, each organization prepares a standardized short-presentation in order to provide an overview about its service/technology areas, customer and market segments and its need for collaboration. The assessment of collaboration potentials of each participating organization is undertaken on the basis of these shortpresentations. As a starting point each organization analyses the input of all other network members and performs a questionnaire-based evaluation in order to identify specific collaboration potentials. The assessment is structured by the three collaboration subjects Customer, Resource and Topic Collaboration. In order to quantify the assessment a scale from “0” to “10” is used, as stated below. x x x

“10” indicates the highest collaboration potential with great prospective benefits. “5” indicates a medium level of collaboration potential with moderate benefits for the involved parties. “0” indicates that no beneficial collaboration is possible. The collaboration potential equals zero in this case.

Following this rating the assessment is visualized in network graphs, one for each collaboration subject. More precisely a force-directed graph is applied, which is the current method of choice for analysing relational data sets [13]. The graph consists of nodes and weighted edges. The nodes symbolize the organizations. The edges are weighted by the collaboration potential on the scale “1” to “10” (see above). The layout of the force-directed graph simulates a physical system in order to specify a certain sub-network (community). Nodes repulse each other and edges attract their nodes. Through these forces a movement is created, which converges to a balanced state. Technically, the graph is created through the graph visualization software Gephi [14] (see Fig. 2).

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of the respective organizations, are taken from the Collaboration Pitch. In the process of the session common challenges are aggregated and discussed and exemplary best-practices are elaborated and discussed in order to provide possible solutions. These specific best-practices are later generalized in order to be suitable to all participants of a community or even for the whole innovation network. The results of the Best-Practice Transfer session are therefore those generalized best-practices that can be transferred and adapted systematically as well as the action plan for further best-practice provision (see Fig. 3).

Best-Practice User

Fig. 2: Exemplary Visualization of a Network with 23 Nodes and 3 Communities (Different Colors)

Finally, for each of the three collaboration subjects communities (sub-networks) are identified. Afterwards in the workshop, manageable groups will be defined on this basis. Therefore, the Louvain method to identify communities as it “allows to detect communities quickly and efficiently with enlightening results” is applied [15]. The algorithm is “intuitive and easy to implement, has unsupervised outcome, is extremely fast and circumvents the resolution limit problem of modularity” [16]. The automatically identified communities are merely a recommendation. They can be discussed in the workshop and modified afterwards. The detailed workshop procedure following the overall methodology is described below. 3.2. Workshop Procedure According to the methodology, the workshop is also divided into the three main steps: Collaboration Pitch, Collaboration and Best-Practice Transfer. The Collaboration Pitch offers the opportunity to get to know the other organization in more detail. Each organization provides an overview over their service/technology areas, customer and market segments and its need for collaboration. Moreover strengths and weakness are pointed out, which can later serve as examples for collaboration potentials in the Collaboration session or as best-practices in the Best-Practice Transfer session. The result of the collaboration pitch should be an overview on what an organization needs and what it has to offer in terms of collaboration (see above). As a second step the Collaboration session takes place. The inputs for this session are the current collaboration assessments based on Customer, Resource and Topic Collaboration and if applicable the workshop results of the previous year. In this session, possibilities for collaboration are evaluated and barriers as well as success factors for collaboration success are identified. As a result, an action plan for joint collaboration activities among each identified community is derived. If the workshop took already place in the previous year, the progress of collaboration is evaluated as well. The last step of the workshop is the Best-Practice Transfer. The inputs for this session, the strengths and weaknesses

Common Challenges

Best-Practice Provider

Systematic Transfer to the Network

Individual BestPractices

Fig. 3: Best-Practice Transfer

It is important to mention that the single steps of the workshop are conducted in the identified communities (subnetworks). Furthermore the workshop structure follows the pattern of the three collaboration subjects Customer, Resource and Topic Collaboration. Hence, there is a need for three workshop sessions in total. Each one follows the three steps described above. Below, the focus of each workshop session is described more in detail. The session on Customer Collaboration is directed at the identification of collaboration potential based on the market pull approach. The exact procedure varies depending on the research area of the community. The communities elaborate maps of their market segments in order to identify and discuss special market demands. Another possibility for mapping is given through the current and planned marketing activities leading to collaboration potentials through joint marketing strategies. Services can be mapped as well in order to create an aligned and complemented service portfolio for the community. Although the results of this workshop session depend heavily on the structure of the network, pilot projects can be already agreed during the session as a first common result. The session on Resource Collaboration aims at the identification of collaboration potentials concerning resources such as equipment, software and human resources. All communities map the existing and planned resources on cards on a metaplan which serves as a basis for discussion on potential collaborations. Connections between the organizations and resources are visualized through the metaplan. As a result of this workshop session visualizations in terms of equipment or software, joint acquisition or sharing of resources as well as overviews on competences and organizational profiles are possible. The session on Topic Collaboration is supposed to identify collaboration potential among the communities based on the technology push approach. Expected results include visualizations of current and planned transversal R&D topics from an

Holger Kohl et al. / Procedia CIRP 26 (2015) 185 – 189

internal community or network perspective. Additionally, an external perspective can be included by identifying and grouping external megatrends that influence the respective industries of the community. In each case the focus is put on the identification and prioritization of opportunities for joint research activities. Possible outcomes of the Topic Collaboration session can be the initiation of common research programs, joint application for public funding, or joint projects such as exhibitions, publications, workshops and/or seminars. Even personnel exchange in form of internships may be taken into consideration. 4. Conclusions Especially for small- and medium-sized enterprises the collaboration in innovation network is an excellent possibility to perform R&D projects even with limited resources. Therefore, the Information Centre Benchmarking (IZB) at Fraunhofer IPK and the Technical University of Berlin developed a methodological benchmarking approach to identify the collaboration potentials of innovation networks in manufacturing industries in the fields of Customer, Resource and Topic Collaboration. The objective of this approach is to generate a multiple-win situation for all participants of the innovation network in terms of x x x

increasing efficiency, optimizing costs and accessing a broader resource base (Resource Collaboration), increasing revenues, mastering broader project scopes and delivering complex solutions (Customer Collaboration) and driving innovation activities and developing new technologies (Topic Collaboration).

At the moment this approach is in a practical pilot testing stage in the second year of application with a large innovation network in Brazil that is strongly connected with manufacturing industries. The current results are very promising to deliver high benefits for all participants of the network. For future applications of the methodology further developments steps are currently planned: x x x x x

Adaption of the methodology and the strategic success factors for other types of organizations or industries, e.g. governmental networks. Including the assessment of intellectual capital in the benchmarking methodology. Integration of the collaboration approach in an evaluation system for applied R&D provider. Development of a visualization tool for reporting and comparison of collaboration results among the innovation network. Development of a planning tool for long-term collaboration activities, e.g. collaboration roadmap.

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5. References [1] [2] [3]

[4]

[5] [6] [7] [8]

[9]

[10] [11]

[12] [13]

[14] [15]

[16]

Camp, R. C. (1994): Benchmarking, Hanser Fachbuchverlag. Mertins, K.; Kohl, H. (2009): Benchmarking – Leitfaden für den Vergleich mit den Besten, Symposion Publishing Verlag, Düsseldorf. Ritter, Thomas; Gemünden, Hans Georg (2003): Network competence. Its impact on innovation success and its antecedents. In: Journal of Business Research (56), pp. 745–755. Rosenfeld, Stuart A. (1996): Does cooperation enhance competitiveness? Assessing the impacts of inter-firm collaboration. In: Research Policy (25), pp. 247–263. Wenger, Etienne (2011): Communities of practice: A brief introduction. In: STEP Leadership Workshop, University of Oregon, October. Kartseva, Vera (2008): Designing controls for network organizations. a value-based approach. Amsterdam: Thela Thesis. Dasgupta, Subhasish (2006): Encyclopedia of Virtual Communities and Technologies. Hershey: Idea Group Inc. Koschatzky, Knut; Kulicke, Marianne; Zenker, Adrea (2001): Innovation Networks. Concepts and Challenges in the European Perspective. Heidelberg: Physica-Verlag. Hansen, Morten T. (2009): Collaboration. How Leaders Avoid the Traps, Create Unity, and Reap Big Results. Watertown: Harvard Business Review Press. Camarinha-Matos, Luis M. (2004): Virtual Enterprises and Collaborative Networks. Boston: Springer. Levermore, David M.; Hsu, Cheng (2006): Enterprise Collaboration. Demand Information Exchange for Extended Enterprises. New York: Springer Science+Business Media, LLC. Audretsch, D.; Vivarelli, M. (1996): Firm size and R&D spillovers: evidence from Italy. Small Business Economics, pp. 249–258 Tamassia, R. (Ed.) (2013), Handbook of Graph Drawing and Visualization: Chapter 12 - Force-Directed Drawing Algorithms, Chapman and Hall/CRC. Gephi Consortium (2013), Gephi Graph Visualization and Manipulation Software, www.gephi.org. Aynaud, T. and Guillaume, J.-L. (2011): “Multi-Step Community Detection and Hierarchical Time Segmentation in Evolving Networks”, Proceedings of the Fifth SNA-KDD Workshop Social Network Mining and Analysis. Blondel V. D.; Guillaume, J.-L.; Lambiotte, R.; Lefebvre, E. (2008): Fast unfolding of communities in large networks, Journal of Statistical Mechanics P10008.

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