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Open Innovation in Smart Factories

Studienarbeit 2020 70 Seiten

Informatik - Internet der Dinge, IOT

Leseprobe

Table of contents

Abstract

List of abbreviations

List of figures

1. Introduction

2. Open Innovation
2.1. Structuring Open Innovation strategies
2.2. Criticism
2.3. Factors for choosing a strategy

3. Smart Factories
3.1. Smart Factory system architecture
3.2. Key technologies in Smart Factories

4. Open Innovation in Smart Factories
4.1. Checking the external factors
4.2. Determining the basis for the strategy
4.3. Developing a strategy
4.4. Recap

5. Closure
5.1. Summary
5.2. Conclusion and Outlook

6. References

Abstract

The promising advantages of Smart Factories are still largely unattainable to this day, as the high complexity of technologies and the encounter of different areas are major challenges for many companies and their innovation processes. One way to overcome these challenges is to open the innovation process to the outside world. The target of this research project was to investigate, if and how such a so-called Open Innovation approach can be applied to enable and accelerate the development of Smart Factories. After examining the topics Open Innovation and Smart Factories separately, a strategy for developing one of the key technologies in Smart Factories, the cyber-physical systems, was designed for small and medium-sized companies from the industrial engineering industry. The investigations of this research project led to the conclusion, that companies can profit most by exchanging knowledge and working together with partners in cooperations, thereby adopting the principles of Open Innovation.

List of abbreviations

AI Artificial Intelligence

BMBF German Federal Ministry of Education and Research

CPS Cyber-physical systems

CPPS Cyber-physical production systems

IIoT Industrial Internet of Things

IoT Internet of Things

IP Intellectual Property

it’s OWL Intelligent Technical Systems Ostwestfalen-Lippe

OECD Organization for Economic Co-operation and Development

R&D Research and Development

RFID Radio-Frequency-Identification

NGO Non-governmental organization

NIST National Institute of Standards and Technology

List of figures

Figure 1: Separating the locus of innovation from the locus of knowledge and the locus of exploitation

Figure 2: Transforming external knowledge into innovations

Figure 3: Possible procedures of externalizing knowledge

Figure 4: External factors and their influence on the degree of openness of companies’ innovation processes

Figure 5: Importance of different firm-specific abilities for the three core processes of Open Innovation

Figure 6: Four layer system architecture for Smart Factories

Figure 7: Structure of cyber-physical systems (CPS) and cyber-physical production systems (CPPS) and their links to the outside world

Figure 8: Evaluation of CPS regarding external factors influencing the degree of openness

Figure 9: Innovation process for CPS with the required openings

Figure 10: Required abilities and efforts, scope of knowledge and risk of different types of cooperation

Figure 11: Overview of possible procedures of a strategy for developing CPS in cooperation

1. Introduction

In recent years, the production sector has been repeatedly confronted with new challenges, like changes in customer requirements (Salesforce Research, 2019) or the climate change (Martínez-Ferrero & Frías-Aceituno, 2015). As an answer to these challenges, technologies and solutions from different fields are combined to take the industrial production to the next level. The result are so-called Smart Factories which offer promising advantages (Sjödin, Parida, Leksell, & Petrovic, 2018). However, the high complexity and the therefore required knowledge from various disciplines stress innovation processes in their early stages, and therefore hamper the development of new technological solutions (Zhou, Liu, & Zhou, 2015).

At the same time, changes occur through the ongoing globalization and the simplification of transferring knowledge, forcing many companies in various industries to adjust their innovation processes (Chesbrough, 2003a). One such procedure that can be observed is the Open Innovation approach, in which companies open their innovation processes in different ways by enabling the in- and outflow of knowledge across the companies’ boundaries. Using Open Innovation, companies can overcome challenges and improve their innovation outcome. In this research project, it will be investigated if and how the methods of Open Innovation can be successfully applied for the development of Smart Factory technologies.

To answer this question, the thesis proceeds as follows. In chapter 2, methods, pros and cons, challenges, and application fields of the Open Innovation approach will be discussed. The following chapter 3 the reviews the relevant technological backgrounds of Smart Factories, thereby including sections about their structure, important technologies, pros and cons as well as specific challenges in developing Smart Factory technologies. In Chapter 4, both topics will be looked at together to investigate whether the Open Innovation practices can help overcoming the challenges of technologies in Smart Factory environments. Given the high complexity of Smart Factories, it is not achievable to do this for all included technologies. Therefore, the investigation is limited to one technology. In the examination, the best suited Open Innovation approach will be determined before a strategy for developing this technology will be designed. In the concluding 5th chapter, the most important findings of the research project will be summarized before a conclusion is drawn and an outlook on possible future research and approaches is given.

2. Open Innovation

For most of the 20th century, key technologies were developed and marketed only within larger companies (Inauen & Schenker-Wicki, 2011) since the attitude that successful innovation requires control (Chandler, 1977) was widespread. Chesbrough (2003a) calls this approach, where the complete innovation process from the idea to the after-sales service takes place within one company, closed innovation.

Since then, many conditions and factors have changed, leading companies to slowly move away from the completely closed innovation process towards a more open one (Chesbrough, 2003a). As a result of the ongoing globalization, competition became increasingly fierce which led to an ever-increasing pressure to innovate (Gerybadze & Reger, 1999). The increasing competitive pressure is forcing companies to reduce their costs which results in the challenge of launching more competitive products on the market while wages are rising and development budgets are shrinking (Gassmann & Enkel, 2006).

The economic globalization has above all been made possible by continually improving information and communication technologies (Gerybadze & Reger, 1999). The internet and the possibility of digitalizing data have made the transfer of knowledge easier and have led to a better accessibility, lowering market entry barriers in many industries. Whereas in the past the largest companies were able to fund the most research and therefore benefited of the most advanced technologies, the described changes enable more and more start-ups to position themselves in the markets (Chesbrough, 2003a). In addition to easier access to knowledge, the increasing availability of venture capital also encouraged the creation and development of start-ups (Teten, Abdel-Fattah, Bremer, & Buslig, 2013). Alongside the start-ups, many universities have also developed into important sources of knowledge as a result of increasing funding and thus more and more research projects (Charles, 2006).

Introduction of the Open Innovation paradigm

Companies recognized that although innovations are essential, they are also very risky because of possible failures. If the entire innovation process takes place in a company, that company also bears the entire risk. As an answer to these developments and changes, companies developed new ways to be innovative while limiting the risk of failed innovations. As these strategies were picked up an discussed in literature a long time ago, Henry Chesbrough (2003a) was the first one to combine all these strategies on a theoretical basis by shaping the term “Open Innovation”. He addresses two main changes companies need to implement in order to optimize their innovation process and therefore success. Chesbrough states that “companies must structure themselves to leverage this distributed landscape of knowledge” (Chesbrough, 2003a, p. 51). With the “distributed landscape of knowledge”, the author refers to the above-mentioned increasing mobility of knowledge. On the one hand, companies need to acknowledge that there are more and new ways to gather knowledge than doing internal research. These new ways do not necessarily replace the previous methods. Instead, Chesbrough’s Open Innovation strategy focuses on “combin[ing] internal research with external ideas” (Chesbrough, 2003a, p. 63). On the other hand, companies need to find new ways to use the output of their innovation processes, i.e. to commercialize it. The search of new methods applies to expected outcomes, i.e. the targets of the development projects, and the not expected outcomes, the so-called spillovers, and reduces the risk of a failed and therefore expensive innovation process (Chesbrough & Bogers, 2014).

Summarized, Chesbrough describes Open Innovation as “the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively” (Chesbrough, van Haverbeke, & West, 2006, p. 1). That also means, Open Innovation is not a single step or procedure in the innovation process but includes the innovation process as a whole (Chesbrough, van Haverbeke, & West, 2014).

In the following, it will be examined which approaches exist for opening up the innovation process, which approach is most suitable for which objectives and which characteristics are required to pursue the respective approach. Afterwards, the Open Innovation paradigm will be critically examined by pointing out its disadvantages. Lastly, it will be analyzed which conditions are suitable for opening up the innovation process and which are not.

2.1. Structuring Open Innovation strategies

Since the introduction in 2003, the concept has been picked up in many academic articles and developed in many directions (Gassmann, 2006; Huizingh, 2011). As Open Innovation “is not a clear-cut concept [but] comes in many forms and tastes” (Huizingh, 2011), it is necessary to develop and define a rough framework. Shortly after Chesbrough coined the term Open Innovation and described the two above-mentioned main changes in the innovation process, the paradigm was structured more detailed by Gassmann and Enkel (2004). In 2004, they presented three core processes, each summarizing Open Innovation methods, as the results of their research projects. In these projects, they gathered data over a 10-year span in partially very close cooperation with 124 companies. Even though some of this data is more than twenty years old by now, the large scale of the research makes this source reliable. Furthermore, the three core processes are a very common concept to structure innovation used by many authors in recent years up until today, for example, by Chesbrough (2017), Manzini, Lazzarotti, and Pellegrini (2017) or Blume (2020), which demonstrates the validity of the processes. Therefore, the respective core processes of Gassmann and Enkel (2004) will be used to structure the Open Innovation approaches in this paper.

To distinguish between these core methods, they describe the relationship between three relevant points: The locus of knowledge, i.e. the place where the knowledge is created or available, the locus of innovation, i.e. the place where the idea is realized and the locus of exploitation, i.e. the place where the product is finally commercialized (Gassmann & Enkel, 2006). In the traditional closed innovation process, all the places lie within the company’s boundaries.

The three core processes are illustrated using the three-point model in Figure 1. The outside-in process is similar to Chesbrough’s strategy of finding new ways to gather knowledge and is about bringing knowledge from the outside into the company. Therefore, the locus of knowledge is outside the company, while the locus of innovation is inside the company’s boundaries. The inside-out process involves exploiting internal innovations outside the company (locus of innovation inside the company, locus of exploitation outside the company) and therefore resembles Chesbrough’s idea of decreasing the risk of innovations by finding new ways to commercialize outputs (Gassmann & Enkel, 2004). The third core process, the so-called coupled process, is a combination of the first two and contains the coupling of integration and externalization of knowledge, for example in alliances and joint ventures (Gassmann & Enkel, 2006).

In addition to defining these three core processes, Gassmann (2006) mentions various perspectives to open up the innovation process. These include the globalization of innovation, outsourcing of Research and Development (R&D), early supplier integration, user innovation and external commercialization of technology. Depending on each process, these aspects are also reflected in the core procedures with varying degrees of emphasis. The following sections will therefore examine the core processes closer while highlighting the different perspectives.

Abbildung in dieser Leseprobe nicht enthalten

Figure 1: Separating the locus of innovation from the locus of knowledge and the locus of exploitation

Source: Adapted from Gassmann & Enkel (2006)

2.1.1 Outside-in process

The outside-in process is based on the recognition, that the locus of knowledge does not necessarily equal the locus of innovation. In this process, knowledge is therefore integrated into the company from various sources using several methods (Gassmann & Enkel, 2004), which will be reviewed in this section. This approach is not new in itself – in 1995, innovations already had an external knowledge input of 34-65% (Conway, 1995). Nevertheless, new methods and sources of knowledge have emerged in recent years. That is why companies need to reorganize their structures and procedures in order to work more closely with a growing number of stakeholders during the innovation process (Inauen & Schenker-Wicki, 2011). Possible external knowledge sources and methods for knowledge integration are examined hereafter.

Possible Sources of knowledge

One possibility of knowledge integration, which has been known for a long time and is widely used, is the inclusion of supply chain members beyond the boundaries of the company. This includes suppliers as well as customers (Fritsch & Lukas, 2001; Gassmann & Enkel, 2004).

The customers abandoned their role as passive receivers of products already in the 1970s and early 1980s and „can now initiate the dialogue[; as] they have moved out of the audience and onto the stage“ (Prahalad & Ramaswamy, 2000, p. 80). User Innovation is also one of Gassmann’s (2006) five most important perspectives of Open Innovation. The importance of including the customer early in the innovation process, for example as lead customers, has been widely recognized (Hippel, 1986; Bonner & Walker, 2004; Lau, Tang, & Yam, 2010). The early integration of customers in the innovation process leads to a better understanding of market needs and therefore to fewer errors in the early development process and a better product quality (Enkel, Kausch, & Gassmann, 2005).

Besides of the customer involvement, the early integration of suppliers plays an important part in Open Innovation, according to Gassmann (2006). The author is validated by many other researchers, agreeing that supplier involvement is undisputed in literature and practice (Hagedoorn, 2002; Petersen, Handfield, & Ragatz, 2003; Zhang, Zhao, Voss, & Zhu, 2016), carrying many advantages. Benefits include an earlier identification of technical problems, improved product features, reduced technical and financial risks and a shorter time-to-market for new products (Ragatz, Handfield, & Petersen, 2002; Gassmann & Enkel, 2004).

Another possible source of external knowledge are intellectual property (IP)-licensed patents (Gassmann & Enkel, 2004). Companies operating with the closed innovation model are using IP to protect their own investments in R&D in order to use the contained knowledge and technologies for themselves and to avoid expensive litigation. The Open Innovation model expands this concept with licensing (Chesbrough, 2006). Licensing is defined as the exploitation of intellectual property from other firms within a certain time frame (Tidd & Bessant, 2018). The reasons of why firms are selling their IPs are analyzed in the next chapter, in which a closer look is taken at the inside-out process. The advancing globalization is making cooperation between firms more interdisciplinary and across industry borders. That is why IP rights are often traded across industries (Inauen & Schenker-Wicki, 2011). In order to take the chance of finding interesting technologies in other industries, companies should not limit their search for knowledge to their own industry.

A further perspective of Open Innovation after Gassmann (2006), which is implemented in the outside-in process, is the outsourcing of R&D. While the strategy to partially outsource R&D and develop innovation together with other companies can be assigned to the coupled process, the development can also be completely outsourced. For this purpose, there are various online forums that all operate on a similar principle and connect experts from companies worldwide with each other and with leading scientists for joint work on research issues (Gassmann & Enkel, 2006). One example is InnoCentive: companies (“seekers”) can post problems on their website with a financial reward for the potential solver for a small fee. Afterwards, various „solvers“, whether they are private individuals, companies or universities, can try to solve the problem. (InnoCentive, 2020).

In addition to all the commercial sources of knowledge, cooperation with non-commercial organizations such as universities or non-governmental organizations (NGO) can also be essential (Inauen & Schenker-Wicki, 2011; Rauter, Globocnik, Perl-Vorbach, & Baumgartner, 2019). Especially through a close cooperation between public universities, companies can profit from “continuous information about new knowledge and developments[,] more speed and flexibility in innovation[,] a valuable network with high level contacts both nationally and internationally” (Inauen & Schenker-Wicki, 2011, p. 510) and reduced R&D costs due to the public financial support of research projects (Harryson, Kliknaite, & Dudkowski, 2008).

In some cases, notably when it comes to sustainability innovations, companies “might particularly require different expertise and input and call for wider societal acceptance” (Rauter, Globocnik, Perl-Vorbach, & Baumgartner, 2019, p. 227). This cooperation is specifically efficient, if the partners have a mutual interest in improving the company’s outcome, which is quite common when it comes to sustainability (Rauter, Globocnik, Perl-Vorbach, & Baumgartner, 2019).

Characteristics of companies using the process as their core process

Transforming knowledge from an external source into an innovation requires some intermediate steps. How well this is achieved depends, according to Cohen and Levinthal (1990), on the absorptive capacity of a company. They describe this capacity as the „ability to recognize the value of new information, assimilate it, and apply it to commercial ends” (p. 128). The absorptive capacity therefore consists of three sub-capabilities, which can be defined as intermediate steps in the information processing procedure. Firstly, the company must comprehend the benefit of the newly gained knowledge. Then this knowledge must be integrated and combined with the already existing knowledge. Finally, this assimilated knowledge must be used to create value. Schreyögg and Duchek (2012) named these three steps acquisition, integration and exploitation. The input of this process is therefore knowledge, the output is innovation (Cohen & Levinthal, 1990). This process, combined with the earlier explained possible external knowledge sources of the outside-in process, is displayed in Figure 2.

Abbildung in dieser Leseprobe nicht enthalten

Figure 2: Transforming external knowledge into innovations

Source: own representation

Cohen and Levinthal's (1990) model has been followed by many revisions since its introduction, some of which have received much attention (Todorova & Durisin, 2007; Zahra & George, 2002), but there has been no fundamental change since. What has not changed either, according to Schreyögg und Duchek (2012), is the lack of detail in the intermediate steps that are ultimately treated as black boxes. Nevertheless, a good absorptive capacity is an essential characteristic for companies implementing the outside-in process (Gassmann & Enkel, 2006).

After defining the absorptive capacity as an essential characteristic, the question still remains for which type of companies the process is suitable. The research of Gassmann und Enkel (2004; 2006) showed, that especially companies with certain characteristics choose the outside-in process as their core procedure for Open Innovation.

As a first characteristic, these companies have a background in the low tech industries, e.g. “companies that expect spillovers from higher tech industries” (Gassmann & Enkel, 2004, p. 9). Another particularity of such businesses is that they often produce highly modular products. Thirdly, they are often positioned in knowledge-intensive industries in which they cannot cover their knowledge demand by internal efforts (Gassmann & Enkel, 2004).

Other studies show that large companies are more likely to gather knowledge from external sources (Inauen & Schenker-Wicki, 2011; Centre for European Economic Research, 2016). However, this does not indicate that the outside-in process is only suitable for large companies.

2.1.2 Inside-out process

In the inside-out process, internally created knowledge is exploited externally, therefore the locus of exploitation lies outside the company’s borders (Gassmann & Enkel, 2004). The knowledge is often externalized through IP-licensing. In the past, patents have been transformed from being only used for protecting technologies into a tradable good. (Gassmann, Enkel, & Chesbrough, 2010). Similar to the outside-in process, the inside-out process does not have a completely defined structure but contains various possibilities for the externalization of knowledge. These possibilities have partly different benefits, which are explained in the following. An overview of possible procedures is shown in Figure 3.

Abbildung in dieser Leseprobe nicht enthalten

Figure 3: Possible procedures of externalizing knowledge

Source: own representation

Technologies developed for a specific application can often be used in other ways. A well-known example for this is Teflon which was originally developed for space travel and is now also used as coatings for pans etc. (Gassmann, 2006). This offers a new possibility for companies to generate profit and therefore create more overall revenue from innovations. These revenues are helping to cover the R&D costs and thus simultaneously reduce the risk of research and development projects. (Gassmann & Enkel, 2004). This approach is the counterpart to the integration of knowledge through licensing IPs in the outside-in process.

One possible procedure is to license other companies from other industries. In addition to the cost reduction and risk diversification, this method allows ideas and technologies to be brought to the market faster than it would be possible within the own company (Gassmann & Enkel, 2004).

Furthermore, the company selling the IP “can also benefit from subsequent improvements in the technology introduced by the IP buyer” (Inauen & Schenker-Wicki, 2011, p. 502). Alternatively, the company can transfer the technology to other industries itself. If the company simply sets up a new business area for this purpose, licensing is not required, which is why this point is bracketed in Figure 3. However, if the technology is not in line with the company’s core business, a so-called spin-off should be established (Inauen & Schenker-Wicki, 2011). This spin-off is an independent company which still belongs to the company group. The spin-off receives the license by the mother firm and develops the new market with the new technology (Enkel, Gassmann, & Chesbrough, 2009). With this approach, the company has a longer time-to-market, has higher investments, and bears the entire risk but also has a higher long-term profit potential.

In addition to using the technology in other applications outside the company’s industry, licensing inside the own industry is also a possible procedure. For example, a company can support its suppliers by issuing licenses within its own supply chain and then benefit from improvements in price and quality (Gassmann & Enkel, 2004).

Characteristics of companies using the process as their core process

According to the research of Gassmann and Enkel (2004) the inside-out process as a core process is mainly chosen by „basic research-driven companies [who] aim at decreasing the fixed costs of R&D and sharing the risks” (p. 11). However, the basic idea of finding further applications for developed technologies and thus increasing the revenue of an innovation should take place in every company.

2.1.3 Coupled process

As the term suggests, the coupled process is a combination of the outside-in process and the inside-out process. A company therefore accesses external knowledge while sharing knowledge with others at the same time. In the coupled process, this exchange happens in the context of a “joint development of knowledge through relationships with specific partners” (Gassmann & Enkel, 2004, p. 12). Possible cooperation partners are all those who already serve as knowledge sources in the outside-in process or as recipients in the inside-out process, namely customers, suppliers, competitors, companies from other industries as well as universities and research institutions (Gassmann & Enkel, 2004; Bogers, 2011; Inauen & Schenker-Wicki, 2011).

One possible type of cooperation is the participation of one company in another in form of minority investments (Kenton, 2019) or corporate venture capital investments (Inauen & Schenker-Wicki, 2011). In both cases, the investing firm generally can access the full portfolio of technologies (Dushnitsky & Lenox, 2005). A further form of cooperation is a joint venture whereby two or more companies found a new company in order to work together towards a common goal (Altmann, 2018). In the case of joint ventures and strategic networks, which can be described as “the coordinated cooperation between several legally and formally independent enterprises that promote long-term strategic cooperation“ (Hinterhuber & Hirsch, 1998, p. 185), the involved companies only expose the technologies and knowledge needed for the joint development (Dushnitsky & Lenox, 2005).

Bogers (2011) states that “because of the increasing complexity of knowledge, more and different kinds of partners are often needed to achieve a certain goal” (p. 95). According to Gassmann and Enkel (2004), such a goal can be an improvement in the competitive position or a risk minimization which can be reached among others by setting a standard or dominant design for a company’s product. The success of such cooperation with different kinds of partners is based on the ability of a company to find the right partner with the required knowledge and competences (Marxt & Link, 2002; Gassmann & Enkel, 2004) and especially “the right balance of give and take” (Gassmann & Enkel, 2004, p. 13). Exchanging knowledge is therefore a critical point in cooperation.

According to Bogers (2011) there can be no standardized strategy for knowledge exchange as this depends on various factors relating to knowledge and the partners. Depending on the type of knowledge, the relationship between the partners and the type of collaboration, a different strategy to exchange knowledge is required (Bogers, 2011).

The knowledge characteristics determine to what extend knowledge can be exchanged (Bogers, 2011). For example, complementary, explicit and teachable knowledge enables a clear exchange of knowledge but is also easy to imitate and therefore requires a better protection than for example very complex, deeply embedded knowledge. Norman (2001) distinguishes between knowledge that is transferred directly and knowledge that is transferred indirectly during collaboration. Nelson and Winter (1982) attempted to describe knowledge characteristics by creating a scale from “public good”, i.e. publicly distributed knowledge, to “deeply embedded in organizational routines”. Most corporate knowledge can be placed somewhere in between these two extreme points (Contractor & Ra, 2002).

In case of a cooperation, a company needs to disclose some knowledge to its partner already during negotiations. If too much is revealed, the recipient can proceed on their own without entering an alliance or paying for the information (Maskell & Malmberg, 1999). If the company discloses too little, a cooperation is not possible. Contractor and Ra (2002) are calling this the “disclosure dilemma”. On the knowledge scale of Nelson and Winter, the highest disclosure dilemma is somewhere on the left side , where the knowledge is part of the company but at the same time easy to replicate if revealed (Contractor & Ra, 2002). According to Contractor and Ra (2002), one way out of this dilemma is the usage of IP rights. Therefore, protecting IP is most important for the type of knowledge which has the highest disclosure dilemma.

There are studies, such as Oxley (1999), Luo (2005) or Alexy, Criscuolo and Salter (2009), which confirm that effective IP protection simplifies the formation of alliances and networks. However, Bogers (2011) argues that the less protection measures and protective attitude there are in a cooperation, the smoother it runs. The author is supported by Hurmelinna-Laukkanen (2011) who states that protection measures are often costly and difficult to apply on certain types of knowledge. In the absence of protection measures, the cooperation is based on trust. At a general level, trust can be described as the readiness to accept vulnerability on the basis of positive expectations about the partner’s intentions (Mayer, Davis, & Schoorman, 1995). As trusting the other party allows people to focus on the common goal instead of using resources for protecting themselves, a higher performance and a better outcome can be achieved (McEvily, Perrone, & Zaheer, 2003). Nevertheless, trust also has limits and can be exploited. On the one hand “successful innovation requires knowledge protection and control also in [an] open environment, […] [on] the other hand, too tight approach towards knowledge protection creates its problems, especially if it blocks needed knowledge exchange” (Hurmelinna‐Laukkanen, 2011, p. 308). As both protecting and sharing knowledge are positively related to innovation performance (Hurmelinna‐Laukkanen, 2011), companies who want to cooperate have to find a middle ground between trusting each other and protecting their knowledge. Bogers (2011) calls this the “tension between knowledge sharing and protection” (p. 94).

On the one hand, the extend of this tension depends on the knowledge characteristics. On the other hand, the type of relationship between the cooperating companies therefore also plays a role (Bogers, 2011). For example, if they come from the same technology field, knowledge which would be perceived as complex and hardly imitable in other industries requires higher protection. Furthermore, factors describing the type of collaboration, like previous experiences, number and size of the partners, duration, and purpose of the collaboration have an influence on the approach of exchanging knowledge (Bogers, 2011).

Measures for protecting knowledge

Norman (2001) divides the knowledge protection mechanisms into three categories: human resources, legal structure of alliance agreements and contracts, and alliance process. She collected measures for each of these categories and examined the effectiveness of these measure in a study through surveys and interviews. The most effective measures are described in this chapter.

In the category human resources, the top management needs to wall off critical knowledge by identifying core competencies and providing enough resources to protect them. It must be specified which knowledge can be shared in the cooperation. The Management also has the task of raising awareness about knowledge protection among their employees, e.g. through appropriate training. Norman (2001) further recommends to designate a person to be responsible for the knowledge exchange and protection in the cooperation who will simultaneously function as a contact person for the employees as well as the top management.

Looking at the legal protection mechanisms, one possible measure is the use of patents (Norman, 2001). By licensing patents, companies can grant their partners the right to use the knowledge for a specific time frame (see chapter 2.1.2). Due to cost reasons, licensing in cooperation is often handled with cross-licensing agreements “in which the partners license each other the knowledge needed for the collaboration” (Bogers, 2011, p. 109). As patents may disclose information to the outside world, this measure can form a risk which additionally is often complex and expensive. Norman (2001) explains that the effectiveness of patens as a knowledge protection mechanism depends on the industry.

Another legal measure, which is effective in all industries, is the establishment of contracts. A contract can specify proprietary data, what information can be shared and can clearly mark critical documents (Norman, 2001). It can also include penalties and set a legal framework for the cooperation (Bogers, 2011).

The third category contains measures for knowledge protection with regard to alliance processes which “are critical determinants of the information, skills, and capabilities that flow between partners” (Norman, 2001, p. 54). By limiting the partner’s access to specific things, the most effective measure according to Norman (2001) is to perform certain functions without the partner. Other limitations can include the partners access to facilities and non-alliance personnel or limiting information flows to specific persons.

The companies in Bogers’ (2011) study all developed their own strategy to deal with the problem of knowledge exchange. Nevertheless, he identified two general strategies, namely the open exchange strategy and the layered collaboration scheme (Bogers, 2011).

The open knowledge exchange focuses on sharing knowledge in an open manner between all partners. However, patenting remains a difficult issue, hampering the openness of the collaboration. One possible solution is the usage of co-owned patents to protect the gained knowledge. For the strategy to work, a tight secrecy agreement to keep knowledge inside the collaboration is required (Bogers, 2011).

In the layered collaboration scheme, sub-collaborations with inner and outer members are established. This leads to an open exchange with the inner circle, while the exchange with the outer members is more limited. This scheme is based on the aspect that not all partners need to know all details. This strategy is the only practical one for a cooperation with a high number of partners (Bogers, 2011).

To develop their own strategy based on one of those two general strategies, companies should keep certain aspects in mind. When choosing protective measures, it is important that they still allow the firm “to be open to the upside and benefits of knowledge sharing” (Norman, 2001, p. 55). Furthermore, it is easier to remove protective power than start implementing it when collaboration has already begun (Hurmelinna‐Laukkanen, 2011).

2.2. Criticism

Although the methods of Open Innovation discussed so far seem to have to potential to be „The New Imperative for Creating and Profiting from Technology“ (Chesbrough, 2003a), the opening of the innovation process is also often criticized in literature. In addition to the problems and difficulties addressed so far, such as the need of companies to build up the ability to effectively integrate and use knowledge (see absorptive capacity, chapter 2.1.1), further concerns have been expressed about this approach.

A first concern is that the in- and especially outflows of knowledge can lead to uncontrolled knowledge spillovers resulting in a loss of control regarding critical internal knowledge (Herzog, 2011; Hopkins, Tidd, & Nightingale, 2014). The actions to prevent such leaks and other emerging tasks, like coordinating a cooperation or looking for possible knowledge sources, result in an increased organizational complexity which in turn lead to augmented time and management costs (Hopkins, Tidd, & Nightingale, 2014; Manzini, Lazzarotti, & Pellegrini, 2017). Besides this additional organizational effort, Open Innovation can even slow down development projects and raise their costs in some cases (Praest Knudsen & Bøtker Mortensen, 2011). Keupp and Gassmann (2009) further indicate that using external knowledge sources and IP can be connected to high transaction costs which affects the feasibility of Open Innovation negatively.

In addition to these problems, companies also have to deal with opposing forces arising from the inside (Manzini, Lazzarotti, & Pellegrini, 2017). The “not invented here” syndrome describes the non-observance of existing knowledge due to the place of origin of the knowledge (Burcharth, Knudsen, & Søndergaard, 2014). In the case of Open Innovation, it can happen that employees ignore external knowledge because it was not their own idea. When pursuing the outside-in or the coupled process, the “not invented here” syndrome can emerge as big problem jeopardizing the whole strategy (Zynga, 2013; Burcharth, Knudsen, & Søndergaard, 2014; Manzini, Lazzarotti, & Pellegrini, 2017). According to Zynga (2013), including “tactics for overcoming internal barriers to adoption” is essential for every plan of switching to a new solution. Therefore, any company that wants to open their innovation process needs to convince their employees for the approach to work.

Further points which are criticized are the missing structure and the lack of clarity regarding the various Open Innovation approaches. Allmendinger and Kuckertz (2016), who are also affirmed by Blume (2020), criticize that there is no concrete structure of how Open Innovation initiatives should look like in order to obtain optimal advantage of each of the three core processes. Even Chesbrough (2017) admits that the conditions, under which Open Innovation will be successful, are not sufficiently defined and proven yet. Even though there are rough frameworks for different approaches, little is known about how exactly collaboration partners influence the innovation performance of a company and how companies really benefit from innovations created in collaborations (Stefan & Bengtsson, 2017).

2.3. Factors for choosing a strategy

However, the topic Open Innovation in literature is so broad and diverse that the disadvantages as well as the advantages described in the previous chapters, do not generally apply. Even if there is no elaborated unambiguous structure for Open Innovation (Dahlander & Gann, 2010), there are various approaches to set borders and define conditions for the success of Open Innovation. In their research, Gassmann and Enkel (2004) identified specific characteristics of companies pursuing the Open Innovation approach. Since the identified characteristics are far from being applicable to every company, this leads to the assumption that “Open Innovation is not an imperative for every company and every innovator” (Gassmann, 2006, p. 223). Beside Gassmann and Enkel, many other authors have dealt with defining factors that have an impact on the success of Open Innovation (Dahlander & Gann, 2010; Praest Knudsen & Bøtker Mortensen, 2011; Hopkins, Tidd, & Nightingale, 2014; Manzini, Lazzarotti, & Pellegrini, 2017). While Gassmann and Enkel (2004) make a clear distinction between the closed and Open Innovation approach and list factors for the respective approach, Manzini, Lazzarotti and Pellegrini (2017) emphasize that there are not only the two extremes, but also stages in between – a degree of openness. Therefore, the extent to which opening up the innovation process has positive or negative effects on a company depends on various factors. In an attempt to summarize these factors from various authors, Manzini, Lazzarotti and Pellegrini (2017) distinguish between two types of factors - external and firm specific factors.

External factors

External factors represent the external environment of a company, most notably the characteristics of the industry they are in. While in some industries, such as software, Open Innovation is the dominant trend (Alexy, Henkel, & Wallin, 2013), the degree of openness in others, such as the food industry, is relatively low (Manzini, Lazzarotti, & Pellegrini, 2017).

The factor with the biggest influence on the openness degree seems to be the state of technology in the industry, a fact that was not only stated by Manzini, Lazzarotti and Pellegrini (2017), but also by various other researchers such as Chesbrough (2003a) or Gassmann and Enkel (2004). As a result of their research, Manzini, Lazzarotti and Pellegrini (2017) defined three forms to describe the state of technology more closely: technological intensity, turbulence and convergence.

The first form, namely technological intensity, can be defined and classified by various approaches (Zawislak, Fracasso, & Tello-Gamarra, 2018). The most widely used classification is the one created by the Organization for Economic Co-operation and Development (OECD) which categorizes industries on four levels ranking from low-technology to high-technology (OECD, 2009). Companies in high-technology industries, where R&D and therefore knowledge have a big impact, are often not able to cope with developing technology on their own, which leads to a higher motivation to open up their innovation processes by integrating or externalizing knowledge (Gassmann & Enkel, 2004; Gassmann, 2006; Manzini, Lazzarotti, & Pellegrini, 2017). While that leads to the assumption that a high technology intensity speaks for a higher openness degree and a low intensity means the opposite, some studies that have dealt with elements of the outside-in process show an exception. According to these studies, many companies from low technology industries expect spillovers from companies with a higher technology degree. This external knowledge is then explored and exploited with success (Gassmann & Enkel, 2004; Segarra-Ciprés, Carlos Bou-Llusar, & Roca-Puig, 2012). Therefore, a high technology intensity may be an indication for a positive effect of cooperate or external knowledge during the coupled or inside-out process, but the outside-in process is independent of this factor.

Similarities can be observed with the technological turbulence, which is also referred to as the speed of industry by Gassmann and Enkel (2004). A high speed or turbulence stands for rapidly changing technology conditions. Therefore, companies need to bring their technologies to the market faster which in turn requires a faster innovation process (Miotti & Sachwald, 2003; Schweitzer, Gassmann, & Gaubinger, 2011; Manzini, Lazzarotti, & Pellegrini, 2017). Accelerating the innovation process is an aim of the outside-in process in many cases but is not a necessity. Therefore, the speed of industry is not relevant for the effectiveness of the outside-in process either (Gassmann & Enkel, 2004; Segarra-Ciprés, Carlos Bou-Llusar, & Roca-Puig, 2012).

The third form of technology is its convergence. Gassmann (2006) describes the phenomenon of shifting or disappearing industry boarders as “technology fusion”. This development leads to the emerging of new business models and, at the same time, to interdisciplinary cross-border research. Due to missing capabilities, companies are not able to provide successful innovations on their own, pressing them to open up their innovation process (Gassmann, 2006; Bröring, 2010). This factor is relevant for all of the three core processes.

Besides the technology, another factor has a big impact on the success of Open Innovation measures, namely globalization (Chesbrough, 2003a; Manzini, Lazzarotti, & Pellegrini, 2017). The increasing competition and the worldwide connection of industries and companies strengthen the technology intensity, turbulence and convergence (Gerybadze & Reger, 1999; Chesbrough, 2003a; Gassmann, 2006). Therefore, innovations must be better and launched even faster to uphold their competitive advantage which is why an globalized environment indicates an high degree of openness for a company (Gassmann, 2006; Manzini, Lazzarotti, & Pellegrini, 2017).

Another factor which is relevant to the choice of whether or not to open up one’s innovation process is the transferability of knowledge (Gassmann, 2006; Manzini, Lazzarotti, & Pellegrini, 2017). As the inflow, outflow and exchange of knowledge are the main parts of Open Innovation (Chesbrough, van Haverbeke, & West, 2006), the ability to transfer knowledge is essential. In chapter 2.1.3, the different types of knowledge which are all transferable to different degrees were shown. The further right the knowledge is placed on Nelson and Winter’s (1982) scale, the more difficult is it to transfer. For example, explicit and teachable knowledge in form of a patent is way easier to transform than deeply imbedded process knowledge. That is why a high transferability of the used knowledge enables a higher degree of openness for a company and knowledge.

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Details

Seiten
70
Jahr
2020
ISBN (eBook)
9783346192035
ISBN (Buch)
9783346192042
Sprache
Englisch
Katalognummer
v902776
Institution / Hochschule
Duale Hochschule Baden-Württemberg, Stuttgart, früher: Berufsakademie Stuttgart
Note
1,0
Schlagworte
Open Innovation Smart Factories Industry 4.0 coupled process inside-out process outside-in process information management cyber-physical systems cloud computing internet of things

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Titel: Open Innovation in Smart Factories