Developed products

Figure 9 shows a piece obtained by plastic deformation where the spiral shape functions as an extension of a flower (Solitary).

 

 

 

The other single one involved casting/foundry and welding process. In both cases, glass test tubes are used to count the flower and the water. Although the company does sand casting, due to its slowness and cost involved, this process is only used in the manufacture of parts that cannot be obtained by other more cost effective processes.

Fruit Bowls: In this product segment, the piece is made of pewter with two elements in epoxy resin or polyurethane, which can be varied from object to object to the formal and the material level, with the possibility of mixing it with pewter powder, sand, mica, coconut fiber or other materials (Figures 10 and 11). 

 

 

 

Candlesticks: This project was conceived with the intention to use a base in carbon fiber, where the pieces of pewter supporting the candles are glued. The parts of pewter were obtained by spinning pewter plates, using a mold obtained from the additive manufacture model (Figures 12 and 13). 

 

 

 

 

In summary, the process of product development was backed by the theoretical clippings: development of the concept of product, development of project scope, production preparation, launch and post-launch of product.

Conceptual model verification and underlying analyses

To solve the research problem and achieve the desired goal, the practices of open innovation of the traditional segment of pewter were identified and then evaluated according to their influence on the prospecting of knowledge according to the respective sources of knowledge. Finally, the optimal rate of value is modeled from the interaction between all dependent variables.

Phase 1: Modeling of the influence of the Open Innovation practices in the prospecting of knowledge of the actors (sources)

This phase is systematized in the following steps:

Step 1) identification of the practices of open innovation. Thus, the following practices of open innovation from the specialized literature were identified and confirmed by experts: Value Chain; Product development through patent licensing; Partnerships for co-development; Relationship between companies and scientific and technological system; Spin-offs; Mergers and acquisitions; Commercialization of technologies via Technology broker; Development of new business from Corporate Venturing; Establishment of non-competitive consortia (innovation networks); and Value Opportunity Web – VOW.

Step 2) identification of the sources of knowledge and their respective knowledge: The identification is systematized in the following: C1:  R&D (Shelanski and Klein, 1995); C2: Clients (Joshi and Sharma, 2004); C3: Suppliers (Horn, 2005; Smith and Tranfield, 2005);  C4: External consultants (Horn, 2005; Smith and Tranfield, 2005); C5: Competitors (Hemphill, 2003);  C6: Joint ventures (Hemphill, 2003); and  C7: universities/other public research centers (Roper et al., 2004).

Step 3) Evaluation of the influence of practices of open innovation in the prospecting of knowledge in high tech industries. This procedure was developed using the multi-criteria analysis Electre III, Promethee II e Compromise Programming and Artificial Neural Network (ANN). Next, these procedures were detailed. The methods used were Compromise Programming, Electre III and Promethee II. The results achieved confirm  Hypothesis 1: The practices of open innovation influence to a greater or lesser degree the prospecting of knowledge of the actors, and assigning values to each criterion, we arrive at a matrix of Criteria x Alternatives that together with the vector weights provides the necessary support to apply the multicriteria methods. In other words, one applies the selection and classification methodology of alternatives, using the Compromise Programming, Promethee II and Electre III methods. The Compromise Programming due to its wide diffusion and application simplicity and understanding renders it an alternative to evaluate problems as referenced in this application. The problem solution compromise is the one that comes closest to the alternative. This method was designed to identify the closest solution to an ideal one; therefore it is not feasible, using a predetermined pattern of distances. In Promethee II there is a function of preferences for each criterion among the alternatives which must be maximized, indicating the intensity of an alternative to the other one, with the value ranging from 0 to 1. Of the Electre family (I,II,III,IV and V), Electre III is the one considered for the cases of uncertainty and inaccuracy to evaluate the alternatives in the decision problem. All these methods enable one to analyze the discrete solution alternatives, and taking into consideration subjective evaluations represented by numerical scores and weights. As these are problems involving subjective aspects, the methods that best fit the situation of this research are the methods of the family Electre and Promethee.  It should be mentioned that although the Compromise Programming method is not part of this classification, it has similar characteristics, showing much simplicity in order to understand its operation, which makes it feasible for this application.

Within this perspective, the multicriteria methods are viable instruments to measure the performance of the practices of open innovation in the prospecting of knowledge for value creation in the high tech enterprises.  The results produced by this prioritization enable managers to better focus their efforts and resources on managing the practices of open innovation that perform best, which results in achieving the goals sought by the companies. The structure of this prioritization (classification by hierarchical analysis) is proposed at three planning levels in a judgment matrix, in which at the first hierarchical structure level it defines­­ the goal, which is to achieve the value creation of the companies that will feed the system; the criteria are in the second level, which are the knowledge (prospecting) of actors: K1: R&D (Shelanski and Klein, 1995); K2: Clients (Joshi and Sharma, 2004); K3: Suppliers (Horn, 2005; Smith and Tranfield, 2005); K4: External consultants (Horn, 2005; Smith and Tranfield, 2005); K5: Competitors (Hemphill, 2003); K6: Joint ventures (Hemphill, 2003); and K7: universities/other public research centers (Roper et al., 2004). The practices of open innovation of the companies are in the third level, the alternatives, which are: P1: Value Chain; P2 Product development through patent licensing; P3: Partnerships for co-development; P4: Relationship between companies and scientific and technological system; P5: Spin-offs; P6: Mergers and acquisitions; P7: Commercialization of technologies via Technology broker; P8: Development of new business from Corporate Venturing; P9: Establishment of non-competitive consortia (innovation networks); and P10: Value Opportunity Web – VOW. The prioritization process obeys the judgment of the evaluators (experts). With the results of the judgment matrix, the methods were applied: Promethee II, Electre III and Compromise Programming to evaluate the innovation capacities in relation to the performance of the companies. Table 1 shows the results produced. 

 

 

 

Open innovation networks introduce highly complex and multifaceted inter-organizational relationships (Jarvenpaa and Wernik, 2011). The results produced by the methods demonstrate the value chain and partnerships practices of open innovation as the most significant ones to ensure the knowledge prospecting and value creation for the companies. In today’s competitive global market, enterprises must possess the capability to design and deliver innovative products with great value to customers in a timely matter. Each organization must focus on its own strong area where it will be uniquely competitive. Hence, all partners should ruminate about where and how values are created, and what contribution they can make based on their core competencies.

In addition, interorganizational relationships must be rapidly built up or dismantled among dynamically networked organizations. Once value chains are composed, all partners hold a definite vision of the coherence within the industry value system to become a collaborative value chain. All members of a given value chain must work together to respond to the changes of market demands rapidly (Chiang and Trappey, 2006). In this kind of environment, enterprises not only must reach out and enhance their relationships with each others, but also need to integrate their business processes (Chiang and Trappey, 2006). In fact, value chain provides enterprises with the opportunity to identify their core competencies and position themselves in the marketplace according to their competitive abilities (Al-Mudimigh et al., 2004; Chiang and Trappey, 2006). Firms benefit from engaging in a range of co-creation activities across the value chain, involving various touch-points and domains, rather than just one way of co-creating value with a particular type of co-creator (Ramaswamy, 2009). This paper presents value co-creation specifically business-to-consumer (B2C). B2C co-creation is rooted in relational marketing theory (Maklan et al., 2008). Atividades de co-criação de B2C são mais propensos a enfatizar formas de mercado como governança co-criação,  porque esses  empreendimentos  estão  criando um mercado para soluções aos desafios co-criação específicas. Perspective on co-creation in management is the study of open innovation (Chesbrough, 2003). Open innovation is characterised by the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively. Open innovation assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as they look to advance their technology (Chesbrough et al., 2006; Roser et al., 2013).

Once value chains are composed, all partners hold a definite vision of the coherence within the industry value system to become a collaborative value chain. Many business model definitions discuss the value network of the firm with terms such as “structure of value chain,” “partner network,” “value network,” “links to external stakeholders,” or “transactional links to exchange partners” (Nenonen and Storbacka, 2010). “Business is fundamentally concerned with creating value and capturing returns from that value, and a model is simply a representation of reality. We define a business model as a representation of a firm’s underlying core logic and strategic choices for creating and capturing value within a value network” (Shafer et al., 2005). “We offer an interpretation of the business model as a construct that mediates the value creation process” (Chesbrough and Rosenbloom, 2002).

All members of a given value chain must work together to respond to the changes of market demands rapidly (Chiang and Trappey, 2006). Organizations create values for themselves and their customers via executing primary and supporting tasks. In the 1980s, value creation mainly depended on cost reduction and industry automation, but modern companies focus on value chain integration to achieve time-to-market and to enhance customer satisfaction (Garetti et al., 2005; Chiang and Trappey, 2006). Thus, the value chain concept offers management a means by which they can evaluate both existing and new strategic opportunities to create customer and partner value (Walters and Rainbird, 2007). Essentially the value creation system is an analytical tool; it facilitates the identification and evaluation of strategic alternatives (Walters and Rainbird, 2007). Value chain analysis identifies the flow of added value through the value creation processes within both the industry and the firm. In the business model of the future, value chains compete rather than individual companies, and the connectivity and process excellence are key challenges (AeIGT: 2003 cited in Johns et al., 2005). In addition, the cooperation in the value chain requires a complex repertoire of behaviors in that members organizations need to learn to mitigate the risks stemming from the other’s opportunism and also to avoid lapses in their respective knowledge-sharing (Jarvenpaa and Wernik, 2011). Increasingly, it has been argued, innovative capacity is dependent upon building linkages through collaborative relationships (Coombs et al., 1996) […] this enables learning which adds to an organization’s existing knowledge base and the creation of completely new knowledge (Inkpen, 1996) and also contributes to “novelty and variety in the economic system” by creating “new economic resources which otherwise simply would not exist” (Coombs et al., 1996). Such collaboration might involve sub-contracting, strategic alliances or joint ventures […] (McLoughlin, 1999; Walters and Rainbird, 2007). Partnership/co-operative innovation combines elements of process innovation management and product innovation manage-ment within a network structure that neither partner can create using its own resources to meet customer/market determined expectations for product and/or service performance at an economic (viable) cost. Thus, the value chain concept offers management a means by which they can evaluate both existing and new strategic opportunities to create customer and partner value. Essentially the value creation system is an analytical tool; it facilitates the identification and evaluation of strategic alternatives (Walters and Rainbird, 2007).

When comparing the results in terms of performance, the Compromise Programming and Promethee II methods did not differ in their classifications.  For Electre III, the results were incompatible. And this is because the p, q and v veto thresholds, respectively, of indifference, strong preference and veto or incomparability have a discrepancy in the structure of their results (classification). Electre III presents a set of solutions with a more flexible hierarchical structure. This is due to the conception of the method, as well as the quite explicit consideration of the indifference and incomparability aspect between the alternatives. The results referenced by the Promethee II and Compromise Programming methods reflect the preference, according to the experts, for value chain and partnerships. The essence of the practices of open innovation is the accumulation of knowledge over time.  Next is the influence of the practices of open innovation in the knowledge prospecting. For this ANN was used. The technique adapts to the case in question.

Prospecting of knowledge using the artificial neural networks – ANN

The artificial neural networks - ANN is understood to simulate the behavior of the human brain through a number of interconnected neurons. A neuron executes weighed additions for the activations of the neurons representing nonlinear relations. The ANN has the capacity to recognize and to classify standards by means of processes of learning and training. The training of the net is the phase most important for the success of the applications in neural network. The topology of the net can better be determined of subjective form, from a principle that consists of adopting the lesser intermediate number of possible layer and neurons, without compromising the precision. Thus, in this application, the layer of the entrance data possess 10 neurons corresponding to the 10 variable referring to practices of open innovation. The intermediate layer possesses 8 neurons, and the exit layer possesses 1 corresponding neuron in a scale value determined for the ANN. The process of learning supervised based in the Back propagation algorithm applying software Easy NN determines the weights between the layers of entrance and intermediate, and between the intermediate and exit automatically. The training process was finished when the weights between the connections had allowed minimizing the error of learning. For this, it was necessary to identify which configuration that would present the best resulted varying the taxes of learning and moment. After diverse configurations to have been tested, the net of that presented better resulted with tax of an equal learning of 0.45 and equal moment 0.92. The data had been divided into two groups, where to each period of training one third of the data is used for training of net and the remaining is applied for verification of the results. The net was trained for attainment of two results’ group for comparison of the best-determined scale for the networks. In the first test the total of the judgment of the agents was adopted; however, only in as test was gotten better scales, next of represented for method of the multi-criteria analysis. With this, the last stage of the modeling in ANN consisted of testing the data of sequential entrance or random form, this process presented resulted more satisfactory. The reached results were satisfactory, emphasizing the subjective importance of scale’s methods to treat questions that involve high degree of subjectivity and complexity. How much topologies are used in the networks; the results showed some configurations of the ANN and compared to the multicriteria analysis, it was observed that ANN 1 is better if approached with the classification obtained from the multi-criteria analysis. Thus, even other topologies do not Tenaha been the best ones, it had been come however close in some practices of open innovation of the multi-criteria analysis. The results can be observed in Table 2 that follows.

 

 

 

In fact, the goal of knowledge is to create value from organizational and individual knowledge.  The benefits derived from good knowledge are multiple, and include: reduced duplication of effort, creation of new knowledge, and increased efficiency and productivity. Knowledge and innovation are the building blocks of sustainable competitive advantage (Porter, 1985), and therefore are a source for sustainable development and growth for enterprises. Co-creating means extending the value chain (Helm and Jones, 2010). Hence, involving co-creators leads to an expansion of organizational boundaries and management of new and different relationships (Sawhney and Prandelli, 2000). In addition, firms require a flexible yet systematic integration and alignment of processes and stakeholder activities across business processes, particularly where customer encounters take place (Payne et al., 2008, 2009; Clarke and Nilsson, 2008). Indeed, the challenge in building a more service oriented and customer centred business model relates to the type of relationships and interactions to be utilized in co-creating value (Roser et al., 2013).

Thus, an innovation is the use of innovative knowledge so as to create effective value for the stakeholders in the value chain. From the perception of the innovation, the innovation value chain may be represented differently. Indeed, innovation starts from an idea that is often embedded with an innovative knowledge, to become somehow a prototypical invention, to finally become an innovative product or piece of technology that is industrially exploited or even commercialized. Porter (1985) argues that firms that optimize their value chain activities vis-a-vis competition stand a better chance of leveraging valuable capabilities into sustainable competitive advantage (Prajogo et al., 2008). Clearly any partnership innovation must be beneficial to all parties (Walters and Rainbird, 2007). The results produced in the light of artificial neural networks confirm value chain and partnerships as the practice of open innovation that shows the most (in greatest degree) influence in the prospecting of knowledge. The value chain is supported by a particular value that creates a logic and its application results in particular strategic postures. Adopting a network perspective, a new economic value is configured to the organizations. Traditionally, value chain has been used as a concept and a tool to understand the analysis of industries and proved to be a useful mechanism for portraying the threaded engagement of traditional activities in industries (Porter, 1980). Moreover, it also shaped the thinking about value and value creation.

The value chain of a company relates to other chains and knowledge coming from different sources (suppliers, competitors, channels and customers, among others), which then become a value chain of the industry. At the same time, a company can make analyses of the links in the value chain between its suppliers, manufacturers and customers’ chain in order to find ways to increase the competition. For the concept of value network, value is co-created by a combination of actors in the network. Business networks are independent. After all, how is value created? A traditional answer to this question is simply the value chain. In this perspective, the knowledge is certainly one of the best resources and the only sustainable competitive advantage. In this context, Additive Manufacturing technologies create parts layer by layer. Thereby, lots of benefits are offered. Especially extended design freedoms provide new potentials for the design of technical parts. To make these benefits accessible to different user groups (Adam and Zimmer, 2014). Recently, important technological and material developments increasingly enable Additive Manu-facturing’s applicability for the creation of end-use parts (Hague et al., 2004).

Thus, Additive Manufacturing more and more turns to a production capable technology (Kruth et al., 1998). Using Additive Manufacturing in terms of Direct Manufacturing – to manufacture end-use parts – new benefits can be gained due to the layer by layer manufacturing  (Adam and Zimmer, 2014). Thereby the extension of design freedoms is one of Additive Manufacturing’s most noteworthy potentials (Levy et al., 2003). It enables the manufacturability of highly complex parts which cannot be produced with conventional technologies like milling or casting. Additionally, Additive Manufacturing decouples parts manufacturing costs from its complexities (Adam and Zimmer, 2014). This increases the industrial relevance of Additive Manufacturing significantly, too (Hague et al., 2003). Here, Additive Manufacturing provides lots of potentials and benefits (Adam and Zimmer, 2014). Thus, in order to be able to enhance both value creation (the consumer’s valuation of the benefit of consumption) and innovation, organizations must be able to create such value. We believe that the enabling factors are these three: individualized immediate feedback, a new organizational logic, and new cooperation structures (Johannessen and Olsen, 2010). Currently, the global products industry finds itself faced with many challenges. These challenges are multifaceted and complex, and the need for the application of innovative ideas and solutions is obvious (Van Horne et al., 2006). Hence, new technology facilitates network logic in the global. Thus, the innovation and economic growth are created. And the innovation of products and processes is seen as a promising answer to many of the challenges faced by the products industry.

Phase 2: Modeling of the optimal effectiveness rate of value creation in the light of the influence of the practices of open innovation in the prospecting of knowledge of the actors under 3D modeling and additive manufacturing

This phase focuses on determining the optimal efficiency rate (OERVC) for value creation in the company using Neurofuzzy modeling. It is a process whose attributes usually possess high subjectivity characteristics, in which the experience of the decision maker is very significant. Thus within this spectrum there is the need for a tool that allows adding quantitative and qualitative variables that converge towards a single evaluation parameter (Cury and Oliveira; 1999; von Altrock, 1997). This model combines the Neural Networks and Logic Fuzzy technology (neurofuzzy technology). Here this model supports the planning of the practices of open innovation on the knowledge and value creation of high-tech companies, as it allows one to evaluate the desirable rate toward the acceptable performance of high-tech companies. The model shown here uses the model of Cury and Oliveira (1999). Based on the Neurofuzzy technology, the qualitative input data are grouped to determine the comparison parameters between the alternatives. The technique is structured by combining all attributes (qualitative and quantitative variables) in inference blocks (IB) that use fuzzy-based rules and linguistic expressions, so that the preference for each alternative priority decision of the optimal rate of value creation determinants, in terms of benefits to the company, can be expressed by a range varying from 0 to 10. The model consists of qualitative and quantitative variables, based on information from the experts. The Neurofuzzy model is described below.

Determination of Input Variables (IV): This section focuses on determining the qualitative and quantitative input variables (IV). These variables were extracted (10 variables: Value Chain; Product development through patent licensing; Partnerships for co-development; Relationship between companies and scientific and technological system; Spin-offs; Mergers and acquisitions; Commercialization of technologies via Technology broker; Development of new business from Corporate Venturing; Establishment of non-competitive consortia (innovation networks); and Value Opportunity Web – VOW) from the independent variables (dimensions of results Influence of practices of open innovation in the prospecting of knowledge for value creation in highly complex environments. The linguistic terms assigned to each IV are: High, Medium and Low. Accordingly, Table 1 shows the IVs in the model, which are transformed into linguistic variables with their respective Degrees of Conviction or Certainty (DoC), with the assistance of twenty judges opining in the process. The degrees attributed by the judges are converted into linguistic expressions with their respective DoCs, based on fuzzy sets and IT rules (aggregation rules), next (composition rules) (Figure 14).

 

 

 

Determination of Intermediate Variables and Linguistic Terms: The qualitative input variables go through the inference fuzzy process, resulting in linguistic terms of intermediate variables (IVar). Thus, the linguistic terms assigned to IVar are: Low, Medium and High. The intermediate variables were obtained from:  Performance of the value chain and partnerships for co-development: PVCPCOD; Performance of relationship between com-panies and scientific and technological system and Spin-offs: PRCSTSO: Performance of mergers and acquisi-tions, product development through patent licensing and commercialization of technologies via Technology broker: PMAPDCTTB; Performance of development of new business from corporate venturing, establishment of non-competitive: DNBENC  consortia (innovation networks); and Performance of Value Opportunity Web – VOW): PDBCENCVO.  The architecture proposed is composed of eight expert fuzzy system configurations, four quail-tative input variables that go through the fuzzy process and through the inference block, thus producing an output variable (OV), called intermediate variable (IVar). 

Then, the IVars, which join the other IVar variables form a set of new IVars, thereby configuring a sequence until the last layer in the network. In the last layer of the network the output variable (OV) of the Neurofuzzy Network is defined. This OV is then subjected to a defuzzification process to achieve the final result: Optimal Efficiency Rate of Value Creation of High-Tech Companies. In summary, the fuzzy inference occurs from the base-rules, generating the linguistic vector of the OV, obtained through the aggregation and composition steps. For example, when the experts’ opinion was requested on the optimal efficiency rate for the technological innovation capacity performance of company A, the response was 8.0. Then the fuzzification (simulation) process was carried out, assigning LOW, MEDIUM and HIGH linguistic terms to the assessment degrees at a 1 to 10 scale. Degree 8, considered LOW by 0% of the experts, MEDIUM by 55% and HIGH by 45% of the experts. In summary, the expert’s response enabled one to determine the degree of certainty of the linguistic terms of each of the input variables using the fuzzy sets.  The results confirm the H2: The optimal efficiency rate depends on the combination and interaction of the innovation capacities of the high-tech companies. The generic fuzzy sets were defined for all qualitative IVars, which always one to exhibit three levels of linguistic terms: a lower, a medium and a higher one. After converting all IVars into its corresponding linguistic variables with their respective DoC, the fuzzy inference blocks (IB), composed of IF-THEN rules, are operated based on the MAX-MIN operators, obtaining a linguistic value for each intermediate variable and output variable of the model, with the linguistic terms previously defined by the judges. With the input variables (features extracted from product development projects), the rules are generated. Every rule has an individual weighting factor, called Certainty Factor (CF), between 0 and 1, which indicates the degree of importance of each rule in the fuzzy rule-base. And the fuzzy inference occurs from the rule-base, generating the linguistic vector of OV, obtained through the aggregation and composition steps.

Determination of Output Variable – Optimal Efficiency Rate of Value Creation

The output variable (OV) of the neurofuzzy model proposed was called Optimal Efficiency Rate of Value Creation in high-tech companies.  The fuzzification process determines the pertinence functions for each input variable. If the input data values are accurate, results from measurements or observations, it is necessary to structure the fuzzy sets for the input variables, which is the fuzzification process. If the input variables are obtained in linguistic values, the fuzzification process is not necessary. A fuzzy set A in a universe X, is a set of ordered pairs represented by Equation 1.

Α={(μ_Α(x),x)|x ? Χ}                                                         (1)

Where (x) is the pertinence function (or degree of pertinence) of x in A and is defined as the mapping of X in the closed interval [0.1], according to Equation 2 (Pedrycz and Gomide, 1998).

µ_A(x):Χ→ [0.1]                                                           (2)

Fuzzy Inference: The fuzzy inference rule-base consists of IF-THEN rules, which are responsible for aggregating the input variables and generating the output variables in linguistic terms, with their respective pertinence functions. According to Von Altrock (1997), a weighting factor is assigned to each rule that reflects their importance in the rule-base. This coefficient is called Certainty Factor (CF), and can vary in range [0,1] and is multiplied by the result of the aggregation (IT part of inference). The fuzzy inference is structured by two components: (i) aggregation, that is, computing the IF rules part; and (ii) composition, the THEN part of the rules. The Degree of Certainty (DoC) that determines the vectors resulting from the linguistic processes of aggregation and composition are defined with Equation 3.

DoC;:max[FC₁ . min{GdC_A11,GdC_A12,...,GdC_1n},...,FCn . min{GdC_An1,GdC_An2,...,GdC_Amn}|                                 (3)

Defuzzification: For the applications involving qualitative variables, as is the case in question, a numerical value is required as a result of the system, called defuzzification. Thus, after the fuzzy inference, fuzzification is necessary, that is, transform linguistic values into numerical values, from their pertinence functions (Von Altrock, 1997). The IT Maximum Center method was popularized to determine an accurate value for the linguistic vector of OV. Based on this method, the degree of certainty of linguistic terms is defined as “weights” associated with each of these values. The exact value of commitment (VC) is deter-mined by considering the weights with respect to the typical values (maximum values of the pertinence functions), according to Equation 4 presented below (Von Altrock, 1997; Cury and Oliveira, 1999).

Where i DoC represents the degrees of certainty of the linguistic terms of the final output variable and i X indicates the end of the typical values for the linguistic terms, which correspond to the maxima of fuzzy sets that define the final output variable. By way of demonstration, using assigned IT (average) hypothetical (Company A) enters-IT into the calculation expression of TPCITj with GdCi of the following linguistic vector of the output variable, also hypothetical: LOW=0.20, MIDDLE=0.53, HIGH=0.17. The numerical value of OERVC at a 0 to 1 scale corresponds to 0.9417, resulting from the arithmetic mean of the values resulting from the defuzzification of each of the simulated twenty judges. This value corresponds to an average value for OERP.  With this result (optimal efficiency rate: 0.9417) produced for a better combination and interaction of strategic practices of open innovation that converged toward a single parameter, it is feasible to assert that this combination of technological innovation activities of the firm at this time, can at least ensure the performance desired by the firm at that time. It is plausible that the company maintains at least this value (0.9417), which ensures the desired performance. It is also plausible to state that, to some degree, there is efficiency in the management of those planning innovation in this category of companies. To illustrate this, assuming that the study-object companies demonstrate the following optimal effectiveness rate of value creation in the light of the influence of the practices of open innovation in the prospecting of knowledge of the actors under 3D modeling and additive manufacturing, in the perspective of multiple products of company (Figure 15):

 

 

The expected reference for value creation for firm (mean) is 0.6596 (Figure 3). It is concluded that: “Synesthesia” Product (0.8442) shows efficiency in the combination of their practices of open innovation in prospecting of knowledge and value co-creation for firm based on the 3D modeling and additive manufacturing. The priorities of practices of open innovation for value creation are dynamic and dependent on constraints and uncertainties that come from the environment at any given time. The environmental contingencies are crucial and essential to adapt the strategies. The modeling approach presented here enables this sophistication refinement for every contingency presented. Thus, it is important to look at the practices of open innovation in the prospecting of knowledge and value creation. Value capture implies focusing on getting the biggest possible cut of the pie, whereas value creation involves innovation that establishes or increases the consumer’s valuation of the benefit of consumption (Priem, 2007). This research investigated the influence of practices of open innovation in the prospecting of value and value creation enhancing innovation and value creation.  The knowledge is the recipient for success of open innovation. We have also seen a change in focus on how value is created. This leads us toward a long-ignored knowledge (and sources of knowledge) lens on both innovation and value creation in company.