## 4 Discussion  
The changes that can be observed on the market influence the decisions of managers in manufacturing companies, in particular in the area of applied AM technologies. Production companies analysing the market and available technologies need a tool to facilitate decision making in the area of implementing new technology. The presented model, based on the BN, can be treated as a solution, which will direct and help to assess the decision vis-a-vis implementing AM technologies. The main goal of our research experiments was to identify the demand for AM in Polish manufacturing companies. Based on the conducted research it is believed that:  
* Management sees the rationale for implementation of AM technologies in the production area and by using the proposed model (results of experiment 1 and 2) it is possible to determine the type of AM technology needing to be implemented (in our case study it is DMLS technology). * The key determinants that influence the decision to adopt AM technology in the company is the need of customers to reduce the waiting time for the product and their demand for personalised products. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test No.** & **Observed Alternatives of Nodes** & **Target P (P = State1)** \\ \hline
0 & - & 0.04 \\ \hline
1 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = A & 0.07 \\ \hline
2 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = B & 0.05 \\ \hline
3 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = C & 0.07 \\ \hline
4 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = D & 0.02 \\ \hline
5 & P10.1 EBM = 0; 10.1 DMLS = 1; P10.1 Welding = 0; IF = E & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the tests for experiment 3b, estimated by the Bayesian Network. There is a need to build a customer-oriented, knowledge-driven framework in order to implement AM technology  
Moreover, the results of the tests of the research experiments in estimating the recent demand for AM technologies, as assessed by the Bayesian network illustrate that:  
* The most hardly accessible knowledge of articles from Journals corresponding to the highest Impact Factor, i.e., IF > 5, does not affect the successful implementation of AM technology within a manufacturing company, in contrast to the positive impact on the implementation of AM technology with readily available knowledge. * Smoothing the distribution and the small variability in the results, in the case of research conducted with \"AM implementation motive\", means that it should not be directly related to the success of the implementation of AM technologies or that key industry determinants should not coincide with the real objectives of individual enterprises. * The practical needs of enterprises, symbolised by the achievement of goals, contributes to success in implementing AM technology. The results of the experiment 1 illustrate that although empirical studies have obtained results showing that companies in the automotive industry use AM technologies mainly for prototyping (23.2%), as a result of combining these results with analysis of the literature on results, the purpose of implementing AM technology is production. Experiment 2 enabled to indicate AM technology that should be implemented in the company--in our case study, it is DMLS technology. We know that managers can determinate the knowledge transfer in a manufacturing company [82] and therefore the obtained models, that is, the results of experiments 1 and 2, can be useful for managers in terms of assessing the decision vis-a-vis the implementation of AM technologies in the following way. * The research questions indicated in the research methodology should be put to managers in the company. * The answers obtained should then be added to the database on which the model is currently built. * The Bayesian network will be again subjected to training.