(1) Background: Products, manufactured using additive manufacturing technologies (AM) are increasingly present on the market. The research was undertaken to determine the possibilities of increasing the use of AM technology in Polish manufacturing companies. The aim of the paper is to determinate the level of the AM technology use of Polish Metal and Automotive Manufacturing and the influence of AM technology use on the increase of manufacturing company’s competitiveness–in the context of Polish Manufacturing Companies. (2) Methods: This paper uses literature studies to determinate the AM technology used within the production processes in the automotive and metal industry companies (so called dimensions) and a questionnaire survey, which was carried out on a sample of 250 Polish Metal and Automotive Manufacturing Enterprises. (3) Results: The results were verified by a statistical analysis, using correlation coefficients. Based on the data obtained, it was determined that both metal and automotive Polish companies use, or have in their investment plans, the implementation of AM technology, due to the need to reduce production costs and increase speed and flexibility when responding to customer needs. Moreover, the relationship between applied additive manufacturing technologies and the effects of their use, in enterprises, was analysed. The novelty of our work is defining the dimensions of the AM technology use for our empirical research and determining the influence of AM technology use on the increase Polish manufacturing company’s competitiveness. (4) Conclusions: The possibilities of using the results of research in economic practice were demonstrated. We also highlighted the impracticality for managers to support the selection and implementation of AM technology in the context of obtaining possible benefits for a manufacturing company.

This work deals with the verification of a decision-making system for additive manufacturing (AM) technology adoption specified by a Petri net. An innovative verification technique of a Petri net-based system is oriented toward practical applications, and can detect errors at the early design and modelling stage. The idea is illustrated by a real-life case study of supporting decision making in AM technology adoption affecting supply chain management (SCM). Two main issues are addressed. Firstly, making optimal decisions about AM technology requires models rarely possessed within a company. Therefore, this work proposes a model supporting the decision making related to the implementation of AM technology, based on a Petri net, by utilizing its main advantages: graphical modelling, and strong mathematical support of formal verification techniques. Contrary to the most popular analysis methods (which are bounded exponentially in a general case), it is proved that the presented method is bounded by a cubic polynomial with net size. Secondly, an investment in AM technology is often financially assessed and does not affect other processes, as in our SCM case. Hence, strong connectivity within the proposed Petri net-based model is examined.

In the era of smart manufacturing and Industry 4.0, the rapid development of modelling in production processes results in the implementation of new techniques, such as additive manufacturing (AM) technologies. However, large invest-ments in the devices in the field of AM technologies require prior analysis to identify the possibilities of improving the production process flow. This paper proposes a new approach to determine and optimize the production process flow with improvements made by the AM technologies through the application of the Petri net theory. The existing produc-tion process is specified by a Petri net model and optimized by AM technology. The modified version of the system is verified and validated by the set of analytic methods safeguarding against the formal errors, deadlocks, or unreachable states. The proposed idea is illustrated by an example of a real-life production process.

Combining the knowledge about additive manufacturing technologies available in the literature with the results of empirical research in Polish manufacturing enterprises, regarding the implementation of AM, using the Bayesian network, will allow the recent demand for AM technologies to be defined in the context of an industry’s needs. The main purpose of the study is to build a new model that integrates: (1) knowledge about the implementation of AM in manufacturing companies, gained from the literature, (2) knowledge about the demands and state of the use of AM from 250 Polish metal and automotive manufacturing enterprises, and (3) Bayesian networks. The results reveal that the model developed is able to accurately detect the key determinants of the implementation process of AM technologies within a manufacturing company and identify the specific requirements for the further implementation of additive technologies. The freshness of our work is defining the demand for AM technology based on the knowledge gained from literature and knowledge received through empirical study. The possibilities of using the results of research in economic practice were demonstrated. This new approach can be treated as a solution, which will both direct and help mangers to take the decision to implement AM technologies.

The changes caused by Industry 4.0 determine the decisions taken by manufacturing companies. Their activities are aimed at adapting processes and products to dynamic market requirements. Additive manufacturing technologies (AM) are the answer to the needs of enterprises. The implementation of AM technology brings many benefits, although for most 3D printing techniques it is also relatively expensive. Therefore, the implementation process should be preceded by an appropriate analysis, in order, finally, to assess the solution. This article presents the concept of using the Bayesian network when planning the implementation of AM technology. The use of the presented model allows the level of the success of the implementation of selected AM technology, to be estimated under given environmental conditions.