## 7 Conclusions  
This section is divided into theoretical, managerial, and empirical conclusions, in addition to an explanation of the limitations of the research performed and a description of the potential future research derived from the findings. Theoretical conclusions: This study identifies manufacturing challenges such as the difficulties of organizations to identify the logic behind non-successful organizational performance. For that purpose, the paper tackles this by developing a holistic approach for modeling manufacturing organizations based on their functions. In order to achieve these goals, the conceptual model also provides a classification of functions based on their evolution along the industrial revolution. From this analysis, it can be seen how manufacturing was the first area to initiate the development of industrial capabilities starting from the substitution of human and animal  
Figure 10: Human factor integration in I4.0 environments: a revolution (the authors’ own elaboration). force by machines and developing continuously until now. Secondly, it was shown that the revolution of the productive systems followed with Taylorismus in the second industrial revolution and has, until now, developed methodologies such as lean manufacturing and Industry 4.0. Third, between the second and the third industrial revolution, it has been shown how the developments in quality and maintenance management started. As a fourth step, once the process is performed based on good planning, quality, and stability maintained over time, it was time for opening the development of global value chains. For that purpose, two new areas of development were initiated in the third industrial revolution, i.e., information systems and logistics. By doing so, globalized supply chains with ubiquitous management and control of operations were possible. Finally, the capabilities to gather and analyze large amounts of data are for the very first time possible due to the technological development of the fourth industrial revolution that makes it possible to collect and process data in real time and convert it into virtual enabling decision-making based on updated information and analysis. Moreover, virtual models enable the ability to reproduce past scenarios and simulate future ones in order to predict the behavior of the system to obtain valid conclusions for any process providing lessons learned and insights about the measures that need to be performed to prevent the occurrence of failures as well as to avoid corrective measures. Managerial conclusions: The paper provides a description of the main issues in manufacturing organizations that can limit performance and viability over time. From this perspective, the paper is a guideline of what can go wrong, and, therefore, helps managers to identify the final cause leading to inefficient performance. Moreover, the paper provides a perfect framework to reflect and analyze the functions and capabilities that the company has while considering the company's foundation time and the historical development of the company. By doing this, managers can obtain a global picture of why some functions provide expected functionalities and why other functions do not provide them as for instance, in a business transformation process resistance at the department level to not lose \"their part of the cake\", i.e., their \"power of influence on the function\" and as a result, the new function was created with problems, lack of support, and / or team spirit. In this context, the relevant changes related to the manufacturing organization through the industrial revolutions were identified as an opportunity to develop improvement strategies and the conceptual model applied to digital twin models can be a key tool in managerial decision making. Empirical conclusions: To prove the utility of this new concept, a simulation example for a manufacturing organization was created by applying system dynamics. Nine different models were successfully developed.