## 7 Conclusions  
Based on the research performed, current research gaps were successfully indicated based on the literature review. Moreover, the need for integration of the different improvement strategies was demonstrated. In this context, the characterization of relevant improvement strategies provides an overview of their utility based on their focus, scope, principles, and tools. On this basis, the systematic improvement model served as a conceptual research framework for the design and optimization of existing production systems. From a managerial point of view, current challenges of manufacturing systems were described, and the conceptual model showed potential as a guide for managers for identifying challenges, creating vision for Industry 4.0 improvements, developing a plan for optimization as well as how to assess and initiate projects. Moreover, it provided the needed information to determine which steps and improvement strategies were more appropriate to be selected for the specific needs of the production system. Furthermore, Section 6 provided a framework for the development of improvement projects that include Industry 4.0 technologies. Moreover, it provided a holistic approach to the decision-making process based on the capabilities that exist within an organization and those that will be required for optimization. To prove the utility of a new conceptual model, simulations with different improvement sequences according to the developed systematic improvement model were generated. Based on the model, the simulations, and the results, the following statements can be concluded:  
* All the steps along the process provided an improvement in KPIs. * Higher improvement was dependent on market need. * When the internal capabilities were the constraint, the TOC as well as the QRM and the AM principles performed better overall on the experimental production system. * When the market was the constraint, lower demand defined the capacity of the production system, and LM and SS provided better performance overall, as they improved the process effectiveness and efficiency as well as the quality, which are directly related to improved customer satisfaction, service levels, and experience and have the potential to increase the demand for more volume. In these cases, QRM also provided improvement if lead time was the cause of the low demand. Moreover, if the markets were not stable and major disruption had been indicated, AM provided the capability to react to them even while KPIs were required to improve. Some of the research limitations are that the sequence steps of the conceptual model consider relevant improvement strategies; however, other tools and methods also exist and are not included in our research. Moreover, the simulation model was a generic model with reduced complexity, and Industry 4.0 is only discussed in this paper and was not demonstrated in the models or simulations. In this case, future research can lead to the implementation of the conceptual and simulation models for real production systems and managerial decision making as well as to apply the methodological project framework for selecting improvement projects. Additionally, the development of the model with other improvement techniques and strategies can be considered as well to include other factors in the process improvement strategies and project selection by integrating human-machine interaction and sustainability aspects such as the improvements availabel for people with disabilities in Industry 4.0 environments, security and cyber-security requirements, and data as well as software development and maintenance.