The use of simulation to support decision-making in productive processes (goods and services) is already an established research field. However, with the availability of solutions and technologies, simulation is no longer a tool with limited scope and analysis. In this case, the integration of simulation with physical systems is considered to allow virtual models to be sensitive to physical changes and aligned with the current state of processes, forming the so-called Digital Twin. Therefore, the main purpose of this article is to present a systematic literature review of the use of simulation as Digital Twin to support decision-making. We considered studies published in scientific journals and conference proceedings that include the use of Discrete Event Simulation (DES) and/or Agent-Based Simulation (ABS). Although the Digital Twin concept has appeared in recent years, we noted that its principle has been used for decades when it comes to decision-making through simulation. Moreover, there are still many discussions and uncertainties regarding the simulation model in this research field, such as the degree of autonomy, synchronisation, and connection. These and other key issues are discussed and some research opportunities are highlighted, such as the need for constant model validation and integration between various models.

The advent of new technologies brings a significant impact on systems management. The Industry 4.0 looks for increasingly automated, integrated, and digitised processes. We highlight the use of simulation as a Digital Twin, a virtual and intelligent copy capable of mirroring real processes and optimise decision making. This paper analyses the applicability of the Discrete Event Simulation as a Digital Twin in a non-automated process, a challenging scenario on the implementation of Industry 4.0 solutions. Amethod for conducting simulation projects of this nature was proposed, considering its integration with the process data, as well as its constant updating due to changes in the real environment. To verify its applicability, the method was used in a real study object. The proposed approach proved possible from the present research. We also present some discussions related to the use of simulation as Digital Twins, highlighting the main characteristics of such an application.

Paper aims Propose a continuous decision support system, a Digital Twin, integrating two widely used techniques, Discrete Event Simulation and forecasting methods. Originality With the evolution of the industry, there is a growing need for increasingly agile and assertive decision support systems. Also, familiar tools and techniques tend to change over time to suit such a scenario, supporting new researches on their use in the modern industry. Research method The proposed method allows the use of simulation, with the aid of forecasting methods, for continuous decision making, composing the so-called Digital Twin. The method was applied in a real process to validate it. Main findings The Moving Average, Single Exponential Smoothing, and Double Exponential Smoothing forecasting methods were used to supply the simulation model in order to test scenarios and guide decision making. The developed system enabled a virtual copy with a certain degree of intelligence and that provides answers to make the constant decision-making process more efficient. Implications for theory and practice The proposed method can be used for several operational problems like headcount, production planning and covers different levels of decision. Therefore, it can be used both on the shop floor and at managerial levels.

Engineer-to-Order (ETO) manufacturing environments are characterized by high product variety, low repetitiveness, and unstable demand, which pose significant challenges to the application of Lean Manufacturing (LM). This study investigates the application and adaptation of LM principles and tools in an ETO production line using an action research approach integrated with Value Stream Mapping (VSM). The research was conducted at a manufacturer of highly customized electrical equipment. An adapted method for calculating representative cycle times based on weighted production volumes was developed to support line sizing and workload balancing. The proposed future-state design incorporates multifunctional operators, FIFO lanes, daily scheduling, and pitch-based control. The results show a 9.5% reduction in labor requirements, a 61.7% decrease in manufacturing lead time, and a 75.0% reduction in overtime hours. Statistical validation using daily PPC records confirmed significant improvements in actual output, schedule adherence, overtime, and lead time after implementation. In addition to operational improvements, this study offers methodological contributions by proposing practical adaptations of LM tools suitable for high-variability ETO environments, thereby contributing to both theory and industrial practice.

Objective: To evaluate the association between abnormal vaginal flora and cytological evidence of HPV with prematurity in high-risk pregnant women. Study design: A prospective cohort study was designed with high-risk pregnant women who delivered singletons between 20-42 gestational weeks between January to November 2018. Vaginal specimens were collected to obtain material from the upper lateral vaginal vault and cervix for direct microscopic examination of vaginal contents and preparation of Gram-stained and pap smear slides. Potential determinants of infection were assessed using the chi-square test. Poisson regression was used to determine the prevalence ratio and 95% confidence interval of the association between the vaginal flora and cytology with prematurity and a p <0.05 was considered as statistical significance. Results: A total of 68 pap smear and high vaginal swab samples were collected from high-risk pregnant women with a mean age of 30.3 years. There were 26 (38.2%) cases of abnormal vaginal flora and 6 (8.8%) of HPV-induced cytological abnormalities. The preterm delivery rate was comparable between women with normal and abnormal vaginal flora (11.9% vs. 11.5%, prevalence ratio 1.0 [95% confidence interval: 0.8-1.2], aPR 1.0 [95% confidence interval: 0.8-1.2]) and between women with and without abnormal cytology (16.7% vs. 11.3%, prevalence ratio 0.9 [95% confidence interval: 0.6-1.4], aPR 0.9 [95% confidence interval: 0.6-1.4]). Conclusions: We determined no association between abnormal cytology or altered vaginal flora in high-risk pregnancy in terms of overall preterm birth rate.