Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises.

In the industrial sector, production processes are continuously evolving, but issues and delays in production are still commonplace. Complex problems often require input from production managers or experts even though Industry 4.0 provides advanced technological solutions. Small and medium-sized enterprises (SMEs) normally rely more on expert opinion since they face difficulties implementing the newest and most advanced Industry 4.0 technologies. This reliance on human expertise can cause delays in the production processes, ultimately, impacting the efficiency and profitability of the enterprise. As SMEs are mostly niche markets and produce small batches, dynamics in production operations and the need for quick responses cannot be avoided. To address these issues, a decision support method for dynamic production planning (DSM DPP) was developed to optimize the production processes. This method involves the use of algorithms and programming in Matlab to create a decision support module that provides solutions to complex problems in real-time. The aim of this method is to combine not only technical but also human factors to efficiently optimize dynamic production planning. It is hardly noticeable in other methods the involvement of human factors such as skills of operations, speed of working, or salary size. The method itself is based on real-time data so examples of the required I 4.0 technologies for production sites are described in this article-Industrial Internet of Things, blockchains, sensors, etc. Each technology is presented with examples of usage and the requirement for it. Moreover, to confirm the effectiveness of this method, tests were made with real data that were acquired from a metal processing company in Lithuania. The method was tested with existing production orders, and found to be universal, making it adaptable to different production settings. This study presents a practical solution to complex problems in industrial settings and demonstrates the potential for DSM DPP to improve production processes while checking the latest data from production sites that are conducted through cloud systems, sensors, IoT, etc. The implementation of this method in SMEs could result in significant improvements in production efficiency, ultimately, leading to increased profitability.

Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm.

Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee's physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems.

A pilot study on the influence of exercising on unstable training machine on balance control and trunk muscles activity.

BACKGROUND: The main position of the working population is becoming sitting. Immobile prolonged sedentary time may cause negative effects including reduced intervertebral discs nutrition. Main ways of mitigating them are regular position changes and exercising. OBJECTIVE: To evaluate influence of the short term training on unstable training machine on balance control and trunk muscles activity in patients with lower back pain. METHODS: Participants (n=16) experiencing lower back pain were trained on an unstable sculling machine "Rehabili". Their balance tested by (Biodex Balance System) and rectus abdominis, externus oblique, transverse abdominis, multifidus and erector spine muscles activity (measured by surface electromyography) while sitting and standing with usual and aligned body postures both before and after six weeks of training (three 15 minutes sessions per week) were compared in between. RESULTS: Balance control improved after the training program. Besides, more symmetrical activation of both sides rectus and transversus abdominis muscles, as well as increased transversus abdominis muscle activation of 19% (p< 0.05), were observed. CONCLUSIONS: Six weeks short sessions training on unstable training machine improved balance control and increased trunk muscles activity especially in aligned body posture when standing or sitting on unstable surface.