Production Floor Research Articles

Capacity Constraint Analysis Using Object Detection for Smart Manufacturing

The increasing adoption of Deep Learning (DL)-based Object Detection (OD) models in smart manufacturing has opened up new avenues for optimizing production processes. Traditional industries facing capacity constraints require noninvasive methods for in-depth operations analysis to optimize processes and increase revenue. In this study, we propose a novel framework for capacity constraint analysis that identifies bottlenecks in production facilities and conducts cycle time studies using an end-to-end pipeline. This pipeline employs a Convolutional Neural Network (CNN)-based OD model to accurately identify potential objects on the production floor, followed by a CNN-based tracker to monitor their lifecycle in each workstation. The extracted metadata are further processed through the proposed framework. Our analysis of a real-world manufacturing facility over six months revealed that the bottleneck station operated at only 73.1% productivity, falling to less than 40% on certain days; additionally, the processing time of each item increased by 53% during certain weeks due to critical labor and materials shortages. These findings highlight significant opportunities for process optimization and efficiency improvements. The proposed pipeline can be extended to other production facilities where manual labor is used to assemble parts, and can be used to analyze and manage labor and materials over time as well as to conduct audits and improve overall yields, potentially transforming capacity management in smart manufacturing environments.

Integration of lean manufacturing system with novel intuitive fuzzy syncretic lean frame work to improve the overall equipment effectiveness

Lean manufacturing embraces the principle of doing more with less by removing non-value-added activities from manufacturing processes to preserve effectiveness, flexibility, and profitability. Predictive maintenance in production stages are more critical since it involves machines which are prone to failure and a post failure maintenance stagnates the production which creates bottlenecks due to machine failures. Hence in this work a novel intuitive fuzzy syncretic lean frame work has been implemented to incorporate the need of intelligent systems. The framework is divided into phases which employs Fuzzy logic with embedded smart sensors for effective utilization of man machine and materials in order to improve the effectiveness of a production floor. A novel time based forecasting is done in the design phase which implements the lean tool Takt time. The manufacturing phase uses the sensors to determine the predictive maintenance of the machines thereby implementing continuous flow as the lean tool and the inspection phase uses smart sensor system for real time continuous monitoring of the machining process thereby incorporating Poka-yoke as a lean tool. Thus by implementing the framework the overall equipment effectiveness is achieved which helps in achieving continuous flow of products and defect free products in any production firm.

CAPP-GPT: A computer-aided process planning-generative pretrained transformer framework for smart manufacturing

Smart manufacturing (SM) constitutes the backbone of Industry 4.0 (I4.0), allowing for heightened autonomy of the various interacting cyber-physical systems, making the various entities on the production floor. Connectivity, a vital enabler, plays a crucial role through state-of-the-art Digital Twinning (DT) technologies driven by underlying innovations like the industrial Internet of Things, Cloud Computing, and advancements in sensory devices. DT, which plays a vital role in the various planning functions under the production and operations management umbrella, is being used in the developed combined CAPP-GPT (Computer-Aided Process Planning-Generative Pretrained Transformer) and production scheduling approach to address disruptions on the shopfloor and in self-healing of the manufacturing processes at a micro-CAPP level by optimally adapting the process parameters and the developed toolpath on the fly based on online process signature measurements. In a leap commensurate with that which has taken place in Natural Language Processing-Large Language Models (Chat-GPT), similar efforts are currently being undertaken to parse CAD data structures and blueprints, fusing operations research and predictive analytics algorithms to carry out setup planning as well as sequencing and grouping manufacturing sub-operations. A hybridized Optimization and Machine Learning (ML) approach is employed where Logical Analysis of Data is used to solve the problem heuristically, exploiting various generative and variant methods at heart. Another extension of this macro-CAPP problem is being tackled by integrating the problem with delayed product differentiation, lot-sizing, and transfer line balance for futuristic batch-production shops employing Hybrid Manufacturing (HM) and Smart Assembly. At the micro-CAPP level, HM process parameters are optimized using a comprehensive approach employing the Taguchi loss function to assess surface roughness, internal failure costs, and other criteria, including greenhouse gas emissions and expended energy. Online measurements of the process signatures are also employed to adapt the initial set of process parameters using different automatic control schemes. ML is used to identify the process parameters carrying simulations on Simulink before the system is deployed.

Dynamic matching of energy and heat conduction in production workshops and financing for manufacturing enterprises based on the Internet of Things

With the advancement of Industry 4.0, the application of Internet of Things technology in manufacturing enterprises is increasingly extensive, and energy heat transfer, as an important factor affecting production efficiency and cost, needs to be optimized by new technical means. This study explores the energy heat conduction characteristics of production workshops based on the Internet of Things and its impact on the dynamic matching of financing for manufacturing enterprises, providing an innovative energy management model to promote the sustainable development of enterprises. The research uses the Internet of Things technology to monitor the energy flow and heat transfer in the production shop in real time through sensors and data acquisition systems. Combined with big data analytics and machine learning algorithms, in-depth analysis of energy consumption data is performed to identify bottlenecks and optimization points for heat transfer. At the same time, the financing dynamic matching model is constructed to analyze the financing needs and costs of enterprises under different energy management levels. The study found that iot technology can significantly improve the thermal efficiency of production plants and reduce energy waste. On the basis of the optimization of energy heat transfer, the financing demand and financing cost of enterprises have also undergone dynamic changes. The optimization of energy heat transfer based on the Internet of Things not only improves the energy efficiency of the production floor, but also provides a new impetus for the financing of manufacturing enterprises. Implementing an effective energy management strategy will help companies stay ahead of the curve in an increasingly competitive market.

Study of SME employees’ awareness level on lean manufacturing and ergonomics implementation in Malaysian and Indonesian production environments

SMEs are small to medium-sized businesses with relatively fewer workers and lower revenues than large companies. However, SMEs also contribute to the local economic growth of a region, so the smooth production process needs to be considered to increase productivity. Applying lean manufacturing (LM) and Ergonomics concepts in the production process is critical because it can overcome smooth production and maintain the health and safety of SMEs workers. LM focuses on minimizing waste, while ergonomics focuses on humans as a source of energy in the smooth running of production activities. So, this study aims to measure the level of understanding of Malaysian and Indonesian SMEs workers on applying LM and Ergonomics concepts on the production floor and determine the effect of these two concepts using the SPSS and SmartPLS4 applications. SPSS serves to measure the validity, reliability and mean for the category of workers' understanding of LM and Ergonomics. SmartPLS4 helps us understand the influence of the two concepts. Based on the calculation of the mean for each variable of LM and ergonomics, it is found that Malaysian workers understand enough compared to Indonesian SMEs workers. As for the effect, Ergonomics has a significant influence on LM.

Application of Technological Procedure Automated Guided Vehicles in the Production Hall of a Company Due to Increasing the Automation—Case Study

This paper discusses the possibility of deploying automated guided vehicle (AGV) technology in a company’s production hall. Chronometric measurements of material handling and people handling were performed. The handling processes were divided into five sub-processes, which were analysed in terms of production line operation, distance, and carrying capacity of the automated guided vehicles. Based on the measurements, we found that it was necessary to use four vehicles for handling in the foam production hall. The company is applying this automation project to foams, and after successful testing, plans to continue with automation for leather upholstery. To ensure the correct chronometric results, we measured the distances between each workstation on the production floor at the company. Based on an analysis of material handling on the production lines, we evaluated that there is a possibility of applying AGVs on the entire production floor. We analysed the volumes of material transfer and the possibilities of interconnection within the existing material handling in the enterprise. Complications arise when deploying AGVs in the plant because the handling to the production lines is not only provided by the parent company, but also by the component suppliers themselves. Based on this observation, we have identified potential collision points within the operation between automated guided vehicles and other material handling equipment or people. For this reason, we deployed one more AGV on the handling routes to prevent unwanted collisions. This means that a human would be able to perform even more processes than a machine and can circulate 80 times, where a machine under the same conditions would only perform the entire circulation 57 times. There is room here to use humans for other processes and to automate the handling of goods, although it introduces delays that can be eliminated by deploying additional automated guided vehicles. This opens room for further research questions on how these vehicles can be deployed, even in a small space, and how existing material handling can be automated.

Pendampingan Perancangan Gudang Material dan Cutting pada Perusahaan Tas TIJ

Top Intera Jaya (TIJ) is a company that focuses on designing and producing high-quality leather bags, wallets, and accessories. TIJ has a monthly production capacity of 50,000 pcs and plans to increase it to 100,000. The company needs better storage capacity and production floor to support this increase. This Community Service Program (CSP) aims to assist TIJ in redesigning the production floor into a raw material and cutting storage warehouse using the shared storage method. The shared storage method organizes warehouse space using the FIFO (First In First Out) principle, where the fastest delivered items are placed in the storage area closest to the exit. This CSP began by conducting interviews with resource persons, namely warehouse staff, to understand the needs of TIJ. After getting an initial overview, the team designed the training materials that TIJ needed. CSP was conducted on February 2, 2024, with the pre-test and post-test results showing an increase in participants' understanding of warehouse design from 33% to 67% and from the results of the training and mentoring, the team and management of TIJ agreed that the shared storage method with the FIFO principle is the best solution that will be implemented for warehouse layout efficiency at TIJ.

Production Floor Digitalization

The digitalization of production floors is becoming increasingly important in the context of e-commerce manufacturers' interactions with customers. This paper investigates the nuances of production floor digitalization in the context of e-commerce manufacturing, examining the implications for both manufactures and buyers. E-commerce manufacturers, faced with the unique challenges of online retail, are leveraging digital technologies to optimize production processes and improve customer experiences. Data-driven demand forecasting, agile manufacturing practices, and seamless integration with e-commerce platforms are all important aspects of production floor digitalization in this context.Digitalization of the production floor provides opportunities for manufacturers to improve efficiency, reduce costs, and respond more quickly to market demand. Manufacturers can improve customer satisfaction and loyalty by implementing real-time data analytics and automation to streamline production workflows, reduce lead times, and ensure timely order fulfillment.On the other hand, production floor digitalization provides significant benefits to e-commerce buyers. Buyers can make more informed purchasing decisions and enjoy greater convenience and reliability in their online transactions thanks to improved product visibility, accurate delivery estimates, and customized purchasing procedures.

Redesign of workstations to minimize musculoskeletal disorders for guitar workers in Indonesia

This study aims to determine and analyze body parts that experience musculoskeletal disorders (MSDs) and muscles that experience fatigue in guitar-making workers. The method used is the Nordic Body Map (NBM) to determine MSDs complaints, including complaints of pain in 28 parts of the worker's body, and the Muscle Fatigue Analysis (MFA) method to determine muscle fatigue that occurs in each part of the body by determining the level of effort, duration of work, and frequency of work movements. The results of research using NBM were 2 workers with high risk, 3 workers with medium risk, and 7 workers with low risk. The results of the MFA method were that 3 workers experienced muscle fatigue in the very high category with priority in the neck, back, shoulders, right arm, wrist and feet. Efforts to minimize the risk of MSDs by redesigning work facilities using SolidWorks software and reducing the risk of muscle fatigue by improving work facilities, improving the layout of the production floor, and encouraging workers to stretch their muscles and exercise regularly.

Environmentally Sustainable Raised Access Flooring Product Development

Raised access floors are nowadays widely used in buildings. A novel raised access flooring product is developed by this research, with a set of sustainable features, including less environmental impact and high strength. Its floor panels are made of polyurethane (PU) reinforced with glass fibre, which is light-weight and fire-resistant, replacing the traditional floor panel materials, and the panels are supported by simplified steel stringers to reinforce the strength of the flooring product. Instead of the conventional sandwich design consisting of a core material encapsulated by outer layers, the new floor panel design adopts the reinforced PU as its sole material, which not only simplifies the structure but also reduces floor weight and costs. The sustainable advantage is further approved by the environmental life cycle assessments of the new raised flooring product in comparison to traditional ones made of cement and woodchips, with results showing that the new floor product’s total environmental impact is 52% less than cement floor and 47% less than woodchip board floor. Further, the finite element analysis (FEA) was carried out, and the experimental test was conducted to verify the FEA results, indicating that the new product’s strength is higher than the requirements of the raised access flooring product standards. There is no raised access flooring product made of PU reinforced with glass fibre available in the market, and, hence, the new product developed by this research is a novel contribution.

Mitigasi Risiko pada Proses Produksi Tahu Menggunakan Pendekatan Metode Failure Mode and Effect Analysis dan Risk Priority Number

The Bantul Tofu Industry is a medium-sized industry that processes soybeans into tofu located in the Bantul area. In the production process there are still errors that cause the tofu product to be damaged. The aim of this research is identify risks that occur in tofu production, analyze priority risk events, and make recommendations for improvements. The method used is FMEA to analyze the occurrence of risks or failures in a process and RPN to provide a risk assessment score for proposed improvements. Based on the research results, it was found that there were 14 total risk events and there were 5 risks that were mitigation priorities, such as spilling of tofu coconut milk, mixing of soybean quality, incorrect cutting of tofu, slippery production floors, and the presence of liquid waste. Recommendations for improvement are creating SOP, using adequate equipment, maintaining the cleanliness of production areas and routine maintenance. Keywords: Failure Mode and Effect Analysis, Risk Priority Number, Tofu Processing

Gamification Approach for Interactive Learning of Lean Manufacturing Activities

The gamification approach is popular method in education as it has been widely utilized in many fields. The project focuses on designing a game for interactive learning of lean manufacturing activities to create an alternative learning method that would gain interest and efficiency in learning. Integration of gamification approach and interactive learning method was used to illustrate a virtual lean manufacturing education and training by means of users’ interaction towards provided interactive learning materials and participation in playing the designed game. Input of the project which is learning materials was developed by analysing numerous articles regarding common lean manufacturing tools and practices. Interactive elements such as animation, sound effects, lecture voice, and videos were applied in each learning material to deliver an adequate understanding of each topic. Construction of the game platform is being developed using ActivePresenter software which includes simulation videos of the production line using FlexSim software to visualize a real production floor which provides a clearer picture of the production system for users. The game should include a basic assessment of lean manufacturing practices by considering its complexity and difficulty level for users with inadequate or does not have experience in the field. The outcome of the project is expected to successfully design a basic game platform for lean manufacturing education by means of interactive learning. Users will be able to learn lean manufacturing systems using the designed interactive learning materials included in the games and self-assess themselves as the game publishes the correct and best answer option in case they answer wrongly.

PERANCANGAN TATA LETAK RELOKASI LANTAI PRODUKSI DENGAN METODE SYSTEMATIC LAYOUT PLANNING, BLOCPLAN, DAN FLAP

In the development of the industrial revolution 4.0, in carrying out production at small, medium, and large levels, there has been a fairly large and significant increase. Factors that influence the goal of successful production are not only about investment, capital, suppliers, machines, skills, and distributors but also one that must be observed is the layout planning and placement of factory facilities. PT. Eworx Indonesia is a company engaged in the processing industry, especially manufacturing in the metal processing sector. PT. Eworx Indonesia intends to relocate the production floor in order to increase production productivity. In this study, the proposed layout design on the new production floor was carried out using the SLP, Blocplan, and FLAP methods. The design is done by collecting direct observation data and interviews with the operator on duty. It was found that the initial layout had problems with operator movement that was not optimal and the placement of several machines and facilities that did not pay attention to material flow resulting in a slowdown in production time. Therefore, in order to get the best layout, a comparison is made on the results of the three alternative layouts with the smallest displacement distance comparison so that the production process will run more optimally. The best proposed layout is the systematic layout planning (SLP) method with the smallest material displacement distance of 147.4 meters and material handling costs Rp. 223,958 per day. Flexsim simulations show this layout can increase production output by 15%-34%.

The pathways to lean manufacturing for circular economy: Implications for sustainable development goals

Purpose This study aims to address the critical challenge of implementing lean manufacturing (LM) in emerging economies, where sustainability complexities on the production floor hinder production efficiency and the transition towards a circular economy (CE). Addressing a gap in existing research, the paper introduces a path analysis model to systematically identify, prioritize and overcome LM implementation barriers, aiming to enhance performance through strategic removal. Design/methodology/approach The authors used a mixed-method approach, combining empirical survey data with literature reviews to pinpoint key LM barriers. Using the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) along with the Network Knowledge (NK) method, they mapped causal relationships and barrier intensities. This formed the basis for developing a path simulation algorithm, integrating heuristic considerations for practical decision-making. Findings This analysis reveals that the primary barriers to LM adoption is the negative perception and inadequate understanding of lean tools and CE principles. The study provides a strategic framework for managers, offering new insights into barrier prioritization and overcoming strategies to facilitate successful LM adoption. Research limitations/implications This research provides a strategic pathway for overcoming LM implementation barriers, empowering managers in emerging economies to enhance sustainability and competitive advantage through LM and CE integration. It emphasizes the significance of structured barrier management in the manufacturing sector. Originality/value This research pioneers a systematic exploration of LM implementation barriers in the CE context, making a significant contribution to the literature. It identifies, evaluates barriers and proposes a practical model for overcoming them, enriching sustainable manufacturing practices in emerging markets.

EVALUASI AKTIVITAS NON VALUE ADDED DENGAN MENGGUNAKAN METODE VALUE STREAM MAPPING DAN PROCESS ACTIVITY MAPPING

Samsul Blacksmith is a blacksmith who produces palm harvesting tools (dodos), in the production process there is still waste which causes the daily production target not to be achieved, this study aims to determine the root causes of waste and provide alternative improvements. This research was conducted with 4 stages, first mapping all production activities using value stream mapping (VSM), the second stage identifying waste using the Process activity mapping (PAM) method, the third stage identifying the root causes of waste using the 5 whys method, the last stage providing alternative improvements to reduce or eliminate waste. The results showed that waste occurred due to the absence of workpiece transfer tools and long transfer distances, simple furnaces without the use of heat shields, and inefficient workbenches, the proposed alternative re-layout of the production floor, designing furnaces, and designing work areas, was able to reduce the lead time of the production process from 6.260 seconds to 4.907,8 seconds (improvement with MOST and MTM), and increased the PCE value from 51.6% to 65.8%.

A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions

In the semiconductor manufacturing industry, the Dynamic Flexible Job Shop Scheduling Problem is regarded as one of the most complex and significant scheduling problems. Existing studies consider the dynamic arrival of jobs, however, the insertion of urgent jobs such as testing chips poses a challenge to the production model, and there is an urgent need for new scheduling methods to improve the dynamic response and self-adjustment of the shop floor. In this work, deep reinforcement learning is utilized to address the dynamic flexible job shop scheduling problem and facilitate near-real-time shop floor decision-making. We extracted eight state features, including machine utilization, operation completion rate, etc., to reflect real-time shop floor production data. After examining machine availability time, the machine's earliest available time is redefined and incorporated into the design of compound scheduling rules. Eight compound scheduling rules have been developed for job selection and machine allocation. By using the state features as inputs to the Double Deep Q-Network, it is possible to acquire the state action values (Q-values) of each compound scheduling rule, and the intelligent agent can learn a reasonable optimization strategy through training. Simulation studies show that the proposed Double Deep Q-Network algorithm outperforms other heuristics and well-known scheduling rules by generating excellent solutions quickly. In most scenarios, the Double Deep Q-Network algorithm outperforms the Deep Q-Network, Q-Learning, and State-Action-Reward-State-Action (SARSA) frameworks. Moreover, the intelligent agent has good generalization ability in terms of optimization for similar objectives.

Robust Control Chart Application in Semiconductor Manufacturing Process

Statistical Process Control (SPC) charts are frequently used in the semiconductor manufacturing environment to monitor process quality and detect special-cause variations, hence, to take corrective actions when necessary. The important aspects of control charts to consider on production floors are identifying the primary objective of implementing control charts, the type of data to monitor and the most appropriate control limits to establish. When the quality data is a type of attribute data like the proportion of defectives from a production lot, a p-chart approach is most suitable. In p-chart applications, although the assumption of normally distributed process data is not mandatory, the widespread practice is to assume the normal distribution of process data when establishing the control limits. Yet again, the reality of industrial settings is that process data are most likely influenced by outliers, resulting in highly skewed distributions. This paper addresses these issues by proposing robust SPC charting techniques to detect special-cause variations in the semiconductor manufacturing processes. Here, we present a case study of a semiconductor company in Malaysia, Dominant Opto Technologies Sdn. Bhd. to propose three robust statistical approaches for monitoring the proportion of defectives in production lots. We apply M-estimates, median, and interquartile range to calculate the upper control limits (UCL) and found that robust estimators are more effective in detecting early process deterioration and capturing the out-of-control (OOC) conditions better than traditional control charts. By proposing robust methods, this study enlightens the practical aspects of process quality improvement for real-life manufacturing setups. Because a high OOC rate may impact manufacturing productivity, we recommend the decision-makers choose the types of control charts based on the implications of each robust approach toward quality and productivity. The significance of this study includes providing insights into setting up the appropriate attribute control charts for detecting defective proportions for professionals and SPC researchers working in these areas.

Modified Levey-Jennings Chart with Robust Estimator: A Case of Semiconductor Manufacturing Process

In the era of Industrial Revolution 4.0 and smart manufacturing, the development and deployment of control charts used in the semiconductor industry need to be automated. Consequently, artificial intelligence-based automation methods typically encompass the deployment of statistical software such as JMP. Automation involves frequent dataset updates; the control limits are recalculated as the parameters change (non-stationary behaviour). This requires the user to define the control chart type before its deployment on the production floor. An initially normally distributed dataset may be skewed during the process owing to the influence of outliers. If a user selects a chart based on normality assumptions, detecting a process-mean shift may be impossible if the recalculated limit fluctuates. In the semiconductor industry, a process-mean shift occurs owing to special cause variations in the process. This signals process deterioration, which may affect the quality of the product. It is unknown when outliers will affect the equilibrium of normality assumptions; therefore, it is important to develop an automated, robust control chart that can detect special cause variations under non-stationary conditions. This study proposes the use of Huber’s M-estimators in the Levey-Jennings chart to detect special cause variations in a semiconductor manufacturing process. This study computes the robust M-estimates of all available samples to calculate the new limits in the Levey-Jennings chart. This new chart is referred to as the modified Levey-Jennings with a robust Huber M-estimator (MLVHM). Using production data from Dominant Opto Technologies Sdn. Bhd., Malaysia, a statistical comparison of the MLVHM and Levey-Jennings charts was performed. While the MLVHM is stable, the absolute difference in dispersion between the two charts ranges between 25.26% and 47.91% owing to standard deviation variation in the Levey-Jennings chart in non-stationary situations with outliers. The study concludes that the MLVHM chart is robust and suitable for industrial automatic flow applications.

CMMS- Streamlining Maintenance management for Food and Beverage Industry to capture breakdowns

CMMS stands for Computerized Maintenance Management System. In today’s world, when technology is advancing each day at an astonishing rate, any organization must have a detailed knowledge and understanding of its current state of machinery, the number of resources spent on the production floor to maintain equipment reliability, and resource allocation. CMMS is a critical and powerful tool that assists and helps any manufacturing industry to make full use of its capabilities, manage or direct workforce, improve the machine reliability, find innovative ways to improve productivity, stay in compliance with food and dietary regulatory standards as per the safe quality foods (SQF) and national sanitation foundation (NSF) standards and achieve series of goal and benefits the organization overall. Today, the food and beverage industry has grown tremendously and is the backbone of the manufacturing industry. Every company looks at creative ways to help them excel by selecting a reliable CMMS that suits their operations and processes to make educated decisions by monitoring their KPIs. This theoretical study aims to provide a building framework to set up plant maintenance modules to manage work orders, including emergency breakdowns and scheduled maintenance repairs in the food and beverage industry.

Making Cognitive Ergonomics in the Human–Computer Interaction of Manufacturing Execution Systems Assessable: Experimental and Validation Approaches to Closing Research Gaps

Cognitive ergonomics and the mental health of production workers have attracted increasing interest in industrial companies. However, there is still not much research available as it is regarding physical ergonomics and muscular load. This paper designs an experiment to analyze the cognitive ergonomics and mental stress of shop floor production workers interacting with different user interfaces of a Manufacturing Execution System (MES) that is adjustable for analyzing the influence of other assistive systems, too. This approach is going to be designed with the Design of Experiments (DoE) method. Therefore, the respective goals and factors are going to be determined. The environment will be the laboratories of the University of Applied Sciences Amberg-Weiden and its Campus for Digitalization in Amberg. In detail, there will be a sample assembly process from the automotive supplier industry for demonstration purposes. At this laboratory, the MES software from the European benchmark SAP is installed, and the respective standard Production Operator Desk is going to be used with slight adaptions. In order to make the cognitive ergonomics measurable, different approaches are going to be used. For instance, body temperature, heart rate and skin conductance as well as subjective methods of self-assessment are planned. The result of this paper is a ready-to-run experiment with sample data for each classification of participants. Further, possible limitations and adjustments are going to be discussed. Finally, an approach to validating the expected results is going to be shown and future intentions are going to be discussed.