Analysis of Production Process Parameters Using Computer Simulation at the Planning Stage

Abstract

Computer simulation is one of the tools that is often used to support activities related to the management of the production process. Computer simulation allows for the analysis of the parameters of production or logistics processes, among others. The study presents a comparative analysis of analytical methods and computer simulation (FlexSim 22.2.1 software) in relation to the subject matter. The use of computer simulation allowed for the verification of the number of production resources, determined by analytical methods, and for the proposal of improvements that would allow for shortening the duration of the production process and reducing its costs. The proposed improvements at the stage of planning the production process in terms of the number of production resources used to implement the assumed production plan allowed for the reduction of the duration of the production process and its costs. Therefore, it is justified to use computer simulation in solving problems related to various aspects of the functioning of an enterprise.

1. Introduction

Modern enterprises are increasingly looking for methods aimed at improving productivity [1]. In industrial practice, the possibility of applying simulation tools is being considered, allowing for the optimization of processes and the identification and reduction of identified losses, as well as for the shortening of the working time and the achievement of the desired economic efficiency without the need to introduce costly changes [2]. In the literature on simulation issues, the analysis covers methods based on discrete event simulation (DES) [1,3,4], which are widely used in modeling health care systems [5], production processes [6], supply chain management, and logistics [7,8], among others applications. Discrete event simulation models present the system in such a way that the decision maker can observe the way the process works, as well as propose improvements that have a significant impact on the process efficiency [1,2].

The methods of computer modeling and simulation are widely used in business practice due to the fact that analytical solutions are complicated, and direct experimentation on a real system is time-consuming, expensive, or sometimes impossible to carry out. The solutions used allow the decision maker to learn about different variants of the solutions and thus choose the variant that meets the decision maker’s expectations. The software used allows for the creation of models for simulating production processes, transport, distribution, storage, or the individual needs of the decision maker, which indicates the wide application possibilities of modeling and simulation in solving complex problems in various branches of the economy.

In connection with the above, the aim of the article was to verify the possibility of using computer simulation (FlexSim software) as a tool supporting analytical methods at the stage of planning the production process. For the purposes of the study, the actual parameters of the production process were analyzed in terms of selecting the appropriate number of production resources ensuring the shortening of the duration of the production process and the reduction of costs, among other benefits. The use of computer simulation within the existing production process can supplement the research conducted in the field possible simulation application at the stage of planning the production process, along with its evaluation. In industrial practice, analytical methods were used; therefore, the possibility of using computer simulation was proposed, using FlexSim software in the scope of the analyses in question, based on real data. The implementation of the assumed goal of the work will allow for filling the research gap in the scope of the possibilities of using computer simulation (FlexSim simulation software) to determine the number of production resources necessary to implement the assumed production plan.

The production process subjected to analysis included the production of rotating parts for use in the machine industry. The process consisted of six production operations carried out on individual, non-automated production stations. Thanks to the data on the duration of individual production operations, which were provided by the company, as well as those related to the form of production organization and the layout of the production stations, the discussed production process was an appropriate model for conducting an analysis in the scope of the defined work goal.

The article includes a literature section (Section 2), in which selected simulation software and areas of their application in the field of analysis of production process parameters were characterized. Section 3, presents a case study, which is the subject of the conducted analysis (a detailed description of the production process) and defines a five-stage methodology. In Section 4, simulations of the production process were carried out, and then discussions of the obtained results were conducted in Section 5.

2. Literature Review

In the literature devoted to the possibilities of computer simulation applications, there are many tools that enable the carrying out experiments in various areas of enterprise operation. These tools are based on discrete-event simulation DES [1]; these tools includes software such as FlexSim, Plant Simulation, Enterprise Dynamics, ARENA, and SIMO, which are used in solving production problems (Table 1). FlexSim makes it possible to conduct many experiments involving problems related to the flow process [1], the assembly system and logistics in workshops [7], and the identification of bottlenecks in production processes [8] and warehouses [9], specifically in regards to proposals for actions enabling the implementation of solutions to improve production and warehouse processes, taking into account various alternatives for identified bottlenecks. It is also used in the field of educational and training activities, allowing for the creation of a virtual post-production plant, allowing users to gain insight into the functioning of the workplace [10]. The use of the software also allows for conducting analyses in the field of emergency evacuation and obtaining answers to questions regarding time spent on evacuation, as well as the possibility of shortening the evacuation time [11], which may play an important role in protecting the life and health of not only public utility unit users, but also of medical facility occupants. FlexSim also allows for finding solutions and alternatives in the regards to shortening queues (queuing systems) in health care facilities [12], as well as determining the total length of a patient’s stay and identifying the need for personnel in order to avoid delays in treatment [13].

The studies also present the application possibilities of the Tecnomatix Plant Simulation software, which allows for simulation and analysis of the product throughout the production process [14]. Using the above-mentioned software, it is possible to identify bottlenecks occurring in the production process and implement solutions that allow for their effective reduction by introducing shift production, intensifying quality control before the bottleneck, expanding the machine system, or, as a last resort, purchasing semi-finished products manufactured as a result of the bottlenecks [14]. The Plant Simulation software is also used to detect bottlenecks and shortcomings in the logistics and transport systems of the company in terms of improving the throughput of the entire logistics flow and reducing inter-operational stocks [15]. Within one simulation model, application possibilities are also noted in the field of simulation of the production logistics area and city logistics [16], as well as in the field of scheduling [17].

Enterprise Dynamics (ED) is a software that provides an object-oriented programming environment for simulation, which is easy to use and comprehensive in terms of functionality [18]. This software has been used in the simulation of the production process using CNC machines [18], production, and logistics in the construction industry [19], taking into account different scenarios. This allows for the identification of bottlenecks, logistics, and spatial conflicts in logistics processes. Analyses of the occurrence of bottlenecks allow for remedial actions to be taken by the management staff in construction. The possibility of introducing changes in the designed logistics system without incurring costs is also recorded [20].

ARENA is a software that allows for creating simulation models that reflect virtually any system [21]. This software is widely used in the field of traffic simulation, among others, in terms of determining bottlenecks and defining proposals for improvements that allow for solving problems related to road traffic, taking into account various scenarios, as well as the consequences before and after their implementation [22]. ARENA is also used to plan operations of technological machines in terms of minimizing the time to complete a detail [23], as well as to evaluate the transport system, taking into account various scenarios allowing for the assessment of productivity [24]. ARENA simulation software is also used in the quantitative assessment of the impact of the improvements applied in production processes related to the implementation of Lean tools. The improvements in question may include shortening the waiting time, determining the number of items in the queue with appropriate resources, identifying inventories disturbing the dynamics of the flow, and increasing the number of parts produced, resulting in an increase in the efficiency and effectiveness of the applied processes [25].

The Simo modeling system was designed to simplify model building by promoting a paradigm shift from process orientation to object orientation. The software allows for simplifying model building by changing the modeling paradigm [26,27]. Simo has been used in the field of aircraft ground service analysis to estimate the impact of disruptions on the timeliness of performed operations [28], as well as in the field of simulation scenario analyses in industrial processes, allowing for the identification of bottlenecks in the assembly line [29].

It is also possible to conduct comparative analyses of the software used by simulation practitioners. The analysis covers aspects related to, among others, the concept of simulation models, the development of animation, the philosophy of modeling, the library, the templates used, the concept of units, and the tool interface [30]. Attention is also paid to the development of industry, where in the case of research, the concept of industrialization (Industry 5.0) refers to the integration of cyber–physical systems, artificial intelligence, machine learning, and big data with operators in order to create a more personalized production environment [29,30], which is why there is continuous development and a wider use of computer simulation in solving the advanced problems of modern industrial enterprises.

Table 1

Examples of the use of simulation models in various sectors of the economy.

Author(s)/Year	Software Used	Usage Characteristics
Barosz, P.; Gołda, G.; Kampa, A. (2020) [1]	FlexSim software	Analysis of the production flow problem and assessment of productivity on the production line.
Wang, Y.; Chen, A.N. (2016) [7]		Simulation of the assembly system and production logistics of the workshop.
Sravan Kumar, U.; Shivraj Narayan, Y. (2015) [8]		Identification of bottlenecks and proposal of remedial actions determined using statistical tools.
Huihui, S.; Xiaoxia, M.; Xiangguo, M. (2016) [9]		Identification of bottlenecks in the warehouse process in terms of streamlining warehouse work.
Kim, J.W.; Park, J.S.; Kim, S.K. (2020) [10]		Supporting educational activities in regards to designing a virtual production plant (smart factory).
Mikulik, J.; Cempel, W.A.; Kracik, S.; Dąbal, D. (2014) [11]		Emergency evacuation simulation in medical units.
Santos, M.; Júnior, E.L.S.B.; Palermo, G.S.; Walker, R.A.; Reis, M.F. (2018) [12]		Queuing systems in healthcare facilities.
Patvivatsiri, L.; Montes, E.J.; Xi, O. (2007) [13]		Elimination of treatment delays in the event of various scenarios related to a bioterrorist attack.
Kikolski, M. (2016) [14]	Plant Simulation software	Identification of bottlenecks in view of their reduction in the production process.
Pekarcikova, M.; Trebuna, P.; Kliment, M.; Dic, M. (2021) [15]		Identification of bottlenecks in logistics and warehousing processes in terms of their reduction.
Fedorko, G.; Molnár, V.; Strohmandl, J.; Horváthová, P.; Strnad, D.; Cech, V. (2022) [16]		Micro-simulation of traffic in the fields of production logistics and city logistics.
Ferro, R.; Cordeiro, G.A.; Ordóñez, R.E.C.; Beydoun, G.; Shukla, N. (2021) [17]		Supporting production planning in making decisions about batch sizes and production scheduling.
Gola, A.; Wichetek, Ł. (2017) [18]	Enterprise Dynamics software	Modeling and simulation of the production process using CNC machines.
Galić, M.; Thronicke, R.; Schreck, B.M.; Feine, I.; Bargstädt, H. (2015) [19]		Modeling and simulation of construction processes in terms of reducing bottlenecks.
Dobrzańska, M. (2016) [20]		Reduction of costs of implemented logistics processes.
Kamrani, M.; Abadi, S.M.H.E.; Golroudbary, S.R. (2014) [22]	ARENA software	Simulation of traffic at intersections during rush hours.
Nawara, G.M.; Hassanein. W.S. (2013) [23]		Workshop scheduling process.
Hashemi, A.S.; Sattarvand, J. (2015) [24]		Productivity analysis in the mining industry.
Dias, A.S.M.E.; Antunes, R.M.G.; Abreu, A.; Anes, V.; Navas, H.V.G.; Morgado, T.; Calado, J.M.F. (2022) [25]		Predicting the results inherent to implementing process improvements related to the application of Lean tools.
Kwasiborska, A.; Postół, J. (2021) [28]	Simo software	Airport ground-handling simulation.
Breznik, M.; Buchmeister, B.; (2023) [29]		Evaluation of design options and operational scenarios on the assembly line.

In connection with the above, based on the analysis of literature [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29], various possibilities for using computer simulation for the analysis of production process parameters and logistics processes are recorded in areas such as the analysis of bottlenecks in the production process, the flow of materials and semi-finished products, the supply chain in logistics processes, support at the stage of planning production processes, production scheduling, or the elimination of waste.

One of the key aspects related to the production process is the proper determination of the number of production resources, which directly determines the justification for the implementation of the production process in terms of its profitability. Therefore, the study presents the possibility of using simulation software for comparing the number of production resources necessary to execute the assumed production plan with the results obtained using analytical methods, with particular emphasis on the FlexSim simulation software (comparative analysis). The choice of software is justified by its availability, as well as by its extensive library. In connection with the above, a broader analysis of the possibilities of using simulation software in the scope of research work was carried out. Therefore, a bibliometric analysis was conducted, which is a scientific data mining method that allows for the analysis of specific data [31,32], in this case, data devoted to the use of FlexSim simulation software. For this purpose, the keyword was “FlexSim”, and the exploration was carried out using two databases in which peer-reviewed studies are published, i.e., Scopus and Web of Science. The time frame for the analyses was 2010–2024. Based on the data analysis (Figure 1), it can be concluded that the number of publications using FlexSim simulation software is increasing, especially since 2017, compared to that available between 2010 and 2016. The largest number of publications in the case of the WoS database is registered in 2017–2018 (499 and 373 studies, respectively), while in the case of the Scopus database, the largest number was registered in the year 2023 (60 studies). The largest number of publications in the case of the WoS database was recorded in the following categories: Computer Science Theory Methods, Engineering Electrical Electronic, Engineering Industrial, Engineering Manufacturing. Therefore, recognizing the application possibilities of simulation software, it was used in the remaining portion of the work.

3. Materials and Methods

3.1. Description of the Research Problem

A case study is defined as the study of a single unit in order to generalize the results to a larger number of units characterized by similar features. In connection with the above, a case study is understood as a special way of analyzing a specific group of variants and not a way of analyzing them or modeling the cause-and-effect relationships between them. A case study aims to better understand the specificity of the problem characterized by individual features of the variant, which would be impossible in the case of only theoretical considerations [33,34]. The study presents an analysis of the actual process of manufacturing rotating parts for use in the machine industry (case study). The production process that was the subject of the analysis included six production operations carried out on individual, non-automated production stations. The duration (unit time and preparation–completion time) of individual production operations was determined at the stage of creating technological documentation of the production process. In order to precisely estimate its production capacity, the production plant assumed a potential delay for each operation, amounting to 5% of its duration. Additionally, the labor costs of the stations involved in the production process were determined for each production operation (Table 2).

Table 2 lists six technological operations marked as 10–60. Operation 10 involved cutting a rod with a diameter of ϕ = 50 mm and a length of l = 200 mm using a band saw. Operation 20 included the technological process of rough turning and shaping using a conventional lathe. Operation 30 included making a keyway with a diameter of ϕ = 45 mm using a vertical milling machine. Operation 40 included the process of polishing selected surfaces in accordance with the technical documentation. Operation 50 involved removing sharp edges resulting from previously performed subtractive machining operations. The last operation, Operation 60, was the quality control of the finished product. In addition to production (technological) operations, the production cycle also included auxiliary and transport activities, as well as break time during one production shift.

The work on the production process was carried out in one work shift, the duration of which was 7 h and 30 min; the planned production volume was 3500 pieces; the maximum production plan implementation time was set at 75 working days; the available work time A_wt = 562 h and 30 min; the production batch size was set by the production plant at 30 pieces.

The flow of semi-finished products in the production process took place in a parallel system, in which the product, after completing the production operation, is immediately transported to the next work station (production operation). Inter-operational transport was carried out by operators of individual production stations. After completing a given operation, the semi-finished products were transferred from the production station to the inter-operation warehouse, which was located between groups of stations carrying out subsequent production operations (10–60). The area of realization of the production process, in addition to the work surface, included the input materials warehouse (A) and the finished product warehouse (B) (Figure 2).

3.2. Methodology of the Study

The achievement of the assumed goal was possible thanks to the development of a five-stage methodology created by the authors of this study (Figure 3).

During the first stage, an analytical method was used to define the number of production stations necessary to execute the assumed production plan P_s.

(1)${\mathrm{P}}_{\mathrm{s}}={\displaystyle \frac{{\mathrm{O}}_{\mathrm{p}}}{{\mathrm{A}}_{\mathrm{w}\mathrm{f}}}},$

where P_s—complete occupation of the production station; A_wt—available work time.

(2)${\mathrm{O}}_{\mathrm{p}}=(\mathrm{n}\ast {\mathrm{t}}_{\mathrm{p}\mathrm{c}}+{\mathrm{t}}_{\mathrm{u}}\ast {\mathrm{P}}_{\mathrm{v}})$

where t_pc—preparation and completion time; t_u—unit time; n—number of production batches; P_v—production volume.

The second stage consisted of selecting simulation software. For the purposes of the study, FlexSim 22.2.1 software was used, which has extensive capabilities in the area of simulation of production processes and analysis of their production parameters. The choice of FlexSim simulation software was motivated by its availability, as well as by the possibilities it offers, allowing for the achievement of the assumed goals of the study, which included the following:

• The possibility of building the model logic in the form of programmed block diagrams;

• Analysis of the system through generated diagrams, reports, and spreadsheets;

• The possibilities of the software for use in the field of industrial production, i.e., analysis of the production line load, analysis of the size of inventories in the supply chain, mapping of the production flow, mapping of the process of supplying work stations with raw and other materials, and analysis of the load of production stations and inter-operational warehouses.

In the third stage, data for the number of production resources necessary to execute the assumed production plan, which were obtained in the first stage, were implemented in the simulation software. The simulation carried out, reflecting the actual conditions of the production process, was aimed at identifying the parameters obtained by the production process in terms of its efficiency, the number of inter-operational stocks, and its production costs. As a result, it was possible to identify bottlenecks in the production process and indicate potential areas for improvement, which is emphasized in the studies [8,9,14,15,19].

In the fourth stage, in the case of identifying areas that could be changed (e.g., bottlenecks), which would increase the efficiency of the production process, actions were taken to improve efficiency. The improvements introduced concerned changing the number of production resources and then verifying the results using computer simulation.

In the fifth stage, a comparative analysis of the results obtained using analytical methods and those generated by the FlexSim simulation software was performed, and its evaluation was carried out in terms of the reduction of the production process duration.

The proposed methodology (Figure 3) may constitute a basis for using the proposed strategy of actions in the analysis of data from other production processes.

4. Results

In accordance with the adopted methodology (Section 3.2), first of all, the number of production resources necessary to execute the assumed production plan was determined using analytical methods. Calculations related to determining O_p and P_s were determined based on Equations (1) and (2), while P_{s_cal} is a rounding to an integer referring to the number of positions required to realize the selected production positions (Table 3). To perform the calculations, the data presented in Table 2 were used.

Based on the results presented in Table 3, it was found that the largest number of stations necessary to engaged in order to implement the assumed production plan concerned operation number 20 (turning process) and was equal to five (P_{s_cal,} Table 3). The production operations which required only one station each were operations number 10 (material cutting), 40 (polishing), 50 (removing sharp edges) and 60 (quality control). The total number of production stations needed to complete the assumed plan was 12. It was also found that the occurrence of potential delays at the level of 5% of the operation duration does not affect the change in the number of production stations. Then, a simulation model was built in the FlexSim software consisting of the production stations, operators, inter-operational warehouse (queue), sources, warehouse of output and input materials, and infrastructure objects. Objects participating in the production process were arranged on the work board of the FlexSim simulation software in accordance with the actual state (Figure 4).

The parameters regarding the duration of the production operation from Table 2 were implemented for each object, taking into account the potential level of underestimation, i.e., up to 5% of the operation duration (Figure 5).

The last stage of creating the simulation model was the selection of statistics in the FlexSim software, allowing for the analysis of selected parameters generated by the manufacturing process (Table 4).

As part of the third stage of the research methodology, a computer simulation was carried out. For the purposes of the study, 20 replications (repetitions) of the production process simulation were assumed (Figure 6).

As a result of the data obtained, generated by the simulation software, it was found that the longest production process execution time was 575 h and 46 min. The shortest duration of the simulated process was 540 h and 41 min. The discrepancy between the production process duration in the simulation software resulted from taking into account a potential delay factor of up to 5% of the operation duration. Based on the analyses performed, in five of the replications, the results showed that it was not possible to implement the production plan within the designated time interval of 562.5 h (A_wt). Failure to perform a specific production batch size using the defined production resources does not indicate improper use of the method in the scope of the analytical determination of the production operation, but it did not take into account the inter-operational transport factor. As a result of the simulation performed without the use of means of transport, it was found that in each of the cases, it was possible to execute the assumed production plan, which is graphically presented in Figure 7.

In the case of the analysis of the level of inter-operational stocks in the warehouses, it was found that in the case of warehouse number 1 (Queue 1) and number 3 (Queue 3) there is a high level of inter-operational stocks at the level of 97–99% of the duration of the manufacturing process (Figure 8).

The highest percentage level of the utilization of the production stations was recorded for Operations 20 (turning process—station PO_20_1 ÷ 5) and 40 (polishing—station PO_40_1) and amounted to approximately 97% (Figure 9).

The results obtained in terms of the high level of use of inter-operational warehouses and stations performing Operation 20 (turning process) and 40 (polishing) indicate the occurrence of a bottleneck in the manufacturing process. The occurrence of a bottleneck in the manufacturing process may result in a decrease in process efficiency, while increasing production costs. In the case analyzed, the total cost of the production process amounted to USD 207,110.4.

In the fourth stage of the methodology, it was decided to implement additional production stations to perform Operations 20 (turning process), station PO_20_6, and 40 (polishing), station PO_40_2. The changes introduced were aimed at increasing the efficiency of the production process by reducing the bottleneck phenomenon, which was to ensure the implementation of the assumed production program within the designated time period. The use of FlexSim software to verify process parameters in connection with the planned change in the number of production resources was aimed at verifying the use of computer simulation at the stage of planning the production process and its evaluation. (Figure 10).

As a result of the simulation (number of replications: 20), it was found that in each case, the duration of the production process ensured the implementation of the production plan of 3500 pieces within 562.5 working hours (Figure 11).

As a result of using two additional production stations, a change was recorded in the area of the level of use of the inter-operational warehouses (Figure 12). In the case of warehouse number 3 (Queue 3), preceding operation number 40 (polishing), a reduction in the level of inter-operational stocks was observed from 99.57% to 11.26%. The decrease in the percentage share of the use of the inter-operational warehouse reduces the bottleneck phenomenon in the production process. It should be noted, however, that the implemented changes caused an increase in the level of inter-operational stocks on warehouses number 1, 2, and 4 (Queue 1, Queue 2, Queue 4). The largest percentage increase in warehouse use was observed in the case of warehouse number 2 (Queue 2), which increased from 4.43% to 15.84%.

In the case of the percentage share of the use of stations in the production process (Figure 13), the change applied to all stations. The reduction in the percentage share of the station was observed for operation number 40 (polishing—station PO_40_1 ÷ 2) and amounted to 59.63%, which indicates a decrease of about 37 percentage points.

The costs of the production process are correlated with the time function; therefore, they were also reduced by about USD 8356.0 compared to the results generated by the simulation model that does not take into account additional production stations. The maximum production cost was USD 19,8754.4.

The obtained results, generated by the FlexSim simulation software, regarding changes in the production process parameters, including the reduction of the production process duration and consequently, the increase in the number of production resources, indicate the validity of using computer simulation at the production planning stage.

5. Discussion

Computer simulation is one of the tools that allows for the low-cost analysis of manufacturing process parameters. Scientific publications describe methods of using simulation to verify selected solutions in the field of manufacturing process management [1,8,14,15,17,18,23,29] or logistics [7,9,16,20,22], as well as those related to the analysis of bottlenecks [8,9,14,15,19]. In each case, computer simulation was an important tool for conducting analyses related to the implemented production tasks.

This paper analyzes the parameters of the manufacturing process at the production planning stage in terms of selecting the appropriate number of production resources using the FlexSim 22.2.1 simulation tool, which is often used by researchers to solve this type of manufacturing problem [1,8]. The analytically determined numbers of stations necessary to perform a given operation were implemented in the FlexSim simulation software, which enables the verification of the correctness of the determination of the number of production stations. Based on the analysis, it was found that it was not possible to produce the assumed production plan within the designated time period in every case. Failure to execute the assumed production plan does not indicate improper use of the method in the analytical determination of the number of production stations. However, it does indicate failure to take into account all factors affecting the duration of production (i.e., means of transport). As a result of the high complexity of manufacturing processes and many factors that are difficult to predict, it seems justified to use computer simulation as a tool to support the planning of production processes.

It should be noted, however, that there are many other solutions in the field of production process management that allow for the elimination or reduction of bottlenecks in the production process, which in turn allows for shortening the time and reducing the costs of production processes. The most commonly used methods for balancing the production line include the following [35]:

• Redistribution of all or part of the observed operations to the previous or next operation;

• Improvement of the working methods and thereby, the reduction of the time per unit;

• Analysis of data regarding the duration of individual production operations using statistical distributions, allowing for the identification of the actual duration of production operations (especially at the planning stage of the production process).

The activities described above may constitute subsequent stages of research, allowing for the selection of the best among the analyzed solutions using computer simulation tools.

6. Conclusions

Based on the data regarding the duration of the production process, the number of production stations necessary to implement the assumed production plan, i.e., 12 in total, was determined using analytical methods. The obtained data was implemented into the FlexSim simulation software, and then a simulation was carried out. As a result, data were obtained indicating that the duration of the production process is between 575 h and 46 min and 540 h and 41 min, which indicates the potential possibility of not implementing the assumed production plan within 62 h and 30 min. In connection with the above, the number of production resources was increased for Operations 20 (turning process) and 40 (polishing). These operations were burdened with the highest level of utilization of all the production stations, amounting to about 97%, and the inter-operational warehouses preceding these production operations were characterized by a high level of utilization at the level of 97–99%, which indicated the occurrence of a bottleneck in the production process. As a result of the introduced changes, i.e., implementing additional production stations, a reduction was observed in the production process duration to approximately 450 h and 20 min, thus ensuring the possibility of implementing the assumed production plan. In addition, the percentage share of the production stations in Operation number 40 (polishing) was reduced to the level of 59.63%. As a result of introducing improvements to reduce the production process duration, a decrease in the costs of the production process was noted from the level of approximately USD 207,110.4 to USD 198,754.4. The use of computer simulation in the case subjected to analysis allowed for the verification of production resources, determined by analytical methods, and the implementation of improvements allowing for the shortening of the production process duration and its costs. Therefore, it is justified to use computer simulation to solve problems related to various aspects of the functioning of an enterprise. Computer simulation is a tool that enables decision makers to make decisions based on the data obtained as a result of analyses, the effect of which is directly related to the actual data concerning the production process being carried out.

At the same time, attention should be paid to the limitations and potential dangers associated with the use of computer simulation at the stage of planning the production process. The convergence of the results obtained as a result of the simulation with the parameters generated by the actual production process is conditioned by the appropriate quality of the input data implemented during the creation of the model. It is also necessary to take into account the dynamics of changes occurring in production processes, especially those with a low level of automation. Delays may lead to an extension of the time of execution of selected production operations, which may consequently have a negative impact on the obtained results. In connection with the above, it is important to continuously analyze the parameters of the production process and update the input data in real time, allowing for obtaining results with the highest possible level of accuracy in relation to the actual production process.

For the purposes of the study, the influence of the number of production resources on the reduction of time and costs of the production process was analyzed. Due to the numerous changes taking place in production processes analyzed using simulation software, it is possible to verify other solutions in terms of, e.g., the layout of production stations or organizational activities, which may constitute a further direction of research.

The methodology of the authors’ own research proposed in the study is adapted to the specificity of the proposed research problem, as presented in Section 3. At the same time, the high level of universality of the proposed methodology may constitute the basis for using the proposed strategy of action to analyze other production processes and verify the results for other research subjects.

Author Contributions

Conceptualization, S.P.; methodology, S.P.; software, S.P., T.M. and A.F.; validation, S.P., T.M. and A.F.; formal analysis, S.P. and T.M.; investigation, T.M. and S.P.; resources, T.M., S.P. and A.F.; data curation, T.M., A.F. and S.P.; writing, S.P., T.M. and A.F.; writing—review and editing, S.P. and T.M.; visualization, T.M. and S.P.; supervision, T.M., S.P. and A.F.; funding acquisition, S.P. and A.F. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. Application of FlexSim software in scientific research—bibliometric analysis (2010–2024).

Figure 2. Production process diagram.

Figure 3. The methodology of the authors (five stages).

Figure 4. Model of the production process in the FlexSim simulation software.

Figure 5. Implementation of data regarding the duration of selected production operations.

Figure 6. Simulation of the production process in the FlexSim simulation software.

Figure 7. Duration of the production process based on the obtained simulation results.

Figure 8. Percentage of usage of inter-operational warehouses (queue).

Figure 9. Percentage of utilization of production stations.

Figure 10. Model of the production process including additional production stations.

Figure 11. Duration of the production process before and after the implementation of additional production stations.

Figure 12. Percentage of use of inter-operational warehouses (queue) after implementing additional production stations.

Figure 13. Percentage of production station utilization after implementing additional production stations.

Table 2

Summary of production process parameters, taking into account the types of operations.

Operation Number	Operation Description	Duration of Operation		Costs [USD/h]
		Unit Time [h]	Preparation and Completion Time [h]
10	Cutting a 50 mm diameter rod to a length of 200 mm using a band saw.	0.10	0.30	15
20	Rough turning and shaping of the product in accordance with the technical documentation.	0.75	1.00	40
30	Milling a 15 mm long keyway on the main surface of the product.	0.30	0.45	35
40	Polishing selected product surfaces.	0.14	0.25	25
50	Removing sharp edges.	0.05	0.10	10
60	Final inspection of the finished product.	0.05	0.08	10

Information on the duration of production operations and the labor costs of the stations were obtained from the enterprise that was the subject of the analysis.

Table 3

Estimated number of production resources.

Operation Number	10	20	30	40	50	60
Op	350.10	2742.00	1172.65	519.25	186.70	184.36
Ps	0.62	4.87	2.08	0.92	0.33	0.33
Ps_cal	1	5	3	1	1	1

Table 4

Indicators used in the analysis of parameters in the FlexSim simulation software.

Indicator	Characteristics
Duration of the process	The duration of the production process from its initiation to its completion, taking into account unit time, preparatory and completion time, inter-operational transport time, and breaks.
Level of utilization of inter-operational warehouses	Information about a high level of inter-process inventory in the warehouse in the case of parallel flow may indicate a bottleneck in the process.
Level of utilization of production stations	It shows the level of utilization of production stations during the production process. A high result may indicate that the station is overloaded.
Financial analysis of the process	Financial analysis indicates the costs related to the production resources used during the production process.

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