*Article* **Determination of Benefits of the Application of CMMS Database Improvement Proposals**

**Ladislav Stazi´c \*, Nikola Raˇci´c, Tatjana Stanivuk and Ðorde Dobrota ¯**

**Featured Application: This article concludes the study of CMMS databases and the measures the authors developed to improve data quality in these databases. It includes a calculation of the benefits that the proposed measures can have for improving data quality in CMMS databases. The proposed measures have already been published in several articles.**

**Abstract:** Computerized maintenance management systems (CMMSs) are software packages that support or organize the maintenance tasks of assets or equipment. They are found in the background of any ship maintenance operation and are an important part of maintenance planning, spare parts supply, record keeping, etc. In the marine market, there are a number of CMMSs that are competing fiercely to program a better and more modern program that will capture the market, which has been accompanied by published analyses and scientific papers. At the same time, the quality of the data entered into CMMS databases is questionable, a fact that has been ignored in practice and scientific circles; until recently, there were no published analyses and there was no way to measure the quality of the data entered. This article presents two proposals for improving the quality of CMMS databases and calculates their potential benefits. By implementing the first proposal, the evaluation methodology for the ship's Planned Maintenance System database, between 10% and 15% of databases will have significant financial or safety benefits. This measure will also have an impact on more than 40% of the other databases that can also be improved. The second proposal will have a smaller impact of only 4%. The overall benefit of these proposals is to improve more than 60% of the databases and will result in a significant increase in safety or financial savings.

**Keywords:** computerized maintenance management systems; planned maintenance; database; benefits; quality

#### **1. Introduction**

Ship maintenance is one of the most researched topics in the industry, and numerous articles have been published on its various aspects [1–3]. An important part of the organization of successful maintenance is performed with the help of CMMS (Computerized Maintenance Management System). The term started long ago as a simple Planned Maintenance System (PMS) and gradually evolved into computerized systems with many modules and multiple functions. Today, there are many different computer programs for CMMS in the maritime industry, the total number of which is estimated to be more than 70. These systems differ in design, quality, and functionality.

PMS and CMMS as tools to reduce downtime and maintenance costs have been widely researched [4–6]. Research has shown that the adoption of PMS brought tremendous financial and safety benefits, and the adoption of CMMS continued this process [7]. At the same time, it is very difficult to find data to measure the benefits that have resulted from the introduction of both systems. The rare values published in scientific articles vary considerably, explaining improvements in maintenance from 30 to 50% (variations of more than 50%) depending on the example (case studied) [8,9].

**Citation:** Stazi´c, L.; Raˇci´c, N.; Stanivuk, T.; Dobrota, Ð. Determination of Benefits of the Application of CMMS Database Improvement Proposals. *Appl. Sci.* **2023**, *13*, 2731. https:// doi.org/10.3390/app13042731

Academic Editor: José A. Orosa

Received: 31 January 2023 Revised: 18 February 2023 Accepted: 19 February 2023 Published: 20 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Faculty of Maritime Studies, University of Split, 21000 Split, Croatia

**<sup>\*</sup>** Correspondence: lstazic@pfst.hr; Tel.: +385-(0)21-619-467

PMS in paper form was a significant step in improving maintenance and enhancing ship safety. The introduction of CMMSs in shipping brought improvements in terms of ease and speed of communication with the office, easier monitoring of maintenance and procurement, or simplicity of data exchange. Since the communication is mostly done via a satellite link, the size of the exchanged data packets must be very small, usually less than 200 kb [10]. The size of the data packet rarely exceeds the specified values even in the case of major changes to the database. This small size of data packets allowed the introduction of CMMS applications running in the cloud and becoming more and more popular in this market [10].

CMMSs or, as they are also known, computerized Planned Maintenance Systems (PMSs) are in daily use on a wide variety of ships. Although they are widely used, there is neither adequate scientific follow up of these systems nor systematic analysis of the systems and their data. Planned maintenance in shipping was addressed in scientific articles in the late 20th century, mainly in Europe and North America [11–13]. The research topics at that time focused mainly focused on the application of CMMS and aspects of the system used. Today, authors still analyze and research similar topics [14,15]. Another frequently researched topic is the performance of different CMMSs and their comparison [16,17].

Alan Mortimer, a former UK Chief Engineer, echoing various opinions on the quality of CMMS, wrote: "Commercial Planned Maintenance (PM) systems are a collection of very variable beasts, some good, some bad, and some indifferent" [18].

Although this opinion is widely held in the maritime industry, it is hard to believe that there are products (in this case, computer programs) on the commercial market (i.e., they have survived competition) that are poor and do not meet the needs of users. According to this statement, the research team assumed that the cause of the problem can be found in different places and consists of two known facts. In their research, the researchers came across two claims that together describe the problem much better. The first possible cause is declared by Davies, who states that computerization of poor management systems only leads to poor results more quickly [19]. The second possible cause is the well-known fact of the GI–GO (Garbage In–Garbage Out) effect, which is well described in the article by Kilkenny and Kerin [20].

This assumption that poor databases are the root cause of all CMMS problems formed the basis for the research conducted by the CMMS research team at the Maritime Faculty in Split. A large number of CMMS databases had to be examined and analyzed to verify this assumption. The quality of databases and their impact have been studied by many authors [21–24], but only for the land industry. This research topic is very limited or non-existent in the maritime industry.

The first discovery at the beginning of the research was that a large number of ship databases have very poor data and numerous problems. At this point, the team faced a major problem, a major challenge to solve. Although it was clear that the databases were in poor condition, their conclusion was based only on subjective opinion and personal experience. There was no tool or method in the industry to evaluate CMMS databases, measure their quality, identify areas for improvement, and determine the steps needed to improve database quality.

To solve the problem, the team's first task was to develop a universal tool to assess the quality of the CMMS database. The main method used to create the new tool was DQA (Data Quality Assessment), shown in Figure 1, which is based on the idea designed by Pipino et al. [25].

DQA is a methodology developed to provide the general principles for the definition of data quality metrics [26] and the method; according to the authors of the cited text (Batini et al. and Ballou et al.) [27], the main characteristic of the methodology is that it is tailor-made, created specifically for each task. The solution where "one size fits all" in different circumstances cannot be a solution [26]. There are many examples of the DQA methodology in practice and the use of the methodology for different aspects and different types of research [28,29].

**Figure 1.** DQA in practice, based on [25].

The research team encountered an interesting problem in studying databases to determine how the database improvement proposal program works. In examining 17 vessels from two companies, seven similar improvement requests were found on three vessels, each claiming that there were no manufacturer's maintenance schedules on board and requesting that the company provide them. The number of improvement requests for this type of deficiency is relatively low, mainly due to the fact that both companies only purchase new vessels. This type of issue often occurs when a company buys a used vessel and the previous crew takes all the operating manuals with them, along with the maintenance logs, data, etc., so the new crew starts from scratch, often without the manufacturer's operating manuals. These seven deficiencies were identified during the CMMS system implementation phase and then reported to the company, which worked to correct them. Five of these deficiencies were successfully corrected, while two were not. The reason for the failure to correct this issue was not identified, although the company's SMS was reviewed to determine whether it contained instructions or recommendations for correcting this deficiency.

Consequently, in five out of seven cases, the maintenance plan and spare parts were added to the CMMS by copying the data from the manual received, while in two cases, the items were still missing. Reviewing various articles and books, the research team found that no one has yet answered the question of how to create the equipment maintenance plan without using the manufacturer's manual.

From the above, it can be concluded that a significant improvement in database quality (read: maintenance and safety) can be achieved if these two database problems are solved. These tasks are the focus of the research team, and this paper presents the potential benefits of these two solutions. The design and methods used to create the Evaluation Methodology for the Ship PMS are described in Section 2, while the methods used to solve the second problem are explained in Section 3. The results and discussions are presented in Section 4, followed by the Conclusion, which summarizes the overall benefits of applying these proposals and highlights the importance of the research.

#### **2. The Evaluation Methodology**

Another example of the application of the DQA methodology is the evaluation methodology for the ship's Planned Maintenance System database. The methodology was developed at the beginning of the research to establish firm rules for CMMS database assessment. All DQA assessment strategies [26] were considered when creating the methodology [30]:


A tool called the Evaluation Methodology for Ship PMS [30] was developed and field tested to verify its functionality. It consists of the questionnaire with thirty questions divided into six groups: Machinery and Equipment, Jobs inside DB, Special jobs and Rules, DB Jobs general, Spare Parts, and Miscellaneous (Table 1). In front of each question, there is a field (mark) indicating the importance of the question for the quality of the database. The "traffic light" principle (R, Y, G) is used to determine the colors in the field and to describe the importance of the question; red mark has the highest importance, and the deficiencies revealed by these questions have a significant impact on the quality of maintenance. Any deficiencies uncovered by these questions should be taken seriously and corrected to improve the database and the quality of maintenance. The questions with the yellow mark are of medium importance. This group of questions has a lower impact on database quality, and the deficiencies revealed by these questions mainly impact user workload, while the impact on maintenance quality and reliability is negligible. The deficiencies revealed by these questions should be corrected due to unnecessary work of staff [31], which may cause aversion to the system. The questions should be answered with a mark from one to five.


**Table 1.** The questionnaire.


#### **Table 1.** *Cont.*

The marks should have the following meaning:

Mark 1—Completely negative evaluation result;

Mark 2—Predominantly negative evaluation;

Mark 3—Predominantly positive evaluation with a significant number of irregularities;

Mark 4—Predominantly positive evaluation with a small number of irregularities;

Mark 5—Completely positive evaluation.

Questions rated five and four are considered satisfactory and require no changes to the database. Questions rated four have room for improvement, but DB changes are not recommended (there will be no significant quality improvement). Questions rated three, two, or one are considered unsatisfactory and data improvement should be made here. The schedule for data changes in the database should correspond to the color schedule (R, Y, G).

After the development of the methodology, serious efforts were made to test it in practice and to study various aspects of its application. The methodology was used (from 2017 to 2019) to analyze the state and quality of forty-four CMMS databases in five different shipping companies operating different types of vessels (one company operates passenger vessels, two companies operate a mix of bulk carriers and tankers, one company operates bulk carriers, and one company operates VLCCs). Testing of the methodology in different companies, with different working practices and methods, and on different types of vessels has shown that it can be used as a universal tool for evaluating CMMS databases and paper-based PMSs.

After testing, the following claims about the methodology were made and verified:


#### *Evaluation Results*

The results of the evaluation of forty-four CMMS databases were published in the article [33]. The testing of the functionality of the methodology is described in the same article and an analysis of the related results is presented. Further analysis of the obtained results was not performed, nor was an analysis of the identified deficiencies. Therefore, the necessary conclusions for maintenance planning that could affect the quality of maintenance were not derived from the evaluation. The deficiencies identified during this evaluation are listed in Tables 2 and 3.


**Table 2.** Deficiencies discovered in companies A, B, and C.


**Table 3.** Deficiencies discovered in companies D and E.

Tables 2 and 3 reflect this breakdown and represent a cumulative analysis of the identified deficiencies. Each row represents a database, while the columns reflect the total number of deficiencies identified, sorted by the scores obtained and indicated by the color of the group.

In accordance with the recommendations for the application of the methodology described above, all deficiencies rated as four are considered minor and no action is required to correct them. Notwithstanding the fact that no action is required, the CMMS can still be improved in these areas. Tables 2 and 3 show that there is not a single area where no deficiencies were identified, i.e., areas can be improved. At the same time, the lowest number of deficiencies was found to be four, and this was in only one database.

Since the scoring methodology recommends ignoring all items rated four (i.e., there is no need for improvement actions in these areas), new tables have been created (Tables 4 and 5) that include only deficiencies rated three or worse, and all green and yellow boxes have been removed. These tables still contain a very large number of databases and a large number of deficiencies.


**Table 4.** Serious deficiencies in databases A, B and C.

**Table 5.** Serious deficiencies in databases D and E.


The analysis of Tables 2–5 shows that the analyzed databases have a very large number of deficiencies; in total, there were 220 major deficiencies in the analyzed databases, of which 47 were rated one, 30 were rated two, and 143 were rated three.

Further reflection on the results presented in Tables 1–4 leads to the following findings:

• When evaluating the databases based on methodology, deficiencies were found in all of the databases examined;


Further review of the assessment results showed that Company D was not paying enough attention to the CMMS, i.e., it had not recognized the benefits that the system can provide.

These poor assessment results show that the CMMS in Company D was neglected both in the offices and on the ships.

Since it is the largest of the companies studied with a large number of vessels, these results could affect the objectivity of the entire research. In order to obtain the most objective picture, the results of the evaluation of Company D's vessels were excluded from the final consideration.

After excluding Company D's vessels from the analysis, the following picture emerges:


It can be concluded that more than 60% of all databases could be improved, 16% of them in more than one area. The results of this analysis show that only 1/3 of CMMS databases were in good condition. These poor results were not unexpected, because the only other information found about the condition of CMMS databases of ships declared 1/4 of the databases to be good [34].

#### **3. CMMS Development Problem**

A possible solution to the missing books problem was published in two articles [1,35], the first [1] showing the preparation of the methodology and the second [35] showing the creation of the maintenance plan.

Fault Tree Analysis (FTA) [36,37] is a widely used method for evaluating the reliability of systems [38], which is used either as static or dynamic. The method is also used to analyze fault causes, improve early fault detection, and improve fault diagnosis during engine operation by reducing false conclusions and inappropriate corrective actions [39]. In this part of the study, the method is used to analyze the turbocharger system of marine diesel engines to identify possible faults in the turbocharger system and determine areas (components) that should be serviced. In this study, the faults identified with the FTA analysis are simulated using the Wartsila-Transas 5000 engine room simulator on the propulsion system of the tanker LCC (Aframax) with the main engine MAN B&W 6560 MC-C [40]. The use of the E/R simulator together with the FTA simplifies the preparation of the fault list and allows its verification from different working aspects.

By combining these two tools, a comprehensive fault list of the turbocharger system of marine diesel engines is created and analyzed in detail. The article [1] once again shows that FTA is extremely useful and practical in analyzing system reliability, energy efficiency, and maintenance costs.

After making a comprehensive list of the faults of the turbocharger system of a marine diesel engine and analyzing what maintenance work needs to be done to avoid these faults, it was necessary to derive the maintenance schedule for the system from the fault list. Each fault from the FTA list is analyzed, and then appropriate preventive maintenance activities

are assigned to prevent the occurrence of each fault, resulting in a detailed maintenance plan for the turbocharger system of the marine diesel engine. Several maintenance plans were prepared by the experiment participants (authors of the articles), and each author used his or her own (personal) experience in marine engineering to prepare the maintenance plan.

These plans were compared and a slight variation was found in the maintenance plans for different tasks. These differences are attributed to the different experiences and practices of the authors [41]. To verify the obtained results, the maintenance plans prepared by the authors using the FTA list were compared with the maintenance instructions for the turbocharger system of the marine diesel engine [42,43]. The comparison showed that these schedules differ slightly from the manufacturer's maintenance recommendations, but the overall verdict is that they are very similar and the end goal is achieved in both cases.

The conclusion of this part and the contribution to the overall objective is to show that FTA combined with engineering experience can be a substitute for missing manufacturer's maintenance recommendations when creating the CMMS database. Although the newly created maintenance plan is not the same as the manufacturer's recommended plan, it is very close to it and is a good substitute for it.

#### **4. Benefits of These Proposals**

The first step in improving the entire CMMS system is a detailed review of the database and the data it contains using the Evaluation Methodology for Ship PMS (Figure 2).

**Figure 2.** CMMS database evaluation process.

This will uncover all the data needed for the improvement effort. This requires expertise, i.e., a good knowledge of the computer programs used and a good knowledge of seamanship, more specifically, marine engineering.

The evaluation methodology for a ship's PMS [30] should be applied during the development of the CMMS database and during the use of the system to avoid deficiencies of the database and to allow proper use with all its benefits. They are relatively easy to calculate using the basic equation:

$$B = \frac{Nsd}{Nq \times Nv} \tag{1}$$

where:

*Nsd*—The number of discovered deficiencies;

*Nq*—Total number of questions;

*Nv*—The number of analyzed vessels.

The application of the methodology will result in the following:


In order to calculate the benefit of the second part of the research (missing books problem), it is necessary to determine how many books are still missing when the database is created. According to two database factories (companies that specialize in creating databases), this number varies. It depends on whether the ship is new or used, whether the data is in electronic or paper form, and where the ship was built, etc.

By studying all available databases according to [33] and calculating the number of these cases compared to the number of ship equipment, the estimated benefit of this part of the research will be the potential improvement of 4% of the databases (4% of the equipment will have a maintenance plan that will allow better maintenance of these systems).

The given value was calculated for newbuildings (all analyzed ships were taken as newbuildings), and the value of solving the problem of missing books for second hand ships remains open as a task for future analysis.

#### **5. Conclusions**

This paper presents two solutions to improve data quality in the CMMS database. The first and far more significant improvement proposal is the evaluation methodology of the ship's PMS, which allows a clear evaluation of the data quality and the identification of areas in the database that can be improved in order to improve the overall maintenance process. The significance of this proposal is that, for the first time, a tool has been created to clearly assess whether the CMMS data is valid and whether the assessment results are the same or similar, even if different people perform the assessment. By incorporating the vessel PMS evaluation methodology into the design and daily use of the CMMS database, the potential benefits described in Section 3 can result in thousands of dollars in maintenance savings if maintenance is not properly adjusted. At the same time, the impact on the safety of the vessel, crew, cargo, and environment can be measured in extremely large amounts (millions or more) if maintenance of certain equipment is properly adjusted and/or performed. The side effect of applying the methodology and improving the quality of the data in the database is to demonstrate to the crew that the CMMS is an important system on board and that it receives the attention it deserves, which further motivates the crew to work with the system on a daily basis. An accurate calculation of the value of this proposed improvement is reflected in the expected improvement of up to 60% of all CMMS databases, including up to 16% in more than one area.

The second proposed improvement is seemingly insignificant, but it is very useful in the case of second-hand vessels, especially those built in failed shipyards or equipped with equipment manufactured by failed companies. The actual financial impact of this proposal is very difficult to calculate after the fact, since each of the possible events can be expressed differently. The benefits of solving the missing books problem calculated in this paper are small, but not insignificant. According to the calculations in this paper, this benefit amounts to 4% of the equipment that will benefit from this proposal, i.e., 4% fewer potential failures and 4% less probability of severe damage.

The calculation of the benefits from the application of these proposals has been made very conservatively, assuming lower values for improvements and for vessels that are purchased as newbuildings. Regardless of how the benefits of these two proposals are calculated, it is clear that both proposals will reduce deficiencies in more than 60% of the databases, improve vessel maintenance, and increase vessel safety.

The main problem with the proposed methods is their current status. Despite the great potential for improvement and the fact that they are publicly available, they are not widely used in practice. The only demonstrated use in practice are the companies that the team contacted personally and the companies that acted as test companies. The next steps the team should take are to analyze why the measures have not been expanded and what should be done to expand their use.

**Author Contributions:** Conceptualization, L.S. and N.R.; methodology, L.S., N.R. and T.S.; validation, Ð.D.; formal analysis, L.S. and T.S.; investigation, L.S.; writing—original draft preparation, L.S., writing—review and editing, L.S., N.R., T.S. and Ð.D.; supervision, N.R., T.S. and Ð.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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**Stanisław Gucma, Maciej Gucma, Rafał Gralak and Marcin Przywarty \***

Faculty of Navigation, Maritime University of Szczecin, 70-500 Szczecin, Poland; s.gucma@am.szczecin.pl (S.G.); m.gucma@am.szczecin.pl (M.G.); r.gralak@am.szczecin.pl (R.G.)

**\*** Correspondence: m.przywarty@am.szczecin.pl

**Abstract:** Context: From the perspective of marine traffic engineering, a system of port waterways is composed of a set of waterways (port areas), such as approach channels, port entrance, inner fairways (port channels, rivers, lakes), turning basins and port basins of various terminals. The sea waterway must be adjusted to the navigation of specific types of ships, characterized by length, breadth, draft and airdraft. The primary requirement for shipping in sea waterways is the safety of navigation. Each sea waterway has traffic restrictions for the ships using it. These restrictions are called conditions of sea waterway operation or conditions of ship operation in the sea waterway. Problem: There are a number of empirical, deterministic or probabilistic methods to determine the safe width of maneuvering areas on port waterways. The direct application of empirical methods to determine the conditions for the safe operation of ships on the complex waterway, such as the Swinouj´ ´ scie–Szczecin fairway, was impossible due to the complexity of the waterway and various restrictions on its individual parts. Method: The paper presents the assumptions and calculation procedure of a method allowing for the determination of maximum safe parameters of ships in existing complex waterways. Results: The proposed method was used in the preparation of port regulations for the dredged and widened Swinouj´ ´ scie–Szczecin waterway. The results of these calculations are presented as a practical application of the method. Conclusions: This article defines conditions for the safe operation of ships in complex port waterways systems and presents the methodology for determining maximum safe parameters of ships in existing complex port waterways systems.

**Keywords:** maritime transport routes; safety of navigation; maritime transport; design of waterways; marine simulation; full-mission ship simulator; maritime traffic engineering; safe maneuvering area; safe operation of the ship; navigational risk

Received: 27 June 2022

Accepted: 28 July 2022 Published: 30 July 2022

**Citation:** Gucma, S.; Gucma, M.; Gralak, R.; Przywarty, M. Maximum Safe Parameters of Ships in Complex Systems of Port Waterways. *Appl. Sci.* **2022**, *12*, 7692. https://doi.org/ 10.3390/app12157692 Academic Editor: José A. Orosa

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

#### *1.1. Waterway System in Marine Traffic Engineering*

The sea waterway must be adjusted to the navigation of specific type of ships, characterized by length, breadth, draft and airdraft [1]. The primary requirement for shipping in sea waterways is the safety of navigation [2]. Navigational safety comprises all of the issues related to smooth ship conduct from point A to point B of the sea route.

The sea waterway system in marine traffic engineering is composed of a number of separate sections (*n*). A complex port waterways system is usually composed of the following sections:

	- Each of the waterway sections consist of three basic elements [3]:

waterway, and refer to:

These elements affect each other and have an important impact on the system characteristics. Each sea waterway has traffic restrictions for the ships using it [4,5]. These restrictions are called conditions of sea waterway operation or conditions of ship operation in the sea


The conditions for the safe operation of ships in a port regarded as a system composed of various types of waterways are dependent on the conditions of the safe operation of ships in each specific waterway section within the port. The parameters of each waterway within the port determine the conditions of the safe operation of ships maneuvering in that area [6].

There are a number of empirical, deterministic or probabilistic methods to determine the safe width of maneuvering areas on port waterways [3]. The most important methods include:


They are widely used, in particular, at the preliminary stage of waterway design. For example, the most widely known PIANC method was used during the initial localization of the LNG terminal in India [7] to determine the approach fairway in the Chinese port of Panjin [8] and to determine the parameters of fairways in Korean waters [9]. However, the use of these methods was limited to determining the parameters of one or a maximum of several types (sections) of waterways (e.g., straight sections, bends, turning basins).

The direct application of the above methods to determine the conditions for the safe operation of ships on a complex waterway, such as the Swinouj´ ´ scie–Szczecin fairway, was impossible due to the complexity of the waterway and various restrictions on its individual parts.

This article:


#### *1.2. Conditions of Safe Operation of Ships in Port Waterways Systems*

From the perspective of marine traffic engineering, a system of port waterways is composed of a set of waterways (port areas), such as approach channels, port entrance, inner fairways (port channels, rivers, lakes), turning basins and port basins of various terminals.

In a port regarded as a system of sea waterways, different areas make up separate waterway sections that can be grouped into several criteria [3]:


The conditions for the safe operation of ships in the *i*-th section of the waterway can be written as a set:

$$\mathbf{W}\_{i} = \begin{bmatrix} t\_{yp}, L\_{ci}, B\_{i\prime}, T\_{i\prime}, \mathbf{H}\_{sti\prime}, V\_{i\prime}, \mathbf{C}\_{i\prime}, \mathbf{H}\_{i} \end{bmatrix} \tag{1}$$

where:


$$H\_i = \begin{bmatrix} d/n\_{i\prime}s\_{i\prime} \ \Delta h\_{i\prime} \ V\_{wi\prime}, V\_{pi\prime}h\_{fi} \end{bmatrix} \tag{2}$$

where:


The conditions for the safe operation of ships passing through the waterways system consisting of a set of *n* sections can be written in this form:

$$\mathbf{W} = \begin{bmatrix} t\_{yp\_{\prime}} \, L\_{c\prime} \, B\_{\prime} \, T \, \_{\prime} H\_{st\_{\prime}} V\_{i\prime} \, C\_{i\prime} \, \mathbf{H} \end{bmatrix} \tag{3}$$

where:

*typ* — Type of 'maximum ship';

*Lc* — Maximum overall length of ships that can safely pass through the waterway system (port entrances);


$$H = \begin{bmatrix} d/n \ \text{s} \ \text{s} \ \text{ } V\_w \end{bmatrix} \tag{4}$$

where:

*d/n* — Allowable time of day in the waterway system;

*s* — Allowable visibility in the waterway system;

*Vw* — Allowable wind speed in the waterway system.

Additional restrictions may occur, causing temporary changes in vessel traffic in the waterway system (or restrictions of maximum ship parameters). These are:


The conditions for the safe operation of ships, defining the parameters of waterway system components, are defined separately for one-way and two-way traffic. In two-way traffic sections, the conditions for the safe operation of fairways can be written as follows:

$$\mathbf{W}\_{i} = f\_{2} \begin{bmatrix} t\_{yp'}^{in} L\_{c}^{in}{}\_{}^{}, B^{in}{}\_{}, T^{in}{}\_{}, V\_{i}^{in}{}\_{}, C\_{i}^{in}, H\\ t\_{yp}^{out}, L\_{c}^{out}{}\_{}, B^{out}{}\_{}, T^{out}{}\_{}, V\_{i}^{out}{}\_{}, C\_{i}^{out}, H \end{bmatrix} \tag{5}$$

where *in* means a ship entering the port and *out* refers to a departing ship.

The state vector of safe ship operation conditions in waterway systems is a function of the parameters of this system [10,11]:

$$\mathbf{W} = F \begin{bmatrix} \mathbf{A}\_i \\ \mathbf{N}\_i \\ \mathbf{Z}\_i \end{bmatrix} \tag{6}$$

where:

*W* — Conditions of safe ship operation (state vector); *<sup>A</sup><sup>i</sup>* — Subsystem of *<sup>i</sup>*-th section of the waterway, determining the area parameters and the

type of maneuver performed in that area (area subsystem);

*<sup>N</sup><sup>i</sup>* — Subsystem of ship position determination, characterizing parameters of navigational systems in use (navigational system);

*Z<sup>i</sup>* — Subsystem of traffic control, characterizing its parameters and waterway capacity.

In cases where port waterways are covered by the identical system of regulations, each of the waterway sections consist of two basic components [12,13]:


These elements affect each other and have a vital impact on the system characteristics. The parameters of subsystems of individual port waterway sections determine the conditions for the safe operation of ships in the waterway system:

$$\mathbf{W} = F \begin{bmatrix} \mathbf{A}\_i \\ \mathbf{N}\_i \end{bmatrix} \tag{7}$$

Conditions for the safe operation of ships in seaports are subject to two restrictions [11,12]:

1. The basic maximum parameters of ships that can safely pass through the waterway system cannot be greater than maximum parameters of ships safely passing through all of the sections of the system. Therefore:

$$\begin{aligned} L\_c &= \min\_i L\_{ci} \\ B &= \min\_i B\_i \\ T &= \min\_i T\_i \end{aligned} \tag{8}$$

2. The hydrometeorological conditions that allow for maneuvering in the given waterway system of ships with maximum parameters are identical. This applies to the time of day (*d/n*), visibility (s) and wind speed (*Vw*).

#### **2. Methods**

The problem of maximum safe parameters of ships arises in the case of the construction, conversion or modernization of a port waterways system. This particularly applies to complex port waterways systems, composed of several port basins (cargo handling terminals), fairways (straight sections and bends) and turning basins.

Individual sections of waterways (system components) differ in technical and operational parameters:


In addition, mooring ships are taken into account in port basins and quays or piers located along the fairways of the system, anchorages and lay-by berths.

A complex port waterway system is usually composed of the following sections:

• Approach channel (from an anchorage);


In order to take into account all constraints on individual sections of the fairway, the following procedure is proposed. It allows us to determine the maximum safe parameters of ships that may use the system between specific turning basins:

1. Preliminary determination of ships' maximum drafts in specific fairway sections:

$$T\_{\rm ui} = h\_i - \Delta\_{\rm ni} \tag{9}$$

The underkeel clearance Δ*wi* was determined for the pre-defined ship speed *Vwi*.

2. Determination of maximum overall lengths *Lco* of ships in turning basins, taking into account the possibility of turning in port basins of the terminals; location of turning basins in the fairway accounting for the length overall of ships safely turned:

$$L\_{\odot} \max \mathbf{x}, \dots, \; L\_{\odot} \min \mathbf{n} \tag{10}$$

It was assumed that maximum lengths of turning ships decrease along with the increase in turning basin distance from the fairway entrance.

	- First section: from port entrance (into fairway) to the turning basin where the maximum length ship can be turned (*Lcomax*);
	- Next sections were determined between subsequent turning basins in the fairway. Further calculations were made separately for each section between the turning basins.

$$L\_c^{zak} = \min\_z L\_{cz} \tag{11}$$

$$B^{zak} = \min\_z B\_z \tag{12}$$

5. Determination of maximum safe breadths of ships in straight one-way fairway sections (*Bp*); here, we should assume the safe ship breadth for all straight fairway sections (*p*) from the considered turning basin as:

$$B^{pr} = \min\_{p} B\_p \tag{13}$$

6. Determination of maximum safe lengths and breadths of ships in particular sections between the considered *j*-th turning basin:

$$L\_{c\circ} = L\_c^{zak} \text{ for } L\_{c\circ} \prec L\_{c\circ} \tag{14}$$

$$B\_{\dot{j}} = \min\left(B^{zak}, B^{pr}\right) \tag{15}$$

7. Determination of ship's safe draft and allowable speeds in specific fairway sections of the considered waterway, taking into account ship and fairway section parameters. The ship speed in individual waterway sections is calculated from this formula:

$$T\_i = h\_i - \Delta\_i \tag{16}$$

The underkeel clearance is a function of ship speed and other parameters, fairway parameters and the performed maneuver:

$$
\Delta\_i = f(V\_i) \tag{17}
$$

The maximum safe draft of the ship in the considered *j*-th section of the waterway to be assumed:

$$T\_i = \min\_i T\_i \tag{18}$$


$$\mathbf{W}\_{\mathbf{j}} = \begin{bmatrix} \mathbf{t}\_{\mathbf{y}\mathbf{p}\mathbf{j}\prime} \, L\_{\mathbf{c}\mathbf{j}\prime} \, B\_{\mathbf{j}\prime} \, T\_{\mathbf{j}\prime} \, H\_{\mathbf{sr}\prime} \, V\_{\mathbf{i}\prime} \, \mathbb{C}\_{\mathbf{i}\prime} \, H \end{bmatrix} \tag{19}$$

The algorithm of the parameters determination is shown in Figure 1.

**Figure 1.** The algorithm of the process of determining maximum parameters of the ship in complex port waterways systems.

Notably, safe parameters of ships (*Lc*, *B*) maneuvering in turning basins and fairway bends can be determined by simulation or empirical methods [3,14,15]. Simulation methods using full mission bridge simulators are more accurate than empirical methods. In straight sections of the fairway, empirical methods are sufficiently accurate.

#### **3. Results**

The developed method for determining the maximum safe parameters of ships in complex port waterways systems was used for the calculation of the conditions for safe ship operations in the modernized Swinouj´ ´ scie–Szczecin fairway, dredged from 10.5 m to 12.5 m and widened. The requirements for determining the conditions for the safe operation of ships included parameters of 'maximum ships' passing through this fairway.

The modernized Swinouj´ ´ scie–Szczecin fairway is 68 km in length, and 12.5 m deep. It includes the Swinouj´ ´ scie entrance channel and a number of straight sections and bends with various technical parameters. The main sections of the fairway, together with their parameters, are shown in Table 1. The basic sections are connected with transition sections, on which, fairway parameters change. The whole waterway system comprises four turning basins. In addition, turning is possible in the Swinouj´ ´ scie–Szczecin fairway for ships entering and departing from two other ports (Figure 2):



**Table 1.** Sections and the parameters of the modernized Swinouj´ ´ scie–Szczecin fairway.

**Figure 2.** A simplified model of the complex port waterways system (four terminals).

Maximum safe parameters of ships passing through the Swinouj´ ´ scie–Szczecin fairway were determined in a procedure composed of the following steps:

1. Preliminary maximum draft of ships is:

$$T\_w = 11 \text{ m}$$

where the underkeel clearance (Δ*wi* = 1.5 m) was calculated using the method of components and adopting:


The maximum ship lengths in the turning basins of Northern, Mieli ´nska and Przesmyk Orli were determined using simulation methods, whereas the maximum ship length in the Parnica Turning Basin was estimated using the empirical method.

Example results of simulation tests are presented for the Northern Turning Basin. The tests were conducted on the FMBS simulator from Kongsberg. The preliminary assumed conditions for the safe operation of bulk carriers entering the Port of Swinouj´ ´ scie were as follows:


Two simulation models of this bulk carrier were built to conduct three test series of ship arrivals and departures. The simulation experiment consisting of 12 passages in one series was performed by port pilots. Each test series was conducted in different least favorable hydrometeorological conditions. Figure 3 presents statistically developed test results of the port entry by a loaded bulk carrier, and Figure 4 depicts the turning of the bulk carrier under ballast, at wind N 10 m/s and ingoing current 0.8 knots. The test results are shown as safe maneuvering areas determined at three levels of confidence: maximum (red line), mean (green line) and 95% (magenta line).

	- Northern Turning Basin (1.7 km)—port entrance (0.0 km of the fairway). The maximum overall length and breadth of ships entering the port and approaching the Northern Turning Basin were determined by the simulation method (Figure 3), Lc = 300 m, B = 50 m, T = 13.2 m;
	- The Mieli ´nska Turning Basin (4.9 km of the fairway)—Northern Turning Basin. The maximum overall length and breadth of ships safely passing from the Northern to Mieli ´nska Turning Basins were determined by the simulation method Lc = 270 m, B = 40 m, T = 11.0 m [16]. In addition, the navigational risk of maximum ship passage was examined in connection with the planned arrivals of a ferry Lc = 220 m at berth No 2 of the ferry terminal in Swinouj´ ´ scie. The risk that the maneuvering ship moves out of the available navigable area and passenger fatalities occur R = 2.8 × <sup>10</sup>−<sup>7</sup> [year<sup>−</sup>1] is lower than the acceptable risk [18].
	- Przesmyk Orli Turning Basin (63.7 km of the fairway)—Mieli ´nska Turning Basin. The maximum overall length *Lzak <sup>c</sup>* and breadth *Bzak* of ships safely maneuvering in the fairway bends (turns) between Mieli ´nska and Przesmyk Orli Turning Basins were determined by the simulation method. The tests were conducted using a Kongsberg-made FMBS simulator in three fairway bends: Ma ´nków, I ´nskie-Babina and Swi ˛ ´ eta. A simulation experiment was conducted for a cruise ship Lc = 260 m, B = 33.0 m, T = 9.0 m, and container ship Lc = 250 m, B = 33.0 m, T = 11.0 m [16]. Two series of tests were conducted in least favorable hydrom-

eteorological conditions for each bend. The simulation experiment consisting of 12 passages in one series was performed by port pilots. Figure 5 shows statistically processed test results for the Swi ˛ ´ eta bend. The results refer to the safe maneuvering areas for a cruise ship *Lzak <sup>c</sup>* sailing through the bend in the least favorable hydrometeorological conditions;

**Figure 4.** Safe maneuvering areas of turning in the Northern Turning Basin in Swinouj´ ´ scie, bulk carrier under ballast Lc = 300 m, TD = 7.4 m, TR = 9.0 m/s, ingoing current 0.8 knots, UTM coordinates (zone 33U).

**Figure 5.** Swi ˛ ´ eta fairway bend. Safe maneuvering area of cruise ship passage Lc = 260 m in least favorable hydrometeorological conditions. Wind S and W, 10 m/s, outgoing current 0.7 knots, UTM coordinates (zone 33U).

4. The maximum breadth of ships safely maneuvering in straight fairway sections was determined after transforming the condition of navigational safety, defined in the empirical CIRM method [3,15]. This condition can be written as:

$$dD\_j \ge d\_m + 2d\_{n(1-\infty)} + d\_r^p + d\_r^l \tag{20}$$

where:


The maneuvering component is the sum of the basic width of the swept path *dmp* and additional corrections of the swept path width *dmd*

$$d\_m = d\_{mp} + d\_{md} \tag{21}$$

while:

$$d\_{mp} = k \cdot B \tag{22}$$

where:

*<sup>k</sup>* — Coefficient determining the ship's maneuverability, dependent on ship type *k* = *f* (*typ*)*.*

Additional corrections of the swept path width depend on the ship speed, technical parameters of the fairway and hydrometeorological conditions, i.e.:

$$d\_{md} = f(V\_{i\prime}A\_{i\prime}, H\_i) \tag{23}$$

The width allowances depend on the ship speed and technical parameters of the fairway:

$$d\_r = f(V\_{i\prime} \mid A) \tag{24}$$

The navigational component is determined by the subsystem of ship position determination in the given fairway section.

$$d\_n = f(N\_i) \tag{25}$$

Transforming this relationship (20) leads to the determination of the maximum safe width of the ship in straight fairway sections between the turning areas: (*p* sections):

$$B\_p = \left(D\_p - d\_{md} - 2d\_{n(1-\alpha)} - d\_r^p - d\_r^l\right) / k \tag{26}$$

where all data necessary for determining the right-hand side terms of the equation are known:

$$B\_p = f\left(A\_{i\prime}N\_{i\prime}\ H\_{i\prime}V\_{wi\prime\prime}t\_{yp}\right) \tag{27}$$

and these were used to determine the maximum safe breadths Bp of ships in each section and between the examined turning areas *Bpr*.

$$B^{pr} = \min\_{\mathcal{P}} B\_{\mathcal{P}} \tag{28}$$

	- Northern Turning Area—port entrance channel: Lc = 300 m, B = 50 m, T =13.2 m;
	- Mieli ´nska to Northern Turning Areas: Lc = 270 m, B = 40 m, T = 11.0 m;
	- Przesmyk Orli to Mieli ´nska Turning Areas:

Lc = 260 m, B = 33 m, T = 9.0 m—cruise carrier;

Lc = 240 m, B = 33 m, T = 11.0 m—container ship;

Lc = 230 m, B = 33 m, T = 11.0 m—bulk carrier;


$$T \ge h - \Delta \tag{29}$$

where:

Δ − Underkeel clearance.

There are two methods for determining the underkeel clearance:


The safe allowable speeds of ships in specific fairway sections were determined using the static method of components [15]. In the method, the allowance Δ was divided into two components: static allowance Δ*<sup>s</sup>* and dynamic allowance Δ*d*:

The static allowance does not depend on ship movement and is constant for the given area and the ship. Its components include:


In the Swinouj´ ´ scie–Szczecin fairway, no water allowance for waves (Δ<sup>9</sup> = 0) was made, so the condition of navigational safety can be written as:

$$T \le h - \sum\_{l=1}^{7} \Delta\_l - \Delta\_8 \tag{30}$$

The underkeel clearance was determined by five empirical methods recommended by PIANC in the given conditions [19]:


It was calculated for container ships (block coefficient Cb = 0.62) and bulk carriers (Cb = 0.8) by adopting appropriate bottom profiles for individual fairway sections. Given the obtained results, specific safe allowable speeds were identified in all fairway sections. For ships T > 10 m, the following values apply:


The guidelines were also formulated for the implementation of the dynamic underkeel clearance system, which will increase the allowable maximum draft to T = 11.2 m.

7. The passing of two ships in the Swinouj´ ´ scie–Szczecin fairway require the determination of maximum safe breadths of the meeting ships, depending on their draft and the fairway channel slope (Table 2).

The breadths of ships safely passing each other in straight fairway sections were determined based on the condition of the navigational safety of two-way traffic [13]:

$$D\_{it} \ge d\_m^{in} + d\_m^{out} + 2d\_{n(1-\infty)}^{in} + 2d\_{n(1-\infty)}^{out} + d\_r^{in} + d\_r^{out} + d\_r^s \tag{31}$$

where:



**Table 2.** Determination of safe breadths of passing ships in straight sections of the Swinouj´ ´ scie– Szczecin fairway.

#### **4. Discussion**

The main objective of the research presented in the article is to develop a general method for determining the maximum parameters of ships in complex port water systems. This goal was achieved by developing a computational procedure (algorithm) that takes into account various constraints specific to individual sections of the fairway. The developed method stands out from the methods known in the literature (PIANC, ROM, Japanese, CIRM), which focus on determining the maximum parameters of a ship on individual types of waterways, but do not include the procedure for their use for complex waterways.

The developed method was used to determine the conditions for the safe operation of ships on the modernized Szczecin–Swinouj´ ´ scie fairway. The obtained results showed that the deepening and widening of the waterway will, obviously, allow for the passage of larger ships. However, the increase in ship dimensions relates more to the draft and length of the ship than to its breadth. For example, for the port of Szczecin, the maximum vessel draft increased from 9.15 m to 11.0 m and the length from 215 m to 260 m, and the width of the maximum vessel only increased from 31 m to 33 m. This is due to the fact that the horizontal dimensions of the safe maneuvering area depend not only on the width of the vessel, but also on its length, the draft ratio and the available depth.

The proposed method does not introduce new dependencies allowing for the determination of safe ship parameters, but, thanks to the systematization of calculations, it allows for the determination of these parameters on complex waterways. The limitations of its use result from the limitations of the detailed methods used; however, due to the possibility of using various methods (both empirical and simulation), it can be treated as a universal method allowing for the determination of safe parameters of ships on complex waterways. In the future, work on the development of this method will focus on the partial automation of calculations, which will allow us to shorten the time necessary to obtain results.

In conclusion, the developed method allows us to determine the maximum safe parameters of ships in complex port waters, which has been practically confirmed by defining the conditions for the safe operation of ships on the modernized Szczecin–Swinouj´ ´ scie fairway.

#### **5. Conclusions**

The article presents a new method for determining the maximum safe parameters of ships in existing complex port waterway systems and the conditions for safe ship operations.


The prosed method was used for the determination of conditions for the safe operation of ships on the modernized 68-kilometer Swinouj´ ´ scie–Szczecin fairway. The modernization of the fairway resulted in deepening the channel from 10.5 m to 12.5 m and an appropriate widening of the channel. By dredging the fairway to 12.5 m, the maximum permissible draught of vessels calling at Szczecin was increased to approx. 11.0 m (before 9.15 m), and, thus, the availability of the Szczecin port to a certain group of large vessels was ensured. There will be no need for them to be discharged at Swinouj´ ´ scie before continuing on to the Szczecin port.

The results were used to draw up detailed port regulations defining the conditions for safe fairway operations of ships heading for the ports of Swinouj´ ´ scie, Szczecin, Police and Skolwin.

**Author Contributions:** Conceptualization, S.G.; methodology, S.G.; software, M.G., R.G. and M.P.; validation, S.G.; formal analysis, S.G., M.G., R.G. and M.P.; investigation, S.G., M.G., R.G. and M.P.; resources, R.G. and M.P.; data curation, M.G, R.G. and M.P.; writing—original draft preparation, S.G.; writing—review and editing, M.G., R.G. and M.P.; visualization, M.P.; supervision, S.G.; project administration, R.G. and M.P.; funding acquisition, M.G., R.G. and M.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

