Scientific Journal of Gdynia Maritime University

ANALYSIS OF THE PROCESS OF IMPLEMENTING THE 5S METHOD AT ZPS "LUBIANA" S.A.

2025-02-27T13:12:45+00:00

The article presents the results of efforts aimed at improving the production process at a company, ZPS "Lubiana" S.A., through the implementation of tools and organizational practices associated with the Lean Management concept. The authors focused on discussing the effects of implementing the 5S analysis tools within the company as a management method where the main visual goal is the orderly organization of the workplace. Introduced primarily as part of Lean Manufacturing, it helps eliminate waste, increase work efficiency, and improve employee safety and morale.
During the implementation work, in order to obtain a comprehensive view of the processes, the authors also applied other tools in addition to the 5S analysis, such as: Kaizen, root cause analysis, value stream mapping (VSM), standardization, and the determination of the OEE indicator based on the analysis of manual work.
Results demonstrate significant workplace organization improvements including reduced search times, fewer errors, and enhanced safety compliance in the packaging department. The study reveals that successful 5S implementation in porcelain manufacturing requires extensive visual management systems, detailed cleaning schedules adapted to ceramic dust environments, and comprehensive audit frameworks with color-coded effectiveness tracking. This research contributes a practical implementation methodology specifically tailored for traditional manufacturing environments and identifies employee engagement through visual progress documentation as critical for sustaining 5S practices in established production facilities.

THE NUTRI-SCORE SYSTEM FOR FRONT-OF-PACK NUTRITIONAL LABELLING: ALGORITHM UPDATES, BENEFITS, AND RISKS

2025-05-13T16:15:54+00:00

Front-of-pack (FOP) nutritional labelling systems, such as Nutri-Score, play an increasingly important role in promoting healthier dietary choices and enhancing consumer awareness of food nutritional quality. Nutri-Score offers a simplified, color-coded summary that aligns with public health recommendations and enables product comparisons within the same category. Its regulatory implementation has been adopted voluntarily by several European countries, while efforts toward EU-wide harmonization are ongoing. In 2024, a revised Nutri-Score algorithm has come into effect, aiming to better reflect current dietary recommendations through changes in product classification. For example, milk and dairy-based drinks are now categorized as beverages, with their scores primarily based on fat and sugar content, diet beverages containing sweeteners are also rated less favourably. Improved scores have been assigned to nutritionally beneficial products, such as whole-grain bread, oily fish, vegetable oils, and low-salt cheeses, while lower scores have been applied to sweetened yoghurts, breakfast cereals, and red meat products. Although Nutri-Score facilitates more informed purchasing decisions, it does not fully support consumers in constructing balanced diets and is not equally applicable across all food categories, particularly for single-ingredient or traditional products. This article examines the benefits and limitations of Nutri-Score, considering both regulatory developments and recent scientific literature related to its algorithmic modifications. However, the future of Nutri-Score remains uncertain amid growing political, institutional, and industry-level criticism across parts of Europe.

ANALYSIS OF THE POSSIBILITY OF USING EXHAUST GAS COMPOSITION IN THE DIAGNOSIS OF A DIESEL ENGINE

2025-03-25T08:58:12+00:00

The article analyzes the impact of malfunctions in compression-ignition engine systems on exhaust composition and cycle parameters. A naturally aspirated, single-cylinder, four-stroke Farymann Diesel D10 engine was studied, with simulated common failures.
The research included laboratory experiments and computer simulations using DIESEL-RK, a tool for optimizing engine processes and thermodynamic cycles.

Malfunctions in the injection and intake systems altered the exhaust temperature, pressure rise, and maximum combustion pressure. Changes in exhaust composition were noted, especially in the nitrogen oxides (NO_x) and carbon monoxide (CO): intake throttling reduced these concentrations, while a lower injector opening pressure increased them. Laboratory and simulation results were cross-validated, ensuring reliability and providing a comprehensive analysis of the engine's condition.

SINGULAR VALUE VERSUS EIGENVALUE DECOMPOSITION EFFICIENCY IN COMPUTING PRINCIPAL COMPONENTS FOR DIMENSIONALITY REDUCTION OF LARGE DATASETS

2025-04-09T07:20:16+00:00

Principal component analysis, being one of the best techniques for dimensionality reduction, is implemented by using one of the two high-accuracy algorithms: the singular value decomposition (SVD) and eigenvalue decomposition (EVD). The EVD is generally faster than the SVD, except for datasets with fewer observations or when the observation has fewer features. Apart from cases of shallower datasets consisting of just a few hundred double-precision observations, the EVD speeds up computing principal components by at least 4.5%, whereas the average speedup in 45% widely varies from 12% to 92%. The speedup on non-shallower single-precision datasets is roughly similar, but it nonetheless makes no sense due to EVD poor accuracy while operating on numeric data with single precision. The EVD is efficient if the dataset consists of no fewer than a few hundred observations (objects) having at least three double-precision features.

SELECTED REINFORCEMENT LEARNING METHODS APPLIED TO DETERMINE THE OPTIMAL PATH OF TRANSITION

2025-05-05T08:19:29+00:00

The focus of this work is the determination of the optimal path for a mobile agent to take in an environment with static obstacles, using reinforcement learning (RL). The paper explains the work examining different RL algorithms, such as Q-learning and Sarsa in the classic version and enhanced with the Adam gradient optimiser. The work investigates the impact of the Adam gradient optimiser on the rate and stability of finding the optimal solution. The analysis includes a comparison of the learning rate, the number of steps in a single episode and the stability of the learning process. The results reveal that the considered Q-learning and Sarsa algorithms supplemented with the Adam optimiser achieve a higher performance, characterised by a faster determination of the optimal transition path, than the same algorithms without the Adam gradient optimiser. The results could be particularly useful in practical applications for routing transitions in fields like mobile robotics.