Argilovski, Aleksandar
Preferred name
Argilovski, Aleksandar
Official Name
Argilovski, Aleksandar
Main Affiliation
6 results
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Item type:Publication, INTEGRATION OF LEAN PRINCIPLES AND AUTOMATION FOR DIGITAL TRANSFORMATION IN MANUFACTURING(University Ss. Cyril and Methodius in Skopje, 2025-12-29); ; ; ; <jats:p>Introducing automation in manufacturing can lead to increasing efficiency in the assembly process, reducing Lean production waste, and enhancing operator ergonomics. The purpose of combining automation and Lean is to bridge the gap between digital transformation and human-centric automation, ensuring technological evolution together with the operator's well-being while driving industrial optimization, innovation, and efficiency. According to the review, synergy is required; however, challenges remain in effectively aligning automation with Lean principles. This paper aims to analyze the possibilities for integrating automation and Lean management according to literature, exploring similarities and the implementation practices to achieve sustainable and competitive manufacturing.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Statistical analysis and machine learning-based modelling of kerf width in CO2 laser cutting of PMMA(Jan Evangelista Purkyne University in Usti nad Labem, 2024-12-21); ; ; Engineering polymers like PMMA have increasingly replaced traditional materials where feasible, with CO2 laser cutting gaining attention for its high quality and speed in processing these materials. Precise cuts are essential for product accuracy, with kerf width being a key quality attribute for ensuring the final product's quality. This study focuses on the impact of three process variables: stand-off distance, laser power, and cutting speed, on the kerf width in CO2 laser cutting of PMMA. A full-factorial experiment systematically varies process parameters to understand their individual and interaction effects on the cutting process. The kerf width is measured as an indicator of precision to evaluate the quality of the laser cuts. In order to address the non-linear relationships between these process parameters and kerf width, several machine learning models were utilized. Performance comparisons indicated that the Artificial Neural Network (ANN) model provided the highest accuracy, with R² values of 0.98 for the validation dataset and 0.95 for the testing dataset. The optimized ANN model is a robust tool for parameter optimization, determining optimal settings to achieve the desired kerf width and ensure productivity. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, BRIDGING THE GAP: QUALITATIVE COMPARATIVE ANALYSIS OF INDUSTRY 4.0 AND INDUSTRY 5.0(University Ss. Cyril and Methodius in Skopje, 2024); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing manufacturing efficiency - A Lean Industry 4.0 approach to retrofitting(Mechanical Engieneering-Scientific Journal, 2023-12); ; Industry 4.0 technologies are already affecting global supply chains by revolutionizing how companies manufacture and distribute their products and services. Many companies are affected by this transformation, especially small and medium-sized enterprises (SMEs) that are keen to enhance their market competitiveness as quickly and easily as possible. The transformation from traditional approaches to Industry 4.0 can bring many benefits, however, this transition involves adopting technologically advanced machinery with a high level of digitalization and communication. The cost and time to replace old machines could be unsustainable for many SMEs and that is why these enterprises seek alternative solutions for the digitalization of their legacy machines, such as retrofitting. This paper conducts a review of both retrofitting and Lean Industry 4.0 (Lean 4.0) to identify the challenges and benefits of both concepts and explore how they can interact and merge with each other to help SMEs to increase their market competitiveness. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Six Sigma Approach to Enhance Concurrency of the Procurement Process for Raw Materials(Tehnički glasnik, 2021-11-01); This paper presents the improvement of the procurement process for raw materials in a real manufacturing company supported by the Lean Sig Sigma as structured approach to deliver the improvement. The manufacturing company that is a subject of this paper have received a significant amount of complains regarding the internal purchase orders approval process and the overall procurement process of the company from both internal employees and external vendors and contractors. Considering that the company is procuring the raw goods from selected vendors, therefore the entire manufacturing plan depends on these materials, the company decided to improve this process trough Lean Six Sigma. The Lean Six Sigma approach was selected in order to obtain the improvement in a short time, based on indicators that were previously analyzed and prioritized. The paper focuses on simplifying of the process trough decreasing the number of mandatory steps. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Product traceability in manufacturing - A review of the concepts for enhanced digital transformation(2023-06); ; ; Peneva, GabrielaThis review provides insights into the role of product traceability in enhancing the digital transformation of manufacturing companies and provides an initial guidance for organizations and researchers that are looking into the possibilities to implement or improve traceability systems. The review highlights several classifications when it comes to product traceability in the manufacturing industry. Various traceability concepts and technologies, including Barcodes, QR codes, Data Matrix codes, RFID, NFC, BLE and GPS are presented, defined, and compared according to selected criteria.