Decision Making in Manufacturing and Services
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ISSN 1896-8325
e-ISSN: 2300-7087
Call number
Volume
Vol. 18
Date
2024
Description
Journal
Decision Making in Manufacturing and Services
AGH University of Science and Technology Press (2007-)
ISSN: 1896-8325 e-ISSN: 2300-7087
ISSN: 1896-8325 e-ISSN: 2300-7087
journal.volume.project
Contains
Journal Issues
Articles
Resilience of Robotic Solutions under Extreme Conditions
(Wydawnictwa AGH, 2024) Sala, Dariusz; Pikulin, Pavlo; Sobczuk, Valentyn; Kotsan, Igor
This study is devoted to the problems of the use of modern advanced technologies by logistics companies in their efforts to increase the speed of their technological operations and transform their business processes; this is aimed at reducing their financial costs, increasing the efficiency of their use of labor resources, and minimizing their risks. Today, this is a decisive factor in increasing a company’s competitiveness in the market, increasing its profitability, and realizing its long-term leadership. Innovative logistics is an effective tool for streamlining flow processes through the introduction of high-tech innovations in the operational and strategic management of the market structures that are aimed at improving the quality of their customer service, increasing the efficiency of their flow processes, and reducing the total cost of their implementation in order to achieve key business objectives. The paper examines approaches to the automation of business processes in the logistics sector in the context of the robotization of technological operations while taking those features that are due to the functioning of enterprises under conditions of constant exposure to extreme risks into account. The concept of the robotization of processes has been developed, which will increase the productivity and efficiency of businesses, help reduce their operating costs, reduce their likelihood of personnel errors, and contribute to improving their business security. The results are implemented in the practice of a number of logistics companies in the real sector of the economy.
Six Sigma vs. Other Quality Improvement Tools: Comparative Analysis of Trends over Period of 1985–present
(Wydawnictwa AGH, 2024) Nakielski, Marcin; Ludwig, Anna
Six Sigma is a widely adopted method in various industries that is aimed at process improvement and quality management. Understanding the evolving interest and utilization of Six Sigma can provide valuable insights into its current significance and prospects. Using data from Google Trends, Google Books, Web of Science, and Scopus, this study examined the search volumes and interests in keywords and phrases that were related to Six Sigma over a specified period of time. The global analysis revealed the overall direction of interest in Six Sigma worldwide, highlighting periods of peak interest and potential significant shifts in the method’s popularity. By identifying those times with the highest concentrations of interest, the article provides a deeper understanding of the adoption and perception of Six Sigma. On top of this, Six Sigma was compared in popularity (by trends) with other known methods such as Lean, Kaizen, PDCA, and TQM. This research contributes to the existing body of knowledge by shedding light on the current trends and future directions of Six Sigma globally. The findings offer valuable insights for practitioners, researchers, and organizations that seek to leverage Six Sigma for process improvements and quality management.
Predictions and Application of Queueing Analysis: Case of Regional Hospital Limbe, Cameroon
(Wydawnictwa AGH, 2024) Machangara, Daphne T.; Boubacar, Habiboulaye Amadou; Andreatta, Giovanni; Ndolo, Antony
In this work, we applied queue analysis and the predictions of waiting times at Regional Hospital Limbe (RHL) in Cameroon. The main purpose of the work was to be able to make mathematical sense of a real-life scenario that concerned queues (waiting lines) and try to come up with models for performance measurements and improvements; this could be achieved by using queueing theory concepts that were composed of queueing models that provided some operational insights because of their analytical nature. The observations included studying patient arrival and waiting times, along with doctor service times; the results showed busy departments in the hospital, busy days, and busy times. Long waiting times were mainly found to exist in general practitioner (GP) and specialist consultations. The queueing concept was applied to only one service segment – GP consultation. Although strong scientific conclusions cannot be made on the queuing models that were obtained due to inefficient data, the value of this work lies mainly in the methodologies and proposals of different operating systems that could be adopted. Furthermore, some predictions were made using machine learning to see how long a patient could wait in a queue for service; the model predictions had an average of 10 minutes and 53 seconds of error.
Analyzing Activities of Mobile App Users Who are Preparing for Driving Tests as Sources of Knowledge about Consumer Behavior
(Wydawnictwa AGH, 2024) Zapiór, Anna
This article presents the partial results from ongoing research that uses mobile applications that help individuals prepare for their driving license exams. The aim of the presented research is to analyze the activity of the users of these applications (including their daily activities, any tasks that are performed, and the lengths of times that are spent on sample exams and tests). The theoretical implication of the article is to draw attention to the time of the highest consumer activity, while the practical implication is to emphasize the importance of using ICT (particularly, mobile applications) in knowledge and information management, marketing decision-making, and education.
Application of Basic Machine-Learning Classifiers for Automatic Anomaly Detection in Shewhart Control Charts
(Wydawnictwa AGH, 2024) Woźniak, Aleksander; Krawiec, Klaudia; Książek, Roger
In today’s dynamic technological environment, innovation plays a crucial role – especially for manufacturing enterprises that constantly strive to improve the quality of their products. This article examines the quality-management issue in a company producing car rims. It was identified that real-time quality control can sometimes be unreliable due to controller fatigue, leading to erroneous data interpretation or delayed responses to deviations in the production process. The study aimed to investigate the possibility of eliminating or significantly reducing these errors by employing a tool that is based on artificial intelligence. The article covers the preparation of training data, the training of classifiers, and the evaluation of their effectiveness in analyzing control charts in real time. The adopted hypothesis assumes that machine-learning classifiers can be effective methods of support for quality controllers. The research began with collecting measurement data from the machine and dividing it into training and test sets. The obtained results were evaluated using standard quality measures for machine-learning models. The results showed that the use of artificial intelligence can bring significant benefits in improving quality supervision in the production process of car rims.