https://www.um.edu.mt/library/oar/handle/123456789/122482 Sat, 25 Apr 2026 18:59:24 GMT 2026-04-25T18:59:24Z https://www.um.edu.mt/library/oar/handle/123456789/122754 Title: Construction of regression models predicting lead times and classification models Authors: Olszewski, Paweł; Gil, Leszek; Rak, Natalia; Wołowiec, Tomasz; Jasieński, Michał Abstract: PURPOSE: This article presents the process of building and applying regression models to predict lead time and classification models in supply chain management.; DESIGN/METHODOLOGY/APPROACH: The article presents the construction of regression models predicting lead times and classification models for partial orders and complete orders.; FINDINGS: Using classification and regression models in the furniture industry increases customer satisfaction through timely order fulfillment, reduced costs associated with delays, and effective management of company resources.; PRACTICAL IMPLICATIONS: Using regression models to determine forecast delivery times for delayed orders allows you to manage customer expectations better and minimize delays' impact on the entire supply chain. With accurate lead time forecasts, the company can make informed decisions about resource allocation, production planning, and logistics, contributing to operational efficiency.; ORIGINALITY/VALUE: Using predictive models in the procurement management process allows for continuous improvement of logistics processes by analyzing historical data and identifying trends. Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/122754 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/122753 Title: Enhancing customer service in shopping malls with an advanced chatbot-integrated concierge device Authors: Maj, Michał; Dmowski, Artur; Nowak, Ryszard; Mędzelowski, Tadeusz; Cieplak, Tomasz Abstract: PURPOSE: The primary objective of this study is to enhance customer service in shopping malls by introducing an innovative solution: a Concierge device equipped with an advanced chatbot assistant. The aim is to simplify mall navigation for customers by offering information on store locations, services, promotions, and events.; DESIGN/METHODOLOGY/APPROACH: The Concierge device features a monitor displaying an interactive mall map that users can navigate to find store details, locations, and opening hours. A chatbot integrated with the device provides a personalized shopping experience, recognizing customers via onboard cameras and adjusting interactions based on their emotions. The device also visualizes routes to stores and highlights promotions along the way. Key services such as toilets and ATMs are easily located through the device, which uses the chatbot to suggest nearby points.; FINDINGS: Preliminary research indicates that this advanced assistant enhances the customer shopping experience by providing tailored information and seamless navigation. The device effectively routes users to destinations while promoting additional services and events, ultimately improving shopping mall service efficiency.; PRACTICAL IMPLICATIONS: Implementing this Concierge device in shopping malls can significantly improve customer satisfaction by providing accurate, real-time information and personalized guidance. Retailers and service providers can expect increased customer engagement and better promotion of their products and services through this system.; ORIGINALITY/VALUE: This solution integrates state-of-the-art emotion recognition and chatbot technology with interactive maps, offering a unique and innovative approach to shopping mall navigation. By providing personalized service and improving customer engagement, it adds significant value to the current shopping mall customer service landscape. Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/122753 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/122752 Title: Chatbot to support the customer service process Authors: Smutek, Tomasz; Marczuk, Marcin; Jarmuł, Michał; Jurczak, Ewelina; Pliszczuk, Damian Abstract: PURPOSE: This article aims to discuss the potential benefits and challenges associated with implementing chatbots in customer service. With their ability to automate tasks, answer FAQs, and engage in conversations, chatbots offer unique opportunities for enhancing customer service.; DESIGN/METHODOLOGY/APPROACH: This article provides a comprehensive analysis of chatbots' potential advantages, such as 24/7 availability, quick response times, cost reduction, and increased customer engagement capacity. The challenges that must be addressed for effective implementation are also highlighted.; FINDINGS: The analysis indicates that chatbots can significantly enhance customer service by offering immediate assistance, reducing wait times, and automating repetitive tasks. This automation allows customer service agents to focus on more complex issues, improving customer satisfaction and reducing operational costs.; PRACTICAL IMPLICATIONS: The practical implications include reducing the workload on human agents, cost savings due to automation, and providing consistent and efficient customer support at any time. Chatbots' scalability can help organizations meet customer demand without expanding the support team.; ORIGINALITY/VALUE: This article offers valuable insights into how chatbots can transform customer service through automation and efficiency. It provides guidance on maximizing chatbots' potential while identifying and addressing challenges that arise during implementation. Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/122752 2024-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/122751 Title: Product knowledge graphs : creating a knowledge system for customer support Authors: Przysucha, Bartosz; Kaleta, Paweł; Dmowski, Artur; Piwkowski, Jacek; Czarnecki, Piotr; Cieplak, Tomasz Abstract: PURPOSE: This article explores developing and integrating a product knowledge graph within an e-commerce customer support system to improve product discovery and recommendation processes.; METHODOLOGY: The methodology involves a structured development process for the knowledge graph, utilizing natural language processing (NLP) to extract relevant entities from product data and machine learning algorithms to establish and categorize relationships between products. The approach integrates data from multiple sources, including vendor catalogs, online reviews, and customer interactions, ensuring a comprehensive data set.; FINDINGS: The research resulted in the creation of a dynamic, scalable knowledge graph that significantly enhances the accuracy and personalization of product recommendations. The graph’s ability to link seemingly disparate data points allows for a nuanced understanding of user behavior and preferences, improving customer satisfaction and sales performance.; PRACTICAL IMPLICATIONS: The presented method has significant implications for retailers looking to enhance their online presence and customer interaction. By implementing this knowledge graph, retailers can expect to streamline their product recommendation processes and gain deeper insights into customer trends, which can inform broader marketing and inventory decisions.; VALUE: This study's novelty lies in applying a comprehensive knowledge graph tailored explicitly for e-commerce systems. This graph integrates abstract and concrete entities to offer a richer, more interconnected dataset than traditional relational databases. Mon, 01 Jan 2024 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/122751 2024-01-01T00:00:00Z