Browsing by Author "Aydin, Mehmet Nafiz"

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Adoption of Design Thinking in Industry 4.0 Project Management
(IGI Global, 2021) Dilan, Ebru; Aydin, Mehmet Nafiz; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Management of Industry 4.0 projects needs to have a distinct discourse, be flexible, iterative and creative. These projects are tightly linked with the way people work which is directly related to both their capabilities and their ways of thinking. Challenging Industry 4.0 projects entail out-of-the-box thinking. The basic premise of this research is that the complex transformation accompanying Industry 4.0, which involves various dimensions, requires extensive and effective project management that can leverage novel approaches and techniques such as design thinking. This new approach may overcome the limitations of the dominant model of standard project management and has the potential to bridge the gap between a refreshed project management perspective and the tools/techniques in practical use. Deciding whether, and to what extent, design thinking needs to be adopted in practice in Industry 4.0 project management is a challenge. However, it is time to start exploring the challenges governing the interface between agile approaches such as design thinking and Industry 4.0 project management. © 2025 Elsevier B.V., All rights reserved.
Citation - WoS: 1
Citation - Scopus: 1
AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
(MDPI, 2025) Yildirim, Senda; Yucekaya, Ahmet Deniz; Hekimoglu, Mustafa; Ucal, Meltem; Aydin, Mehmet Nafiz; Kalafat, Irem; Industrial Engineering; Economics; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning-Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)-were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project.
Citation - WoS: 16
Citation - Scopus: 22
Design and Implementation of a Smart Beehive and Its Monitoring System Using Microservices in the Context of Iot and Open Data
(Elsevier Sci Ltd, 2022) Aydin, Sahin; Aydin, Mehmet Nafiz; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
It is essential to keep honey bees healthy for providing a sustainable ecological balance. One way of keeping honey bees healthy is to be able to monitor and control the general conditions in a beehive and also outside of a beehive. Monitoring systems offer an effective way of accessing, visualizing, sharing, and managing data that is gathered from performed agricultural and livestock activities for domain stakeholders. Such systems have recently been implemented based on wireless sensor networks (WSN) and IoT to monitor the activities of honey bees in beehives as well. Scholars have shown considerable interests in proposing IoT- and WSN-based beehive monitoring systems, but much of the research up to now lacks in proposing appropriate architecture for open data driven beehive monitoring systems. Developing a robust monitoring system based on a contemporary software architecture such as microservices can be of great help to be able to control the activities of honey bees and more importantly to be able to keep them healthy in beehives. This research sets out to design and implementation of a sustainable WSN-based beehive monitoring platform using a microservice architecture. We pointed out that by adopting microservices one can deal with long-standing problems with heterogeneity, interoperability, scalability, agility, reliability, maintainability issues, and in turn achieve sustainable WSN-based beehive monitoring systems.
Detection of Early School Drop Out in Vocational and Technical High Schools in Turkey
(Sage Publications inc, 2025) Korkmaz, Ozgur; Aydin, Mehmet Nafiz; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
This study investigates the factors contributing to early school dropout in vocational and technical high schools in Turkey, utilizing machine learning techniques to analyze a dataset of personal, socio-economic, familial, and academic variables. The data was collected via a detailed survey administered to students at one of the largest Vocational and Technical High School in Istanbul, capturing 35 features (factors) relevant to dropout rates. Various classifiers, including Decision Trees and Random Forest, were employed to identify at-risk students with high accuracy. The Decision Tree model, enhanced by the Synthetic Minority Over-sampling Technique (SMOTE), demonstrated the best results for identifying potential dropouts, indicating its effectiveness in educational settings where early intervention is critical. By feature importance analysis this research reveals that parental education levels, family structure, and financial hardships are significant predictors of dropout likelihood. Despite the study's limitations, such as a small dataset and some features with zero-filled columns, the results underscore the importance of data-driven approaches in developing targeted interventions to reduce dropout rates. This research not only enhances the understanding of dropout phenomena in Turkish vocational education but also provides practical insights for policymakers and educators to improve student retention through early and informed interventions. The findings highlight the potential of machine learning to enhance educational support systems, ensuring that every student can succeed.
Citation - WoS: 3
Citation - Scopus: 7
Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace
(Mdpi, 2021) Kafkas, Kenan; Perdahci, Ziya Nazim; Aydin, Mehmet Nafiz; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Marketplace platforms gather and store data on each activity of their users to analyze their customer purchase behavior helping to improve marketing activities such as product placement, cross-selling, or customer retention. Market basket analysis (MBA) has remained a valuable data mining technique for decades for marketers and researchers. It discovers the relationship between two products that are frequently purchased together using association rules. One of the issues with this method is its strict focus on binary relationships, which prevents it from examining the product relationships from a broader perspective. The researchers presented several methods to address this issue by building a network of products (co-purchase networks) and analyzing them with network analysis techniques for purposes such as product recommendation and customer segmentation. This research aims at segmenting products based on customers' purchase patterns. We discover the patterns using the Stochastic Block Modeling (SBM) community detection technique. This statistically principled method groups the products into communities based on their connection patterns. Examining the discovered communities, we segment the products and label them according to their roles in the network by calculating the network characteristics. The SBM results showed that the network exhibits a community structure having a total of 309 product communities, 17 of which have high betweenness values indicating that the member products play a bridge role in the network. Additionally, the algorithm discovers communities enclosing products with high eigenvector centralities signaling that they are a focal point in the network topology. In terms of business implications, segmenting products according to their role in the system helps managers with their marketing efforts for cross-selling, product placement, and product recommendation.
Citation - WoS: 1
Citation - Scopus: 1
Innovation mentor community of practice: a social network analysis perspective
(Emerald Group Publishing Ltd, 2023) Altinisik, Gunda Esra; Aydin, Mehmet Nafiz; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
PurposeTo exploit collaboration-driven innovation, in recent years, many government-sponsored innovation programs and mentor services have emerged. These services support an effective exchange of knowledge among innovation actors, including innovation mentors and enable mentor connectedness as an important factor to develop and sustain effective innovation mentors' community of practice (CoP). The purpose of this paper is to examine the degree of connectedness in an innovation mentor CoP. Design/methodology/approachIn this study, the innovation mentors CoP as part of a national innovation program is considered a network. The connectedness and assortative mixing of this CoP and the effects of these two on each other were examined by using social network measures, including component analysis, the giant component (GC) and assortativity. FindingsThe authors provide the analytical interconnectedness results for both the GC and the whole network with network analysis and assortativity measurements of three attributes of mentors (institution, title and degrees). The degree of correlation of community for the GC shows preferential attachment between high-ranking and low-ranking mentors, while preferential attachment was not observed for the whole network. The correlation coefficient for the institution attribute has the highest value for GC, while the title has the highest value for the whole network. Originality/valueThe study is one of the early attempts to apply social network analysis for an innovation mentor CoP. This study reveals the criticality of evaluating the GC and the whole network separately and provides a number of research and practical directions that will contribute to the development of the innovation mentor CoP.
Citation - WoS: 1
Citation - Scopus: 1
Network analysis of innovation mentor community of practice
(Emerald Group Publishing Ltd, 2023) Altinisik, Gunda Esra; Aydin, Mehmet Nafiz; Perdahci, Ziya Nazim; Pasin, Merih; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
PurposePositive effect of knowledge sharing (KS) on innovation has come to the fore and government-supported innovation and mentoring communities or mentor networks have become widespread. This article aims to examine the community connectedness and mentors' preferences for professional competency-based KS of such innovation community of practice networks (CoPNs).Design/methodology/approachThe paper constructs a directed weighted CoPN model with a node-attribute-based novel fingerprint edge weights. Based on the CoPN, Social Network Analysis (SNA) metrics and measures including Giant Component (GC) were proposed and analyzed to identify mentors' connectedness preferences. The fingerprint was proposed as a novel binarized node attribute of competence. Jaccard similarity of fingerprints was proposed as edge weights to reveal correlations between competences and preferences for KS.FindingsThe work opted to conduct a survey of 28 innovation mentors to measure a CoPN. Both a name generator question and a second set of questions were employed to invite respondents to name their collaborators and indicate their professional competence. SNA metrics result in differing values for GC and the rest, which lead us to focus on GC to reveal salient metrics of connectedness. Jaccard similarity analysis results on GC demonstrate that mentors collaborate in an interdisciplinary manner.Originality/valueBased on the CoPN, the methods proposed may be effective in predicting preferred relationships for interdisciplinary collaborations, providing the managers with an analytical decision support tool for KS in practice.