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    A FUZZY QFD-BASED APPROACH FOR CUTTING MACHINE SELECTION IN THE FURNITURE INDUSTRY
    (Bursa Uludağ Üniversitesi, 2025) İnce, Melike Nur; Taşdemir, Çağatay; Yildiz, Aytaç
    To survive in an increasingly competitive business environment, companies are placing a greater emphasis on customer demands. Demand-driven manufacturing has become a key business priority in light of these developments. Furthermore, customer demand has emerged as a pivotal consideration in strategic decision-making processes within the business sector. Adopting a customeroriented approach to decision-making in various operational areas, including purchasing, logistics, and production, could increase business profitability. In this study, a fuzzy AHP-based fuzzy QFD approach was developed for a cutting machine that a medium-sized furniture company sought to procure. This analysis identified eight customer requests and determined their relative importance using the Fuzzy AHP methodology. The results indicated that Precision (CR 3) was the most critical customer request, with a weight of 0.300, followed by Cutting Quality, which was identified as the second most crucial customer request, with a weight of 0.229. Subsequently, these weighted customer requests were input into the Fuzzy QFD methodology. Subsequently, ten technical requirements for machine selection were identified. The study results showed that the best-performing alternative was the laser cutting machine, with a percentage value of 28.00. In contrast, the worst-performing alternative was the autogenous flamecutting machine, with 19.40%. Although the employed methodology was explicitly focused on machine selection for metal components in the furniture industry, the findings offer significant insights with broader applicability. These insights provide a reference point for addressing complex decision-making problems of a similar nature, making the research valuable to practitioners and academics working in furniture manufacturing, machine selection, and multi-criteria decision-making.
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    Facility layout planning through the ALDEP Method in the wooden cable reels industry
    (2024) İnce, Melike Nur; Tasdemir, Cagatay
    Facility layout planning plays a pivotal role in manufacturing system design, impacting vital metrics such as lead time, handling costs, and space optimization. While a significant portion of research has been invested in refining existing facility layouts, there is a noticeable research gap in devising optimized layouts for new establishments, especially in the value-added wood products domain. Addressing this lacuna, this research focused on designing an efficient department-level layout for a wooden cable drums manufacturing facility in an area of 4150 m2. This facility included both office and production areas. The investigative process was segmented into four distinct phases: Deciding the strategic positioning of the facility on the available plot, defining the functional and spatial requirements for each department, establishing the intricate relationship dynamics between these individual units, and rigorously documenting the most optimal department-level facility layout. For precision in layout creation, the ALDEP algorithm was employed, which was further visualized to offer a comprehensive three-dimensional representation. The final layout seamlessly organized seven departments within the 1st Floor Office Area, eight in the 2nd Floor Office Area, and thirteen within the Production Floor. Efficiency evaluation of these areas yielded scores of -811, 184, and -318, respectively. Conclusively, this research furnished actionable insights for manufacturers within the wood products sector and was expected to be an invaluable reference for academics delving into facility planning and value-added wood products manufacturing.
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    Optimizing Energy Efficiency in Wood Processing Plants through Data Analysis: A Case Study on Value-Added Wood Products Manufacturing
    (Sakarya University of Applied Sciences, 2024) İnce, Melike Nur; Taşdemir, Çağatay
    Energy consumption in value-added wood products manufacturing facilities has significant environmental and economic impacts. High energy usage increases costs and expands the carbon footprint, making it challenging to achieve sustainability goals. Inefficient energy management in wood processing plants elevates operational costs and exacerbates the environmental burden. Therefore, optimizing energy efficiency through data analysis techniques is critically important. This study analyzes energy consumption data to identify inefficiencies and propose effective optimization strategies. Historical data encompassing operational parameters, energy consumption, environmental conditions, and production output from five high-capacity wood processing machines in the wood products industry were collected daily over the past three years. The dataset includes ten categories: Date, Machine ID, Runtime Hours, Load Percentage, Electricity Usage, Gas Usage, Temperature, Humidity, Production Output, and Energy Efficiency. Initially, the data were loaded into a pandas Data Frame, converted to date and time format, and checked for missing and outlier values, followed by standardization of numerical features. Descriptive statistics were calculated for each feature, and data consistency was verified. The distributions of critical features were visualized with histograms, and the relationships between numerical features were illustrated using a correlation matrix heatmap. Trends and seasonal patterns in energy consumption and production output were analyzed by resampling the data monthly. Principal Component Analysis (PCA) was applied to reduce the dimensionality of the dataset while retaining significant information, and three clusters were formed using the K-Means algorithm. The clusters were visualized in the PCA-reduced feature space, and their characteristics were analyzed to prioritize machines for energy efficiency improvements. Cluster 2, characterized by an average energy usage of 209.79 kWh, an average gas usage of 107.90 m³, an average production output of 1595.03 units, an average energy efficiency of 5.26 units/kWh, and an average load percentage of 75.35%, demonstrated low energy consumption and high production output, indicating highly efficient operations. Therefore, it is recommended that the best practices from this cluster be standardized and implemented across other clusters. Additionally, investing in technological advancements to enhance energy efficiency and conducting continuous improvement efforts to maintain and improve efficiency are suggested.
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    The Impact of Quality Dimensions and Some Other Critical Factors on Consumers’ Furniture Purchasing Decisions
    (2024) İnce, Melike Nur; Tasdemir, Cagatay
    This research examined the influence of quality dimensions and various other factors on consumer choices in the Turkish furniture market, aiming to bridge a literature gap by leveraging theoretical insights and empirical data. Utilizing a detailed survey, the study captured consumer perceptions of factors influencing furniture purchases, focusing on Garvin's eight quality dimensions: Suitability, Perceived Quality, Features, Aesthetics, Service, Durability, Reliability, and Performance. The methodology included a 19-question survey targeting Bursa's population to gather data on demographic characteristics and purchasing influences, which was analyzed via Microsoft Excel. The findings underscored the paramount importance that consumers placed on durability and performance, suggesting a pragmatic approach to furniture buying where functionality trumped aesthetics. A notable preference for sustainable and eco-friendly furniture emerged, aligning with broader environmental trends. Demographically, most respondents were young, university-educated adults, indicating a market segment with distinct tastes and preferences, particularly toward modern-style furniture. These insights advocated for furniture industry stakeholders to adopt marketing strategies emphasizing product durability, performance, and environmental friendliness, aligning with consumer expectations for quality and sustainability. This alignment could be crucial for guiding product development and design to cater to contemporary consumer needs.