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    Defective products management in a furniture production company: A data mining approach
    (Wiley, 2022) Ersoz, Taner; Guven, Ilker; Ersoz, Filiz
    Quality is one of the main focuses of the manufacturing companies. Therefore, this issue takes attention of many researchers from both academic and professional environment. In industries like furniture where company types are most likely workshop or small-medium enterprise and production method is traditional, effective methods to prevent faulty production must be considered. Traditional or statistical methods are good to track defective products and keep the within desired levels, however not as good to prevent them from occurring. These methods come with an acceptance to some level of defective production. In this study, it is aimed to evaluate the in the furniture production sector and to reveal the source of the defective production and the factors that cause the defect in terms of which department. In addition, finding the most appropriate methods to accurately analyze the data coming from the company is another research topic of this study. In the research, artificial neural networks and decision tree models were established and inferences were made from the data sets. The model established in the application revealed the causes of the problems experienced in the production process of the company, thus root cause of defective products can be found and prevented. By using data mining techniques, this study developed an effective approach to improve the quality of the production process and to predict and prevent errors before they occur. According to results of the study classification and regression tree algorithm outperformed other methods by yielding 90.12% correct prediction rate. 87.5% of the defects caused by cover and seat cushion problems account for defects in the textile department.
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    Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods
    (Pergamon-Elsevier Science Ltd, 2020) Guven, Ilker; Simsir, Fuat
    In this study, product variety has been taken into account and sales forecasting has been performed by using artificial intelligence to minimize error rate, in the retail garment industry. In this context, artificial intelligence models such as artificial neural networks (ANN) and support vector machines (SVM) have been established and inferences from the datasets have been made. During the establishment of the models, datasets have been prepared with and without color details of the products, for nine different products as separately and one combined dataset which consists all products, then the forecast process was carried out. Thus 20 different models were established and compared. Along with color detail, other variables that may have an effect on the sales performance such as weather, gender, special days etc., have been added to proposed models. In the comparison of methods root mean square error has been taken into consideration. As a result of this study, it has been determined that ANN outperformed SVM on seven datasets out of ten for the datasets without color and their performances were even for the datasets with color. The reliability of this study has been increased by comparing the results of the methods.
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    Machine Learning Algorithms with Intermittent Demand Forecasting: An Application in Retail Apparel with Plenty of Predictors
    (E.U. Printing And Publishing House, 2021) Guven, Ilker; Uygun, Ozer; Simsir, Fuat
    Demand forecasting is a key factor for apparel retail stores to sustain their business, especially where there are variety of products and intermittent demand. In this study, two of the most popular machine learning methods, random forest (RF) and k-nearest neighbour (KNN), have been used to forecast retail apparel's intermittent demand. Numerous variables that may have an effect on the sales, have been taken into account one of which is defined as special day that might trigger intermittency in the demand. During the forecast application, four different datasets were used to provide reliability. 28 different variables were used to increase accuracy of the forecasting and experience of the behaviours of the algorithms. Root mean square error (RMSE) was used to evaluate performance of the methods and as a result of this study, RF showed better performance in all four datasets comparing to KNN.
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    Selecting Display Products for Furniture Stores Using Fuzzy Multi-criteria Decision Making Techniques
    (Springer International Publishing Ag, 2018) Uygun, Ozer; Guven, Ilker; Simsir, Fuat; Aydin, Mehmet Emin
    Efficient marketing in which the right products are supplied to the right consumer plays a crucial role for a profitable business in the age of highly accessible and competitive global market. This fact enforces producers to clearly identify and analyze the needs of consumers and to display their products respecting locality based on customers' needs. The position of the business is strengthened within the market and its competiveness increases by supplying and displaying the products suitable to regional consumers' preferences. In this study, an integral fuzzy multi criteria decision making technique is proposed for an effective decision making process to select the most suitable display products to the consumers' needs and preferences. The approach has been applied to identify the most suitable set of modular furniture products to be displayed at a local store that locates in Bursa city of Turkey. The approach uses Fuzzy DEMATEL method to work out the interrelations of chosen criteria, which are weighted with Fuzzy ANP and finally suggest a rank-based list of products with Fuzzy PROMETHEE. The results are verified with the expert view and found very useful.