Browsing by Author "Ocal Tasar, Ceren"

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Citation - WoS: 32
Citation - Scopus: 30
Data analytics for quality management in Industry 4.0 from a MSME perspective
(Springer, 2025) Gorkem Sariyer; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Ceren Ocal Tasar; Sunil Luthra; Sariyer, Gorkem; Tasar, Ceren Ocal; Luthra, Sunil; Mangla, Sachin Kumar; Kazancoglu, Yigit; Ocal Tasar, Ceren
Advances in smart technologies (Industry 4.0) assist managers of Micro Small and Medium Enterprises (MSME) to control quality in manufacturing using sophisticated data-driven techniques. This study presents a 3-stage model that classifies products depending on defects (defects or non-defects) and defect type according to their levels. This article seeks to detect potential errors to ensure superior quality through machine learning and data mining. The proposed model is tested in a medium enterprise—a kitchenware company in Turkey. Using the main features of data set product customer country production line production volume sample quantity and defect code a Multilayer Perceptron algorithm for product quality level classification was developed with 96% accuracy. Once a defect is detected an estimation is made of how many re-works are required. Thus considering the attributes of product production line production volume sample quantity and product quality level a Multilayer Perceptron algorithm for re-work quantity prediction model was developed with 98% performance. From the findings re-work quantity has the highest relation with product quality level where re-work quantities were higher for major defects compared to minor/moderate defects. Finally this work explores the root causes of defects considering production line and product quality level through association rule mining. The top mined rule achieves a confidence level of 80% where assembly and material were identified as main root causes. © 2025 Elsevier B.V. All rights reserved.
Citation - WoS: 18
Citation - Scopus: 29
Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining
(SAGE Publications Ltd info@sagepub.co.uk, 2020) Gorkem Sariyer; Ceren Ocal Tasar; Sariyer, Gorkem; Ocal Tasar, Ceren
Diagnostic tests are widely used in emergency departments to make detailed investigations on diagnosis and treat patients correctly. However since these tests are expensive and time-consuming ordering correct tests for patients is crucial for efficient use of hospital resources. Thus understanding the relation between diagnosis and diagnostic test requirement becomes an important issue in emergency departments. Association rule mining was used to extract hidden patterns and relation between diagnosis and diagnostic test requirement in real-life medical data received from an emergency department. Apriori was used as an association rule mining algorithm. Diagnosis was grouped into 21 categories based on International Classification of Disease and laboratory tests were grouped into four main categories (hemogram biochemistry cardiac enzyme urine and human excrement related). Both positive and negative rules were discovered. Since the nature of the data had the dominance of negative values higher number of negative rules with higher confidences were discovered compared to positive ones. The extracted rules were validated by emergency department experts and practitioners. It was concluded that understanding the association between patient’s diagnosis and diagnostic test requirement can improve decision-making and efficient use of resources in emergency departments. Association rules can also be used for supporting physicians to treat patients. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 14
Citation - Scopus: 17
Predicting cost of defects for segmented products and customers using ensemble learning
(Elsevier Ltd, 2022) Gorkem Sariyer; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Lei Xu; Ceren Ocal Tasar; Tasar, Ceren Ocal; Kumar Mangla, Sachin; Kazancoglu, Yigit; Sariyer, Gorkem; Xu, Lei; Mangla, Sachin Kumar; Ocal Tasar, Ceren
Due to technological advances Big Data Analytics (BDA) has become increasingly important over the last few years. This has led companies to evolve BDA capabilities (BDAC) to manage operations and make better decisions. In this study we propose a model Clustering Based Classifier Ensemble Method for Cost of Defect Prediction (CBCEM-CoD) incorporating clustering classification prediction and learning techniques of BDA for quality management in the manufacturing industry. CBCEM-CoD (1) is fact-driven as it is based on a fundamental problem of the manufacturing industry (2) integrates different BDA techniques in a specific way when an output of one technique is used as an input of another and (3) extracts insights from real-world big data and directly offers many implications for practice. In the first stage of the CBCEM-CoD k-means and agglomerative clustering techniques are used comparatively for segmenting customers and products. CoD values of each product and customer segment are predicted using ensemble learning techniques in the second stage. The model is tested using a case data set from the kitchenware industry. As a result 53 and 720 different types of customers and products in the train data set are segmented in optimal numbers of 4 and 20 clusters. Around 89% accuracy is obtained for CoD predictions in the test data set. These results have substantial business value since they inform managers how to prioritize their focus on specific products and customer types to reduce the cost of a defect. We also highlight the importance of developing BDAC in dynamically changing environments to create a competitive advantage. © 2022 Elsevier B.V. All rights reserved.