Predicting cost of defects for segmented products and customers using ensemble learning

Sariyer, Gorkem, Mangla, Sachin Kumar, Kazancoglu, Yigit, Xu, Lei and Ocal Tasar, Ceren (2022) Predicting cost of defects for segmented products and customers using ensemble learning. Computers and Industrial Engineering, 171: 108502. ISSN 03608352

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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.

Item Type: Article
Keywords: Big data analytics | Cost of defect | Decision making | Ensemble learning | Segmentation
Subjects: Social Sciences and humanities > Business, Management and Accounting > General Management
JGU School/Centre: Jindal Global Business School
Depositing User: Amees Mohammad
Date Deposited: 11 Aug 2022 11:02
Last Modified: 11 Aug 2022 11:02
Official URL:
Funders: SAFEA High-End Foreign Experts Project, National Natural Science Foundation of China, Civil Aviation University of China


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