Promoting a novel method for warranty claim prediction based on social network data

Abstract Warranty plays an important role in retaining consumers' loyalty, increasing the competitive advantage and the profit of companies. Moreover, warranty claim prediction based on social media is a novel area, enabling managers to foresee problems in production and take the proper measures to mitigate them. The higher the precision of the warranty claim predictions, the lower the risk the company faces. This paper examines the impacts of utilizing social media data on daily warranty claim prediction. In this paper, we showed that social media data could enhance the accuracy of daily warranty claim predictions. We cooperated with Sam Service Warranty Company that provides warranty and aftersales services for Samsung products in Iran. Warranty operational data along with Twitter data analyses were used to improve the precision of warranty claim prediction. Operational data from Sam Service Company include the total number of warranties, the number of warranties for new customers, and the number of warranties for those who return. A novel framework was presented that uses the Random Forrest algorithm for prediction of the number of daily warranty claims. The results show that our framework improves the accuracy of out-of-sample warranty claims predictions, with respective development at a range of 14.98% to 21.90% across various timeframes. Improving prediction accuracy enables managers to effectively minimize warranty-related costs, inventory levels, waste, and customer dissatisfaction while maximizing the return on investment, profit, efficiency, and customer satisfaction.

[1]  Tsan-Ming Choi,et al.  When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management? , 2020 .

[2]  Fabrice Rossi,et al.  Mean Absolute Percentage Error for regression models , 2016, Neurocomputing.

[3]  Jun Deng,et al.  A random forest approach for predicting coal spontaneous combustion , 2018, Fuel.

[4]  Mohammed Alkahtani,et al.  A decision support system based on ontology and data mining to improve design using warranty data , 2019, Comput. Ind. Eng..

[5]  Kathryn Wifvat,et al.  Copula Model Selection for Vehicle Component Failures Based on Warranty Claims , 2020, Risks.

[6]  Sama Azadi,et al.  Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran. , 2016, Waste management.

[7]  Ron Kohavi,et al.  Mining e-commerce data: the good, the bad, and the ugly , 2001, KDD '01.

[8]  Minghong Lin,et al.  Stochastic analysis of file-swarming systems , 2007, Perform. Evaluation.

[9]  Zongsheng Huang,et al.  Money-back guarantee and pricing decision with retailer's store brand , 2020 .

[10]  Bhanu S. Ragu-Nathan,et al.  The impact of supply chain management practices on competitive advantage and organizational performance , 2006 .

[11]  Amandeep Dhir,et al.  Tweeters on Campus: Twitter a Learning Tool in Classroom? , 2013, J. Univers. Comput. Sci..

[12]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[13]  Zhenguo Yang,et al.  Random Forest Exploiting Post-related and User-related Features for Social Media Popularity Prediction , 2018, ACM Multimedia.

[14]  Kunal K. Ganguly,et al.  Social Media in Operations and Supply Chain Management: A Systematic Literature Review to Explore the Future , 2021 .

[15]  David Zilberman,et al.  Demonstrations and money-back guarantees: market mechanisms to reduce uncertainty , 2001 .

[16]  Sajjad Shokouhyar,et al.  Promoting consumer's attitude toward refurbished mobile phones: A social media analytics approach , 2021 .

[17]  Slawomir Nowaczyk,et al.  Early Prediction of Quality Issues in Automotive Modern Industry , 2020, Inf..

[18]  Exploring the Impacts of Service Guarantee Strategy , 2012 .

[19]  Andrea Bonaccorsi,et al.  Technical Sentiment Analysis. Measuring Advantages and Drawbacks of New Products Using Social Media , 2020, Comput. Ind..

[20]  Kuldip Singh Sangwan,et al.  Evaluation of collection methods in reverse logistics by using fuzzy mathematics , 2015 .

[21]  Reza Khoshkangini,et al.  Warranty Claim Rate Prediction Using Logged Vehicle Data , 2019, EPIA.

[22]  R. Zmeureanu,et al.  A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings , 2021, Energies.

[23]  Zhen He,et al.  A novel probabilistic graphic model to detect product defects from social media data , 2020, Decis. Support Syst..

[24]  Nipon Theera-Umpon,et al.  A Partially Interpretable Adaptive Softmax Regression for Credit Scoring , 2021, Applied Sciences.

[25]  D. N. Prabhakar Murthy,et al.  Warranty menu design for a two-dimensional warranty , 2016, Reliab. Eng. Syst. Saf..

[26]  S. Seshadri,et al.  Sales Forecasting with Financial Indicators and Experts' Input , 2008 .

[27]  Yong He,et al.  Compensation and information disclosure strategies of a green supply chain under production disruption , 2020 .

[28]  Deyun Chen,et al.  System dynamics research of remanufacturing closed-loop supply chain dominated by the third party , 2017, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[29]  M. Zahedifard,et al.  E-mail Marketing: Advantages, Disadvantages and Improving Techniques , 2012 .

[30]  Rob Law,et al.  Review of tourism forecasting research with internet data , 2021 .

[31]  Shaomin Wu,et al.  Support vector regression for warranty claim forecasting , 2011, Eur. J. Oper. Res..

[32]  Wei Zhang,et al.  Warranty strategy in a supply chain when two retailer’s extended warranties bundled with the products , 2015 .

[33]  Matthias Grabert,et al.  Warranty Cost Forecast Based on Car Failure Data , 2007, 2007 International Joint Conference on Neural Networks.

[34]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[35]  D. Murthy,et al.  New product warranty: A literature review , 2002 .

[36]  Shaomin Wu,et al.  Forecasting warranty claims for recently launched products , 2012, Reliab. Eng. Syst. Saf..

[37]  Shaomin Wu,et al.  Warranty Data Analysis: A Review , 2012, Qual. Reliab. Eng. Int..

[38]  Morteza Nagahi,et al.  Analysis of a Warranty-Based Quality Management System in the Construction Industry , 2020, 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI).

[39]  Jan Bosch,et al.  Customer Feedback and Data Collection Techniques in Software R&D: A Literature Review , 2015, ICSOB.

[40]  Felician Campean,et al.  Vehicle Warranty Claim Prediction from Diagnostic Data Using Classification , 2019, UKCI.

[41]  Bharatendra Rai,et al.  Warranty Spend Forecasting for Subsystem Failures Influenced by Calendar Month Seasonality , 2009, IEEE Transactions on Reliability.

[42]  Ling Shao,et al.  Deep attentive and semantic preserving video summarization , 2020, Neurocomputing.

[43]  N. Sanders,et al.  Forecasting sales in the supply chain: Consumer analytics in the big data era , 2019, International Journal of Forecasting.

[44]  Vladimir M. Popovic,et al.  Warranty optimisation based on the prediction of costs to the manufacturer using neural network model and Monte Carlo simulation , 2015, Int. J. Syst. Sci..

[45]  K. Srinivasan,et al.  Signaling Quality with a Money-Back Guarantee: The Role of Transaction Costs , 1995 .

[46]  Andre Kleyner,et al.  Forecasting the cost of unreliability for products with two-dimensional warranties , 2006 .

[47]  R Yunanto.  Android-based Social Media System of Household Waste Recycling: Designing and User Acceptance Testing , 2018 .

[48]  Sule Gündüz Ögüdücü,et al.  Identifying topical influencers on twitter based on user behavior and network topology , 2018, Knowl. Based Syst..

[49]  Ammar Y. Alqahtani,et al.  Money-back guarantee warranty policy with preventive maintenance strategy for sensor-embedded remanufactured products , 2018 .

[50]  Nanua Singh,et al.  Forecasting warranty performance in the presence of the ‘maturing data’ phenomenon , 2005, Int. J. Syst. Sci..

[51]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[52]  Fei Tao,et al.  Analysis of Customer Reviews for Product Service System Design based on Cloud Computing , 2019, Procedia CIRP.

[53]  Wei Xie,et al.  Two-dimensional aggregate warranty demand forecasting under sales uncertainty , 2017 .

[54]  Tonya Boone,et al.  Can Google Trends Improve Your Sales Forecast? , 2018 .

[55]  Zhen He,et al.  Warranty claims forecasting based on a general imperfect repair model considering usage rate , 2016, Reliab. Eng. Syst. Saf..

[56]  M. Nasrollahi.  The Impact of Firm's Social Media Applications on Green Supply Chain Management , 2018 .

[57]  Jie Chen,et al.  Research on classification method of spare parts inventory based on warranty data , 2016, 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI).

[58]  T. Ramayah,et al.  The role of social media on recycling behaviour , 2019, Sustainable Production and Consumption.

[59]  Jestin Joy,et al.  Comparative analysis of Machine Learning approaches for early stage Cervical Spondylosis detection , 2020, J. King Saud Univ. Comput. Inf. Sci..

[60]  Wei Xie,et al.  Two-dimensional warranty: A literature review , 2017 .

[61]  Chun Wang,et al.  Social Media Prediction Based on Residual Learning and Random Forest , 2017, ACM Multimedia.

[62]  Davorin Kofjač,et al.  Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period , 2020, Business Systems Research Journal.

[63]  Bin Liu,et al.  A performance-based warranty for products subject to competing hard and soft failures , 2020, International Journal of Production Economics.

[64]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[65]  Sajjad Shokouhyar,et al.  Toward consumer perception of cellphones sustainability: A social media analytics , 2021 .

[66]  D. Carless,et al.  Authentic feedback: supporting learners to engage in disciplinary feedback practices , 2020, Assessment & Evaluation in Higher Education.

[67]  Shaomin Wu,et al.  A review on coarse warranty data and analysis , 2013, Reliab. Eng. Syst. Saf..

[68]  Kazuyuki Suzuki,et al.  Analysis of warranty claim data: a literature review , 2005 .

[69]  Rd Rohmat Saedudin,et al.  Comparison between ANN and Multiple Linear Regression Models for Prediction of Warranty Cost , 2018, International Journal of Integrated Engineering.

[70]  Rim Faiz,et al.  Extracting Product Features for Opinion Mining Using Public Conversations in Twitter , 2017, KES.

[71]  Kum Fai Yuen,et al.  Social Media Engagement of Stakeholders: A Decision Tree Approach in Container Shipping , 2020, Comput. Ind..

[72]  David L Loudon,et al.  The Marketing Research Guide , 1997 .

[73]  Yiwen Zhang,et al.  Manufacturer warranty service outsourcing strategies in a dual-channel supply chain , 2020, Int. Trans. Oper. Res..

[74]  Kang Li,et al.  Financial innovation: Credit default hybrid model for SME lending , 2016, Expert Syst. Appl..

[75]  J. Lee,et al.  The impact of K-beauty social media influencers, sponsorship, and product exposure on consumer acceptance of new products , 2021, Fashion and Textiles.

[76]  I. J. Orji,et al.  The critical success factors of using social media for supply chain social sustainability in the freight logistics industry , 2020, Int. J. Prod. Res..

[77]  Antonio Moreno,et al.  The Operational Value of Social Media Information , 2018 .

[78]  Vasudeva Akula,et al.  Identifying customer knowledge on social media through data analytics , 2019, J. Enterp. Inf. Manag..

[79]  Tsan-Ming Choi,et al.  Pareto Improving Supply Chain Coordination Under a Money-Back Guarantee Service Program , 2017 .

[80]  Tong Peng,et al.  Designing differential service strategy for two-dimensional warranty based on warranty claim data under consumer-side modularisation , 2020 .

[81]  Robbi Rahim,et al.  Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error , 2017 .

[82]  Terence A. Oliva,et al.  The Role of the Internet in Supply Chain Management , 2000 .

[83]  Zhen He,et al.  Warranty Claims Forecasting for New Products Sold with a Two-Dimensional Warranty , 2019 .