Warranty claim cost forecasting based on ARIMA Box-Jenkins approach
List of Authors
  • Ahmad Kadri Junoh , Nursafian Haris

Keyword
  • ARIMA Box-Jenkins, Warranty Forecasting, Box-Cox Transformation

Abstract
  • Warranty claims reported in recent months might carry more up-to-date information than those reported in earlier months. Depending on the technological development, forecasting of produced quantity rejection is main aspect of a manufacturing company to the plan of services; predict the approximate warranty cost and customer satisfactions. An attempt has been made to develop a forecasting model from the existing seasonal timely behaviour warranty claim cost using Box-Jenkins approach - Auto Regressive Integrated Moving Average (ARIMA) methodology for building forecasting model. This includes the observation and monitoring of warranty trend from the existing actual warranty data, by plotting warranty claim cost over Repair Month or Complaint Month. These trends have been deployed into statistical means of Box-Jenkins approach, to define the best Model Equation. The proposed model equation has a significant impact towards a reliable and convincing figure – another key factor in warranty budgeting and accrual task.

Reference
  • 1. Abdullah, L. (2012). ARIMA Model for Gold Bullion Coin Selling Prices Forecasting. International Journal of Advances in Applied Sciences, 1(4). https://doi.org/10.11591/ijaas.v1i4.1495

    2. Ahammed, B., & MesbahulAlam, M. (2012). Forecasting failure number using warranty claims in multiplicative composite scale. International Conference on Statistical Data Mining for Bioinformatics Health Agriculture and Environment, 540–653.

    3. Ahmad, W. M. A. W., Naing, N. N., & Halim, N. A. (2008). An Application Of Box-Cox Transformation To Biostatistics Experiment Data. Journal of Bioscience, 19(1), 137–145. http://myais.fsktm.um.edu.my/6680/

    4. Ali, A., Ch, M. I., Qamar, S., Akhtar, N., Mahmood, T., Hyder, M., & Jamshed, M. T. (2016). Forecasting of Daily Gold Price by Using Box-Jenkins Methodology. International Journal of Asian Social Science, 6(11), 614–624. https://doi.org/10.18488/journal.1/2016.6.11/1.11.614.624

    5. Alsharif, M. H., Younes, M. K., & Kim, J. (2019). Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea. Symmetry, 11(2), 1–17. https://doi.org/10.3390/sym11020240

    6. Bakar, N. A., & Rosbi, S. (2017). Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction. International Journal of Advanced Engineering Research and Science, 4(11), 130–137. https://doi.org/10.22161/ijaers.4.11.20

    7. Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal Ofthe Royal Statistical Society. Series B (Methodological, 26(2), 211–252.

    8. Chen, T. T., & Takaishi, T. (2014). Box-Cox transformation of firm size data in statistical analysis. Journal of Physics: Conference Series, 490(1). https://doi.org/10.1088/1742-6596/490/1/012182

    9. Dritsaki, D. C. (2015). Forecasting Real GDP Rate through Econometric Models: An Empirical Study from Greece. Journal of International Business and Economics, 3(1), 13–19. https://doi.org/10.15640/jibe.v3n1a2

    10. Eni, D., & Adeyeye, F. J. (2015). Seasonal ARIMA Modeling and Forecasting of Rainfall in Warri Town, Nigeria. Journal of Geoscience and Environment Protection, 03(06), 91–98. https://doi.org/10.4236/gep.2015.36015

    11. Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10, 1–9. https://doi.org/10.1177/1847979018808673

    12. Gopal, K., Abdul, R. M. F., & Adam, M. B. (2017). Box-cox transformation of monthly Malaysian gold price range. Malaysian Journal of Mathematical Sciences, 11(S2), 107–118.

    13. Hipel, Keith W., & McLeod, A. Ian. (1994). Time series modelling of water resources and environmental systems. In Time series modelling of water resources and environmental systems. Elsevier Science B.V. https://doi.org/10.1016/0022-1694(95)90010-1

    14. Iqbal, N., Bakhsh, K., Maqbool, A., & Abid Shohab, A. (2005). Use of the ARIMA Model for Forecasting Wheat Area and Production in Pakistan. Journal of Agriculture and Social Science, 1(2), 120–122.

    15. Ismail, A., Truong, H. L., & Kastner, W. (2019). Manufacturing process data analysis pipelines: a requirements analysis and survey. Journal of Big Data, 6(1), 1–26. https://doi.org/10.1186/s40537-018-0162-3

    16. Judge, G. G., Carter Hill, R., Griffiths, W. E., Lütkepohl, H., & Lee, T.-C. (1982). Introduction to the Theory and Practice of Econometrics. John Wiley & Sons. Inc.

    17. Judge, G. G., Hill, C., Griffiths, W. E., Lutkepohl, H., & Lee, T.-C. (1988). Introduction to the Theory and Practice of Econometrics. Journal of the American Statistical Association, 83(404), 1229. https://doi.org/10.2307/2290184

    18. Li, L., Ma, Z., Liu, L., & Fan, Y. (2013). Hadoop-based ARIMA Algorithm and its Application in Weather Forecast. International Journal of Database Theory and Application, 6(5), 119–132. https://doi.org/10.14257/ijdta.2013.6.5.11

    19. Mečiarová, Z. (2007). Modeling and Forecasting Seasonal Time Series. Journal of Information, Control and Management Systems, 5(1), 73–80.

    20. Mishra, N., & Jain, E. A. (2014). Time Series Data Analysis for Forecasting – A Literature Review. International Journal Of Modern Engineering Research (IJMER) ISSN: 2249–6645, 4(7), 1–5.

    21. Mohamed, N., Ahmad, M. H., Ismail, Z., & Suhartono. (2010). Double Seasonal ARIMA Model for Forecasting Load Demand. Matematika, 26(2), 217–231.

    22. Mondal, P., Shit, L., & Goswami, S. (2014). Study of Effectiveness of Time Series Modeling (Arima) in Forecasting Stock Prices. International Journal of Computer Science, Engineering and Applications, 4(2), 13–29. https://doi.org/10.5121/ijcsea.2014.4202

    23. Mong, T., & Ngan, U. (2016). Forecasting Foreign Exchange Rate by using ARIMA Model : A Case of VND / USD Exchange Rate. Research Journal of Finance and Accounting, 7(12), 38–44.

    24. Nopiah, Z. M., Lennie, A., Abdullah, S., Nuawi, M. Z., Nuryazmin, A. Z., & Baharin, M. N. (2012). The Use of Autocorrelation Function in the Seasonality Analysis for Fatigue Strain Data. Journal of Asian Scientific Research, 2(11), 782–788.

    25. Nwakuya, M. T., & Nwabueze, J. C. (2018). Application of Box-Cox Transformation as a Corrective Measure to Heteroscedasticity Using an Economic Data. American Journal of Mathematics and Statistics, 8(1), 8–12. https://doi.org/10.5923/j.ajms.20180801.02

    26. Oliveira, P. J., Steffen, J. L., & Cheung, P. (2017). Parameter Estimation of Seasonal Arima Models for Water Demand Forecasting Using the Harmony Search Algorithm. Procedia Engineering, 186, 177–185. https://doi.org/10.1016/j.proeng.2017.03.225

    27. Osborne, J. W. (2010). Improving your data transformations: Applying the Box-Cox transformation. Practical Assessment, Research and Evaluation, 15(12).

    28. Rai, B. K., & Singh, N. (2009). RELIABILITY ANALYSIS and PREDICTION with WARRANTY DATA. Issues, Strategies, and Methods. In CRC Press. Taylor & Francis Group LLC.

    29. Rotela Junior, P., Salomon, F. L. R., & de Oliveira Pamplona, E. (2014). ARIMA: An Applied Time Series Forecasting Model for the Bovespa Stock Index. Applied Mathematics, 05(21), 3383–3391. https://doi.org/10.4236/am.2014.521315

    30. Sakia, R. M. (1992). The Box-Cox Transformation Technique: A Review. The Statistician, 41(2), 169. https://doi.org/10.2307/2348250

    31. Shafiee, M., & Chukova, S. (2013). Maintenance models in warranty: A literature review. European Journal of Operational Research, 229(3), 561–572. https://doi.org/10.1016/j.ejor.2013.01.017

    32. Singh, A., & Mishra, G. C. (2015). Application of box-jenkins method and artificial neural network procedure for time series forecasting of prices. Statistics in Transition, 16(1), 83–96. https://doi.org/10.21307/stattrans-2015-005

    33. Vélez, J. I., Correa, J. C., & Marmolejo-Ramos, F. (2015). A new approach to the Box–Cox transformation. Frontiers in Applied Mathematics and Statistics, 1(October), 1–10. https://doi.org/10.3389/fams.2015.00012

    34. Verband der Automobilindustrie. (2009). Joint Quality Management in the Supply Chain Marketing and Service, Field failure analysis (1st ed., Issue 1). Henrich Druck + Medien.

    35. Zurita, D., Delgado, M., Carino, J. A., Ortega, J. A., & Clerc, G. (2016). Industrial Time Series Modelling by Means of the Neo-Fuzzy Neuron. IEEE Access, 4, 6151–6160. https://doi.org/10.1109/ACCESS.2016.2611649