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.