Determining the Optimum Historical Data Period for Forcasting with Price Floating Rates

A reliable forecast surely directs the budget control towards to achieve optimal. To suitably select the period of historical data has been critical and rebellious in determining the accuracy of forecasts. The random demand, order quantity, lead-time and fraction nonconforming of an order, as well as the holding cost, ordering cost, and the penalty cost are considered simultaneously into this study. This study concentrates on the determination of historical data period with respect to the random/constant environment in forecasting of the material ordering/inventory cost budget through the price floating rates. In addition, the computer program on dynamic. determination of the historical data period for forecasting with Monte Carlo Mathematical Method is also proposed. This study definitely provides an efficient and practical tool to evaluate the optimal historical data period in the price floating rates of material ordering and inventory system. 1. Introduction Since two decades ago, the way to prevent and to manage risks in organizations has been significantly noticed [1 -4]. Truly, a precise forecast of the future uncertainty can minimize the risk of cost overestimation or underestimation, and a reliable forecasting method can avoid these problems successfully. Although there have several applicable forecast techniques in this viewpoint, none is well fit for today's dynamic circumstance. This study is to investigate a better historical data period in the budget forecasting of material ordering and inventory under price floating situation for future uncertainty. Most inventory management studies mainly emphasize on the unstable material demand [5-7]. However, the uncertain demand is presented in corresponding to the practical situation in this paper, and a random number is proposed to represent the uncertain material demand for every single time interval. Most studies assume that lead-time is a given parameter, and the reorder point is also given. This study focuses on the unstable lead-time, uncertain order quantity, random demand, and fixed reorder point to make it closer to real industry cases. In addition, the period and unit time-interval for simulation are one quarter of a year and a week respectively. Moreover, the procedure with the use of Monte Carlo Method [8,9] to determine the optimal period of historical data under the different range of price floating rate is introduced in this study. Truly, this paper provides a more precise decision criterion for uncertain future in cost budget of material ordering and inventory. 2. Assumptions and Notations Before introducing this study, several assumptions and notations are to be made. They are described as follows: 2.1 Assumptions 1. The ordering cost, penalty cost and holding cost are known, and are fixed during the simulation period. 2. The change rage of price floating rate is set as 0.01 per time in this study. …