Abstract: A method for determining an optimum buffer inventory size by generating a time-based demand pattern that is based on processing discreet demand data through a sliding time window and summing the demand quantity in that time window, and repeating that process as the time window then moves through the time-line of discrete demand, thereby establishing a pattern of cumulative demand in windows-of-time along a time-line. The process moves along the demand time-line from one demand point to the next demand point, summing up all of the demand in a window of time that corresponds to the Time-to-Reliably Replenish (TRR). The method plots each summation of demand for its corresponding window of time (TRR) on a time-line at a point in time coinciding with the first demand data element in each summation. The resulting buffer size is based on the conversion of discrete demand data into time-based demand patterns.

Abstract: A method for determining an optimum buffer inventory size by generating a time-based demand pattern that is based on processing discreet demand data through a sliding time window and summing the demand quantity in that time window, and repeating that process as the time window then moves through the time-line of discrete demand, thereby establishing a pattern of cumulative demand in windows-of-time along a time-line. The process moves along the demand time-line from one demand point to the next demand point, summing up all of the demand in a window of time that corresponds to the Time-to-Reliably Replenish (TRR). The method plots each summation of demand for its corresponding window of time (TRR) on a time-line at a point in time coinciding with the first demand data element in each summation. The resulting buffer size is based on the conversion of discrete demand data into time-based demand patterns.