System and Method of Simultaneous Computation of Optimal Order Point and Optimal Order Quantity

Abstract

A system is disclosed for simultaneous computation of optimal order point and optimal order quantity. The system includes one or more memory units and on ore more processing units, collectively configured to receive initial inputs, initialize a first, at least second and final locations and the initial inputs and compute a first baseline inventory performance of the first level. The system is further configured to compute at least a second inventory performance of the at least second level and perform optimization iterations by simultaneously determining a change in inventory performance for the first and the at least second level when the re-order point (R) is incremented by a specified R increment value and when the re-order quantity is incremented by a specified Q increment value. The system is further configured to report the reorder point and reorder quantity for the first, the at least second, and the final location.

What is claimed is: 
     
         1 . A system, comprising:
 a computer, comprising a processor and memory, the computer configured to:
 receive initial inputs for item-locations of a multi-echelon supply chain network, each of the item-locations comprising a location level; 
 calculate inventory performance values for an item-location at a lower location level based, at least in part, on a customer wait time and a customer wait time variance of an item-location at a higher location level; 
 compare the inventory performance values of the item-location at the lower location level with a target performance of the item-location at the lower location level; 
 calculate a performance derivative ratio for the item-location at the lower location level for a system performance metric based on decrementing a reorder point, a reorder quantity, or both, the performance derivative ratio indicating a change in system performance for a change in inventory cost; and 
 adjust a size of the ordering quantity of at least one of the item-locations based, at least in part, on the decremented reorder point, reorder quantity, or both. 
   
     
     
         2 . The system of  claim 1 , wherein the computer is further configured to:
 perform a hypothesis of decrementing the reorder point or the reorder quantity by comparing the performance derivative ratio for each of one or more iterations to determine a least reduction in inventory performance for a highest reduction in the inventory cost.   
     
     
         3 . The system of  claim 2 , wherein the computer is further configured to:
 remove an item-location from being evaluated in the one or more iterations when the decrementing of the reorder point, reorder quantity or both results in violation of performance constraints or target performance.   
     
     
         4 . The system of  claim 1 , wherein the computer is further configured to:
 calculate one or more of expected fill rate, expected back order, stock level, average inventory cost and cycle stock.   
     
     
         5 . The system of  claim 1 , wherein the computer is further configured to:
 compute a forecast error by calculating a mean square error of a comparison of a historical forecast with actual customer orders.   
     
     
         6 . The system of  claim 1 , wherein the computer is further configured to:
 compute a lead time error by calculating a mean square error of a comparison of a historical production and shipment plan with actual production and shipments.   
     
     
         7 . The system of  claim 1 , wherein an item at an item-location is based on the item being procured or produced. 
     
     
         8 . A computer-implemented method, comprising:
 receiving initial inputs for item-locations of a multi-echelon supply chain network, each of the item-locations comprising a location level;   calculating inventory performance values for an item-location at a lower location level based, at least in part, on a customer wait time and a customer wait time variance of an item-location at a higher location level;   comparing the inventory performance values of the item-location at the lower location level with a target performance of the item-location at the lower location level;   calculating a performance derivative ratio for the item-location at the lower location level for a system performance metric based on decrementing a reorder point, a reorder quantity, or both, the performance derivative ratio indicating a change in system performance for a change in inventory cost; and   adjusting a size of the ordering quantity of at least one of the item-locations based, at least in part, on the decremented reorder point, reorder quantity, or both.   
     
     
         9 . The computer-implemented method of  claim 8 , further comprising:
 performing a hypothesis of decrementing the reorder point or the reorder quantity by comparing the performance derivative ratio for each of one or more iterations to determine a least reduction in inventory performance for a highest reduction in the inventory cost.   
     
     
         10 . The computer-implemented method of  claim 9 , further comprising:
 removing an item-location from being evaluated in the one or more iterations when the decrementing of the reorder point, reorder quantity or both results in violation of performance constraints or target performance.   
     
     
         11 . The computer-implemented method of  claim 8 , further comprising:
 calculating one or more of expected fill rate, expected back order, stock level, average inventory cost and cycle stock.   
     
     
         12 . The computer-implemented method of  claim 8 , further comprising:
 computing a forecast error by calculating a mean square error of a comparison of a historical forecast with actual customer orders.   
     
     
         13 . The computer-implemented method of  claim 8 , further comprising:
 computing a lead time error by calculating a mean square error of a comparison of a historical production and shipment plan with actual production and shipments.   
     
     
         14 . The computer-implemented method of  claim 8 , wherein an item at an item-location is based on the item being procured or produced. 
     
     
         15 . A non-transitory computer-readable medium embodied with software, the software when executed:
 receives initial inputs for item-locations of a multi-echelon supply chain network, each of the item-locations comprising a location level;   calculates inventory performance values for an item-location at a lower location level based, at least in part, on a customer wait time and a customer wait time variance of an item-location at a higher location level;   compares the inventory performance values of the item-location at the lower location level with a target performance of the item-location at the lower location level;   calculates a performance derivative ratio for the item-location at the lower location level for a system performance metric based on decrementing a reorder point, a reorder quantity, or both, the performance derivative ratio indicating a change in system performance for a change in inventory cost; and   adjusts a size of the ordering quantity of at least one of the item-locations based, at least in part, on the decremented reorder point, reorder quantity, or both.   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein the software when executed further:
 performs a hypothesis of decrementing the reorder point or the reorder quantity by comparing the performance derivative ratio for each of one or more iterations to determine a least reduction in inventory performance for a highest reduction in the inventory cost.   
     
     
         17 . The non-transitory computer-readable medium of  claim 16 , wherein the software when executed further:
 removes an item-location from being evaluated in the one or more iterations when the decrementing of the reorder point, reorder quantity or both results in violation of performance constraints or target performance.   
     
     
         18 . The non-transitory computer-readable medium of  claim 15 , wherein the software when executed further:
 calculates one or more of expected fill rate, expected back order, stock level, average inventory cost and cycle stock.   
     
     
         19 . The non-transitory computer-readable medium of  claim 15 , wherein the software when executed further:
 computes a forecast error by calculating a mean square error of a comparison of a historical forecast with actual customer orders.   
     
     
         20 . The non-transitory computer-readable medium of  claim 15 , wherein the software when executed further:
 computes a lead time error by calculating a mean square error of a comparison of a historical production and shipment plan with actual production and shipments.