System and method of simultaneous computation of optimal order point and optimal order quantity
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.
1. A computer-implemented method, comprising:
receiving initial inputs for a multi-echelon supply chain network, over a computer network, the multi-echelon supply chain network comprises at least one first entity that produces an item and at least one final entity that fills a customer demand, each of the two or more entities comprising at least one item-location;
comparing a target performance of an item-location of the at least one final entity with a base performance of the item-location;
when the base performance does not meet the target performance, calculating a system performance derivative for each item-location in response to increasing the ordering quantity by an ordering quantity increment and the reorder point by a reorder point increment;
calculating a system performance in response to increasing the ordering quantity or the reorder point at the item-location having a best system performance derivative; and
when the system performance meets the target performance, adjusting the ordering quantity of at least one final entity such that the size of the order is based, at least in part, on the increased ordering quantity or the increased reorder point of the item-location having the best system performance derivative.
2. The computer-implemented method of claim 1 , further comprising:
iteratively calculating the system performance derivative for each item-location in response to increasing the ordering quantity by the ordering quantity increment and the reorder point by the reorder point increment and the system performance in response to increasing the ordering quantity or the reorder point at the item-location having the best system performance derivative, until the new system performance meets the target performance of each final entity of the multi-echelon supply chain network.
3. The computer-implemented method of claim 2 , wherein the system performance derivative is calculated based, at least in part, on a system inventory performance and a system inventory cost.
4. The computer-implemented method of claim 3 , further comprising:
when the best system performance derivative is calculated for increasing the reorder point increment, setting the reorder point of the item-location having the best system performance to the reorder point plus the reorder point increment; and
when the best system performance derivative is calculated for increasing the ordering quantity increment, setting the ordering quantity of the item-location having the best system performance to the ordering quantity plus the ordering quantity increment.
5. The computer-implemented method of claim 4 , wherein the system performance is calculated by:
creating a sorted list by sorting each of the two or more entities in the multi-echelon supply chain network according to an item and a level of the item-location;
determining whether an item-location comprises a source;
when the item-location comprises a source, adding a customer wait time of the source to a lead-time of the item-location and adding a customer wait time variance to a lead-time variance of the item-location.
6. The computer-implemented method of claim 5 , wherein the system inventory cost is calculated by computing an average inventory cost.
7. The computer-implemented method of claim 6 , wherein the target performance comprises one or more of a fill rate and the customer wait time.
8. A system, comprising:
a computer, comprising one or more processors and a memory, configured to:
receive initial inputs for a multi-echelon supply chain network, over a computer network, the multi-echelon supply chain network comprises at least one first entity that produces an item and at least one final entity that fills a customer demand, each of the two or more entities comprising at least one item-location;
compare a target performance of an item-location of the at least one final entity with a base performance of the item-location;
when the base performance does not meet the target performance, calculate a system performance derivative for each item-location in response to increasing the ordering quantity by an ordering quantity increment and the reorder point by a reorder point increment;
calculate a system performance in response to increasing the ordering quantity or the reorder point at the item-location having a best system performance derivative; and
when the system performance meets the target performance, adjust the ordering quantity of at least one final entity such that the size of the order is based, at least in part, on the increased ordering quantity or the increased reorder point of the item-location having the best system performance derivative.
9. The system of claim 8 , wherein the computer is further configured to:
iteratively calculate the system performance derivative for each item-location in response to increasing the ordering quantity by the ordering quantity increment and the reorder point by the reorder point increment and the system performance in response to increasing the ordering quantity or the reorder point at the item-location having the best system performance derivative, until the new system performance meets the target performance of each final entity of the multi-echelon supply chain network.
10. The system of claim 9 , wherein the system performance derivative is calculated based, at least in part, on a system inventory performance and a system inventory cost.
11. The system of claim 10 , wherein the computer is further configured to:
when the best system performance derivative is calculated for increasing the reorder point increment, set the reorder point of the item-location having the best system performance to the reorder point plus the reorder point increment; and
when the best system performance derivative is calculated for increasing the ordering quantity increment, set the ordering quantity of the item-location having the best system performance to the ordering quantity plus the ordering quantity increment.
12. The system of claim 11 , wherein the computer is configured to calculate the system performance by:
creating a sorted list by sorting each of the two or more entities in the multi-echelon supply chain network according to an item and a level of the item-location;
determining whether an item-location comprises a source; and
when the item-location comprises a source, adding a customer wait time of the source to a lead-time of the item-location and adding a customer wait time variance to a lead-time variance of the item-location.
13. The system of claim 12 , wherein the system inventory cost is calculated by computing an average inventory cost.
14. The system of claim 13 , wherein the target performance comprises one or more of a fill rate and the customer wait time.
15. A non-transitory computer-readable medium embodied with software, the software when executed:
receives initial inputs for a multi-echelon supply chain network, over a computer network, the multi-echelon supply chain network comprises at least one first entity that produces an item and at least one final entity that fills a customer demand, each of the two or more entities comprising at least one item-location;
compares a target performance of an item-location of the at least one final entity with a base performance of the item-location;
when the base performance does not meet the target performance, calculates a system performance derivative for each item-location in response to increasing the ordering quantity by an ordering quantity increment and the reorder point by a reorder point increment;
calculates a system performance in response to increasing the ordering quantity or the reorder point at the item-location having a best system performance derivative; and
when the system performance meets the target performance, adjusts the ordering quantity of at least one final entity such that the size of the order is based, at least in part, on the increased ordering quantity or the increased reorder point of the item-location having the best system performance derivative.
16. The non-transitory computer-readable medium of claim 15 , wherein the software when executed further:
iteratively calculates the system performance derivative for each item-location in response to increasing the ordering quantity by the ordering quantity increment and the reorder point by the reorder point increment and the system performance in response to increasing the ordering quantity or the reorder point at the item-location having the best system performance derivative, until the new system performance meets the target performance of each final entity of the multi-echelon supply chain network.
17. The non-transitory computer-readable medium of claim 16 , wherein the system performance derivative is calculated based, at least in part, on a system inventory performance and a system inventory cost.
18. The non-transitory computer-readable medium of claim 17 , wherein the software when executed further:
when the best system performance derivative is calculated for increasing the reorder point increment, sets the reorder point of the item-location having the best system performance to the reorder point plus the reorder point increment; and
when the best system performance derivative is calculated for increasing the ordering quantity increment, sets the ordering quantity of the item-location having the best system performance to the ordering quantity plus the ordering quantity increment.
19. The non-transitory computer-readable medium of claim 18 , wherein the software when executed calculates the system performance by:
creating a sorted list by sorting each of the two or more entities in the multi-echelon supply chain network according to an item and a level of the item-location;
determining whether an item-location comprises a source; and
when the item-location comprises a source, adding a customer wait time of the source to a lead-time of the item-location and adding a customer wait time variance to a lead-time variance of the item-location.
20. The non-transitory computer-readable medium of claim 19 , wherein the system inventory cost is calculated by computing an average inventory cost.