Abstract:
A system and method is disclosed for root cause analysis and early warning of inventory problems. The system includes a server coupled with a database and configured to access the data describing inventory policy parameters of a supply chain network, the data describing one or more demand patterns and one or more replenishment patterns of the supply chain network, and the data describing the supply chain network comprising a plurality of entities, each entity configured to supply one or more items to satisfy a demand. The server is further configured to optimize the inventory policy parameters for each of the one or more items according to the one or more demand patterns and the one or more replenishment patterns and store the optimized inventory policy parameters in the database for each of the one or more items.
Abstract:
Determining an inventory target for a node of a supply chain includes calculating a demand stock for satisfying a demand over supply lead time at the node of the supply chain, and calculating a demand variability stock for satisfying a demand variability of the demand over supply lead time at the node. A demand bias of the demand at the node is established. An inventory target for the node is determined based on the demand stock and the demand variability stock in accordance with the demand bias.
Abstract:
In one embodiment, determining order lead time for a supply chain includes generating probability distribution for expected order lead time options, where each probability distribution for expected order lead time option is associated with a category. A category that corresponds to a supply chain is identified. The supply chain has nodes, including a starting node and an ending node that supplies a customer, and designates a path from the starting node to the ending node. A probability distribution for expected order lead time option associated with the identified category is selected as a probability distribution for expected order lead time for the supply chain. The probability distribution for expected order lead time describes ending node demand for the ending node versus order lead time.
Abstract:
In one embodiment, estimating demand for a supply chain includes accessing a probability distribution for expected order lead time of the supply chain. The supply chain has nodes including a starting node and an ending node and a path from the starting node to the ending node. The probability distribution for expected order lead time describes ending node demand for the ending node versus order lead time. The path is divided into order lead time segments, and the order lead time segments are associated with the probability distribution for expected order lead time by associating each order lead home segment with a corresponding order lead time range of the probability distribution for expected order lead time. A demand percentage is estimated for each order lead time segment in accordance with the probability distribution for expected order lead time in order to estimate demand for the supply chain. Each demand percentage describes a percentage of a total ending node demand associated with the corresponding order lead time segment.
Abstract:
In one embodiment, optimizing inventory includes accessing service level band sets. Each service level band set is associated with a policy group, and includes service level bands. Each service level band of a service level band set has a service level priority with respect to any other service level bands of the same service level band set. An inventory band set is determined for each service level band set. Each inventory band set includes inventory bands, where each inventory band satisfies a corresponding service level band assuming an unconstrained network. Each inventory band of an inventory band set has an inventory priority with respect to any other inventory bands of the same inventory band set. A feasible supply chain plan that satisfies the inventory band sets is generated in order of the inventory priorities until a constrained network is depleted.
Abstract:
In one embodiment, optimizing inventory for a supply chain includes generating an inventory plan for the supply chains. Execution of a supply chain plan associated with the inventory plan is initiated at the supply chain. The supply chain is monitored to generate metric values. A watchpoint triggered by a metric value is detected, and a cause of the triggered watchpoint is identified using a causal tree. The inventory plan is adjusted in response to the detected triggered watchpoint and in accordance with the identified cause, and the supply chain plan is adjusted in accordance with the adjusted inventory plan. Execution of the adjusted supply chain plan is initiated, and new metric values are measured to determine performance. The performance is evaluated, and the causal tree is updated in response to the evaluation.
Abstract:
A system and method is disclosed for estimating demand of a supply chain including accessing a probability distribution of order lead time of the supply chain. The supply chain has nodes including a starting node and an ending node and a path from the starting node to the ending node. The probability distribution of order lead time describes ending node demand of the ending node versus order lead time. The path is divided into order lead time segments which are associated with the probability distribution of order lead time by associating each order lead time segment with an order lead time range of the probability distribution of order lead time. A demand percentage is estimated for each order lead time segment in accordance with the probability distribution of order lead time, such that each demand percentage describes a percentage of a total ending node demand of an order lead time segment.
Abstract:
Determining an inventory target for a node of a supply chain includes calculating a demand stock for satisfying a demand over supply lead time at the node of the supply chain, and calculating a demand variability stock for satisfying a demand variability of the demand over supply lead time at the node. A demand bias of the demand at the node is established. An inventory target for the node is determined based on the demand stock and the demand variability stock in accordance with the demand bias.
Abstract:
Locations that include supply, manufacturing, demand locations, and channels are defined. A demand is computed for each part at each location. An availability lead-time is estimated for each part at each location and for each part at each channel. A total landed cost is calculated for each part at each location and each channel. A lead-time demand is computed for each part at each location using the availability lead-times for the part. A demand over lead-time is computed for each part at each location using the availability lead-times for the part. A completely filled demand is determined from the lead-time demands and the stock levels, and a partially filled demand is determined from the lead-time demands and the stock levels. A coverage function is generated for the parts at the locations and the channels from the completely filled demand and the partially filled demand.
Abstract:
A computer-implemented system, a method thereof, and a computer-readable medium comprising a supply chain visualizer providing a plurality of graphical elements in a multi-dimensional supply chain network view and a plan display providing a plurality of graphical elements in a multi-dimensional tabular view, a multi-directional capability for traversing between the supply chain visualizer and the plan display operable to receive input from a planner during a planning session specifying a plan problem, reflecting changes to data associated with the plan display back to data associated with the supply chain visualizer, reflecting changes to data associated with the supply chain visualizer back to data associated with the plan display, traversing the multi-dimensional supply chain network via a real-time interaction between the multi-dimensional tabular view of the plan display and the visual representation of the graphical view of the supply chain visualizer, and providing the planner the ability to generate a plan.