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: To provide isolated extensibility for webpages, thereby enabling webpages to provide additional functionality supported by instructions whose execution is independent of execution of the instructions of the webpage, a unified manifest is utilized which comprises information regarding each of the extensions that can extend the functionality of the webpage, and a common extensibility platform script is invoked that provides the setup functionality to support such isolated extensibility. The extensibility platform script can be utilized by each extension thereby centralizing the relevant extension instantiation functionality. To provide for a unified manifest, a manifest associated with one extension can be copied into a manifest associated with another extension if the first extension indicates that it is capable of extending the second extension, or if the first extension is actually invoked by the second extension.

Abstract: To provide isolated extensibility for webpages, thereby enabling webpages to provide additional functionality supported by instructions whose execution is independent of execution of the instructions of the webpage, a unified manifest is utilized which comprises information regarding each of the extensions that can extend the functionality of the webpage, and a common extensibility platform script is invoked that provides the setup functionality to support such isolated extensibility. The extensibility platform script can be utilized by each extension thereby centralizing the relevant extension instantiation functionality. To provide for a unified manifest, a manifest associated with one extension can be copied into a manifest associated with another extension if the first extension indicates that it is capable of extending the second extension, or if the first extension is actually invoked by the second extension.

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: 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: Various techniques for chronology based content processing are disclosed herein. For example, in one embodiment, a method includes receiving a content containing a plurality of facts and determining a subject and a chronological value for each of the facts contained in the content. The subject includes at least one of a person, a place, an object, or an event, and wherein the chronological value includes at least a date. The method also includes assembling the facts in the content based on the determined subjects and corresponding chronological values for the individual facts.

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: Various techniques for chronology based content processing are disclosed herein. For example, in one embodiment, a method includes receiving a content containing a plurality of facts and determining a subject and a chronological value for each of the facts contained in the content. The subject includes at least one of a person, a place, an object, or an event, and wherein the chronological value includes at least a date. The method also includes assembling the facts in the content based on the determined subjects and corresponding chronological values for the individual facts.

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: 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: 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: 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: 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: Optimizing inventory targets for nodes of a supply chain to satisfy a target customer service level may include accessing a supply chain model that has an assumed value for each of a number of inputs. An optimized inventory target is calculated according to the supply chain model to satisfy the target customer service level, and a measured actual customer service level and a measured actual value for each input are accessed. If the measured actual customer service level fails to satisfy the target customer service level, deviations between the measured actual and assumed values for each input are determined. An input for which the deviation is significant is identified to be a root cause of the failure. For a subsequent time period, using the deviation for the identified input as feedback, the assumed value for the identified input is adjusted, and a reoptimized inventory target is calculated to satisfy the target customer service level.

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.

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: Determining a policy parameter for an entity of a supply chain includes establishing attributes of the entities of the supply chain. Attribute segments are established for each attribute, where an attribute segment includes one or more values of the corresponding attribute. Rules are formulated using the attribute segments to define policy groups, and policy parameters are assigned to each policy group. A policy group corresponding to an entity is identified in accordance with the rules. The policy parameters assigned to the identified policy group are determined and selected for the entity.

Abstract: Determining a policy parameter for an entity of a supply chain includes establishing attributes of the entities of the supply chain. Attribute segments are established for each attribute, where an attribute segment includes one or more values of the corresponding attribute. Rules are formulated using the attribute segments to define policy groups, and policy parameters are assigned to each policy group. A policy group corresponding to an entity is identified in accordance with the rules. The policy parameters assigned to the identified policy group are determined and selected for the entity.

Abstract: Determining a policy parameter for an entity of a supply chain includes establishing attributes of the entities of the supply chain. Attribute segments are established for each attribute, where an attribute segment includes one or more values of the corresponding attribute. Rules are formulated using the attribute segments to define policy groups, and policy parameters are assigned to each policy group. A policy group corresponding to an entity is identified in accordance with the rules. The policy parameters assigned to the identified policy group are determined and selected for the entity.