Hewlett-packard Development Company, L.p.

Hewlett-packard Company

Shailendra K Jain

Cipriano A Santos

Dirk Manfred Beyer

Xin Zhang

Jerry Zhaolin Shan

Fereydoon Safai

Bin Zhang

Alex Xin Zhang

Kemal Guler

Julie Ward Drew

Kay-Yut Chen

Maria Teresa Gonzalez Diaz

Paul F Williams

Ernesto Brau

Ivan Adrian Lopez Sanchez

Jose Luis Beltran Guerrero

Andrei Fuciec

Joachim Resch

Ming Hu

A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does.

Behavior and information model to yield more accurate probability of successful outcome

A tool receives parameters relating to target enterprise objective of an enterprise, the cost of an enterprise resource associated with the enterprise, and an enterprise resource capacity. The tool calculates an amount of an enterprise resource to be assigned in an enterprise based on the received parameters relating to the target enterprise objective, the enterprise resource cost, and the enterprise resource capacity.

Calculating an amount of enterprise resource to be assigned based on received parameters

A method for product portfolio analysis includes selecting a set of solution points along a curve of order benefit as a function of the number of products in a product portfolio and performing a heuristic between an adjacent pair of solution points that approximates the curve between the adjacent pair of solution points. An apparatus that can perform the product portfolio analysis is also described.

Method and apparatus for product management

A method of managing product end of life over an end of life horizon includes the step of selecting an objective of either maximizing gross profit or minimizing writeoff costs for a selected plurality of products being discontinued. The products may have parts in common. A subset of all possible combinations of product demand levels is selected. Each combination is associated with a probability. A procurement plan that optimizes the selected objective is generated based on the selected subset of demand levels given a non-zero pre-existing inventory of at least some parts of the selected products.

Method and apparatus for managing product end of life

A method for determining call center resource allocation can include modeling call center performance over an operations time period using a computer. A number of replicas of the modeled call center performance are simulated, using the computer, over a planning time period, each replica having random contact arrivals and contact service times following a stochastic arrival and service process according to a probability distributions of inter-arrival time and service time. Multiple iterations of each simulation are run on the computer to optimize call center resource allocation. A particular simulation iteration is tested against a criterion of convergence, and call center resource is allocated based on the particular simulation iteration with a successful criterion of convergence.

Call center resource allocation

An initial external bid of an enterprise that is based on initial component bids of plural entities is received by a tool. The tool receives margin values representing margins acceptable to the entities. A new external bid is received that is different from the initial bid, and adjusted component bids of the plural entities are generated by the tool according to the new external bid and the margins of the entities.

Generating adjusted bids of entities according to a new external bid

An iterative, assignment-dependent, method of allocating manufacturing resources to perform operations required in the manufacture of multiple products provides for improved conformance with actual manufacturing situations and for solutions which approximate optimal allocation while requiring only modest computational power and time. The first step involves attributing complex costs to potential assignments of operations to resources. Complex costs include two components, combined money-costs and combined times. Combined cost is an assignment dependent variable which can equal operational cost or the sum of operation cost and set-up cost depending on assignments that have already been made. Combined time is likewise assignment dependent. In a second step, combined cost is selected as a parameter to evaluate each potential operation-resource pair. In a third step, a lowest cost and a second lowest cost resource are determined for each unassigned operation. In a fourth step, a maximum penalty operation is identified by finding the maximum difference between lowest and second lowest costs. In a fifth step, the maximum penalty operation is assigned to its lowest cost resource. In a sixth step, combined costs are re-evaluated to take the most recent assignment into account. In a seventh step, iteration is continued by returning to the third step until all operations have been assigned to resources. Once all operations have been assigned, the assignments are reported as a solution to the allocation problem. The inventive method readily handles capacity constrained resource allocation problems, which are difficult or impossible to solve with conventional techniques.

Assignment-dependent resource allocation method

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Sunnyvale, CA, United States of America

Shailendra K Jain