International Business Machines Corporation

Faisal Aqlan

Sreekanth Ramakrishnan

Warren Boldrin

Jason McAllister

Alex R Jorge-Ortiz

Juan F Garzon

Keila Y Martinez Camacho

Sarah S Lam

Kevin F Becker

Anthony G Tiano

Technical solutions are described to for assigning a team to resolve a defect during a manufacturing process. The method includes identifying a root cause of a defect from a root cause database by generating a reference code. The method also includes generating a defect score based on characteristics of the defect and the root cause, and identifying a set of solutions for the defect based on the defect score. The method also includes selecting a first solution from the set of solutions to implement and determining a candidate skill for the implementation. The method also includes identifying, from an employee database, a candidate employee that has the candidate skill and assigning the candidate employee to the team to resolve the defect.

Defect management

A method, data processing system and computer program product for managing inventory used in a manufacturing operation to manufacture a product. The method includes a processor communicating with at least one database containing manufacturing operation data. Several inventory locations and components are identified within the manufacturing operation data. A current component inventory and space data, historical component usage data and a demand forecast are retrieved. A statistical analysis is performed upon the current component inventory and space data, the historical component usage data and the demand forecast to obtain a target inventory level range for each of the components at each of the inventory locations. An inventory level control chart is generated, based on the statistical analysis, for selected components associated with at least one inventory location.

Performing hierarchical data-driven inventory and warehouse management in manufacturing environments

Embodiments of the invention relate to statistical and mathematical optimization techniques to predict completion time of parts and subassemblies in one production line. A real-time visibility of products and work-in-process is produced. Optimization techniques are utilized to allow for dynamic modification of a transportation schedule and/or batch size, thereby reducing transportation costs.

Dynamic batch size and internal transportation

A method for order prioritization includes calculating a cycle time for a product order of a plurality of product orders using an artificial neural network, determining a first order priority of the product order based on a priority index using an analytic hierarchy process, determining a second order priority of the product order based on event based simulation model, and determining a shipping date for the product order based on the second order priority. The artificial neural network calculates the cycle time based upon product order type and a plurality of component counts. The analytic hierarchy process determines a first order priority based upon a plurality of product order attributes. The simulation model determines a second order priority and completion time based upon the first order priority, product model, product type, a plurality of component counts, manufacturing capacity and inventory data, and production time data for historical product orders.

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Poughkeepsie, NY, United States of America

Faisal Aqlan