No abstract

In 1994, IBM began to reengineer its global supply chain. It wanted to achieve quick responsiveness to customers with minimal inventory. To support this effort, we developed an extended-enterprise supply-chain analysis tool, the Asset Management Tool (AMT). AMT integrates graphical process modeling, analytical performance optimization, simulation, activity-based costing, and enterprise database connectivity into a system that allows quantitative analysis of extended supply chains. IBM has used AMT to study such issues as inventory budgets, turnover objectives, customer-service targets, and new-product introductions. We have implemented it at a number of IBM business units and their channel partners. AMT benefits include over $750 million in material costs and price-protection expenses saved in 1998.

We present WatsonPaths, a novel system that can answer scenario-based questions. These include medical questions that present a patient summary and ask for the most likely diagnosis or most appropriate treatment. WatsonPaths builds on the IBM Watson question answering system. WatsonPaths breaks down the input scenario into individual pieces of information, asks relevant subquestions of Watson to conclude new information, and represents these results in a graphical model. Probabilistic inference is performed over the graph to conclude the answer. On a set of medical test preparation questions, WatsonPaths shows a significant improvement in accuracy over multiple baselines.

Abstract|W e argue that intelligence is necessary in robots used for rehabilitation in order to reduce the amount o f mental activity needed by the user of these robots. With this in mind, the areas of research relevant to imparting robotic systems with the capability of assuming a more intelligent role are identi ed. We describe our implementation of functionalities such as fuzzy command interpretation, object recognition, face tracking, and task planning and learning, which are part of the ISAC, an intelligent system designed to feed individuals with physical disabilities.

We describe a discrete event simulator developed for daily prediction of WIP position in an operational 300mm wafer fabrication factory to support tactical decision-making. The simulator is distinctive in that its intended prediction horizon is relatively short, on the order of a few days, while its modeling scope is relatively large. The simulation includes over 90% of the wafers being processed in the fab and all process, measurement and testing tools. The model parameters are automatically updated using statistical analyses performed on the historical event logs generated by the factory. This paper describes the simulation model and the parameter estimation methods. A key requirement to support daily and weekly decision-making is good validation results of the simulation against actual fab performance. Therefore, we also present validation results that compare simulated production metrics against those obtained from the actual fab, for fab-wide, process, tool and product specific metrics.