Sugato Bagchi

Watson: beyond jeopardy!

This paper presents a vision for applying the Watson technology to health care and describes the steps needed to adapt and improve performance in a new domain. Specifically, it elaborates upon a vision for an evidence-based clinical decision support system, based on the DeepQA technology, that affords exploration of a broad range of hypotheses and their associated evidence, as well as uncovers missing information that can be used in mixed-initiative dialog. It describes the research challenges, the adaptation approach, and finally reports results on the first steps we have taken toward this goal.

Experience using the IBM supply chain simulator

The IBM Supply Chain Simulator (SCS) is a software tool that can help a company or a group of companies make strategic business decisions about the design and operation of its supply chain. SCS and its predecessors were originally developed by IBM Research to improve IBMs internal supply chains. The tool has played an important role in the resurgence of IBM over the last six years (1992-8). In 1997 the IBM Industry Solution Units began using the tool to help its clients improve their supply chains. After about a year of business, successful engagements have been completed in a variety of geographies and business segments. SCS deploys a mix of simulation and optimization functions to model and analyze supply chain issues such as site location, replenishment policies, manufacturing policies, transportation policies, stocking levels, lead times, and customer service. The paper reviews the capabilities of SCS and presents experience from practical studies.

Extended-Enterprise Supply-Chain Management at IBM Personal Systems Group and Other Divisions

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

Data Quality Management using Business Process Modeling

750 million in material costs and price-protection expenses saved in 1998.

Task planning under uncertainty using a spreading activation network

The quality of data contained in the enterprise information systems has significant impact, both from the internal business decision-making perspective and the external regulatory and shareholder obligations. This paper addresses data quality assessment in business processes by proposing a modeling framework to quantify the data quality in an information processing system. We present a business process modeling framework for data quality analysis and develop the mathematical formulation for error propagation. This is overlaid with a business controls framework where the placement and effectiveness of the controls alter the propagation of errors. This framework enables the estimation and management of data quality when faced with changes in various aspects of the business process. It also allows the formulation of optimization problems that trade off the cost of business controls with the level or cost of the resultant data quality. We illustrate the modeling framework and analyses with a revenue management process

An analytic approach for quantifying the value of e-business initiatives

As robotics and automation applications extend to the service sector, researchers have to increasingly deal with performing robotic actions in uncertain and unstructured environments. A traditional solution to this problem models uncertainty about the effects of actions by probabilities conditioned on the state of the environment, making it possible to select plans that have the highest probability of success in a given situation. Reactive systems use another approach to handling uncertainty, by employing a set of predefined situation-response rules that make it possible to move toward the goal from any situation, whether expected or unexpected. This paper describes a planner that combines the two approaches. A proactive component generates plans that are biased toward picking the most reliable action in a given situation, and a reactive component can alter the selected actions based on unexpected situations that may arise in uncertain environments. Action selection is driven by a spreading activation mechanism on a probabilistic network that encodes the domain knowledge. A decision-theoretic framework incorporates quantitative goal utilities and action costs into the action selection mechanism. Experiments conducted demonstrate the ability of the planner to plan with hard and soft domain constraints and action costs, modify plans as a reaction to unexpected changes in the environment or goal utilities, and plan in situations with multiple conflicting goals.

A full-factory simulator as a daily decision-support tool for 300mm wafer fabrication productivity

We describe an IBM strategic consulting offering involving a methodology and an analytic tool. The methodology, the Risk and Opportunity Assessment, provides a systematic approach for diagnosing problems in the value chain of the enterprise, and for selecting and prioritizing e-business initiatives. Applying this methodology involves the use of an analytic tool, the Value Chain Modeling Tool, that uses management science and operations research techniques, as well as techniques from the domains of finance and supply chain management, to model the end-to-end value chain of the enterprise. This approach has been successfully used to improve the financial and operating performance of several enterprises.

A real options approach for prioritization of a portfolio of information technology projects: a case study of a utility company

We describe a discrete event simulator developed for daily prediction of WIP position in an operational 300 mm 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.

Simulation of grid computing infrastructure: challenges and solutions

The valuation of information technology (IT) investments is particularly challenging because it is characterized by long payback periods, uncertainty, and changing business conditions. Corporate budgeting methods use accounting-based criteria like return on investment (ROT), internal rate of return (IRR), and payback period which were designed for projects with no option features. However, the uncertainties underlying IT investment decisions and the inability of traditional discounted cash flow (DCF) methods to incorporate the impact of flexibility on project valuation, force executives to rely on gut instinct when finalizing IT investment decisions. Real options analysis (ROA) has been suggested as a capital budgeting tool because it explicitly accounts for the value of future flexibility in management decision making. This paper deals with the application of a nested real options model to evaluate and prioritize a portfolio of information technology projects. It elaborate the valuation and prioritization of a real-world portfolio of IT initiatives under consideration for funding. It is illustrated using real world data from EnergyCo, a large utility facing challenges on many fronts due to uncertainties surrounding energy deregulation and Internet-based energy trading.

WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information

Recent advances in middleware technologies such as grid computing have provided IT architects with the ability to design infrastructures that are more flexible and less dedicated to specific application workloads. However, the capabilities of design and analysis tools that IT architects use have not kept pace. In this paper, we describe our progress in developing an IT infrastructure modeling environment that supports an extensible set of analysis tools. We focus in particular on a discrete-event simulator for analyzing the performance of computational workloads that are running on a grid. The unique modeling requirements and challenges presented by the grid computing infrastructure domain are discussed. Efficient event queue management and other simulation techniques to address these challenges are developed. Finally, we position the role of simulation analysis in the larger context of estimating the business and financial returns from grid computing investments.