Ching-Hua Chen-Ritzo

Instrumenting the planet

During the last 50 years, population growth, along with increasingly affluent societies, has resulted in a greater demand for our limited physical infrastructures and natural resources than ever before. In addition, the risks of climate change have heightened the need for more sophisticated ways of controlling carbon emissions. Today, numerous streams of data are being collected from sensors that monitor the environment. When used in conjunction with computational models, these streams can be important sources of data for understanding physical phenomena and human behavior. In this paper, we present a vision of a pervasively instrumented world in which these streams of real-world data are combined with mathematical models to improve the ability to manage the consumption of increasingly scarce resources. Such an instrumented world requires a class of information technology systems that combine very large numbers of sensors and actuators with computing platforms for capturing and analyzing such data streams. We provide details on the characteristics, requirements, and possible applications of such platforms and the key roles that they will play in addressing various societal challenges.

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

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.

Sales and operations planning in systems with order configuration uncertainty

This paper addresses the problem of aligning demand and supply in configure-to-order systems. Using stochastic programming methods, this study demonstrates the value of accounting for the uncertainty associated with how orders are configured. We also demonstrate the value of component supply flexibility in the presence of order configuration uncertainty. We present two stochastic models: an explosion problem model and an implosion problem model. These models are positioned sequentially within a popular business process called sales and operations planning. Both models are formulated as two-stage stochastic programs with recourse and are solved using the sample average approximation method. Computational analyses were performed using data obtained from IBM System and Technology Group. The problem sets used in our analysis are created from actual industry data and our results show that significant improvements in revenue and serviceability can be achieved by appropriately accounting for the uncertainty associated with order configurations.

Information technology for healthcare transformation

Rising costs, decreasing quality of care, diminishing productivity, and increasing complexity have all contributed to the present state of the healthcare industry. The interactions between payers (e.g., insurance companies and health plans) and providers (e.g., hospitals and laboratories) are growing and are becoming more complicated. The constant upsurge in and enhanced complexity of diagnostic and treatment information has made the clinical decision-making process more difficult. Medical transaction charges are greater than ever. Population-specific financial requirements are increasing the economic burden on the entire system. Medical insurance and identity theft frauds are on the rise. The current lack of comparative cost analytics hampers systematic efficiency. Redundant and unnecessary interventions add to medical expenditures that add no value. Contemporary payment models are antithetic to outcome-driven medicine. The rate of medical errors and mistakes is high. Slow inefficient processes and the lack of best practice support for care delivery do not create productive settings. Information technology has an important role to play in approaching these problems. This paper describes IBM Researchs approach to helping address these issues, i.e., the evidence-based healthcare platform.

Component rationing for available-to-promise scheduling in configure-to-order systems

We address the problem of rationing common components among multiple products in a configure-to-order system with order configuration uncertainty. The objective of this problem is to maximize expected revenue by implementing a threshold rationing policy. Under this policy, a product is available to promise if fulfilling the order for the product will not cause the inventory of any one of its required components to fall below the components threshold level for that product. The problem is modeled as a two-stage stochastic integer program and solved using the sample average approximation approach. A heuristic is developed to generate good feasible solutions and lower bound estimates. Using industry data, we examine the benefit of component rationing as compared to a First-Come-First-Served policy and show that this benefit is correlated to the average revenue per product and the variability in the revenue across products whose components are constrained.

Experiences in implementing simulation-based support for operational decision making in semiconductor manufacturing

We describe a discrete event simulator that has been deployed in a 300 mm wafer fabrication plant to aid short-term, operational decision-making. Our simulator has been designed and calibrated to produce reliable predictions over simulation horizons as short as a few days to several weeks. It has been automated to run daily, without the need for manually provided data. We provide motivating examples illustrating the need for short-term predictions, and share insights on aspects of the simulator design that contribute to its usefulness and accuracy. Finally, we include several examples of how the simulator has been used to aid operational and tactical decision-making at the IBM wafer fabrication plant in East Fishkill, NY. [Received: 01 June 2009; Revised: 16 November 2009; Accepted: 01 February 2010].

A framework for evidence-based health care incentives simulation

We present a general simulation framework designed for modeling incentives in a health care delivery system. This first version of the framework focuses on representing provider incentives. Key framework components are described in detail, and we provide an overview of how data-driven analytic methods can be integrated with this framework to enable evidence-based simulation. The software implementation of a simple simulation model based on this framework is also presented.

Applying a framework for healthcare incentives simulation

At WinterSim 2011, we originally proposed an agent-based framework for healthcare simulations, enabling flexible integration of multiple simulation models, including models of disease progression, effects of provider interventions, and provider behavior models that are responsive to contractual incentives. In this paper, we report results using our proposed framework to integrate two examples of provider behavior models, two examples of disease models, and four examples of payment models. We explore multiple combinations of these models and simulate the impact that alternative payment models may have on health and financial outcomes. These examples test the robustness of the simulation framework, and illustrate the value of such simulations to the policy makers who design incentives to improve cost and health outcomes, and to providers who wish to evaluate the financial impact of proposed incentives on their practice.