Indranil R. Bardhan

Plant information systems, manufacturing capabilities, and plant performance

Firms have been investing over

An Interdisciplinary Perspective on IT Services Management and Service Science

5 billion a year in recent years on new information technology and software in their manufacturing plants. In this study, we develop a conceptual model based on the theory of dynamic capabilities to study how manufacturing plants realize improvements in plant performance by leveraging plant information systems to enable implementation of advanced manufacturing capabilities. We develop hypotheses about relationships between information systems, their impact on manufacturing practices, and the overall impact on plant performance. Analysis of survey data from 1,077 U.S. manufacturing plants provides empirical support for the dynamic capabilities model and suggests that manufacturing capabilities mediate the impact of information systems on plant performance. Our results underscore the importance of manufacturing and organizational capabilities in studying the impact of IT on manufacturing plant productivity, and provide a sharper theoretical lens to evaluate their impact.

Prioritizing a Portfolio of Information Technology Investment Projects

The increasing importance of information technology (IT) services in the global economy prompts researchers in the field of information systems (IS) to give special attention to the foundations of managerial and technical knowledge in this emerging arena of knowledge. Already we have seen the computer science discipline embrace the challenges of finding new directions in design science toward making services-oriented computing approaches more effective, setting the stage for the development of a new science—service science, management, and engineering (SSME). This paper addresses the issues from the point of view of service science as a fundamental area for IS research. We propose a robust framework for evaluating the research on service science, and the likely outcomes and new directions that we expect to see in the coming decade. We emphasize the multiple roles of producers and consumers of services-oriented technology innovations, as well as value-adding seller intermediaries and systems integrators, and standards organizations, user groups, and regulators as monitors. The analysis is cast in multidisciplinary terms, including computer science and IS, economics and finance, marketing, and operations and supply chain management. Evaluating the accomplishments and opportunities for research related to the SSME perspective through a robust framework enables in-depth assessment in the present, as well as an ongoing evaluation of new knowledge in this area, and the advancement of the related management practice capabilities to improve IT services in organizations.

Information Technology, Production Process Outsourcing, and Manufacturing Plant Performance

Although the use of real options for valuation of information technology (IT) investments has been documented, little research has been conducted to examine its relevance for valuing and prioritizing a portfolio of projects. Complexities of IT projects along with the effect of project interdependencies raise several challenges in applying real options for prioritization of IT investments. We examine a large U.S.-based energy utility firm in a deregulated environment that is considering investment in a portfolio of 31 projects to provide a range of Internet-enabled energy services to customers. Using real data on expected project benefits and costs for different competitive scenarios, we develop a nested options model that extends prior research by incorporating the impact of project interdependencies to calculate the option value of all projects. Our nested options model provides a better understanding of project interdependencies on valuation and prioritization decisions, and provides insights into the business value of IT infrastructure projects that provide the managerial flexibility to launch future projects. We present a real options portfolio optimization algorithm for dynamic multiperiod portfolio optimization by incorporating the project values based on real options analysis in a portfolio management model with budget constraints.

Understanding the Impact of Collaboration Software on Product Design and Development

What is the role of information technology (IT) in enabling the outsourcing of manufacturing plant production processes? Do plant strategies influence production outsourcing? Does production process outsourcing influence plant performance? This research addresses these questions by investigating the role of IT and plant strategies as antecedents of production outsourcing, and evaluating the impact of production outsourcing and IT investments on plant cost and quality. We develop a theoretical framework for the antecedents and performance outcomes of production outsourcing at the plant level. We validate this theoretical framework using cross-sectional survey data from U.S. manufacturing plants. Our analysis suggests that plants with greater IT investments are more likely to outsource their production processes, and that IT investments and production outsourcing are associated with lower plant cost of goods sold and higher product quality improvement. Our research provides an integrated model for studying the effects of IT and production outsourcing on plant performance.

A note on returns to scale in DEA

Prior research suggests that supply chain collaboration has enabled companies to compete more efficiently in a global economy. We investigate a class of collaboration software for product design and development called collaborative product commerce (CPC). Drawing on prior research in media richness theory and organizational science, we develop a theoretical framework to study the impact of CPC on product development. Based on data collected from 71 firms, we test our research hypotheses on the impact of CPC on product design quality, design cycle time, and development cost. We find that CPC implementation is associated with greater collaboration among product design teams. This collaboration has a significant, positive impact on product quality and reduces cycle time and product development cost. Further analyses reveal that CPC implementation is associated with substantial cost savings that can be attributed to improvements in product design quality, design turnaround time, greater design reuse, and lower product design documentation and rework costs.

A Simulation Study of Joint Uses of Data Envelopment Analysis and Statistical Regressions for Production Function Estimation and Efficiency Evaluation

Abstract This brief note adds computational convenience and efficiency to the article by Banker and Thrall on returns to scale in DEA by modifying one of their suggestions to avoid the need for examining all alternate optima in order to reach a decision.

Health information technology and its impact on the quality and cost of healthcare delivery

A previous paper by Arnold, Bardhan, Cooper and Kumbhakar (1996) introduced a very simple method to estimate a production frontier by proceeding in two stages as follows: Data Envelopment Analysis (DEA) is used in the first stage to identify efficient and inefficient decision-making units (DMUs). In the second stage the thus identified DMUs are incorporated as dummy variables in OLS (ordinary least squares) regressions. This gave very satisfactory results for both the efficient and inefficient DMUs. Here a simulation study provides additional evidence. Using this same two-stage approach with Cobb-Douglas and CES (constant elasticity-of-substitution) production functions, the estimated values for the coefficients associated with efficient DMUs are found to be not significantly different from the true parameter values for the (known) production functions whereas the parameter estimates for the inefficient DMUs are significantly different. A separate section of the present paper is devoted to explanations of these results. Other sections describe methods for estimating input-specific inefficiencies from the first stage use of DEA in the two-stage approaches. A concluding section provides further directions for research and use.

Chapter 12 New uses of DEA and statistical regressions for efficiency evaluation and estimation — with an illustrative application to public secondary schools in Texas

The impact of health information technologies (HIT) on the quality of healthcare delivery is a topic of significant importance and recent research has yielded mixed evidence. We use archival data on HIT usage in combination with data on quality of care processes to conduct a three-year longitudinal study of a large panel of U.S. hospitals. Our analysis extends earlier research on the association between HIT and healthcare quality among healthcare providers that have previously focused on outcomes associated with cost reduction. We study the impact of HIT applications, not only on hospital operating expenses, but also on the process quality associated with evidence-based measures for treatment of four major health conditions. Our results indicate a positive association between usage of clinical information systems and patient scheduling applications and conformance with best practices for treatment of heart attacks, heart failures, and pneumonia. Our results also suggest that usage of financial management systems is associated with lower hospital operating expenses. Furthermore, we find that not-for-profits and urban hospitals are more likely to exhibit greater conformance with process quality metrics, while for-profits exhibit lower operational expenses. Our results have important policy implications for investments in health IT and studying their cost and quality implications.

Research Note---Business Value of Information Technology: Testing the Interaction Effect of IT and R&D on Tobin's Q

This paper examines new combinations of Data Envelopment Analysis (DEA) and statistical approaches that can be used to evaluate efficiency within a multiple-input multiple-output framework. Using data on five outputs and eight inputs for 638 public secondary schools in Texas, unsatisfactory results are obtained initially from both Ordinary Least Squares (OLS) and Stochastic Frontier (SF) regressions run separately using one output variable at-a-time. Canonical correlation analysis is then used to aggregate the multiple outputs into a single “aggregate” output, after which separate regressions are estimated for the subsets of schools identified as efficient and inefficient by DEA. Satisfactory results are finally obtained by a joint use of DEA and statistical regressions in the following manner. DEA is first used to identify the subset of DEA-efficient schools. The entire collection of schools is then comprehended in a single regression with dummy variables used to distinguish between DEA-efficient and DEA-inefficient schools. The input coefficients are positive for the efficient schools and negative and statistically significant for the inefficient schools. These results are consistent with what might be expected from economic theory and are informative for educational policy uses. They also extend the treatments of production functions usually found in the econometrics literature to obtain one regression relation that can be used to evaluate both efficient and inefficient behavior.