Nitin R. Joglekar
Abstract
We examine the management of data accuracy in inter-organizational data exchanges using the context of distributed software projects. Organizations typically manage projects by outsourcing portions of the project to partners. Managing a portfolio of such projects requires sharing data regarding the status of work-in-progress residing with the partners and estimates of these projects' completion times. Portfolio managers use these data to assign projects to be outsourced to partners. These data are rarely accurate. Unless these data are filtered, inaccuracies can lead to myopic and expensive sourcing decisions. We develop a model that uses project-status data to identify an optimal assignment of projects to be outsourced. This model permits corruption of project-status data. We use this model to compute the costs of using perfect versus inaccurate project-status data and show that the costs of deviation from optimal are sizable when the inaccuracy in the data is significant. We further propose a filter to correct inaccurate project-status data and generate an estimate of true progress. With this filter, depending on the relative magnitudes of errors, we show that accuracy of project-status data can be improved and the associated economic benefit is significant. We illustrate the improvement in accuracy and associated economic benefit by instantiating the model and the filter. We further elaborate on how the model parameters may be estimated and used in practice.
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Reviews
Bhavanandan L. Rathinasamy
Distributed software project development is ubiquitous nowadays with the advent of high-speed Internet. Almost all big information technology firms outsource at least a portion of their projects to partner firms, to make use of their human resources and to reduce costs. It is vital for the planning team of the principal firm that outsources projects to have access to up-to-date information about the so-called project portfolio, the status of the projects carried out by the partner firms, and the current project workload. Project portfolios are managed through data sharing between the principal firm and the partner firms. Hence, the accuracy of such data is vital in making predictions about the project assignments and completion times. Often, this data is inaccurate for various reasons. In this paper, Joglekar et al. examine the reasons for inaccuracy in interorganizational data exchange, and propose a model from control theory to identify project assignments for the partner firms that are optimal despite inaccurate data. They use the Kalman-Bucy filter to remove inaccuracy in the data, and demonstrate the economic benefit in doing so. The mathematical analysis is described eloquently, with all the details necessary to enable readers to follow the main results. Extensive numerical analyses are presented to illustrate the efficiency of the proposed control theory-based model and Kalman-Bucy filter. Online Computing Reviews Service
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