A Statistical Framework of Demand Forecasting for Resource-Pool-Based Software Development Services

To adapt to the fast-changing landscape of technology and the increasing complexity of skills needed as a result, an outcome-based delivery model, called crowdsourcing, emerges in recent years for software development. In this model, some of the work required by a large project is broken down into self-contained short-cycle components, and a resource pool of vetted freelancers is leveraged to perform the tasks. The resource pool must be managed carefully by the service provider to ensure the availability of the right skills at the right time when they are needed. This article proposes a statistical framework of demand forecasting to support the capacity planning and management of resource pool services. The proposed method utilizes the predictive information contained in the system that facilitates the resource pool operation through survival models, and combine the results with special complementary time series models to produce demand forecasts in multiple categories at multiple time horizons. A dataset from a real-world resource pool service operation for software development is used to motivate and evaluate the proposed method.

By: Ta-Hsin Li

Published in: RC25585 in 2016


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