Engineering Analytics – Improving Cycle-time Prediction

You have probably heard of business analytics, which involves continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Engineering analytics is similar, but focused on engineering performance, and in this case prediction and estimation of cycle times. Many of the SSCI members and clients are high maturity organizations. They have been improving their process and practices, and capturing data such as control charts that track statistics about their projects. Unfortunately, even with all of the statistical data there are a lot of factors, both technical and non-technical that can cause significant variance from project to project.

We gave a Webinar on Bayesian Networks – A New Class of Management Tools for Prediction, Estimation and Risk Management. We discussed some applications performed with SSCI members in predicting software reliability based on quantitative defect data as well as subjective judgments about factors such as quality, complexity and architectural stability. The key benefit is that a Bayesian Nets (BN) represents causal models that combine sparse data with expert judgment transforming qualitative knowledge about the processes into quantitative predictions.

We have been working on another project applying hybrid BNs to predict project cycle times. We are using the quantitative statistical data collected in their control charts, and the BN combines the subjective judgment about factors such as complexity, quality of the requirements, design reuse, engineers’ expertise, and even judgments about subcontractors and suppliers. The results are quite impressive. In applying the models to about 25 projects, all which last more than one year, the models predictions reduces the predicted variance by more than 50%.

We are interested in hearing your interests and experiences with these type of prediction tools and techniques.


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