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A framework is presented to simulate and analyze the effect of multiple business scenarios on the adoption behavior of a group of technology products. Diffusion is viewed as an emergent phenomenon that results from the interaction of consumers. An agent-based model is used in which potential adopters of technology product are allowed to be influenced by their local interactions within the social network. Along with social influence, the effect of product features is important and we ascribe feature sensing attributes to the consumer agents along with sensitivities to social influence. The model encompasses utility theory and discrete choice models in the decision-making process for the consumers. We use expressive machine learning algorithms that can handle complex, nonlinear, and interactive effects to identify important inputs that contribute to the model and to graphically summarize their effects. We present a realistic case study that demonstrates the ability of this framework to model changes in market shares for a group of products in response to business scenarios such as new product introduction and product discontinuation under different pricing strategies. The models and other tools developed here are envisioned to be a part of a recommender system that provides insights into the effects of various business scenarios on shaping market shares of different product groups.

As manufacturing transitions to real-time sensing, it becomes more important to handle multiple, high-dimensional (non-stationary) time series that generate thousands of measurements for each batch. Predictive models are often challenged by such high-dimensional data and it is important to reduce the dimensionality for better performance. With thousands of measurements, even wavelet coefficients do not reduce the dimensionality sufficiently. We propose a two-stage method that uses energy statistics from a discrete wavelet transform to identify process variables and appropriate resolutions of wavelet coefficients in an initial (screening) model. Variable importance scores from a modern random forest classifier are exploited in this stage. Coefficients that correspond to the identified variables and resolutions are then selected for a second-stage predictive model. The approach is shown to provide good performance, along with interpretable results, in an example where multiple time series are used to indicate the need for preventive maintenance. In general, the two-stage approach can handle high dimensionality and still provide interpretable features linked to the relevant process variables and wavelet resolutions that can be used for further analysis. he has been a software engineer at Intel Corporation's Arizona site working on statistical process control applications. His interests are in the general areas of statistical model building and both supervised and un-supervised learning methods.Mani Janakiram is a Director/Principal Engineer of Supply Chain Strategy at Intel and in his 11+ years at Intel, he has managed several projects in supply chain, strategy roadmap, modeling, capacity planning, process control, analytics and research. He has 20+ years of experience (including Honeywell and Motorola), published 50+ papers and has two patents. Mani is a Six Sigma Master Black Belt, and served on several committees including, ITRS FI, AZ Tech Council, Stanford AIM, ISMI, NSF research panels and Factory Systems of SRC. He is also an adjunct professor at the Thunderbird School of Global Management. Mani holds a 892

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