Braze FeedbackLoop: Concurrent, Data-Driven Support forProduct Development

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BFL is being developed in a two-stage process. Their in'st year focuses on reasoning about the developed product and process. Thus, the first year BFL reasoning is based on relatively simple techniques, including statistical data analysis and basic expert systems combined with failure mode analysis. The second year will be devoted to the achievement of more complex reasoning capability, including statistical machine learning, feature-based reasoning, and automated setup and peffotmmr of finiteelement-analysis model experiments to optimize design parameters for performance.This more advanced reasoning capability will be supported by a distributed agent architecture [Goldsmith 1997]. This paper provides an overview of the first year's efforts.

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Most of the time, customers do not get the advice they need but rather the advice their advisor thinks they need, based on a desire not to deviate to far from conventional wisdom, or on a desire to maximise the potential to sell products to them. This conflicted situation arises so often that an intelligent user can never really know which human generated advise they can really trust.

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Fast food chains make their money on feeding people quickly. Speedy service centers promise an oil change in ten minutes or less. But there's an argument for slowing down to serve the customer better. And as a business or professional looking to maximize your earning potential, it pays to slow down the sale. Kevin Davis, in his New York Times bestseller Slow Down, Sell Faster!: Understand Your Customer's Buying Process and Maximize Your Sales, explore the topic from the standpoint of earning new customers.

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