Abstract: Methods and system for enhancing and monitoring demand forecasting through advanced analytics is described. The advanced analytics demand forecasting system and techniques leverage multivariate data, thereby enhancing modeling through the use of data related to a plethora of aspects of the supply chain. Multivariate data can include market data, econometric data, customer data, lifecycle analytics, and an expanded range of historical data (e.g., 15 years). Multivariate data can be fed into an advanced analytics hyper parameter model, which combines time series, as well as machine learning and deep learning algorithms to improve the accuracy of the demand forecast. In some cases, the advanced analytics hyper parameter model is a stacking ensemble model, which combines the predictions of several other models using a stacking ensemble technique, thereby improving the modeled predictions.

Abstract: In one embodiment, a system comprises logic configured to identify a tip of a pin that has been press fit into a circuit board, logic configured to measure characteristics that pertain to a flat end surface and a chamfered surface of the identified pin tip, logic configured to compare the measured characteristics with at least one of stored reference values and each other, and logic configured to make a final decision as to whether the pin is properly installed based upon results of the comparing performed by the logic configured to compare.