Demand forecasting and financial estimation are two critical issues in supply chain management. Traditional forecasting techniques are either based on historical data (time-series) or causal predictors (regression). Although numerous schemes have been proposed, most cannot accommodate the time-lag causalities between the predictors and the outcome and the interactive dynamics of the supply-chain members. This research presents a novel framework to highlight the following issues: (1) Time-series models are constructed to accommodate product volatility and conduct demand forecasting. (2) Vector autoregression is used to capture the interactive dynamics of the supply-chain members to conduct financial estimation. (3) Regression methods are applied to conduct sensitivity analyses that can measure the impact on the sales revenue of a firm by increasing or decreasing a specific predictor. Experimental results demand forecast for consumer products can successfully predict the sales revenues of chip-design firms. For chip manufacturers and packaging and testing (P&T) firms, interactive dynamics can be competition (one suffers from the growth of the other) or cooperation (a win-win scenario). If one is strong and the other is weak (asymmetric relationship), the dynamics is cooperative. If two firms perform almost equivalently, the dynamics is competitive.