GATE Industrial Engineering: Forecasting Fundamentals

Master the core mathematical models of demand forecasting required for industrial engineering exams. This text-based course breaks down complex quantitative methods into clear, step-by-step written explanations and calculations. You will transition from struggling with forecasting formulas to confidently calculating moving averages, exponential smoothing, regression analysis, and forecast errors. What you'll learn: - Understand foundational forecasting terminology, demand patterns, and qualitative versus quantitative approaches. - Calculate simple and weighted moving averages to smooth out historical demand data. - Apply exponential smoothing models and select the optimal smoothing constants. - Analyze linear regression techniques to project long-term demand trends. - Measure forecasting accuracy using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and tracking signals. - Practice exam-style quantitative problems with step-by-step written solutions. The course begins with essential definitions and qualitative concepts before moving systematically through mathematical forecasting formulas, error metrics, and solved practice problems. This course is designed for engineering students preparing for competitive exams like GATE, as well as professionals seeking a solid, first-principles foundation in industrial forecasting. No prior forecasting experience is required. Start reading today to master the mathematical foundations of industrial forecasting.