Demand Forecasting-Supply Chain : End to End Guide

Define demand and forecasting, contrast qualitative and quantitative methods using historical data and domain knowledge, and outline core components like trend, seasonality, cyclic, level, and noise.

Apply three forecasting principles: acknowledge forecasts will be wrong and reduce error. Forecasts are more accurate for groups than for individual items; aggregation improves stability.

Learn how rolling forecast updates a one-year outlook with quarterly actuals to revise future quarters, shift orders as needed, and adapt to market volatility for better financial planning and agility.

Balance quarterly demand against capacity by applying the capacity to demand ratio within a rolling forecast, demonstrated with an Excel example that shifts demand to keep the ratio above one.

Identify the forecasting purpose, define the forecast level and unit, set the planning horizon, compile data, select and test models, reach a consensus forecast, and continuously improve.

Compare time series and causal forecasting models for demand planning. Learn about time as index with trend and seasonality, and tools like moving average, ARIMA, random forest, and XGBoost.

Align data formats with the forecast horizon and capture special events, then segment by skill, location, and customers to enable organized demand forecasting.

Balance market opportunity and supply network capability to form a consensus demand plan that guides demand forecasting, planning, and customer satisfaction.

Differentiate demand from forecast by showing forecast as a historical data based prediction, while demand planning uses forecast as a starting point and adds distribution, inventory, capacity, and materials considerations.

Identify real demand as more than sales data, including backlog and inventory replenishment. Learn how different demand layers—customer orders, backlog, seasonality, and promotions—shape forecasting.

Explore the product lifecycle from introduction to decline and how the four stages—introduction, growth, maturity, and decline—shape demand planning and marketing decisions.

Explore naive forecasting, using previous month data to project future sales, and understand its limitations with outliers and limited history, plus how it compares to moving average and seasonal methods.

Explore moving average forecasting to smooth sales data with 3- and 4-month windows, noting lag, outlier dampening, and inability to capture trend or seasonality.

Engage with humor to reinforce practical concepts in demand forecasting and end-to-end supply chain management, from procurement to delivery using route optimization software.

Learn how forecasting KPIs, including accuracy and bias, guide model evaluation for neo forecasting and moving average, using metrics like MAPE and MSE, and how to compare models through experimentation.

Explore key forecasting KPIs including mean absolute percentage error, mean absolute error, and RMSE, with hands-on Excel calculations and interpretation for end-to-end demand forecasting.

Learn how to select forecasting KPIs such as RMSE, MAE, and MAPE through data-driven experiments, considering average versus median forecasts, seasonality, and outliers to achieve unbiased, accurate demand forecasts.

Forecast review process improves accuracy with benchmarking and periodic tracking of unit-based, percentage-based, and relative-based metrics across SKUs and product families.

Learn to build exception review reports that rank thousands of SKUs by error contributions and by volume–error balance, prioritizing top products with error and volume considerations.

Demonstrates demand segmentation by classifying products into a, b, c, and d segments based on volume and variability, guiding forecasting effort from high-priority, low-variability items to lower-priority, high-variability ones.

Apply exponential smoothing to forecast demand, weighting near-term data via alpha in the formula forecast = alpha times previous demand plus (1 - alpha) times previous forecast.

Optimize the alpha parameter in exponential smoothing to minimize mape using Excel solver, set constraints, compare to Python, and apply to future forecasts.

Explore the autoregressive model (AR) within ARIMA for time series forecasting, using past observations via linear regression with lagged predictors to forecast future values in supply chain and markets.

Explore building an autoregressive AR model in Excel, using an 80/20 training–testing split, with lag one, lag two, and lag three inputs, regression analysis, and iterative refinement to forecast equation.

Learn linear regression as a time-series forecasting tool for demand, using y = mx + c to link dependent and independent variables and explain the slope's role in sales prediction.

Forecast demand with a linear regression model by fitting a trendline, applying Y = MX + C, and projecting August–November using the slope, intercept, and R-squared from Excel.

Explore causal models and multiple linear regression to forecast demand by incorporating historical sales alongside factors like promotions, discounts, price, market share, and promotion spend.

Learn to build a causal model with multiple linear regression to forecast sales using price change and promotion spend. Derive the forecast equation and interpret the coefficients and R square.

Apply a best model selection framework that weighs forecast accuracy and bias, using a rating system to identify the best fit model for a product family in demand forecasting.

Apply winsorization to detect and correct outliers, replacing extreme values with percentile-based points (e.g., 10th and 90th) to ensure clean input for forecasting.

Split historical data into training and testing sets (80/20 or 70/30) to evaluate forecast accuracy on unseen data using exponential smoothing and alpha optimization.

Explore model initialization and data leakage in demand forecasting, highlighting exponential smoothing, handling the first forecast point, and removing the initial point from accuracy calculations to avoid leakage.

Celebrate your completion of the demand forecasting course and reinforce your holistic understanding of demand forecasting within the supply chain, guided by an end-to-end approach.