Forecasting Using Quantitative and Qualitative Methods

Explore moving averages, including three- and five-month simple moving averages, to forecast demand when data show little trend; learn calculations, pros and cons, and practical Excel use.

Calculate a three period moving average forecast from the latest data to predict period six demand, and a four period moving average forecast from the most recent four points.

Explore weighted moving average forecasting, assigning higher weights to recent periods to capture trends; apply with two- and three-period examples using weights like 60-40, 70-30, and 3-2-1.

Learn to compute a four-week weighted moving average on the dataset using the most recent data, assigning higher weights to newer observations through a practical exercise solution.

Apply exponential smoothing to forecast demand by updating the prior forecast with alpha times the difference between actual demand and the forecast. Select alpha (0-1) to minimize forecast error.

Practice exponential smoothing by solving an exercise that requires generating a forecast from given data.

Forecast future sales using trend projection from historical data and the trend line in Excel, which estimates future points from the line's equation.

Explore three trend-based forecasting methods—extrapolation with a trend line, forecast, and trend functions—using time as the x axis to estimate August values around 280.

practice trend prediction by extrapolating the next five months from existing data using a trend line, forecast forward, and compare forecasting methods.

Learn to forecast with quantitative trend analysis and qualitative judgment, using trend lines and extrapolation, while evaluating anomalies that may warrant adjusting the forecast.

Apply trend extrapolation to forecast 2019 for product one using the trend line and known values, building familiarity and readiness for the next exercises.

Review the trend exercise by adjusting data, fitting a trend line, and using extrapolation to forecast 2019 sales, totaling about 897k units.

Qualitative forecasting, also called subjective methods, relies on the personal assessment of sales managers and industry experts to produce short-term forecasts, such as the next month, quarter, or year, when historical data is unavailable or limited. It can be used alone or with quantitative methods to validate results, but it depends on expert opinions and may introduce biases or mistakes.

Explore the jury of executive opinion method, a small cross-functional panel that discusses and reaches consensus to forecast demand, highlighting its efficiency and biases from dominant voices.

Delphi method, a qualitative forecasting approach, aggregates independent expert forecasts with reasons, shares feedback, and iterates toward consensus without member meetings.

Explore a qualitative Excel template that captures forecaster estimates for customers, products, and quarters, automatically calculating expected quantity, unit price, and achievement percentage.

Apply a case study approach to forecast next year's demand for ten products by integrating quantitative revenue trends from 2013–2020 with qualitative data templates, considering sales team insights and escalations.

Compare quantitative and qualitative data in a case study to forecast 2021 with trend methods, then assess differences and build best, most likely, and worst case scenarios.

Celebrate completing the forecasting course on quantitative and qualitative methods by obtaining your completion certificate and sharing your review and comments.