Operations Management

Chapter 3 : Forecasting Demand

Forecasting in Operations
Forecasting Methods
Qualitative Method
Delphi Method
Nominal Group Technique
Time-series Methods
Simple Moving Average
Weighted Moving Average
Exponential Smoothing
Causal Quantitative Methods
Linear Regression
Selecting a Forecasting system
Time span
Data availability
Cost and Accuracy
Measures of Forecasting Accuracy
Mean Absolute Deviation
Mean Square Error
Mean Forecast Error
Mean Absolute Percentage Error
Tracking signal
Monitoring and Controlling Forecasts

Chapter Summary

Forecasting in operations management involves the use of quantitative and qualitative tools for estimating and predicting future demand for products and services and the resources needed to produce these products and services. Accurate forecasts are critical for the survival and profitability of organizations in the long run.

In this chapter, we discussed three basic forecasting methods: qualitative methods, time-series methods and causal methods. Qualitative methods are judgmental and subjective in nature and based on the estimates and opinions of individuals. Time-series methods make a systematic use of past data to estimate future trends.

Causal methods evaluate the relationship between different variables and determine how variation in the value of one variable affects other variables. These methods therefore probe cause and effect relationships. Managers consider several factors, like cost and accuracy, data availability, and time span, before selecting a forecasting method.

Since forecasts set to predict the future, they cannot be error-free. Managers use several measures that determine to determine accuracy of the model. We discussed commonly used measures of accuracy: Mean Absolute Deviation, Mean Square Error, Mean Forecast Error and Mean Absolute Percentage Error.

Next Chapter>>