# Operations Management

Topics: Exponential smoothing, Moving average, Forecasting Pages: 38 (8098 words) Published: February 16, 2013
FORECASTING FUNDAMENTALS

Forecast: A prediction, projection, or estimate of some future activity, event, or occurrence.

Types of Forecasts
* Economic forecasts
* Predict a variety of economic indicators, like money supply, inflation rates, interest rates, etc. * Technological forecasts
* Predict rates of technological progress and innovation. * Demand forecasts
* Predict the future demand for a company’s products or services.

Since virtually all the operations management decisions (in both the strategic category and the tactical category) require as input a good estimate of future demand, this is the type of forecasting that is emphasized in our textbook and in this course.

TYPES OF FORECASTING METHODS

Qualitative methods: These types of forecasting methods are based on judgments, opinions, intuition, emotions, or personal experiences and are subjective in nature. They do not rely on any rigorous mathematical computations.

Quantitative methods: These types of forecasting methods are based on mathematical (quantitative) models, and are objective in nature. They rely heavily on mathematical computations.

QUALITATIVE FORECASTING METHODS

Qualitative Methods

Delphi
Method

Approach in which consensus agreement is reached among a group of experts

Sales Force Composite

Approach in which each salesperson estimates sales in his or her region Executive
Opinion

Approach in which a group of managers meet and collectively develop a forecast Market
Survey

Approach that uses interviews and surveys to judge preferences of customer and to assess demand

QUANTITATIVE FORECASTING METHODS

Quantitative Methods

Time-Series Models

Time series models look at past patterns of data and attempt to predict the future based upon the underlying patterns contained within those data. Associative Models

Associative models (often called causal models) assume that the variable being forecasted is related to other variables in the environment. They try to project based upon those associations.

TIME SERIES MODELS

Model| Description|
Naïve| Uses last period’s actual value as a forecast| Simple Mean (Average)| Uses an average of all past data as a forecast| Simple Moving Average| Uses an average of a specified number of the most recent observations, with each observation receiving the same emphasis (weight)| Weighted Moving Average| Uses an average of a specified number of the most recent observations, with each observation receiving a different emphasis (weight)| Exponential Smoothing| A weighted average procedure with weights declining exponentially as data become older| Trend Projection| Technique that uses the least squares method to fit a straight line to the data| Seasonal Indexes| A mechanism for adjusting the forecast to accommodate any seasonal patterns inherent in the data|

DECOMPOSITION OF A TIME SERIES

Patterns that may be present in a time series

Trend: Data exhibit a steady growth or decline over time.

Seasonality: Data exhibit upward and downward swings in a short to intermediate time frame (most notably during a year).

Cycles: Data exhibit upward and downward swings in over a very long time frame.

Random...