Lecture 13_Outline - Forecasting

# Lecture 13_Outline - Forecasting - Lecture 13 Outline...

This preview shows pages 1–5. Sign up to view the full content.

Lecture 13 - Outline 1 Lecture 13: Forecasting (Chapter 13) Objectives z Forecasting across the Organization z Demand Patterns z Key Decisions on Making Forecasts z Time Series Methods z Linear Regression z Moving Average z Weighted Moving Average z Simple Exponential Smoothing z Putting It All Together Forecasting z Forecasts are critical inputs to business plans, annual plans, and budgets z Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan: output levels, purchases of services and materials, workforce and output schedules, inventories, and long-term capacities z Forecasts are made on many different variables z Forecasts are important to managing both processes and managing supply chains Demand Patterns z A time series is the repeated observations of demand for a service or product in their order of occurrence z There are five basic time series patterns ± Horizontal ± Trend ± Seasonal ± Cyclical ± Random

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Lecture 13 - Outline 2
Lecture 13 - Outline 3 Key Decisions: z Deciding what to forecast ± Level of aggregation ± Units of measure z Choosing a forecasting system z Choosing the type of forecasting technique ± Judgment and qualitative methods ± Causal methods ± Time-series analysis z Key factor in choosing the proper forecasting approach is the time horizon for the decision requiring forecasts Linear Regression z A dependent variable is related to one or more independent variables by a linear equation z The independent variables are assumed to “cause” the results observed in the past z Simple linear regression model is a straight line z The sample correlation coefficient, r ± Measures the direction and strength of the relationship between the independent variable and the dependent variable. ± The value of r can range from –1.00 r 1.00 z The sample coefficient of determination, r 2 ± Measures the amount of variation in the dependent variable about its mean that is explained by the regression line ± The values of r 2 range from 0.00 r 2 1.00 z The standard error of the estimate, s yx

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Lecture 13 - Outline 4 ± Measures how closely the data on the dependent variable cluster around the regression line
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 02/14/2011 for the course MGSC 395 taught by Professor Zimmer during the Fall '10 term at South Carolina.

### Page1 / 14

Lecture 13_Outline - Forecasting - Lecture 13 Outline...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online