Operations Management Heizer 8th ed Ch 4
About this set
Created by:
carrieab on March 27, 2012
Classes:
Production and Operation Management
Log in to favorite or report as inappropriate.
Order by
31 terms
Terms | Definitions |
|---|---|
 Forecasting | The art and science of predicting future events. |
| Economic Forecasts | Planning indicators that are valuable in helping organizations prepare medium to long range forecasts. |
| Three Time Horizons | Short, medium, and long randge forecasts |
| Short-range forecast | Up to a year, but generally less than 3 months. planning purchasing, job scheduling, workforce levels, job assignments, production levels |
| Medium-range forecast | 3 mths to 3 yrs; sales planning, production planning and budgeting, and analysis of various operating plans. |
| Long-range forecast | 3 years or more; planning for new products, capital expenditures, facility location or expansion, and research & development. |
| 3 features that distinguish medium and long range from short term forecasts | 1. M&L deal with more comprehenisve issues 2. ST employs different methodologies 3. ST tends to be more accurate than m&L |
| Product Life Cycle Stages | 1. Introduction 2. Growth 3. Maturity 4. Decline |
| 3 Types of Forecasts | Economic Technological Demand |
| Economic Forecasts | Address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators. |
| Technological Forecasts | Concerned with rates of technological progress, which can result in the birth of excitingnew products, requiring new plants and equipment. |
| Demand Forecasts | Projections of demand for a company's products or services. Also called sales forecasts, drive a company's productuction, capacity, and scheduling systems and serve as inputs to financial, marketing, and personnel planning. |
| Forecasting Approaches/Models | Quantitative Qualitative Simulation Miscellaneous Models |
| Quantitative | Emply mathmatical modeling to forecast demand |
| Qualitative | incorporate such facotrs as the decision maker's intuition, emotions, personal experiences, and value system. |
| Qualitative Examples | Market Survey (Market Research) Expert Opinion (Jury of Executive Opinion) Sales Force Consensus Estimate Delphi Method Historical Analogy |
| Quantitative Examples | Time Series Last Period (Naive Approach) Arithmetic Average (not in text) Moving Average Weighted Moving Average Exponential Smoothing (EWMA) - Level Correction - Level and Trend Correction - Level, Trend, and Seasonality Correction (not in text) Causal Models (Associative Models) Simple Regression (Trend projection) Multiple Regression |
| Time Series Definition (class) | Assuming time factors drive the demand |
| Causal Models (class) | Assuming certain factors drive the demand, identify the factors |
| Principles of Forecasting | Forecasts are (almost) always wrong Forecasts should include an estimate of error Near-term forecasts are more accurate than long-term forecasts. Forecasts are more accurate for a product group than a single product. (more variability) |
| Key Factors in Forecasting | Past Data Subjective or Objective Continuous or Snapshot Cost Accuracy External Factors Model |
| Forecasting Process (slides) | Item(s) to be forecasted Determine Time interval Forecast horizon Collect data Discover patterns / components Consider internal / external factors Select model Forecast and implement Monitor performance Revise / replace model |
| Time Series Analysis (slides) | Process of discovering demand pattern with respect to time. Process of projecting the past behavior in the future Demand is related to time |
| Time Series Analysis Components (See graphics on slide 11-15) | Trend (Upward / Downward) gradual up or down movement Seasonality: any specific pattern that occurs periodically and is repetitive Cyclical Variations: an up and down movement that repeats itself over a long period of time Random (Unexplained Variations): fluctuations with no specific/discernible pattern |
| Two Opposing Properties of Forecasting Models | Responsiveness: Ability to quickly respond to change in demand AND Desirable in situations with a trend Stability: Ability to stabilize demand (not to overreact to random variations) AND Desirable in situations with steady (level) demand and no trend |
| Characteristics Last Period Demand | Only one previous data point is used Cannot dampen (filter) unexplained variations (actually overreacts to unexplained variations) Responds well to demand with trend Not appropriate for seasonal demand |
| Characteristics Arithmetic Average | All past data are used Appropriate for level demand (dampens unexplained variations well) Responds to trend with a lag Not appropriate for seasonal demand |
| Characteristics Moving Average | More flexible than both Last Period and Arithmetic Average models If n is small: behaves more like Last Period If n is large: behaves more like Arithmetic Average |
| Characteristics Weighted Moving Average | Same as Moving Average but more flexible because both n and weights can be controlled |
| EWMA value of alpha | Controls the responsiveness and stability of the model. when a = 0; least responsive, most stable a = 1; most responsive, least stable |
| Start back up at slide 30 | ... |
First Time Here?
Welcome to Quizlet, a fun, free place to study. Try these flashcards, find others to study, or make your own.